After a long limbo, RNA has gained its credibility as a druggable target, fully earning its deserved role in the next generation of pharmaceutical R&D. We have recently probed the trans-activation response (TAR) element, an RNA stem–bulge–loop domain of the HIV-1 genome with bis-3-chloropiperidines (B-CePs), and revealed the compounds unique behavior in stabilizing TAR structure, thus impairing in vitro the chaperone activity of the HIV-1 nucleocapsid (NC) protein. Seeking to elucidate the determinants of B-CePs inhibition, we have further characterized here their effects on the target TAR and its NC recognition, while developing quantitative analytical approaches for the study of multicomponent RNA-based interactions. These results are in good agreement with the previous observation that the reactivity of Lys-derivatives is lower compared to the aliphatic B-CePs [To elucidate the effects of the different linkers connecting the reactive moieties of B-CePs, we estimated the relative abundance of the different alkylated RNA species obtained upon reactions with the test compounds. Briefly, spectra acquired from samples obtained by reacting 10 μM of compounds nditions B. In thic B-CePs .1–5 as described above  under native conditions, which has been shown capable of enabling the detection of non-covalent complexes between RNA and NC ,20,21,22ed above A. The re4 and 5, which had afforded lower adduct stoichiometries. Conversely, a lower incidence of protein binding was observed for the aliphatic-linker compounds 1–3, which had produced higher adduct distributions. In particular, compound 1—which had produced the greatest number of adducts —was also the B-CeP that exhibited by far the most pronounced inhibition of NC binding. Nevertheless, not only the B-CePs-induced stoichiometry of TAR alkylation but also the quality of the linker impairs the binding of NC to the alkylated TAR. The signal intensities of the various species were employed with proper precautions to estimate the percentages of free and bound components in solution, as detailed in 1 across key guanines in the TAR hairpin prevented both thermal and NC-mediated melting of the double-stranded stem [1–3 by comparing the positions of the nucleotides involved in the bridging conjugates. This task was accomplished by performing RNAse A digestion of the adducts followed by mass-spectrometric characterization of the cleavage products. The representative data obtained from TAR RNA treated with either compound 2 or 3  present on the TAR construct. To gain additional insights into the inhibition mechanism, we sought to map the position of B-CePs adducts onto the RNA structure. In previous studies, we demonstrated that these compounds possessed the exquisite ability to form bifunctional cross-links that bridged across opposing RNA strands . We demoded stem . We have or 3 . It should be noted that, consistent with our previous observations [2 and 3 may place G12 within striking distance of other susceptible guanines in the G-rich loop . The ability to form alternative conjugates could be supported also by the intrinsic flexibility of the loop structure and by the rather unprotected, accessible context of these single-stranded nucleotides. On the other hand, the G2 and G27 nucleotides involved in XL2 are located in a base-paired stretch that narrowly constrains their mutual distance, as well as the distances between them and other possible guanines in the region 6– linker characteristic of compound 3 may just exceed the distance between such nucleotides without reaching those necessary to bridge alternative targets. Signal intensities were once again employed to estimate the percentage of conjugated XL1 and XL2 species observed in the various spectra see , with thrvations , the care region C. The st1–3 alkylation of both TAR and cTAR, and the effects of such alkylation reactions on the NC-mediated formation of the TAR/cTAR hybrid. For this purpose, we employed electrophoretic mobility shift assay (EMSA), a simple electrophoretic approach capable of detecting the formation of stable nucleic acids adducts by retardation of electrophoretic migration [1–3  and allowing them to react for 2 h at 37 °C. The spectra contained intense signals corresponding to unreacted cTAR, as well as several 1 adducts. Such products were readily assigned to combinations of mono- and bi-functional adducts with stoichiometries increasing from 2:1 to 3:1 in a concentration-dependent manner  was heated to 95 °C for 5 min and then ice-cooled in order to assume the proper hairpin structure. The full-length recombinant NC protein was obtained in house as reported [All oligonucleotides were synthesized by Metabion International AG  and stored at −20 °C in 10 mM Tris-HCl pH 8.0. TAR is the 29-mer RNA sequence 5′-GGCAGAUCUGAGCCUGGGAGCUCUCUGCC-3′ and cTAR is its DNA complementary sequence 5′-GGCAGAGAGCTCCCAGGCTCAGATCTGCC-3′. Dilutions were made in DEPC-treated water . The typical folding procedure consisted of snap-cooling: either TAR RNA or cTAR DNA diluted in 1X BPE buffer  to achieve a final 1 μM concentration of total nucleic acid substrate. Samples were analyzed by direct infusion electrospray ionization (ESI) on a Thermo Fisher Scientific  LTQ-Orbitrap Velos mass spectrometer. The analyses were performed in nanoflow mode by using quartz emitters produced in-house by using a Sutter Instruments Co.  P2000 laser pipette puller. Up to 5 μL samples were typically loaded onto each emitter by using a gel-loader pipette tip. A stainless-steel wire was inserted in the back-end of the emitter to supply an ionizing voltage that ranged between 0.8 and 1.2 kV. The source temperature and desolvation conditions were adjusted by closely monitoring the incidence of ammonium adducts and water clusters. Data were processed by using Xcalibur 2.1 software .Possible effects induced by B-CePs on the specific binding of NC protein to TAR substrate were evaluated by analyzing samples in which TAR was pre-reacted with each B-CeP (50:1 compound:RNA ratio) for 2h at 37 °C and then incubated with equimolar amount of NC protein (1 μM each) in 150 mM ammonium acetate. ESI-MS performed under nondenaturing conditions was applied to unambiguously identify all species present at equilibrium in solution. Samples were analyzed in negative ion mode via direct infusion nanospray ionization as described above on a Synapt G2 HDMS traveling-wave ion mobility spectrometry (IMS) mass spectrometer . Data were processed by using Mass Lynx  software. To evaluate the binding of the full-length NC to TAR construct, abundances of protein-free and NC-complexed TAR species in each experiment were calculated, expressed as percentage, and compared.2 or 3 at final concentrations of 10 μM, i.e., a 10:1 compound/substrate ratio. Reaction mixtures were incubated at 37 °C for 2 h and quenched by ethanol precipitation. Aliquots of unreacted and reacted TAR were submitted to digestion with ribonuclease A (RNAse A) in 150 mM ammonium acetate for 1 h at 37 °C. Samples were stored at -20 °C until immediate analysis by ESI-MS on the Thermo Fisher Scientific  LTQ-Orbitrap Velos mass spectrometer, as reported above. Signal intensities of fragments G1:C3 and G10:C13 were used to calculate the abundances of crosslinked species in each experiment and rate the compounds as excellent (XL > 80%), limited (2% < XL < 80%), or negligible (XL < 2%).Consistently with previous analysis , typical1–3 to directly interact with TAR RNA and cTAR DNA constructs, and the effects of such alkylation on the NC-mediated formation of the TAR/cTAR hybrid. Prior to incubation with compound, nucleic acids were heated to 95 °C for 5 min and then ice-cooled in order to assume the proper hairpin structure. TAR and cTAR construct (1 μM) were then incubated with increasing concentrations (from 0 to 50 μM) of B-CePs, at 37 °C for 2 h in 1X BPE buffer. In addition, aliquots of each construct, after reaction with B-CePs as described above, were mixed together, NC solution was added (8 μM), and then the mixture was incubated for other 15 min at room temperature. The samples were then added gel loading buffer containing SDS , at which point they were kept on ice, and resolved by 12% non-denaturing polyacrylamide (PAA) gels containing 1X TBE (Tris Borate EDTA) buffer at room temperature. Unreacted and reacted RNA and DNA on the gel were stained with SybrGreen II® . Fluorescence in gel system was detected on a Geliance 600 Imaging System .Electrophoretic mobility shift assay (EMSA) was used to assess the ability of com- pound 1–3 highlighted the importance of the distance between reactive moieties in driving the formation of covalent bridges between residues placed in different positions of the RNA structure. Cross-linker spacing could thus represent a convenient parameter for finely tuning the reactivity of potential B-CeP-based therapeutics against specific viral structures instead of undesired host targets. Furthermore, it has been shown that putative reactions taking place in the cytoplasmic environment can significantly reduce the extent of damage of genomic DNA caused by the aliphatic compounds 1–3 as compared to that of their Lys-analogs 4 and 5 [With the goal of developing RNA-targeting agents, we evaluated B-CePs as RNA cross-linking agents and assessed their ability to interfere with NC-mediated remodeling of TAR and cTAR secondary structures. We determined that B-CeP reaction with TAR RNA produced a series of different effects, in addition to the strong stabilization induced on TAR construct . The ali 4 and 5 . Taken tFinally, the results afforded by the TAR/cTAR/NC system demonstrated the merits of the experimental approaches employed here, which could represent a broad base, effective platform for the characterization of multicomponent RNA-based complexes and for the investigation of the effects of small-molecule ligands on their specific interactions. The ability of B-CePs to conjugate nucleotides placed in well-defined structural contexts, and the possibility to adjust their reach by modulating the linker length could pave the way for their utilization as nucleic-acid specific structural probes or interactomic reagents, alongside other bifunctional cross-linkers employed exclusively with protein substrates.


N4-hydroxycytidine (NHC), is currently in clinical trials for COVID-19 therapy. However, the biochemical mechanisms involved in molnupiravir-induced mutagenesis had not been explored. In a recent study, Gordon et al. demonstrated that NHC can be incorporated into viral RNA and subsequently extended and used as template for RNA-dependent RNA synthesis, proposing a mutagenesis model consistent with available virological evidence. Their study uncovers molecular mechanisms by which molnupiravir drives SARS-CoV-2 into error catastrophe.Molnupiravir, a prodrug of the nucleoside derivative β-D- Outbreaks of highly pathogenic severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002 to 2003 and Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012 raised the alarm on the potential threat of coronaviruses to human health. Today, we are witnessing a huge pandemic caused by SARS-CoV-2 that has infected millions of people, while causing more than 3.7 million deaths around the world. Although a handful of vaccines are now available, there is a lack of effective antiviral drugs to fight the disease caused by SARS-CoV-2 infection (COVID-19).Coronaviruses are enveloped viruses with a positive-sense single-stranded RNA genome of 26.4 to 31.7 kilobases . The coronavirus genome encodes nonstructural proteins that are responsible for the replication and transcription of the viral genome, the main component of which is a multifunctional protein that contains a central RNA-dependent RNA polymerase (RdRp) domain (N4-hydroxycytidine (NHC). NHC is a broad-spectrum antiviral compound that inhibits the replication of multiple viruses in cell culture   . The triar range , 4. Thisar range , and NHCar range . Molnupiet al.  and Kd (nucleotide binding affinity) were not obtained, their results clearly showed that all rNTPs are more efficiently incorporated than the NHC triphosphate. However, as expected, the most efficient competition was observed with cytosine triphosphate (CTP). In a different set of experiments, Gordon et al. found that once incorporated, primers having NHC monophosphate at their 3’ end were efficiently extended, particularly at high rNTP concentrations.In their recent work, Gordon et al.  used a Sin vitro and in vivo . Approvet al.  showed t (et al.  could thAvailable evidence suggests that molnupiravir could become a paradigmatic example in the use of lethal mutagenesis as an antiviral strategy. However, there are inherent risks in this approach. NHC can be metabolized by the host cell to the 2′-deoxyribonucleoside form by the ribonucleotide reductase and then incorporated into the host cell DNA. The mutagenic effect of NHC has been shown in animal cell cultures , raisingThe author declares that he has no conflicts of interest with the contents of this article.


We apply a CRISPR/Cas9-mediated saturation mutagenesis approach to generate libraries of mutations near three natural editing substrates at their endogenous genomic loci. We use machine learning to integrate diverse RNA sequence and structure features to model editing levels measured by deep sequencing. We confirm known features and identify new features important for RNA editing. Training and testing XGBoost algorithm within the same substrate yield models that explain 68 to 86 percent of substrate-specific variation in editing levels. However, the models do not generalize across substrates, suggesting complex and context-dependent regulation patterns. Our integrative approach can be applied to larger scale experiments towards deciphering the RNA editing code.Adenosine-to-inosine (A-to-I) RNA editing catalyzed by ADAR enzymes occurs in double-stranded RNAs. Despite a compelling need towards predictive understanding of natural and engineered editing events, how the RNA sequence and structure determine the editing efficiency and specificity (i.e.,  The RNA sequence and secondary structure regulate RNA editing by ADAR. Here the authors employ a CRISPR/Cas9-mediated saturation mutagenesis and machine learning to predict RNA editing efficiency of specific substrates. In animals, the predominant type of RNA editing is the hydrolytic deamination of adenosine (A) to form inosine (I), catalyzed by adenosine deaminase acting on RNA (ADAR)4. Abnormal A-to-I RNA editing is strongly linked to autoimmune diseases, neurological disorders and cancers6. Humans have two catalytically active ADAR proteins, ADAR1 and ADAR2, responsible for the editing of millions of RNA editing sites8. Adenosines in perfect or nearly perfect dsRNA duplexes, formed mainly by inverted repeats, are promiscuously edited9; in contrast, adenosines in imperfect dsRNA structures, can be edited by ADARs with high specificity and efficiency10. How RNA editing is regulated to determine its efficiency and specificity is poorly understood. Both the primary sequence and secondary structure  have been proposed to regulate ADAR editing16. A preferred sequence motif has been defined, including the 5′ and 3′ nearest-neighboring positions (−1 and +1 nt) to the editing site13. Editing can be enhanced or suppressed by deviations from perfect base-pairing , suggesting complex structural contributions to editing specificity17. The quantitative trait loci (QTL) mapping approach has been used to identify genetic variants associated with variability in RNA editing in Drosophila and humans, suggesting that many editing QTLs act through changes in the local and distal secondary structure for edited dsRNAs, consistent with the importance of RNA structure19. However, previous studies have generally been limited to small numbers of natural or engineered variants. We lack the systematic sequence and structure variations required for the development of predictive models of editing, therefore a high-throughput, systematic mutagenesis approach is needed.RNA editing greatly diversifies the transcriptome and proteome in higher eukaryotescis-regulatory RNA sequence and structural elements that affect ADAR-mediated RNA editing. As proof-of-concept, we choose three representative RNA editing substrates and introduce hundreds of mutations at the endogenous loci in human cells, using the CRISPR-mediated approach. We use supervised machine learning to build predictive models of substrate-specific RNA editing levels based on a variety of cis-sequence and structural features. We identify highly edited structures different from wild-type (WT) structure , and general, as well as idiosyncratic features that determine editing efficiency of individual substrates, highlighting the complexity of the cis-regulatory editing code. Our integrative approach, named predicting RNA editing using sequence and structure (PREUSS), lays the foundation for developing predictive models of RNA editing.Here, we combine CRISPR/Cas9 genome engineering, next-generation sequencing, and machine learning to decipher cis-regulatory elements of RNA editing, we applied the CRISPR/Cas9 technology .To interrogate the effects of ogy Fig.  to introIn a pilot experiment to introduce one or more mutations near the editing site, we used a single degenerate donor oligonucleotide with mutations at each position to randomize the region from −3 to +3 positions of the editing strand for NEIL1 Fig.  and a 103a–20. We were able to reliably detect >90% of our designed variants after using stringent quality control filters. The KI results and editing measurements were highly reproducible  and location of the mutation across all three RNA substrates Fig. c. We useZ-score < 0), with the largest effects observed at positions +1 and +2 relative to the editing site  of G mutations downstream from the editing site in the ECS strand. An example of how a G mutant changes secondary structure is shown in Supplementary Fig. Z-score < −1) for the transversion mutations that also disrupted base-pairing (transversion + break) or caused other structural changes (transversion + shift) at positions in close vicinity to the editing site , and at the 3′ side of the editing site (*+1 to *+8). Double-transversion mutations in the editing strand of NEIL1 had overall pronounced effects on editing  were observed when at least one of the mutations was in close proximity to the editing site (positions –6 and +2). The effect was generally smaller (Z-score > −2) if one of the mutations was in a non-base-paired region . For TTYH2 double mutations, the effects were most negative when at least one mutation was located around the editing site  than single mutations in NEIL1 and TTYH2. This difference may be explained by the long distance (524 nt) between AJUBA editing strand and ECS, such that mutations may lower the probability of forming this long-range structure relative to alternative proximal structures; alternatively, primary sequence might have a larger influence on editing of the AJUBA RNA.We only examined the mutations on the editing strand of AJUBA. In contrast to NEIL1 and TTYH2, many single mutations of AJUBA were sufficient to disrupt editing. Also, all double mutations abolished editing regardless of positions , suggesting the importance of this structural feature  reductions in editing levels  compared to lowly (lowest 25 percentile) edited NEIL1 (P < 0.0001) and AJUBA (P < 0.001) variants, based on both the MFE structure  and TTYH2  of similarity score between highly and lowly editing variants for NEIL1 and AJUBA .To quantitatively capture the complex relationship between editing levels and multidimensional RNA sequence and structure features, we turned to machine learning models. A set of 125 features were derived to annotate the RNA variants  in ADAR editing levels for variants in the held-out test set, with a Spearman correlation (Rs) of 0.92 between observed and predicted editing levels. Binary editing status was also predicted accurately . Similarly, high test set predictive performance was obtained for TTYH2 variants  and AJUBA variants . Augmenting the training set for each substrate with variants from the other substrates did not result in any significant improvements in model performance  and TTYH2 variants , as compared to a model trained and tested on NEIL1 variants . Similarly, a model trained on all NEIL1 and TTYH2 variants also yielded lower predictive performance when tested on AJUBA variants , albeit higher than the model trained on either substrate independently. The same held true for all models trained on two of the substrates and evaluated on the third—lower performance compared to within-substrate training and evaluation.Next, we tested whether models trained on variants of one or more substrates could predict editing effects of mutations in a different substrate. We observed a significant drop in model performance for cross-substrate prediction of RNA editing  of distinct substrates available for model training. It is likely that the challenge of cross-substrate training may be solved by training on a larger number of variants from diverse substrates, and future efforts will focus on this task. However, given the success of our substrate-specific models in predicting editing effects for unseen mutations within each substrate, we decided to interpret these models to investigate the features that may be predictive of RNA editing levels.29. The SHAP importance score of a feature with a specific value for a variant of a substrate estimates how much the feature contributes to pushing the model’s output from a baseline editing level to the predicted RNA editing level for the variant. The baseline editing level is defined as the average editing level across all variants in the test set. Examples of how SHAP scores illuminate feature importance are illustrated in Fig. 7e,For each of the three substrate-specific models, we used the TreeExplainer SHAP (SHapley Additive exPlanations) algorithm to quantify the contributions (or importance) of all features to the RNA editing predictions of each variant in the test setsTo illustrate the directionality of predictive association of the features with RNA editing levels, we plotted the SHAP scores of the top 20 features for all test set variants of the three substrates Fig. . We also7–27 . “Mut_pos” values are numbered beginning at the 5′ end of the RNA molecule, so higher values indicate positions further from 5′ and closer to 3′. This result indicates that the nucleotides adjacent to the editing site in the structure is the hot spot dictating activity. Though the “mut_pos” feature had a strong impact on editing level, the directionality varied across substrates, reflecting the interplay of the mutation position with other structural features.Eight features were illuminated as most important for driving model predictions across substrates. Number of mutations (“num_mutations”) was the strongest contributor for AJUBA (47.69%) and NEIL1 (19.18%), and in the top six most important features for TTYH2  would greatly enhance the RNA structure analysis. In addition, advanced experimental methods such as irCLASH36 may be applied together with gene-specific amplification to validate the ECS sequence. Further, while we focused on cis-elements adjacent to the editing site, long-range interactions, such as the editing inducer elements37 important for editing can be investigated using our approach. Although our data focused on the editing level, which is largely determined by the deaminase domain38, the dsRBD 42, in vitro competitive binding experiments and the editing measurements in cells can be conducted in the knockout or overexpression background for the RBP of interest. Because we observed no correlation of RNA abundance on editing level  stem required for ADAR1 (ref. 14) compared to ADAR2 and specific non-stem 3′ (downstream) structure, where the internal loops at the 3′ likely contribute to the ADAR selectivity17. Notably, each of the three RNA substrates we tested has substrate-specific features that dictate the editing efficiency  was co-transfected with 500 ng of the DNA donor using lipofectamine 2000 (Invitrogen). Cells were maintained at 50–90% confluency for 5 days.HEK293T cells (ATCC) were cultured in Dulbecco’s modified Eagle medium (Life Technologies) supplemented with 10% FBS  and penicillin streptomycin (Life Technologies). Cells were maintained at 70–90% confluency. One day before transfection, ~700,000 cells were split to 6-well plates. The next day, 500 ng of Cas9–sgRNA construct in the px330 backbone . Briefly, a 15–20 nt region around the target editing site and the corresponding region on the opposite strand were subject to mutagenesis. All possible nucleotide at any single position was tested, with exceptions where A-to-G mutation was avoided in the +1 and −1 nt of NEIL1 because it potentially becomes indistinguishable with A-to-I editing in RNA-seq results. Combination mutations at two positions were also designed, in which each of the positions is mutated to the nucleotide in the opposite strand to disrupt the original structure. In addition, individual donors with altered length for interrogation of specific features of the RNA substrate were included. For NEIL1, we were able to use donor oligos to introduce compensatory mutation variants because the ECS and editing site are close in sequence space. All oligo sequences used in this study are listed in Supplementary Data For NEIL1 donors, 80mer oligos were purchased from IDT and pooled at equal molar ratio. Oligo pairs NEIL1_leftarm/NEIL1_rightarm, or asymmetrical labeled primer pairs NEIL1_leftarm_biotin/NEIL1_rightarm and NEIL1_leftarm/NEIL1_rightarm_biotin  before ligation to BbsI-linearized PX330 backbone. The ligation mix was transformed into Stbl3 chemical competent cells (Invitrogen) and single clones were sequence verified by Sanger sequencing.gRNA was predicted by the web-based software 54. Two biological replicates were included for each assay. The transfected cells were grown for 5 days before they were seeded onto 10 cm dishes for an additional two days. A total of 10% of the cells were harvested for genomic DNA using Quick-DNA kits (Zymo Research). The remaining cells were used for nuclear extraction using the Nuclear/Cytosolic Fractionation Kit (Cell Biolabs) following the manual. Nuclear RNA was purified from the nuclear extract using the Trizol method. Genomic DNA was removed from the RNA samples using the TURBO DNase (Thermo Fisher Scientific). The primers were designed to make sure pre-mRNA species were amplified for RNA editing analysis. RT was performed using SuperScript III kit (Thermo Fisher Scientific) and the gene-specific primers. All RT products were used in total of 300 µl (50 µl × 6) PCR reaction with Phusion polymerase (Thermo Fisher Scientific), and gene-specific primers with Fluidigm mmPCR adaptor sequences55. Genomic DNA library was amplified using a similar approach, except for the different primer set. All first round PCR products were size-selected on 1.5% agarose gel and purified using Gel purification Kit (Qiagen). Diluted PCR product (1:50) was used in the second round of PCR to add the Illumina sequencing adapter and individual barcode sequences, using Fludigm_universal_F/fludigm_barcode_R (ref. 55). The library was size selected and purified as in the previous step.We used 600 ng single-stranded oligo donor library or 1200 ng double-stranded oligo donor library along with 500 ng gRNA construct to co-transfect into 1 million HEK293T cells using lipofectamine 2000 (Invitrogen). For degenerate donor mediated KI, 1 µl of 10 µM degenerate donor was used. A total of 1 µM L755507 (Sigma Aldrich) was added to the media 1 day after transfection to enhance the HDR efficiency56. The indel variants were mapped individually. The editing level Z-score of each variant is calculated for each RNA library by Eq. (All libraries were sequenced on a NextSeq550 using Basespace (Illumina) for data collection. NEIL1 libraries were sequenced for 75 cycles paired-end and TTYH2 and AJUBA libraries 150 cycles paired end. Quality of reads were evaluated, and reads were filtered by FastQC default settings. To map the variants of the target gene, a reference genome was first built using the GMAP package, where designed mutations were included as SNPs. Briefly, GSNAP was used to detect variants with mismatches inside the interrogated region but not indels. The mapped reads were separated into individual variants based on the unique mutations carried in the region except for the editing site, and RNA editing was called and measured for each variant, as described previously57 (see Supplementary Data 57 except for the reverse transcription step was carried out using the TGIRT-III enzyme (Ingex), which improved efficiency of the reverse transcription reaction 35. The reverse transcription reaction mix (12 µl) were incubated at room temperature for 5 min prior to incubation at 57 °C for 3 h followed by quenching of reaction by adding 5 µl of 0.4 M NaOH at 90 °C for 3 min and then cooled on ice for 3 min by adding 5 µl acid quench mixture . The first strand cDNA was then purified by RNAclean XP beads and amplified by one round of PCR to construct the library to add index and barcodes  then mapped and demultiplexed by cutadapt 1.17 to read and trim the barcodes in three steps59 . The resulting reads were processed by ShapeMapper 2  to detect DMS reactivity followed by structure inferring by Biers in MATLAB61 (default settings except for max_bootstrap = 100). For NEIL1, two replicates are performed by conducting the chemical treatment on separate tubes, while the replicates of TTYH2-ECS are from two different barcodes of the same RNA in one experiment. DMS reactivity data and experimentally inferred MFE structure were deposited in the RMDB database62.We were able to construct RNA libraries by in vitro transcription by T7 polymerase  for the NEIL1 and a portion of TTYH2 (TTYH2-ECS library) variants to probe th e RNA structures in vitro to compare with computationally predicted structures (below). The NEIL1 library (DNA oligo manufactured by IDT) was constructed with 3′ common primer binding sequence (PBS) and 3′ hairpin barcodes similar to previous report18 by examining the 100–1000 bp sequence flanking the editing site and choosing the most sData RNA duplex. For AJUBA, the ECS sequence we predicted also matches the predicted duplex using the RNAhybrid method63. We chose the region flanking the editing site of AJUBA to fold a “minimum” hairpin structure as the AJUBA RNA substrate .The sequence used for WT NEIL1, TTYH2, and AJUBA are shown in Supplementary Data ate Fig.  by omittate Fig.  based onate Fig.  for all 65 method version 1.2.3 to compare the MFE structure between computationally predicted to the experimentally inferred structures (described above) for all NEIL1 variants and a portion of the TTYH2 variants that we were able to experimentally measure. The MFE structures are largely similar between experimental and computational results shown by the high average value of the pairwise normalized similarity score calculated using SimTree65  to generate a hierarchical cluster tree file ile Fig.  and 9. T64 version 2.1.9. The probability of forming the WT-like secondary structures was calculated by Eq. . E_wt is the energy of the state with the WT-like secondary structure, calculated using both the constraints and reference information (listed below) in RNAfold. The details of the usage and the selection criteria for these constrains and reference information are also explained below.The probability of forming WT-like RNA secondary structure was calculated with Vienna RNAfoldThe structure constraints are:NEIL1:…………………………………..>>>….>…>>…>…>…TTYH2:…………………………………………………….>>>>>>>>>>>.>.>.>>>>>>>>>>….AJUBA:…………………………………………………..>>.>>>>.>.>>>.>>>>.>>>>>>>.where “>” indicates that the given base must be paired with a residue that comes before it (5′) in the sequence and “.” indicates no constraint for the given base.The reference information is:NEIL1………………..(((((.(((.(((((……))))).)))..)))))…………………TTYH2:…………………((((((.((.((((((((((((……………………….)))))))))))).)).))))))………………….AJUBA:(((((((((((((((.((((((((.((((((.((.(((((((((.((((……….)))).))))))))).)).)))))).)))))))).))))))))))))))).https://github.com/kundajelab/PREUSS.)The constraints are used to specifically calculate the free energy when fold the RNA sequence according to the constraints. The reference information was used to calculate a penalty by further comparing the base-pairing between the folded structure and the structure indicated by the reference information. We incrementally tested different versions of both the constraints and the reference information, starting from the exact secondary structure of WT to gradually relax the base-pairing starting from the first base-pair below the hairpin loop. This way the constrains and reference information together would preserve majority of the base-pairing information in WT structure near the core region of the RNA (regions that flanking the editing site). The reference information is used so that an additional penalty was added if base pairs could not be formed in the core region of the RNA. Note that penalties are not applied for any additional base pairs that form in the unpaired regions of the reference information. The probability was divided by the number of noncanonical base pairs that would be formed in the core of the WT secondary structure (defined by the reference information) to roughly account for the additional energetic penalty that these base pairs should incur. These constraints and reference information also ensure that the “active conformation” we calculated includes a wide group of core conformations that closely resembles the MFE structure of the WT, but are not limited to the single WT MFE structure. Additionally, the final versions we choose (listed above) are the ones that can compute conformations existed in the majority of RNA variants . The script in is available on GitHub . The conformation of the core RNA residues (nucleotides corresponding to NEIL1 residues 30–39 and 44–53) was taken from the previously solved structure of human ADAR2 bound to double-stranded RNA (PDB ID: 5HP3). The RNA was positioned relative to the protein by aligning the previously solved ADAR2 structure (in complex with dsRNA) to the homology model of ADAR1, then copying the RNA coordinates from the ADAR2-dsRNA structure. Protein residues in the ADAR1 homology model that clashed with the RNA were removed . This model was used as input to RNP-denovo with the -s option. Helical regions of the NEIL1 RNA were modeled as ideal A-form helices, also included with the -s option. Conformations of protein residues were not optimized . Default settings were used for all other options. The complete RNP-denovo command line used is provided below:A 3D model of ADAR1 bound to NEIL1 was built through homology modeling and the Rosetta RNP-denovo methodrna_denovo -fasta fasta.txt -secstruct_file secstruct.txt -s ADAR1_homology_model_and_core_RNA_from_5hp3.pdb RNA_helix_1.pdb RNA_helix_2.pdb RNA_helix_3.pdb RNA_helix_4.pdb RNA_helix_5.pdb -new_fold_tree_initializer true -minimize_rna true -minimize_protein_sc false -out:file:silent build_full_wt_neil1.out -rna_protein_docking true -rnp_min_first false -rnp_pack_first false -cycles 10000 -rnp_high_res_cycles 0 -minimize_rounds 2 -nstruct 2000 -ignore_zero_occupancy false -convert_protein_CEN false -FA_low_res_rnp_scoring true -ramp_rnp_vdw true -dock_each_chunk_per_chain false -use_legacy_job_distributor true -no_filterswhere RNA_helix_1.pdb, RNA_helix_2.pdb, etc. are ideal A-form helices for base-paired regions of Neil1. Possible placements of the double-stranded RNA-binding domains were visualized by aligning the previously solved structure of the ADAR2-dsRNA-binding motif bound to dsRNA (PDB ID: 2L2K) to our model of NEIL1 bound to ADAR1.https://github.com/kundajelab/PREUSSAll feature extraction and model training code are available to access on github: 9. The bpRNA annotations were in turn utilized to extract structural and positional features for each variant. A feature matrix with structure-specific features from the bpRNA  was used to train gradient boosted regression trees to predict Adar editing levels from feature matrices described above. Training was performed both within-substrate and across substrates. Several training and test combinations of datasets were utilized and summarized in Supplementary Data The XGBoostThe dataset was randomly separated into three splits: training on 70% of variants, model validation on 15%, and testing on the remaining 15%. To avoid train/test contamination, base-pair positions along the RNA molecules were assigned to one of the three splits . All features associated with a given base pair position were assigned to the corresponding split. Any feature that was null or non-varying across all variants in a given training split was removed from analysis. A number of variants were characterized by two or more mutations relative to the WT. For these, features were defined for each mutated base pair separately. To avoid train/test contamination, any base pairs that were both mutated in a given variant were included in the same split. The rationale for defining features relative to bases rather than variants is that different combinations of mutations may lead to variations in editing level, and a number of features were derived in reference to mutation type and position  on the validation split. Default parameters were used.R2 value was calculated on the test set to determine the percent of total variance explained by the feature matrix. Other metrics to measure model performance included:Spearman correlation from the scipy.stats Python library.Pearson correlation the scipy.stats Python library.Mean absolute error (MAE) from sklearn.metrics Python library.Mean absolute percent error (MAPE).Root mean square error (RMSE) from sklearn.metrics Python library.Area under the precision-recall curve (auPRC) from sklearn.metrics Python library.Area under the receiver operating characteristic (auROC) from sklearn.metrics Python library.The F score for each feature. The TreeSHAP algorithm29 was applied to interpret feature importance from the XGBoost model. SHAP summary values were computed for each feature as a measure of feature importance using the “shap_values” function within the “TreeExplainer” class. Pairwise interaction values from TreeShap were also calculated to identify highly correlated feature values.Feature importance analysis was performed to identify the subset of features most informative in predicting Adar editing levels. The XGBoost “plot_importance” function was used to calculate the Structure features: stem length, free energy, probability of active conformation.Number of mutations in the variant.Mutation-specific sequence features: mutation position, mutation site reference allele, mutation site alternate allele, distance of mutation site from edited base.Mutation-specific structure features: bpRNA structure designation for the mutation site, bpRNA structure designation for the adjacent upstream site, bpRNA structure designation for the adjacent downstream site, boolean indication of whether or not the mutation is part of the same structure as the editing site.“Other” mutation-specific features: type of mutation , presence/absence of mutation (WT/mutated) in the variant.Editing site sequence features.Editing site structure features.Characterization of the 3 bpRNA structural features upstream of the editing site.Characterization of the 3 bpRNA structural features downstream of the editing site.SHAP values were applied to calculate the combined relative importance of feature subsets. Feature subsets  values were summed across all features, and the percent contribution to the total was obtained for each feature. These percent contributions for each feature were averaged across substrates to determine features that were ranked as high importance consistently across all substrates.Further information on research design is available in the Supplementary InformationPeer Review FileSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Description of Additional Supplementary FilesReporting Summary


The transport of drugs across the placenta is a point of great importance in pharmacotherapy during pregnancy. However, the knowledge of drug transport in pregnancy is mostly based on experimental clinical data, and the underlying biological mechanisms are not fully understood. In this review, we summarize the current knowledge of drug transporters in the human placenta. We only refer to human data since the placenta demonstrates great diversity among species. In addition, we describe the experimental models that have been used in human placental transport studies and discuss their availability. A better understanding of placental drug transporters will be beneficial for the health of pregnant women who need drug treatment and their fetuses. Transporters are membrane proteins that regulate the absorption, distribution, and excretion of substrates in all cells of the body. Transporters contribute to the creation of density gradients inside of and outside of membranes by actively importing and exporting a broad variety of substances. Although transporters were originally classified based on their functions, they are now classified according to the sequence homology of the isolated genes. Transporters are categorized into two superfamilies: ATP-binding cassette (ABC) transporters, which are involved in the efflux of substances, and solute carrier (SLC) transporters, which are mainly involved in the uptake of substances ,2. TransDrug transporters have two main roles: one is to control the uptake of drugs during their circulation from the organs that associated with alimentation, such as the small intestine, and their transport in excretory organs, such as the liver and kidneys. Their other role is to control local drug concentrations in order to protect critical cells, tissues, and organs from xenobiotics, such as at the blood–brain barrier, blood–cerebrospinal fluid barrier, and blood–placental barrier. The body is protected biologically from xenobiotics through the coexistence of ABC transporters and SLC transporters in each organ.The placenta connects the fetus to the mother through the umbilical cord and supplies the fetus with nutrients and oxygen. Due to a history of harmful side effects on the fetus, the placental transport of drugs has always been an important issue. Regarding detoxification from xenobiotics, metabolic enzymes are involved , but traThus, in this review, we summarize published data on human placental drug transporters and discuss the availability of experimental models. Animal data are not mentioned in this review because the placenta is an organ with great diversity among species not only regarding its construction but also its transporter activity ,5.First, we identified publications that discussed human placenta transporters that were published before May 2021 using the search term “transporters in human placenta” in PubMed. From those, we selected transporters that are able to recognize drugs as their substrates and that included data from humans and animals. These selected “drug transporters existing in the human placenta” are listed in In the text and the table, we use the transporter acronyms that were used in the underlying original papers. Additionally, we provide the official gene name as per human gene nomenclature (HGNC).Drug transporters have other functions and transport drugs because of their similarities with physiological substrates. Therefore, we provide a brief overview of the physiological functions and main expression sites (apart from the placenta) of the drug transporters that are described in this review.The ABC transporter superfamily is a group of transporters that discharge drugs from the inside of cells to the outside of cells using ATP hydrolysis energy. Although they were originally identified as a multidrug resistance factor from tumor cells, they are also expressed in normal tissues and are able to transport a variety of drugs with different chemical structures and mechanisms of action . ABC transporters can be classified into seven subgroups, ranging from ABCA to G. Among these, ABCB, C, and G are recognized as drug transporters. The ABC transporter family has been studied more in depth than the SLC family . The funABCB1 (MDR1), which was initially called P-glycoprotein (P-gp), is expressed in the canalicular membrane of hepatocytes, renal proximal tubules, the luminal side membranes of the capillary bile ducts in the liver, the apical surface of intestinal enterocytes, especially in the distal ileum, and in the capillary endothelial cells in the brain and testis. MDR1 recognizes a wide range of lipophilic substrates. In the gastrointestinal mucosa, MDR1 drains substrates to the luminal side, and in cerebrovascular endothelial cells, MDR1 regulates the substrate distribution into the brain tissue ,7,8,9. AABCC1 (MRP1) is widely expressed throughout the body and is suggested to be involved in antioxidant activity ,13. ABCCABCG2 (BCRP) transports uric acid and a wide variety of organic anions into the gastrointestinal tract, bile, and urine .This group of transporters is responsible for secondary active transport and some passive transport. A large number of SLC transporters have been identified, with more than 400 transporters in over 60 families. Unlike ABC transporters, SLC transporters are highly diverse in structure and consist of a variety of folds. The information on the phylogenetic analysis of SLC transporters is also scarce due to a large number of subfamilies and their complex genetic structures ,21.SLCO1A2 (OATP1A2) is mainly expressed in the brain and transports thyroid hormone. SLCO 1B1 (OATP1B1) is responsible for the uptake of bile acids into the hepatocytes. SLCO2B1 (OATP2B1) is widely expressed in organs throughout the body and carries estrone 3 sulfate and dehydroepiandrosterone sulfate. SLCO3A1 (OATP3A1) is upregulated in the liver during cholestasis and contributes to the elimination of the bile acids that accumulate in the liver . SLCO4A1This family includes three subfamilies: organic cation transporter (OCT), organic cation transporter novel type (OCTN), and organic anion transporter (OAT).SLC22A1 (OCT1) is mainly expressed in the liver and is responsible for the uptake of dopamine and other substances into the liver. In the placenta, OCT1 transports acetylcholine as a substrate . SLC22A2SLC22A4(OCTN1) and SLC22A5 (OCTN2) are physiologically crucial transporters for L-carnitine. It has been proposed that OCTN2 plays a major role in L-carnitine transport from the mother to the fetus across the placenta . SLC22A6Both SLC29A1 (ENT1) and SLC29A2 (ENT2) are expressed in a wide range of organs, while SLC29A4 (ENT4) is highly expressed in the adipose tissue. ENT1 and ENT2 are involved in the uptake of nucleic acids into the cells .SLC47A1(MATE1) is present in a wide variety of organs, including the liver and kidney, while SLC47A2 (MATE2) is mainly expressed in the kidney. They excrete substrates into the urine and bile through the exchange transport of H+ and organic cations .The human placenta has a hemochorial structure, where fetal-derived villous trees are surrounded by maternal blood. The cellular layers of villi consist of, from the maternal blood side, syncytiotrophoblast, cytotrophoblast cells, stroma cells, and fetal vascular endothelium, which act to separate the fetal circulation from the maternal circulation . SeveralIn the human placenta, 28 proteins have been reported to function as drug transporters. For each transporter, we show its localization in the placenta and its expression in each trimester of pregnancy and in the cell lines that are frequently used in transporter studies  Figure .ABCB1 (MDR1) is the transporter that was identified first and is the one that has been the most intensively studied. The expression of MDR1 decreases with progressing gestational age, while the expression of ABCB4 (MDR3) increases ,34. The ABCC3 (MRP3) is highly expressed in the placenta, but it is express much less in cell lines, whereas ABCC11 (MRP8) is expressed more in cell lines . It was This transporter has been comparatively well studied and is widely expressed in both the placenta and in cell lines. ABCG2(BCRP) is expressed in the apical side of syncytiotrophoblasts, cytotrophoblasts, and fetal endothelial cells. In BeWo, the expression level is higher than it is in the placenta . AlthougThis group of transporters has been isolated from various animal species, including humans. However, they have more diversity among species than other drug transporters and have many isoforms without homologues between species. SLC21A9 (OATP2B1) expression is far less abundant in cancer-derived cell lines, including in the most frequently used choriocarcinoma cell lines .Although SLC22A1 (OCT1) and SLC22A2 (OCT2) have been reported to be expressed in very small amounts, it is commonly recognized that SLC22A3 (OCT3) is the only OCT family member that is clearly expressed in the human placenta, but there are conflicting reports on its cellular localization. One study reported OCT3 expression in the basal membranes of syncytiotrophoblast ,79, whilSLC29A1 (ENT1) is expressed on the apical side of the syncytiotrophoblasts, while SLC29A2 (ENT2) is expressed at lower concentrations ,94,96,97The placental expression levels of both SLC47A1 (MATE1) and SLC47A2 (MATE2) decrease as the gestational weeks progress .When considering the functional effects of drug transporters, the expression level of the transporters is also an important factor. As mentioned above, the expression levels of some transporters have been shown to change with the number of gestational weeks. In addition, some factors such as drug–drug interactions, some kinds of pathological conditions such as GDM or preeclampsia, and genetic variations such as single-nucleotide polymorphisms (SNPs) have been reported to cause changes in the expression levels of these transporters. The transporter regulation is summarized in the review by Staud and Ceckova .The most commonly used cell line in studies on human placental transport is the choriocarcinoma cell line BeWo, which forms a confluent monolayer with the microvilli on a permeable filter support. BeWo cells are often used as a model for trophoblast maturation because morphological and biochemical changes and syncytialization occur when the intracellular cAMP concentration is increased due to stimulation with forskolin. However, it should be noted that the syncytialization of BeWo may cause changes in the permeability of the monolayer due to the widening of the intercellular spaces and increases in the transport of certain substances . After BPlacental explants are less frequently used for transport studies than cell lines because syncytiotrophoblasts degrade rapidly, but they can be used for functional and toxicological analyses . PlacentThe so-called isolated “membrane vesicles” from term placenta have often been used in transporter studies. Previously, they were isolated by means of the apical membrane preparation  and basaRecently, microfluidic systems called “organs-on-a-chip” have been actively studied, including placenta-on-a-chip. To simulate the placental barrier, co-cultures of BeWo or HTR−8/SVneo and Human Umbilical Vein Endothelial Cells (HUVEC) or Human Placental Vascular Endothelial Cells (HPVEC) have been commonly used ,112. So This model has been commonly used in toxicological studies. Unlike the in vitro models, which are far from the real in vivo situation, the main feature of this model is its ability to reproduce macroscopic transport in human placenta. The limitations of this model are its short maintenance time of 6 h, and situations modelling the first trimester cannot be reproduced. Nonetheless, this model is a human organ models that can mimic the in vivo situation very closely. Indeed, May et al. perfused candidate transporter inhibitors with talinolol and evaluated the transporters that were involved in the materno–fetal transfer of talinolol .During functional transporter analyses, cell lines are frequently used. A common method is to add the inhibitors of specific transporters and to evaluate the changes that take place in terms of the substrate uptake ,41,47,61In this review we have summarized the findings related drug transporters in the human placenta and in experimental models. Transporters have been intensively studied in the kidney, liver, gastrointestinal tract, and at the blood–brain barrier. Pharmacokinetic knowledge is vital in terms of drug efficacy and side effects. Additionally, the placenta has a strong barrier function in order to ensure that maternal and fetal blood avoid contact with each other and do not mingle, but simultaneously, it has a very active transport function that is used to aliment the fetus and to eliminate its waste products. However, from the studies that have been published thus far, most do not explain the complete mechanisms of transportation of particular substances from the maternal surface to fetal circulation. Understanding the placental permeability of drugs is very important for estimating their toxicity to the fetus as well as for the pharmacological treatment of the fetus.There are several difficulties in studying drug transporters. First, in vitro models have limitations, as shown by the different transporter localization reuslts in the human placenta when the membrane preparation method or immunostaining have been used . Despite the technical challenges in studying placental transporters, the continuous improvement of experimental models and the combination of different types of experimental methods will lead to better understanding. Developments in the knowledge of placental drug transporters will be beneficial for the health and optimization of drug treatment for pregnant women and their fetuses.In this review, we described the functions and the expression and localization of drug transporters in the human placenta and in experimental models that have been previously used for their study. Detailed knowledge is fundamental for planning drug applications in pregnancy as well as for the consideration of the potential side effects of those applications.


Adenoid cystic carcinoma (ACC) is a rare, basaloid, epithelial tumor, arising mostly from salivary glands. Radiation therapy can be employed as a single modality for unresectable tumors, in an adjuvant setting after uncomplete resection, in case of high-risk pathological features, or for recurrent tumors. Due to ACC intrinsic radioresistance, high linear energy transfer (LET) radiotherapy techniques have been evaluated for ACC irradiation: while fast neutron therapy has now been abandoned due to toxicity concerns, charged particle beams such as protons and carbon ions are at present the beams used for hadron therapy. Carbon ion radiation therapy (CIRT) is currently increasingly used for ACC irradiation. The aim of this review is to describe the immunological, molecular and clinicopathological bases that support ACC treatment with CIRT, as well as to expose the current clinical evidence that reveal the advantages of using CIRT for treating ACC. Adenoid cystic carcinoma (ACC) is a rare tumor, which has a dual component of myoepithelial and ductal cells. Most of the ACCs arise from minor salivary glands, and account for about 10% of all malignant salivary tumors –4. OveraPhoton beam radiotherapy (RT) technological developments gave a substantial contribution in HN patient prognosis improvement last decades especially in patients bearing squamous cell carcinoma . RT playin vitro or preclinical study have specifically evaluated CIRT irradiation effects on ACC cell lines. Nevertheless, multiple immunological and biological properties justify ACC management with CIRT  of 34 tumor samples from primary and metastatic ACC tumors isolated from eight patients, Liu et al.  demonstrACC is characterized by a biphasic composition made of ductal cells (characterized by CK7 protein expression) and myoepithelial cells . The histological tumoral organization defines three ACC variants based on the predominant anatomopathological pattern: cribriform, tubular (both characterized by CK7 protein expression), or solid pattern . Solid patterns usually have a higher Ki67 index. Multiple histological grading systems have been proposed based on the estimated proportion of the solid pattern component  and [“adenoid cystic carcinoma” OR “ACC”]. Search was independently conducted by PL and EO. Inclusion criteria, defined using the PICOS framework, were the following: clinical studies  evaluating CIRT for ACC, in any setting, and reporting toxicity and efficacy data. Exclusion criteria were pre-clinical or purely dosimetric studies. References from the selected studies were screened for potential additional articles.The current clinical experience of CIRT for ACC is summarized in the An alternative approach has been evaluated at the Heidelberg Ion-Beam Therapy Center, using CIRT as a boost to conventional photon radiotherapy. In 2004, Schulz-Ertner et al.  obtainedJensen et al.  re-irradChen et al.  treated Molinelli et al.  evaluateFor a full CIRT irradiation, ACC is generally irradiated with a sequential strategy consisting of a first phase of nine to ten fractions to a low-risk volume , followed by a second phase of six to seven fractions to a high risk volume (boost), with a unique nominal dose per fraction, according to the protocol adopted in Japan since 1997 . In thisRobust planning is of prime importance for CIRT, to take into account range and positioning uncertainties . FinallyThe predicted range of carbon ion beams should be calculated as precisely as possible during treatment planning and treatment delivery. While small errors in margins quantification with photon RT leads to target underdosage, such errors with CIRT may have critical consequences; due to the sharp dose diminution at the distal edge of the Bragg peak, parts of the tumor might not receive any dose. The location of the distal dose fall-off notably depends on anatomical modifications, and minor anatomical changes might consequently negatively affect target coverage as well as substantially increase OAR toxicity. For head and neck ACC irradiation, where multiple nearby OARs have critical functions, CIRT might thus benefit from advanced techniques to monitor tumor response and potential anatomical changes during irradiation. This could for example be conveniently done by systematic CT scan reevaluations, the implementation of image guided CIRT, or robust planning. The dosimetric interest of adaptative planning for CIRT, relying on daily CT imaging, has been demonstrated for pancreatic irradiation  and mighin vitro or preclinical study have specifically evaluated CIRT irradiation on ACC cell lines to date. In addition, clinical data demonstrate that CIRT is associated with superior local control compared to conventional photon radiotherapy. In the future, further improvement of the outcomes of ACC treatment with CIRT may be possible by personalizing the treatment by taking into account ACC molecular and pathological features. Vered et al. , currently under development in various European and Asian centers. Kato et al.  treated The use of CIRT for ACC management is motivated by immunological, molecular and clinicopathological considerations. Although no prospective randomized trials have been published to this date and might not be easily feasible, due to the rarity of ACCs and the scarce availability of particle beam RT facilities, clinical studies demonstrated that CIRT was well tolerated and associated with a substantial tumor control in diverse clinical situations, especially in advanced unresectable stages. Current technical developments ensure safe treatments.Conceptualization: PL, BV, YK, and EO. Methodology: PL, BV, YK, and EO. Writing: PL. Review and Editing: PL, BV, MB, RI, SR, VV, AB, LG, LM, RD, RP, LL, VC, HM, SC, YK, and EO. Supervision: BV, YK, and EO. All authors contributed to the article and approved the submitted version.The authors declare 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.


The human kallikrein-related peptidase 4 (KLK4) and the transcribed pseudogene KLKP1 are reported to be highly expressed in the prostate. When trying to clone transcripts of KLKP1, we partly failed. Instead, we identified an androgen-regulated transcript, KLK4T2, which appeared to be a splice variant of KLK4 that also contained exons of KLKP1. Expression analysis of KLK4, KLK4T2, and KLKP1 transcripts in prostate cancer cell lines showed high levels of KLKP1 transcripts in the nucleus and in unfractionated cell extract, whereas it was almost completely absent in the cytoplasmatic fraction. This was in contrast to KLK4 and KLK4T2, which displayed high to moderate levels in the cytoplasm. In patient cohorts we found significantly higher expression of both KLK4T2 and KLK4 in benign prostatic hyperplasia compared to both primary prostate cancer and bone metastasis. Analysis of tissue panels demonstrated the highest expression of KLK4T2 in the prostate, but in contrast to the classical KLK4, relatively high levels were also found in placenta. So far, the function of KLK4T2 is still to be explored, but the structure of the translation product indicated that it generates a 17.4 kDa intracellular protein with possible regulatory function. Prostate cancer (PC) is a major global medical problem, with an annual incidence of close to 1.5 million cases in 2020  and in Sweden it is the most frequently diagnosed tumor disease among men with about 11,000 new cases per year (Swedish Cancer Registry). PC is a multifaceted disease, and it is not possible today to predict whether a tumor will develop into an aggressive form or not. The mechanisms behind PC metastasis and castration-resistant tumor growth are largely unknown and identification of molecular pathways involved has the potential to improve both PC prognosis and treatment.The kallikrein-related peptidases, KLKs, are a family of serine proteases, which in humans are encoded by 15 genes situated at a locus on the long arm of chromosome 19 ,3. AberrWe have previously cloned the transcript of KLK3, which perhaps is the most well-known protein in the gene family . It is aBetween KLK2 and KLK4 on human chromosome 19, there is a gene with the HGNC-approved designation KLKP1. Originally it was identified as a transcribed pseudogene containing a nucleotide sequence homologous with the second translated exon of KLK1 . It has For studies on the function of and its potential role in PC development, we set out to clone transcripts of KLKP1. During these efforts, we decided to run rapid amplification of cDNA ends (RACE) in order to better understand the transcripts that were predominating in RNA isolated from benign prostate hyperplasia (BPH) tissue and LNCaP cells. To our surprise, the answer was none of the previously described transcripts. Instead we identified a single product, which appeared to be a splice variant of KLK4.Initially, we intended to clone the KLK31P transcripts (GenBank accession number DQ211698 and AY533562) and the transcripts encoding KRIP1 (GenBank accession number DQ086829), using RNA isolated from BPH tissue and LNCaP cells as template for RT-PCR. Despite repeated efforts, we failed to generate the KLK31P transcripts. We were more successful with the KRIP1 transcript, but in addition to the expected transcript, the RT-PCR also generated a smaller product in which nucleotides 358–787 of DQ086829 were missing. As there were GT and CAG sequences at the ends of the missing piece, it most likely was removed by a splicing event, which also, almost completely, removed the nucleotides coding for KRIP1.The difficulty in obtaining the KLK31P transcripts in combination with our discovery of a new transcript from the KLKP1, prompted us to reinvestigate the products of the gene by RACE analysis. We used the primer KLKP9R, originally synthesized for cloning of KLK31P and KRIP1, in 5′ RACE experiments . To ruleOur result also suggested that KLK4 was a fairly large gene, consisting of at least 8 exons A. The stAn interesting observation was made when aligning the primary structures encoded by exon 3 and exon 7 of the extended KLK4. There were conserved residues that reached approximately halfway into the exons, as seen in The 5′ RACE experiments were run on samples made with total RNA from BPH tissue and LNCaP cells and fractionated RNA isolated from the cytosol and nucleus of LNCaP cells. Priming with oligonucleotide KLKP9R, which hybridized to the 3′ end of KLK4T2 and also of KLK31P and KRIP1, yielded a single predominating product of 1.2 kb in all samples . SequencThe primer KLK4r18, which hybridized to a sequence immediately upstream to the stop codon in KLK4, resulted in a more complex pattern, with a 0.7 kb product that was present in all samples . In the The 3′ RACE experiments were made with samples taken from the same sources as used for the 5′ RACE experiments. The primers used were KLK4Ne2f and KLK4Ne3f, which hybridized with sequences in KLK4 exon 3 and 4, respectively. Both primers gave very similar results, with the exception that priming in exon 3 yielded slightly larger products that reflected the 262 bp difference in location of priming sites . There wThe 5′ RACE cDNA was also synthesized from pooled material consisting of total RNA prepared from bone metastasis of patients suffering from castration-resistant prostate cancer (CRPC) or hormone naïve prostate cancer (HNPC) . Using tBecause of the discovery in the RACE experiments, we decided to quantify transcripts from different compartments of the combined KLK4 and KLKP1 loci. We used primers that would specifically recognize KLK4, KLK4T2, and KLKP1 (KRIP1 and the longer form of KLK31P) in RT-qPCR experiments. This showed that the transcripts encoding KLK4 and KLK4T2 were found in the cytosol fraction A. In conHormone effect on the KLK4T2 expression was studied in androgen-stimulated LNCaP cells. The androgen stimulation resulted in an increase of KLK4T2 transcripts, which was of a similar magnitude as the hormone-induced increase of KLK4 B.In Illumina expression array analysis, two different probes were hybridizing with KLK4-related transcripts, denoted KLK4 and KLKP1. As can be seen in n = 10), primary tumor tissue (n = 13), and bone metastasis from CRPC (n = 9) are displayed. As can be seen, the expression of KLK4 was significantly decreased as the disease progressed, although the most significant difference is seen as the disease progressed into bone metastasis . As mentioned above, the structure contains both several possible phosphorylation sites and a putative cleavage site, which together indicate a protein with high turnover rate.There are several studies reporting that the expression of KLK4 is higher in prostate tumors compared to normal, nonmalignant prostate tissue ,28,29,30KLK4 was originally isolated, characterized, and cloned from porcine transition stage enamel matrix, under the name enamel matrix serine proteinase 1 . The cDNIn this paper, we identified a new splice variant of the KLK4 locus that we denote KLK4T2 (OK423452). KLK4 is mentioned as either classical KLK4, which refers to pre-pro-KLK4 with signal peptide and pro-peptide (NM_004917) ,20, or t2.LNCaP (ATCC CRL-1740) were maintained in RPMI 1640 + GlutaMAX supplemented with 10% fetal bovine serum (FBS), 100 U penicillin/mL and 100 µg streptomycin/mL, 10 mM HEPES, and 1 mM sodium pyruvate ). Cell cultures were performed at 37 °C in a humidified atmosphere containing 10% CO2O, of which 1 µL was used as template in 50 µL PCR reaction using gene-specific primers in combination with the universal primer mix (UPM) supplied with the SMARTer 5′/3′ RACE kit ). The PCR product was purified using the Qiaquick PCR purification kit ) and the DNA concentration was determined by measurement of the absorbance at 260 nm. The yield of product was usually in the range 1–10 µg. The PCR products were sequenced using the BigDye Terminator v3.1 Cycle sequencing kit and an ABI PRISM 3100 Genetic Analyzer ), following the recommendations of the supplier of the kit. DNA sequencing was also made at GATC Biotech, currently Eurofin Genomics . Alternatively, RACE products were isolated from agarose gels by purification of excised bands using the Macherey–Nagel™ NucleoSpin™ Gel and PCR Clean-up kit  according to manual. Transcripts were cloned into either the pRACE vector from the SMARTer kit 5′/3′ RACE kit ) or the pcDNA3.1/V5/His expression vector  and the inserts were DNA sequenced as described above. Primers used are listed in For amplification of 5′ and 3′ ends of the KLK4 transcripts, the SMARTer 5′/3′ RACE kit ) was used. Products were analysed by agarose gel electrophoresis, stained with GelRed ), and visualized by UV light. Larger quantities of products were obtained by reamplification of specific bands on the agarose gel. For this, approximately 1 µL was recovered directly from the visualized band in the agarose gel and diluted in 15 µL HTotal RNA was prepared using either the AllPrep DNA/RNA/Protein kit ) or the Ambion Paris kit ). Cytosolic and nuclear RNA was prepared from patient samples using SurePrep Nuclear or Cytoplasmic RNA Purification Kit ). The Ambion Paris kit was used for fractionation of RNA from LNCaP cells. cDNA was synthesizes using either Revert Aid or SuperScript VILO ). DNA was amplified using BioRad iQmix  for quantification or either Advantage 2 ) ) or Platinum Hot Start PCR mix ) for visualization and/or cloning. For primer sequences, see n = 13) and of hormone-naïve (n = 10) and CRPC (n = 30) bone metastases samples were previously performed based on Illumina bead arrays, as described in [Whole genome expression analysis of nonmalignant and malignant areas of prostatectomy samples (ribed in . From thOne-way ANOVA was used to show that we had significant differences between our groups. The ANOVA study was followed by Tukey’s HSD Test for multiple comparisons.KLK4T2 received GenBank accession number OK423452.We identified an exciting new transcript, KLK4T2, formed by alternative splicing of the KLK4 gene. The transcript is regulated by androgens and primarily high levels in the prostate, but also in the placenta and fetal brain. KLK4T2 is, in contrast to KLKP1 transcripts, expressed in the cytoplasm of the cell, a prerequisite for translation to occur. No signal sequence could be identified in the amino acid sequence, indicating a nuclear location of the protein. Several putative phosphorylation sites and a proposed cleavage site open up for a regulatory protein with fast turnover rate.


A proven effective cure for AGS has not been discovered, and targeting these two pathways may lead to a treatment that improves patients’ immunological symptoms. Hydroxychloroquine (HCQ) interferes with normal antigen processing and presentation and is widely used in the clinical treatment of autoimmune diseases such as systemic lupus erythematosus and rheumatoid arthritis.7 HCQ is also a well-known inhibitor of autophagy that prevents the degradation of autolysosomes. This drug inhibits acidification and maturation of endosomes and increases the pH in lysosomes, resulting in the inhibition of their main functions, such as downstream cell signaling through TLRs.7 Therefore, the aim of our work was to investigate whether HCQ is able to modulate the abnormal inflammatory response driven by RNA:DNA hybrids.Aicardi–Goutières syndrome (AGS) is a rare genetic disease caused by mutations in nine genes that are all involved in nucleic acid metabolism or sensing.RNASEH2A p.R108W + p.F230L mutations, one from a patient carrying the RNASEH2B p.A177T mutation and one from a healthy control. We investigated the presence of RNA:DNA hybrids using the S9.6 antibody and flow cytometry. Quantification of the mean S9.6 fluorescence revealed a significant increase in RNA:DNA hybrids only in the RNASEH2B-mutant LCL : one from a patient carrying the LCL Fig. . RNA:DNAasm Fig. .8 After RNase H treatment, the RNA:DNA hybrid fluorescence signal considerably decreased in the RNASEH2B-mutant LCL (Fig. RNASEH2B-mutant LCL (Fig. The S9.6 antibody may also bind double-stranded RNA but with a lower affinity than with RNA:DNA hybrids, and therefore, to verify the cytoplasmic accumulation of the hybrids in these cell lines, we treated the cells with RNase H.LCL Fig. , the onlLCL Fig. .RNASEH2B- and RNASEH2A-mutant LCLs after treatment with 25 µM HCQ for 24 h. We found that in the treated RNASEH2B-mutant LCL, the RNA:DNA hybrid level decreased (Fig. RNASEH2B-mutant LCL showed no colocalization of S9.6 and Rab5, confirming that, at the basal level, hybrids were internalized by lysosomes, whereas we discovered weak colocalization in the untreated and treated RNASEH2A-mutant LCL. Interestingly, Rigby et al.9 described the accumulation of RNA:DNA hybrids in endosomes in the presence of retroviral infection in B3T3 fibroblasts. After treatment with HCQ, we observed colocalization between Rab5 and RNA:DNA hybrids in the RNASEH2B-mutant LCL, highlighting a possible role of endosomes in RNA:DNA hybrid elimination. No colocalization between Rab5 and RNA:DNA hybrids was observed in the control LCL (Fig. RNASEH2B-mutant LCL.We then evaluated how the RNA:DNA hybrid level changed in the healthy control LCL and the sed Fig. , and a lsed Fig. . Since LLCL Fig. . AGS mim7 we investigated whether RNA:DNA hybrid is depleted upon HCQ treatment via the autophagy pathway. After treating the cells with HCQ, we observed an increase in the LC3-II/LC3-I ratio for each cell line, especially in the RNASEH2B-mutant LCL, with a significant P value < 0.001 (Fig. RNASEH2B-mutant LCL after HCQ treatment (Fig. RNASEH2B-mutant LCL, which became more evident after HCQ treatment (Fig. Since HCQ also interferes with autophagy,001 Fig. . Moreoveent Fig. . In accoent Fig. , D, streRNASEH2B-mutant LCL (Fig. 10 No significant differences in the phosphorylation profile were identified before or after treatment with HCQ (Fig. IFI44 and IFIT1, changed in RNASEH2A- and RNASEH2B-mutant LCLs after treatment. IFI44 and IFIT1 expression levels decreased after HCQ treatment in the RNASEH2B-mutant LCL, with a greater decrease in IFIT1, whereas no differences in the RNASEH2A-mutant LCL were observed (Fig. After HCQ treatment, cGAS protein levels slightly decreased in the LCL Fig. , and subLCL Fig. . To confHCQ Fig. . We alsoved Fig. .RNASEH2B mutation, which may indicate an impairment in the RNA:DNA hybrid degradation process. On the other hand, the RNASEH2A-mutant LCL carries RNA:DNA hybrids in endosomes, possibly explaining the lack of impaired RNA:DNA hybrid degradation in this cell line. Moreover, we found that HCQ is a drug that can induce decreased activation of the IFN-α immune cascade only in the RNASEH2B-mutant LCL, the only cell line presenting high ISGs. Therefore, we hypothesized that HCQ, a European Medicines Agency-approved drug, may be an effective treatment for AGS patients who present abnormal activation of the innate immune response.In conclusion, we found that RNA:DNA hybrids accumulate mainly in the cytoplasm and colocalize with lysosomes in the LCL derived from an AGS patient carrying a Materials and MethodsFigure S1Figure S2Figure S3


Distant organ metastasis, often termed as organotropic metastasis or metastatic organotropism, is a fundamental feature of malignant tumours and accounts for most cancer‐related mortalities. This process is orchestrated by many complex biological interactions and processes that are mediated by a combination of anatomical, genetic, pathophysiological and biochemical factors. Recently, extracellular vesicles (EVs) are increasingly being demonstrated as critical mediators of bi‐directional tumour‐host cell interactions, controlling organ‐specific infiltration, adaptation and colonization at the secondary site. EVs govern organotropic metastasis by modulating the pre‐metastatic microenvironment through upregulation of pro‐inflammatory gene expression and immunosuppressive cytokine secretion, induction of phenotype‐specific differentiation and recruitment of specific stromal cell types. This review discusses EV‐mediated metastatic organotropism in visceral  and skeletal (bone) metastasis, and discusses how the pre‐metastatic education by EVs transforms the organ into a hospitable, tumour cell–friendly milieu that supports the growth of metastatic cells. Decoding the organ‐specific traits of EVs and their functions in organotropic metastasis is essential in accelerating the clinical application of EVs in cancer management. The conceptual framework of tumour progression towards metastasis has been well‐described and simplified into a stepwise sequence of cell‐biological events, which are collectively termed the invasion‐metastasis cascade  of metastatic progression, and the host organ sites , inducing endothelial cell permeability in the BBB and brain metastasis , which promotes cancer cell colonization and brain metastasis by generating a pro‐metastatic environment. Mechanistically, uptake of CEMIP‐positive EVs by brain endothelial and microglial cells stimulates endothelial cell branching and the establishment of a pro‐inflammatory vascular niche by upregulating chemokines and cytokines known to promote brain vascular remodelling and metastasis  protective mechanisms and employ microglia and CNS border‐associated macrophages to achieve CNS colonization , which induces an increase in matrix metalloproteinase (MMP)‐9 expression in the brain stroma and contributes to extracellular proteolysis  cells secrete EV‐packaged miR‐103 that can promote tumour motility by targeting endothelial junction proteins and increasing vascular permeability in vitro. Moreover, they showed that mice with xenografts of miR‐103‐expressing HCC cells exhibit higher levels of miR‐103 in EVs, higher vascular permeability and increased rates of lung metastasis  regulates secretion of miR‐210 contained in EVs, which affects the capacity for lung metastasis. In particular, they showed that EV transport of miR‐210 in endothelial cells promotes angiogenesis and enhances their metastatic initiation efficiency in lungs  subsequently induce the secretion of pro‐inflammatory cytokines, including IL‐6 and IL‐8, to boost lung metastasis ‐derived EVs alter transforming growth factor (TGF)‐β production by Kupffer cells, leading to an increased fibronectin production by hepatic stellate cells and recruitment of bone marrow‐derived macrophages. They further identified macrophage migration inhibitory factor (MIF) as an essential trigger of the pre‐metastatic niche in the liver  cells or neutralization of pro‐apoptotic NK‐cell‐derived TNF‐related apoptosis‐inducing ligand (TRAIL) promotes liver metastasis ‐expressing stromal cells, developing a metastatic‐designated microenvironment  by tumour‐derived EVs is a prerequisite for the recruitment and growth of cancer cells. Hood et al. have shown that EVs derived from melanoma home to SLN and facilitate pre‐metastatic niche formation by increasing the expression of genes related to cell recruitment, ECM deposition and vascular proliferation in the LN  and promote lymphangiogenesis. It has been shown that EVs, derived from cervical squamous cell carcinoma (CSCC), transfer miR‐221‐3p to LECs and promote lymphangiogenesis and lymphatic metastasis through downregulation of vasohibin1 (VASH1), an intrinsic factor with broad‐spectrum anti‐lymphangiogenic activity  cells. EVs derived from GC patients have been found to induce the differentiation of CD252.2Bone is the third most frequent and preferred site of metastasis, second to lung and liver Coleman, , and typRecent findings from Nielsen and colleagues have shown that cell diffusion to distant skeletal sites in MM is favoured by EV‐associated procoagulant phospholipid and tissue factor (TF) activity ‐derived EVs induce EGFR phosphorylation causing an increase of RANKL and osteoclastogenic markers (MMP‐9 and TRAcP) (Taverna et al., Recent developments focused on re‐evaluating traditional aspects of the pathophysiology of PCa and BCa to bone metastasis have revealed the multifarious role of EVs in controlling bone homeostasis and cancer progression in the bone microenvironment (Patil & Soekmadji, Hashimoto and colleagues have shown that hsa‐miR‐940 shuttled by PCa‐derived EVs promotes the osteogenic differentiation of human MSCs by targeting ARHGAP1 and FAM134A (Hashimoto et al., Another aspect worth considering is the role of EVs in promoting dormancy of BCa cells in the bone marrow. Co‐culture studies have shown that BMSC‐derived EVs induce dormant phenotypes in MDA‐MB‐231 cells through miR‐23b mediated suppression of a target gene, MARCKS, which encodes a protein that promotes cell cycling and motility (Ono et al., 3As clinical oncology is progressing towards precision and personalized cancer medicine, a deeper understanding of the biology of metastasis has become increasingly acute. EVs are increasingly shown to be involved in the invasion‐metastasis cascade. EVs can facilitate organ‐specific metastasis and redirecting metastatic distribution which affects the organotropism in metastatic cancers. Identifying the organ‐specific molecular determinants/traits of EVs that drive organ‐specific patterns is an urgent need considering the dire clinical consequences associated with overt metastasis. Delineating the precise mechanisms adopted by EVs to promote the formation of pro‐metastatic environments at distant sites is crucial for understanding metastatic progression. Understanding EV‐driven mechanisms will help drive future potential therapeutic interventions to regulate EV function in metastasis which is an absolute requirement for gaining effective and elusive cure for patients with advanced metastatic cancer.The authors have declared no conflict of interest.


The injection mold is one of the most important elements for the part precision of this important mass production process. The thermal mold design is realized by cooling channels around the cavity and poses as a decisive factor for the part quality. Thus, the objective but specific design of the cooling channel layout is crucial for a reproducible part with high-dimensional accuracy in production. Consequently, knowledge-based and automated methods are used to create the optimal heat management in the mold. One of these methods is the inverse thermal mold design, which uses a specific calculation space. The geometric boundary conditions of the optimization algorithm influence the resulting thermal balance within the mold. As the calculation area in the form of an offset around the molded part is one of these boundary conditions, its influence on the optimization result is determined. The thermal optimizations show a dependency on different offset shapes due to the offset thickness and coalescence of concave geometries. An algorithm is developed to generate an offset for this thermal mold design methodology considering the identified influences. Hence, a reproducible and adaptive offset is generated automatically for a complex geometry, and the quality function result improves by 43% in this example. The mold of the injection molding process provides the boundary conditions for the physical actions taking place from the injection phase until ejection of the molded part. Material-related component shrinkage is one of the greatest challenges of modern plastics processing and poses a challenge for high geometric and shape part precision. This accounts especially for injection molded components, which are characterized by a high functional density and increasingly complex component geometries. From the surface and geometry of the cavity to the heat transfer coefficient between mold and melt as well as the arrangement of cooling channels, the design, chosen material and mold manufacturing methods have a significant influence on the physical properties of the plastic product . These dExperiment-based design;Design and optimization based on the conformal cooling line;Optimization using expert algorithms;Modular/parametrical design of conformal cooling channels;Solid modelling based on topology optimization.Many research groups work in the automatic generation of cooling channels. An extensive literature review has recently been performed by Feng et al. . AccordiThe first category is a hands-on approach, where different designs are created via experience and then validated via simulations ,5,6. In optimization using expert algorithms, but it places low demands on the designer in a pure application. The designer does not have to specify any parameters for the calculation of the cooling channel layout other than the planned cooling time and the targeted demolding temperature. The method calculates an optimal heat balance in the injection mold independently of an existing cooling channel system (see The Institute for Plastic Processing in Industry and Craft at the RWTH Aachen University (IKV) researches the method of the inverse thermal mold design. This method falls into the category of stem see . ThermalThis work uses an optimization algorithm to calculate a thermal balance within the mold, which provokes a cooling of the molded part with minimized warpage. Within the optimization of the inverse mold design, the thermal boundary conditions in the mold are calculated in such a way that a thermally optimal molded part is produced at the end of the cooling phase with regard to the material models and modeling used. This methodology is based on an approach by Agazzi et al. , was extBefore the optimization step, a contour is created in the form of an offset of the part. This geometry maps the steel tool around the molded part. This has the advantage of a smaller calculation space than compared to the whole mold, which is multiple times bigger. Additionally, the contour’s surface is the location of the temperature values, which are varied by the optimizer. These temperatures are the optimization parameters that result in the optimized thermal balance.The used routine for the thermal cooling channel design used in this research is based on the further development of Hohlweck . Figure Comsol Multiphysics, Comsol AB, Stockholm, Sweden for the optimization step. They are particularly important because the approach of Agazzi et al. [Once a suitable offset is created, an injection molding simulation is carried out using commercial models and software. This simulation depicts solely a temperature distribution after multiple cycles without any cooling channels within the mold. This allows a location-dependent temperature distribution to be quickly calculated for the molded part and the offset as a starting condition for the following optimization. Additionally, a time-dependent pressure distribution can be imported from the injection molding simulation to consider the pressure dependencies of the thermal properties and i et al.  is execui et al. . StartinThe optimization deals with the boundary value problem of inverse thermal heat transfer . The bouThe first term of the equation is similar to the quality function from Nikoleizig . It evalThe second row of the equation evaluates the morphology of the molded part. The first term adds up the differences of the local cooling rates e Atot,2 , left. AAn isosurface can be extracted from the optimal thermal heat balance in the following. This isosurface represents the position with the given heat flux that can be provided by cooling channels at this position. Along these isosurfaces, a developed algorithm approximates the segments of cooling channels and creates connections between them ,16. By tMinkowski sum of the input manifold and a sphere with the chosen constant offset radius [P and Q, their Minkowski sum is given by There is extensive literature on how to compute offsets from triangle meshes with constant offset diameters ,27,28,29t radius . Given tMinkowski sum of the input object and a sphere. This can be solved by separating the sum into easier to compute primitives [The approaches to compute constant radius offsets can be separated into three groups, including some hybrid approaches: Mesh Boolean-based, surface-based, and volumetric. Mesh Boolean-based approaches compute the imitives ,29: a spimitives .Surface-based approaches usually try to directly apply offsets to input primitives ,26,30. TFinally, volumetric approaches evaluate the output surface in a grid and use isosurface extraction to extract the final offset mesh. The simplest candidate of volumetric approaches evaluates the signed distance to the input mesh in a regular grid and then uses an isosurface extraction algorithm such as marching cubes  to extra such as  use an aWhile most previous work was done on algorithms to extract offset surfaces with a constant distance to the input mesh, there is little work on variable offset surfaces. The definition for a variable offset varies vastly: Musialski et al.  aim to cThe approach of this work uses a similar offset definition as Woerl et al. : the desThe offset is created manually, which is a time-consuming and error-prone process for complex geometries. It is not viable for complex geometries with e.g., openings, narrow ribs, snap hooks, etc., which corresponds to the real requirements of injection-molded components. Comercial CAD software often provides tools for an automated offset orthogonal to the selected surfaces. However, this method leads to a larger and unrelated distance between the part and the offset at the edges. Even the rounding of edges and corners is only a limited remedy, as, for example, the maximum offset of inner radii is determined by their magnitude or the combination of several complex surfaces leads to geometric defects.Minkowski sum and can create a constant distance from the part geometry [There are algorithms that use the geometry . The aspAs the creation of contours with a constant distance to complex parts poses difficulties, the use of simplified contours is investigated. A case for a single-board computer (SBC) with small and complex elements such as ribs, screw domes, and snap hooks poses as molded part geometry. A minimal offset thickness of 10 mm is used for the investigation, but for the simplified contours, it is larger for various sections. In addition to the methods described above for creating a contour, a simple geometry in the form of a box and shell-shaped contour is created .As a result, the isosurfaces, which indicate the position of the heat flow to be applied in the form of the cooling channels, are compared. With the box contour, the isosurfaces are evenly distributed around the part and have an approximately constant distance to it, whereas using the contoured offset results in a more specific isosurface . Therefoj  of 2 mm are examined. The conventional offset, one with an extra surface along the medial axis within the closed volume and one with a 0.1 mm thick slit along the medial axis, are compared in the thermal optimization  Core(TM) i7-6700K CPU that runs at 4 GHz and has 32 GB RAM. The timings for different inputs are shown in function  and the putation  scale wiputation  that canComsol Multiphysics was found to increase drastically with higher triangles counts. Therefore, the number of output triangles is reduced during post-processing (see The pipeline is tested on several inputs. A selection is shown in sing see .The generated offset is imported to the thermal optimization model, and a solid geometry is derived from the triangulated surface. This step is necessary to create geometric boundaries that can be selected for the definition of the materials, evaluation areas, and surface for the optimization parameters. An insufficient description of the offset results in defective geometry. The next step is a new mesh of the offset geometry. Accordingly, no surface elements from the input file are used. This work shows the necessity of an automized generation of the contour. The manual design of the offset is not feasible, especially for complex parts such as the raspberry pi case. Additionally, the influence of the variation occurring in manual work needs to be avoided for a reproducible and comparable offset generation.Multiple steps have been identified as variables that have an influence on the thermal optimization result. For one, the offset thickness in combination with the wall thickness of the part has a significant influence on the optimal temperature distribution. The influence of the cooling rate and freeze time on the second part of the quality function will be investigated with more practical dimensions of the geometry. Additionally, a solution for the coalescence of concave geometries and therefore multiplication of the effective offset distance is determined. The calculation and extraction of the medial axis pose as a suitable method to establish fitting offset thicknesses in cavities. For the first tests with one geometry containing a slit at the medial axis, a multiple times better result for the quality function is achieved. In future work, it needs to be determined, whether the gap in the thermal model at the medial axis leads to impossible temperature fields, which cannot be approximated by actual cooling channels. In this case, adaptations and limitations of the extracted medial axis will be considered.The variable offset generation has been successfully implemented into an interactive tool that can be used for further experiments. The developed algorithm considers the examined effects, and the generated offsets can be used in the existing methodology. The first results show a significant improvement of the optimized thermal balance in the mold.