Drug development based on target proteins has been a successful approach in recent decades. However, the conventional structure-based drug design (SBDD) pipeline is a complex, human-engineered process with multiple independently optimized steps. Here, we propose a sequence-to-drug concept for computational drug design based on protein sequence information by end-to-end differentiable learning. We validate this concept in three stages. First, we design TransformerCPI2.0 as a core tool for the concept, which demonstrates generalization ability across proteins and compounds. Second, we interpret the binding knowledge that TransformerCPI2.0 learned. Finally, we use TransformerCPI2.0 to discover new hits for challenging drug targets, and identify new target for an existing drug based on an inverse application of the concept. Overall, this proof-of-concept study shows that the sequence-to-drug concept adds a perspective on drug design. It can serve as an alternative method to SBDD, particularly for proteins that do not yet have high-quality 3D structures available. Conventional structure-based drug design pipeline is a complex, human-engineered process with multiple independently optimized steps. Here, the authors report a sequence-to-drug concept that discovers drug-like small molecule modulators directly from protein sequences. A typical protein structure-based drug design (SBDD) project starts from the protein sequence and builds a three-dimensional (3D) structure through structural biology or structure prediction. It then identifies binding pockets, including orthosteric sites or allosteric sites, and finally discovers active modulators through virtual screening or de novo design5  and ring finger protein 130 (RNF130), which lacks existing 3D structures. Additionally, we identified ADP-ribosylation factor 1 (ARF1) as a new target for proton pump inhibitors (PPIs). After the proof of concept, the sequence-to-drug concept appears to be a promising direction for rational drug design.Several deep learning models have been proposed to use protein sequences as inputges Fig. . First, 20 and its framework is shown in Fig. 20. Therefore, we ensured that each compound in our dataset exists in positive class and negative class, but pairs with different proteins. Because the compounds with positive and negative labels are exactly the same, ligand bias is greatly reduced in our dataset. Under this criteria, we constructed a ChEMBL dataset containing 217,732 samples in the training set, 24,193 samples in the validation set, and 10,199 in the test set. Consequently, we used the label reversal experiment20 to split the ChEMBL dataset, where some chosen ligands in the training set appear only in one class of samples (either positive or negative interaction CPI pairs), but in the opposite class of samples in the test set. If a model only memorizes the ligand patterns, it is unlikely to make correct predictions because the ligands it memorizes have the wrong (opposite) labels in the test set. Within the scheme of label reversal experiments, the model was forced to utilize protein information along with compound information to understand interaction patterns and thus overcome the ligand bias issue.To build a model that can implement the sequence-to-drug concept, we developed TransformerCPI2.0 based on our previous work20, CPI-GNN19, GraphDTA(GAT-GCN)21, MolTrans29 and Graph Convolutional Networks (GCN)21 were selected as baseline models, and all were retrained on the ChEMBL dataset. We trained TransformerCPI2.0 and baseline models under the same criteria and compared their performance in terms of area under the Receiver Operating Characteristic Curve (AUC) and area under the Precision Recall Curve (PRC)  Fig. . TransfoRC) Fig. . Transfo30 and DEKOIS2.0 set31 and the enrichment factor  for the screen power assessment32, which is calculated from the proportion of true active compounds in the selection set in relation to the proportion of true active compounds in the entire dataset .To confirm the feasibility of sequence-to-drug concept, we compared TransformerCPI2.0 with conventional structure-based drug design approaches to test its ability to screen active molecules from compound libraries. We used the benchmark dataset DUD-E set33, but slightly higher than the academic program AutoDock Vina34. From Supplementary Table From Supplementary Table 35. Briefly, we mutated each amino acid of the given protein sequence one by one and examined whether the prediction score changed significantly. We input the wild-type protein and drug into TransformerCPI2.0 to calculate the original prediction score, denoted as s. Then we mutated each amino acid of the protein sequence to all 20 amino acids (including itself) and calculated the prediction score s’. The activity change score ΔS is defined as the difference between s and s′ Then the relative activity change score (ΔR) is defined as the average of ΔS among 20 amino acids at each position, followed by normalization.To investigate whether TransformerCPI2.0 captures correct information about binding sites, we proposed an analysis method named drug resistance mutation analysis that mimics alanine scanning36. It is encouraging that positions with a high ΔR are highly overlapped with the binding sites of doravirine . We considered positions with ΔR above 0.38 as important sites corresponding to the top 5% sites of the entire sequence. As a result, P225, F227, L234 and P236 have been reported as drug resistance mutation sites40 and are correctly retrieved as important sites by TransformerCPI2.0  and reported the first SPOP inhibitor, but it is a challenging target in terms of protein–protein interactions46. In ccRCC cells, SPOP is overexpressed and misallocated in the cytoplasm, inducing proliferation and promoting renal tumorigenesis47. Two substrates of SPOP are phosphatase and tensin homolog (PTEN) and dual specificity phosphatase 7 (DUSP7)47. PTEN acts as a negative regulator of phosphoinositide 3-kinase/AKT pathway, and DUSP7 dephosphorylates extracellular signal-regulated kinase (ERK)48. The accumulation of cytoplasmic SPOP in ccRCC cells decreases cellular PTEN and DUSP7 by mediating the degradation of these two cytoplasmic proteins, leading to an increase in phosphorylated AKT and ERK and promoting ccRCC cell proliferation47.SPOP functions as an adapter of cullin3-RING ubiquitin ligase, mediates substrate protein recognition and ubiquitination50 of 4.51 μM   Fig. . A nucle1C7 Fig. . To demoATH Fig. . Compounsay Fig. , and did53 of 214Å2. Therefore, we conducted hit expansion and obtained 26 structural analogs of 221C7, 19 of which were active in the FP assay  and the smallest IC50 of the FP assay say Fig. . Among tsay Fig. . To detesay Fig. . This su50 of approximately 20 μM compared with non-ccRCC cell lines. To determine if 230D7 is suitable for in vivo studies, we investigated the pharmacokinetics and acute toxicity profile of 230D7. 230D7 can be efficiently absorbed into the blood circulation after intraperitoneal injection and has low acute toxicity  is 1.01. Compared with other tools, iRNF130-63 was highly ranked and discovered only by TransformerCPI2.0  and ARF1 inhibition or knockdown in CSCs leads to accumulation of lipid droplets, further leading to metabolic stress that not only can kill CSCs selectively, but also stimulate an anticancer immune response and achieve lasting therapeutic effects57. Inhibition of ARF1 activity is a promising direction for cancer immunotherapy, therefore, we selected ARF1 for investigation.Benefiting from the end-to-end nature, the sequence-to-drug workflow can be inversely used to enable drug target identification or drug repurposing. This means that we can perform proteome-wide target screening, as only protein sequence information is required except for a given drug molecule as the model input. Here we selected proton pump inhibitors (PPIs) as a drug repurposing case study. To date, preclinical and clinical data support the use of PPIs in cancer treatmentm shifts  and destabilize the protein. Drug resistance mutation analysis was then applied to interpret the prediction of TransformerCPI2.0. The results indicated that amino acids 150 to 165, a region that containing a cysteine (C159), contributed greatly to compound protein binding  PPIs with ARF1WT and the reducing agent dithiothreitol (DTT), which can break disulfide bonds; and (2) PPIs with ARF1C159A where C159 was mutated to alanine. No Tm shifts were observed in either assay  and found that rabeprazole suppressed the nucleotide exchange process in a concentration-dependent manner  using a G-LISA assay. The results showed that rabeprazole effectively inhibited ARF1 activity in CT26 cells in a concentration-dependent manner . PROTACs have been successfully developed for harnessing the ubiquitin–proteasome system to degrade a protein of interest, receiving tremendous attention as a new and exciting class of therapeutic agents that promise to significantly impact drug discovery. Furthermore, through an inverse application of the sequence-to-drug concept, the FDA-approved drug rabeprazole showed promise for expanding its indications to colon cancer treatment by regulating lipid metabolism and inducing an antitumour immune response. Additionally, these targets and their corresponding active molecules are not seen in the training phase, and the hits we reported share low similarity with the known active molecules and fail to be discovered by other tools, which supports that our concept is not replaying the training set but can generalize across protein and chemical space. Overall, our findings provide a proof of sequence-to-drug concept, which we believe will become an essential component of future rational drug design pipelines.However, our work does not guarantee the success to any novel targets. We appreciate and respect other drug discovery tools, and our aim is to add a new perspective to drug design. The rigorous conclusion we draw is that our work can serve as an alternative method to SBDD, and it can be used in combination with other in silico or in vivo tools, to help the community accelerate the drug discovery progress.60, SCUBIDOO61, ZINClick62, GDB-1763, FDB-1764, DrugSpaceX65 and synthesis (REAL) combinatorial libraries66, which cover spaces of 100 million to multiple billions of chemicals, there is a high demand for developing computationally efficient virtual screening approaches. The sequence-to-drug concept and TransformerCPI2.0 can be combined with these large virtual libraries to rapidly discover active scaffolds from the unexplored chemical space.Recently, due to the rapid growth of virtual chemical libraries, such as GDBChEMBLCompared with TransformerCPI, TransformerCPI2.0 has been updated in the following four aspects: (1) removing 3-gram protein word embedding calculated by the Word2Vec algorithm, (2) computing protein sequence representation by a pretrained protein language model named TAPE-BERT, (3) replacing 1D convolutional neural networks and gated linear units with a self-attention-based transformer encoder, and (4) introducing a new atom vector into the atom sequence that carries the interaction information at the molecular level.67 achieved great success in natural language processing (NLP), many efforts have been devoted to protein sequence representation learning. Many pretraining models based on long short-term memory (LSTM) or transformer architectures have been proposed, such as UniRep68 and TAPE69. To maintain the model consistency and gain parallel computing efficiency, we chose the transformer model in TAPE (TAPE-BERT) to calculate protein sequence embedding. The TAPE-BERT model contains 12 self-attention encoder layers, 12 attention heads for each layer, 768 dimensions for the hidden state, and 3072 dimensions for feedforward layers. We first utilized the TAPE Tokenizer from TAPE-BERT to encode the protein amino acid sequence into real values, where numbers 1–23 represent 23 common amino acids, 0 represents the token ‘<pad>’, 24 represents the token ‘<cls>’, and 25 represents the token ‘<sep>’. Then, we input this encoded real value sequence into the pretrained TAPE-BERT model and finally obtained protein embeddings with 768 dimensions. In TransformerCPI2.0, protein embedding from the TAPE-BERT model serves as the input to the encoder of TransformerCPI2.0, replacing the embedding calculated from the Word2Vec model.Word2vec is an unsupervised technique to learn high-quality distributed vector representations that describe sophisticated syntactic and semantic word relationships and maps discrete words to low-dimensional real-valued vectors. However, the final embedding table of Word2vec is stationary, regardless of the upstream and downstream context information of the given word, which may lead to errors regarding the true meaning of the word in its local context. Since BERTSince protein embedding was calculated by the TAPE-BERT model, we replaced 1D convolutional neural networks and gated linear units with the original self-attention-based transformer encoder. Given that the position information of the amino acid sequence has been taken into consideration when computing protein embeddings, position embedding was removed from the transformer encoder. The encoder consists of 3 encoder layers, 8 attention heads for each layer, 768 dimensions for the hidden state, and 3072 dimensions for feedforward layers. To maintain the maximal performance of TAPE-BERT, the hidden state dimension of the encoder was exactly the same as that of TAPE-BERT, ensuring no information loss in this process. After the hyperparameter search, 12 attention heads showed little performance improvement compared to 8 attention heads but much higher training and inference time; therefore, only 8 attention heads were used in TransformerCPI2.0.70 to learn the representation by integrating their own neighborhood information. Notably, only one GCN layer was used and recommended in this process, and more than two GCN layers harmed the performance of TransformerCPI2.0 to a great deal. Too many GCN layers may over smooth the atom features, causing different atom features to tend to be similar to each other. TransformerCPI2.0 then fails to learn compound-protein interaction features when the atom embedding carries excessively similar features. A table of atomic embedding features is shown in the Supplementary Table Each of the atom features was initially represented as a vector of size 34 using the RDKit python package, and the list of atom features can be found in our previous work. In TransformerCPI2.0, we additionally introduced a new virtual atom that carries the information at the molecular level and does not exist in the given compound. This virtual atom was initialized as the average of atom features across the whole compound and was linked to all atoms. All the atom vectors together with the virtual atom vector were put into GCNsProtein embedding and atom embedding serve as the target sequence and memory sequence of the transformer decoder, respectively. Consistent with the encoder, the decoder consists of 3 decoder layers, 8 attention heads for each layer, 768 dimensions for the hidden state, and 3072 dimensions for feedforward layers. In addition, the original transformer was designed to solve seq2seq tasks and utilize a causal mask operation to cover the downstream context of each word in the decoder. We removed the mask operation of the decoder to ensure that our model accesses the whole target sequence. Since we introduced a new virtual atom, as described above, we used the last layer representation of this virtual atom rather than the weighted sum of the last layer atom representation to predict the compound protein interaction probability. The last layer presentation of virtual atoms was fed into fully connected layers and finally returned the compound protein interaction probability.71 optimizer with a learning rate of 1e-5 and a weight decay of 1e-3. The batch size of 1 was selected to ensure that the longest protein sequence fit into the GPU memory. We employed the gradient accumulation technique to expand the actual batch size to 64. Training was performed on one NVIDIA Tesla V100 (16G) GPU. The TransformerCPI2.0 model was trained for ~50 epochs or ~1.5 weeks of wall clock time.The TransformerCPI2.0 model was trained by the RAdam72 database to construct a universal dataset to train TransformerCPI2.0.Inheriting our previous work, we followed two rules to construct a dataset: (i) CPI data was collected from an experimentally validated database and (ii) each ligand should exist in both positive and negative classes. To build a universal deep learning model for all types of proteins, we selected the ChEMBL_23The target type was set to ‘SINGLE PROTEIN’, and the molecule type was set to ‘Small molecule’;Data with a confidence score of 9 and assay type of ‘B’ were reserved;50, EC50, Ki in units of nM were selected.Activity data with activity metrics of ICThe ChEMBL_23 database was released on 1 May 2017 and contains 2,101,843 compound records, 1,735,442 compounds, 14,675,320 activities, 1,302,147 assays, 11,538 targets and 67,722 source documents. We downloaded the whole database and cleaned the data using the following procedure:50, EC50, and Ki to pIC50, pEC50, and pKi and then split the dataset into a positive set and a negative set at the threshold of 6.5. Samples with different labels were removed from the dataset. In the early stage of drug discovery, only hit compounds whose IC50, Ki or EC50 are at the μM or even nM level will be further optimized. Additionally, public data are prone to a certain experimental error, i.e., on average 0.5 log units for IC50 data74. The threshold of 6 considers those CPI pairs whose IC50, Ki or EC50 are smaller than 1 μM as positive samples, which causes the models to select CPI pairs with high activity. To decrease the risk of experimental error in public data, a stricter threshold of 6.5 was used in a previous work75. Therefore, we selected the threshold of 6.5 to define positive data and negative data. Data whose atom number was more than 60 or whose protein sequence length exceeded 4000 were filtered out, guaranteeing that all the data fit into GPU memory. Finally, we constructed a ChEMBL dataset including 3348 proteins, 69,616 compounds, 117,513 positive CPIs, 134,611 negative CPIs and 252,124 samples in total.After cleaning the data, we transformed ICWe selected ligands that exist in both the positive and negative classes to make the compound distribution in positive samples and negative samples exactly the same, trying to eliminate the potential ligand bias as much as possible. Consequently, we used a label reversal experiment to split the ChEMBL dataset. The mechanism of label reversal experiment is that some ligands in the training set appear only in one class of samples (either positive or negative interaction CPI pairs), while have the opposite labels with other proteins in the test set. In this way, the model was forced to utilize protein information to understand interaction modes and make opposite predictions for those chosen ligands. If a model only memorizes the ligand patterns, it is unlikely to make correct predictions because the ligands it memorizes have the wrong (opposite) labels in test set. Therefore, this label reversal experiment is specifically designed to evaluate CPI models and is capable of indicating how much influence the hidden variables have exerted. For the ChEMBL dataset, we randomly selected 2941 ligands and pooled all the negative CPI samples containing these ligands into the test set. Additionally, we selected another 2900 ligands and pooled all their associated positive samples into the test set. The remaining datasets were split randomly into a training set and a validation set at a ratio of 10:1. The validation set was used to determine the hyperparameters, and the best model was evaluated on the test set. Under this experimental design, we finally established a ChEMBL set containing 217,732 samples in the training set, 24,193 samples in the validation set and 10,199 in the test set.All the baseline models and TransformerCPI2.0 were tested on the external test set and time-split ChEMBL27 set. This external set contains compounds that were not previously observed and 1192 new protein targets that were not included in the training set. The total number of CPI pairs is 342,447, and the ratio of positive samples to negative samples is 1:3. This external set can evaluate the generalization ability of TransformerCPI2.0 and baseline models to the new compounds and new proteins. Another time-split dataset named the ChEMBL27 dataset contains compounds that were not previously observed and 637 new protein targets that are not included in the training set, and all the data were collected from the ChEMBL_27 database. The total number of CPI pairs is 92,919, and the ratio of positive samples to negative samples is 1:1.All the baseline models, including TransformerCPI, CPI-GNN, GraphDTA(GAT-GCN), MolTrans and GCN, were trained on the ChEMBL dataset with their own hyperparameters. Only the learning rate, weight decay rate and dropout rate were subjected to a hyperparameter search.Activity change score calculation. First, we input the wild-type protein and drug into TransformerCPI2.0 to calculate the original prediction score, denoted as S here. We analyzed ΔS and found that TransformerCPI2.0 actually learns the key features of compound protein interactions because the pattern of ΔS revealed by heatmap analysis is consistent with that of drug resistance mutation.Here, After calculating the activity change score, we can evaluate whether a mutation at a specific position plays an important role in compound protein interactions. However, Furthermore, the value of R can then characterize the contribution of each amino acid position to TransformerCPI2.0 prediction and help researchers discover novel and potential drug resistance mutation sites. On the other hand, ΔR can reflect the compound–protein interactions in TransformerCPI2.0. An amino acid site whose contribution to compound–protein binding is large can be revealed quantitatively by the relative activity change score ΔR. Finally, we used the activity change score ΔS to plot a heatmap to study the concrete pattern of drug resistance mutations and the relative activity change score ΔR to rank the most important sites for compound protein binding.The relative activity change score Δ3) with trifluoromethyl (TFM or −CF3) is frequently employed in compound optimization. However, the exact effect of −CH3/–CF3 substitution on bioactivity is still controversial. To further investigate whether TransformerCPI2.0 captures the key features of the compound and comprehensively understands compound–protein interaction, we employed TransformerCPI2.0 to predict the substitution effect of the trifluoromethyl group. We utilized a previously reported dataset, removed the redundancy data and finally got a dataset containing 18,217 pairs of compounds and corresponding bioactivity data with the only difference being that −CH3 is substituted by −CF3 to study this problem. We checked this dataset with our training set and found only 1,062 pairs (5.8%) were overlapped. The majority of this dataset are not seen by TransformerCPI2.0 and baseline models during the training phase, so it can measure the generalization of these models to some extent. However, this analysis does not prove that TranformerCPI2.0 can solve activity cliff prediction problems. We stress that this analysis is aim to show that TransformerCPI2.0 can capture activity-related information of compounds, and can served as interpretation tools. The interpretation results should be taken with caution.The replacement of methyl , which contains approximately 1.6 million in-stock compounds, TransformerCPI2.0 was applied to score the compounds, and the top 35,000 molecules  were selected by screening. Second, we filtered pan assay interference compounds (PAINS) and clustered these molecules automatically based on their extended-connectivity fingerprints (ECFP), obtaining approximately 800 clusters. Third, we filtered these compounds by the Lipinski rules and selected representative compounds from top ranked clusters. Finally, a total of 82 candidates were purchased for further experimental evaluation.First, after filtering non-drug-like compounds from the Chemspace Library , which contains approximately 2 million in-stock compounds, TransformerCPI2.0 was applied to score the compounds, and the top 10,000 molecules  were selected. Second, we filtered pan assay interference compounds (PAINS) and clustered these molecules automatically based on their extended-connectivity fingerprints (ECFP), obtaining approximately 200 clusters. Third, we filtered these compounds by the Lipinski rules and selected representative compounds from top ranked clusters. Finally, a total of 87 candidates were purchased for further experimental evaluation.We collected potential proteins from the DrugBank database and selected proteins that already have active modulators. Then, TransformerCPI2.0 was applied to score proteins against four classical PPIs , and the results were sorted by predicted interaction probability. Next, we analyzed the top 20 proteins by evaluating their novelty, importance and feasibility, and finally chose ARF1 for experimental validation.SPOP inhibitors were purchased from ChemDiv Library : 221C7, Y502-3210; 231A10, 5282-0816; 231D8, 8017-3040. 230D7 was synthesized in our laboratory. RNF130 inhibitor was purchased from Chemspace Library : iRNF130-63, CSC138461036. PPIs were purchased from MedChemExpress : rabeprazole, HY-B0656; lansoprazole, HY-13662; Omeprazole, HY-B0113; pantoprazole, HY-17507.O43791-1), PTEN (P60484-1), DUSP7 (Q16829-1), RNF130 (Q86XS8-1), ARF1 (P84077-1) and ARNO (Q99418-1). For plasmid construction, SPOPWT and SPOPcyto (residues 1-366) were subcloned into the pcDNA 3.1 vector with a Flag-tag, SPOPMATH (residues 28–166) and ARNOSec7 (residues 50–250) was subcloned into the pGEX 6p-1 vector with a GST-tag, PTEN and DUSP7 were subcloned into the pcDNA 3.1 vector with a Myc-tag, RNF130 (residues 1–304) was subcloned into the pcDNA3.1 vector with a C-terminal His8-Flag-tag. N-terminally truncated human Δ17ARF1 and C159A-mutant Δ17ARF1 were subcloned into pProEX HTb with a His6-tag. All of the above plasmids were synthesized by Sangon Biotech (Shanghai) Co., Ltd. pCMV-HA-Ub plasmid (CAT#. kl-zl-0513) was purchased from Shanghai Kelei Biological Technology Co., Ltd.Wild-type, truncated or mutant versions of the human proteins were used in this study: SPOP -RIPL Cells , and then the cells were grown in lysogeny broth (LB) medium and induced by isopropyl β-D-1-thiogalactopyranoside (IPTG) at a final concentration of 0.5 mM at 16 °C overnight. Cells were harvested and lysed in solution  by sonication and then centrifuged at 32,914 × g for 1 h at 4 °C. The supernatants were filtered by 0.22 μM syringe filters and purified on GST Trap columns  by elution with 10 mM reduced glutathione. The eluted components were loaded onto desalting columns  to remove reduced glutathione and incubated with PreScission Protease for 6–8 h at 4 °C. The components were reloaded onto GST Trap columns to remove the GST tags and further purified by a Superdex 75 10/300 GL column. The purified SPOPMATH protein was concentrated and stored in buffer  at −80 °C.For expression of SPOP2+-Sepharose 6 Fast Flow resin  and then further purified by gel filtration chromatography with a Superdex S200 column . The purified protein was concentrated and stored in buffer  at −80 °C.RNF130 protein was expressed in Expi-293F (Invitrogen) using Expifectamine transfection reagent according to the manufacturer’s instructions. Cells were collected 3 days after transfection. Proteins were first captured by NiC159A, the His-tagged recombinant plasmid was transformed into BL21-CodonPlus (DE3)-RIPL cells, and then the cells were grown in LB medium and induced by IPTG at a final concentration of 0.1 mM at 25 °C for 6 h. The cells were harvested and lysed in solution  by sonication and then centrifuged at 32,914 × g for 1 h at 4 °C. The supernatants were filtered by 0.22 μM syringe filters and purified on HiTrap column  by elution with 300 mM imidazole. Protein sample was then purified by a Superdex 75 10/300 GL column. Finally, the purified Δ17ARF1 and Δ17ARF1C159A proteins were concentrated and stored in buffer  at −80 °C.For expression of Δ17ARF1 and Δ17ARF1Sec7 protein was the same as that of the SPOPMATH protein except the cells were induced by 0.1 mM IPTG at 37 °C for 3 h.The purification process ARNOMATH protein was incubated with 2 μL of compound for 30 min at room temperature, and 20 μL of reaction buffer containing 200 nM probe was added. Fluorescence polarization (mP) signals were measured by a fluorescence mode  in Spark microplate reader (Tecan).Fluorescence polarization experiments were conducted in a 384-well black plate  using a 42 μL reaction system. FITC-labeled SPOP substrate puc_SBC1 (FITC-LACDEVTSTTSSSTA) (synthesized by GL Biochem (Shanghai) Ltd) was used for the probe. Then, 20 μL of reaction buffer  containing 200 nM SPOPMATH protein. PTS experiments were performed in a 96-well PCR plate (DN Biotech (Hong Kong) Co., Ltd.) with a 20 μL reaction system. A total of 20 μL of reaction buffer containing protein (5 μM for SPOPMATH or 2.5 μM for ARF1), 5 × SYPRO Orange Protein Gel Stain  and indicated concentration of compound. The signals of all reaction systems were continuously monitored and recorded from 25 °C to 90 °C for approximately 45 min. The Tm values of SPOPMATH and ARF1 were measured using CFX manager software version 3.1.The Bio–Rad CFX96 RealTime PCR Detection System was utilized to monitor the thermal stability of ARF1 and SPOP2O containing 5 μM SPOPMATH protein and 5% DMSO‑d6.NMR spectroscopy experiments were performed using a 600 MHz spectrometer  to validate protein–ligand interactions. In Carr-Purcell-Meiboom-Gill (CPMG) and saturation transfer difference (STD) NMR experiments, compound was dissolved to a final concentration of 200 μM in a solution of PBS formed with DThe experiment was performed on the mass spectrometry service platform of Shanghai Institute of Materia Medica, Chinese Academy of Sciences. The protein (100 μM) was incubated with compounds (1 mM) or solvent control overnight at 4 °C, and then the protein molecular weights were determined by Q Exactive (Thermo) and 6545 XT (Aglient) mass spectrometer. For compound binding site identification, the proteins were digested with trypsin (10 ng/μL) at 37 °C for 17 h. The next day, after centrifugation, the supernatant was lyophilized, desalted, and lyophilized again, followed by the addition of 0.1% FA solution to dissolve peptide lyophilized powder. After centrifugation, the supernatant was detected by mass spectrometry (Q-Exactive). The MS data was analyzed via software MaxQuant (version 1.6.5.0). The false discovery rate (FDR) for peptides and proteins was controlled <1% by Andromeda search engine.KD) value was derived using Biacore T200 Evaluation software .The SPR binding assay was performed using a Biacore T200 instrument . The purified RNF130 protein was covalently immobilized onto a CM5 sensor chip (Cytiva) by a standard amine-coupling procedure in 10 mM sodium acetate (pH 4.5) with running buffer HBS . iRNF130-63 was serially diluted and injected onto the sensor chip at a flow rate of 30 μL/min for 120 s (contact phase), followed by 120 s of buffer flow (dissociation phase). The equilibrium dissociation constant  was added to the RNF130 protein. The data were analyzed using MicroCal PEAQ-ITC software.GDP protein. Next, ARF1GDP protein (20 μM) was mixed with compounds and Mant-GTP (10 μM) in reaction buffer and incubated in the dark for 15 min. Exchange reactions were initiated by the injection of ARNOSec7 (1 μM).First, with the participation of EDTA , GDP was loaded onto the ARF1 protein by incubating ARF1 with a 20-fold molar concentration of GDP. Excess magnesium chloride was used to terminate the loading reaction, followed by the removal of excess GDP by a NAP-5 column to produce ARF12 atmosphere.293T (CRL-3216), MDA-MB-231 (HTB-26), 4T-1 (CRL-2539) and CT26 (CRL-2638) cells were obtained from the American Type Culture Collection (ATCC). OS-RC-2 (1101HUM-PUMC000292) and Caki-2 (1101HUM-PUMC000337) cells were purchased from the National Biomedical Laboratory Cell Resource Bank. 786-O (TCHu186) cells were kindly provided by the Cell Bank/Stem Cell Bank, Chinese Academy of Sciences. 293T and MDA-MB-231 cells were cultured in DMEM medium  supplemented with 10% fetal bovine serum  and 1% Penicillin-Streptomycin . 786-O, OS-RC-2, 4T-1 and CT26 cells were cultured in RPMI-1640 medium  supplemented with 10% FBS and 1% PS. Caki-2 cells were cultured in McCoy’s 5A medium  supplemented with 10% FBS and 1% PS. All cells were incubated at 37 °C under a 5% (v/v) COg at 4 °C for 1 min. Protein concentrations from each sample were quantified and adjusted to identical concentration for the assay. ARF1 activity was assessed according to the manufacturer’s instructions.ARF1 activity was measured using corresponding G-LISA Activation Assay Kits . Briefly, CT-26 cells were treated with different concentrations of rabeprazole and lysed using the provided cell lysis buffer, then lysates were collected by centrifugation at 16,260 × g for 15 min at 4 °C. The BCA protein assay kit  was used to quantify the protein concentration. Equal amounts of total proteins were separated by 10% SDS–PAGE and then transferred onto nitrocellulose membranes. The membranes were blocked with 5% skim milk in TBST for 1 h at room temperature and then incubated with primary antibodies overnight at 4 °C. Then membranes were incubated with HRP-conjugated anti-rabbit antibody  for 1 h at room temperature. Finally, the immune complexes were detected with an ECL kit  and visualized as well as quantified using GenGnome XRQ NPC. The following primary antibodies were used: anti-Flag , anti-myc , anti-GAPDH , anti-PTEN , anti-HA , anti-p-ERKT202/Y204 , anti-p-AKTThr308 , anti-ERK , anti-AKT , anti-DUSP7 , anti-SPOP , anti-GST  and anti-β-Tubulin .Total proteins from cells were lysed in RIPA lysis buffer  containing phosphatase inhibitor  and protease inhibitor  on ice. Cell lysates were centrifuged at 13,000 × MATH proteins bound to GST magnetic beads  were incubated with the cell lysates (Myc-PTEN or Myc-DUSP7) in the presence of different doses of compound for 2 h at room temperature. The beads were washed 3 times with PBST, and the precipitated proteins were eluted with 1 × SDS loading buffer  at 100 °C for 5 min and analyzed by Western Blot.The plasmids (Myc-PTEN or Myc-DUSP7) were transiently transfected into 293T cells. After transfection for 24 h, the cells were harvested and lysed in cell lysis buffer for Western and IP  containing protease inhibitor on ice. GST or GST-SPOPg for 30 min at 4 °C, and the supernatants were analyzed by western blot.293T cells transfected with Flag-RNF130 for 48 h were collected and lysed in 20 mM Tris pH 7.5, 150 mM NaCl and 1% Triton X-100. Then, 50 μM iRNF130-63 or DMSO was added to the supernatant and incubated at 25 °C for 30 min. After denaturing at various temperatures for 3 min on a temperature gradient PCR instrument (Eppendorf), the samples were centrifuged at 20,000 × cyto, Myc-PTEN or Myc-DUSP7) were transiently cotransfected into 293T cells. After transfection for 24 h, 293T cells were treated with different doses of compound for another 24 h. The cells were harvested and lysed in cell lysis buffer for Western and IP containing protease inhibitor on ice. Approximately 80% of the total lysates were immunoprecipitated with anti-Flag-conjugated magnetic beads  for 2 h at room temperature, and other lysates were used as input. The magnetic beads were then washed 3 times with PBST, and the immunoprecipitated proteins were eluted with 1 × SDS loading buffer at 100 °C for 5 min. The IP and lysate samples were analyzed by western blot.The plasmids  were transiently cotransfected into 293T cells. After transfection for 24 h, 293T cells were treated with different doses of compound for 24 h. The cells were then treated with 10 μM protease inhibitor MG132 , HY-13259) for another 4 h before harvesting. Next, the cells were lysed in denaturing buffer . The lysates were incubated for 5 min at 100 °C immediately, and then sonicated and diluted with cell lysis buffer for Western and IP. Approximately 80% of the total lysates were immunoprecipitated with anti-Myc-conjugated magnetic beads  for 2 h at room temperature, and the other lysates were used as input. The magnetic beads were then washed 3 times with PBST, and the immunoprecipitated proteins were eluted with 1 × SDS loading buffer at 100 °C for 5 min. The ubiquitination levels were detected using a Western Blot assay.The plasmids  using a variable slope (four parameters) in GraphPad Prism (9.0).Cells were seeded in 96-well plates and incubated with serially diluted compounds for 72 h. Cell viability was determined using the CellTiter-Glo® Luminescent Cell Viability Assay kit  following the manufacturer’s instructions. IC3, the maximal tumor burden permitted by the Institutional Animal Care and Use Committee at the Shanghai Institute of Materia Medica, Chinese Academy of Sciences.All procedures performed on animals were in accordance with regulations and established guidelines and were reviewed and approved by the Institutional Animal Care and Use Committee at the Shanghai Institute of Materia Medica, Chinese Academy of Sciences . NSG mice were obtained from Shanghai Model Organisms Center, Inc; BALB/c mice were obtained from Beijing Huafukang Biotechnology Co. Ltd . Six- to eight-week-old mice were used for the studies and were maintained with free access to pellet food and water in plastic cages at 21 ± 2 °C and humidity (50 ± 10%) conditions and kept on a 12 h light/dark cycle. The tumor size tolerated by the xenograft tumor model mice did not exceed 2000 mmg for 5 min to obtain the plasma fraction. A 10 μL aliquot of plasma was deproteinized with 100 μL acetonitrile/methanol  containing internal standard. After centrifugation, the supernatant was diluted with a certain proportion of acetonitrile/water , mixed and centrifuged at 1807 × g for 10 min. Finally, the aliquots of the diluted supernatant were injected into LC–MS/MS system.The pharmacokinetic profiles of compound 230D7 were determined in male BALB/c mice. The test compound 230D7 was dissolved in solution containing DMSO, PEG400, PBS  and administered via intraperitoneal administration (i.p.) at 10 mg/kg. Serial blood samples (50–100 µL) were collected at 0.25, 0.5, 1, 2, 4, 8, 24 h after dosing and centrifuged at 7227 × n = 3) and treated with different doses of compound 230D7  by intraperitoneal administration daily for a week. The body weights of mice were measured every day and the significant organs  were harvested, weighted and used for histological analysis at the last day.BALB/c mice were used to evaluate the toxicity of compound 230D7. The mice were randomly divided into 3 groups , isolated organ tissues were fixed in 4% neutral paraformaldehyde for 24 h and embedded in paraffin wax. Paraffin slides (4 μm) were then dewaxed and hydrated. Subsequently, the slides were sequentially stained with hematoxylin and eosin. Lastly, the slides were sealed with neutral resin and images were captured by microscopy .7) into the NSG mice. When the tumor reached the volume of approximately 100 mm3, the mice were randomly divided into three groups (n = 7) and intraperitoneally treated with different dosages of 230D7  in solution containing DMSO, PBS  once a day for 16 days. Body weight and tumor size were measured every 2 or 3 days, and tumor volume was calculated using the formula: V = (L × W2)/2 . At the end of the experiment, the mice were euthanized and the tumors were harvested for Western Blot and other studies.NSG mice were used to evaluate the pharmacodynamics of 230D7. The 786-O cells xenograft tumor model was established by the subcutaneous injection of 786-O cells (1 × 105) into mice. When the tumor reached the volume of approximately 100 mm3, the mice were randomly divided into two groups (n = 5) and intraperitoneally treated with rabeprazole  in solution containing DMSO, PEG300, PBS,  once a day for 10 days. The tumor size was recorded using callipers, and tumor volume was calculated using the formula: V = (L × R2)/2. At the end of the experiment, the mice were euthanized and the tumors were harvested for immunohistochemistry and fluorescence-activated cell sorting.BALB/c mice were used to evaluate the pharmacodynamics of rabeprazole . The CT26 cells transplanted tumor model was established by the subcutaneous injection of CT26 cells (2.5 × 10TM II Chamber Slide systems (Thermo) and incubated with rabeprazole or vehicle. Then, the cells were washed with PBS for 15 min, fixed with Immunol Staining Fix Solution  for 30 min and washed with PBS again, followed by treatment with Immunostaining Permeabilization Buffer with Triton X-100  for 30 min and washing with PBS again. To stain the lipid droplets, the cells were incubated with Nile Red (2 µM) in the dark for 10–30 min and then washed with PBS before the nuclei were stained with Antifade Mounting Medium with DAPI . Fluorescence images were captured using an OLYMPUS IX73 fluorescence microscope and Lecia two-photon confocal microscope.CT26 cells were seeded into Lab-TekThe isolated CT26 tumor tissue was fixed with neutral paraformaldehyde, and subsequent staining of cell surface markers was performed by Servicebio Company . In brief, the tumor tissue embedded in paraffin was processed through sectioning, dewaxing, rehydration, and antigen retrieval. Following peroxidase inactivation and blocking with goat serum, the tissue was incubated overnight with the corresponding primary monoclonal antibody overnight at 4 °C. The next day, slides were washed three times and incubated with horseradish peroxidase (HRP)-linked secondary antibodies for 1 h at room temperature. Specimens were washed three times then developed with the DAB substrate kit and counterstained with haematoxylin.2 and 120 μM MgCl2 in RPMI1640 medium). The digested tumor tissues were filtered with 200 mesh gauze, followed by the lysis of red blood cells with ammonium chloride solution, and filtered again to obtain single-cell suspensions in PBS. For discriminating the living and dead cells, 1 × 106 cells were stained on ice with Fixable Viability Stain 700  for 10 min and then terminated with cell staining buffer (PBS containing 2% FBS). Fc receptors on the cell surface are blocked by 1 μg anti-Mouse CD16/32 antibody (10 min on ice). Then, appropriately conjugated fluorescent primary antibodies were added to stain cell surface markers. Finally, cells were suspended with cell staining buffer for flow cytometry analysis using Beckman CytoFelx. The following antibodies were used: anti-CD3 , anti-CD8 , anti-PD1 , anti-TIM3 . The data were analyzed by Flowjo software and cell populations were defined as shown in Supplementary Fig. We analyzed the infiltration of immune cell subsets in CT26 transplanted tumor tissue by fluorescence-activated cell sorting (FACS) analysis. After the mice were euthanized, the tumor tissues were stripped and cut into pieces, then digested at 37 °C for 60 min with tumor tissue digestive buffer , and all coupling constant (J) values are given in Hz. ESI-HRMS and ESI-LRMS data were measured on Thermo Exactive Orbitrap plus spectrometer. Flash column chromatography was performed on Flash 300 Isolera one. Analytical HPLC conditions were as follows: Agilent 1260 Infinity II variable wavelength detector; Waters XBridge C18, 4.6 mm × 150 mm, 3.5 μm particles. Phase A was water with 0.1% TFA, and phase B was MeCN. The entire eluting time was 10 min with a gradient from 10% phase B to 90% phase B in 3.5 min, followed by a 4.5 min hold at 90% phase B, and then a gradient from 90% phase B to 10% phase B in the next 2 min. The flow rate was 1 mL/min. The synthetic routes of the compounds are shown in Supplementary Fig. 1H NMR, 13C NMR, HRMS and HPLC data of intermediates and final products are reported in Supplementary Figs. All reagents and solvents, unless otherwise specified, were purchased from commercial sources and used without further purification. 2CO3 , the mixture was stirred at 60 °C for 5 h. After completion of the reaction, H2O (50 mL) was added and the mixture was extracted with EtOAc (50 mL × 3). The combined organic phases were washed with brine (30 mL × 3), dried over Na2SO4, and concentrated in vacuum. The residue was purified by column chromatography using 20% EtOAc in hexane to obtain the title compound 1  as a white solid. 1H NMR  δ 7.70 , 7.53 , 7.32-7.27 , 6.82 , 5.28 , 4.29 , 1.29 ; 13C NMR  δ 158.23, 154.02, 153.04, 144.79, 132.59, 131.47, 123.40, 119.38, 116.67, 113.47, 113.09, 63.13, 61.24, 14.63; HRMS (m/z): [M + H]+ calcd. for C14H13BrClO4, 358.9680; found, 358.9680; HPLC: purity 98.9%, retention time 9.221 min.To a solution of 4-bromo-2-chlorophenol , ethyl 5-(chloromethyl)furan-2-carboxylate  in DMF (7 mL) was added K1  in MeOH (15 mL) was added 2 M aqueous NaOH , and the mixture was stirred at 50 °C for 2 h. After completion of the reaction, the mixture was concentrated under reduced pressure, then the residue was acidified to pH 4 by the dropwise addition of concentrated HCl at 0 °C. The precipitated solid was filtered to afford the title compound 2  as a white solid. 1H NMR  δ 13.23 , 7.70 , 7.53 , 7.30 , 7.22 , 6.79 , 5.26 ; 13C NMR  δ 159.64, 153.54, 153.07, 145.82, 132.59, 131.47, 123.39, 118.82, 116.65, 113.36, 113.04, 63.18; HRMS (m/z): [M-H]- calcd. for C12H7BrClO4, 328.9222; found, 328.9223; HPLC: purity 99.3%. retention time 8.061 min.To a solution of 2  in SOCl2  was stirred under reflux for 2 h. After being cooled to rt, the solution was concentrated under reduced pressure to remove the excess SOCl2. The residue was then dried under high vacuo for 1 h, and the crude product 3 (0.21 g) as a white solid was used directly for the next step without further purification.A solution of R,7R)−3-(acetoxymethyl)−7-amino-8-oxo-5-thia-1-azabicyclo[4.2.0]oct-2-ene-2-carboxylic acid  in acetone (7 mL) was added saturated aqueous NaHCO3 (14 mL), and then 3  in acetone (5 mL) was added dropwise at 0 °C over 15 min. The reaction mixture was allowed to warm to rt and stirred for 4 h. After completion of the reaction, the pH was adjusted to 4 with 1 M aqueous HCl. The precipitated solid was filtered to obtain a crude product. The crude product was purified by column chromatography using 5% MeOH in DCM, to afford the title compound 230D7  as a white solid. 1H NMR  δ 13.71 , 9.36 , 7.70 , 7.52 , 7.38 , 7.31 , 6.78 , 5.82 , 5.24 , 5.18 , 4.99 , 4.71 , 3.65 , 3.51 , 2.04 ; 13C NMR  δ 170.67, 164.27, 163.31, 158.22, 153.11, 152.64, 147.15, 132.58, 131.47, 127.03, 123.76, 123.44, 116.71, 116.06, 113.30, 113.03, 63.20, 63.14, 59.67, 58.05, 26.04, 21.03; HRMS (m/z): [M+Na]+ calcd. for C22H18BrClN2NaO8S, 606.9548; found, 606.9565; HPLC: purity 98.6%, retention time 8.023 min.To a solution of −7-amino-8-oxo-3-vinyl-5-thia-1-azabicyclo[4.2.0]oct-2-ene-2-carboxylic acid  in acetone (5 mL) was added saturated aqueous NaHCO3 (10 mL), followed by dropwise addition of 3-cyclopentylpropanoyl chloride  in acetone (5 mL) at 0 °C over 15 min. The reaction mixture was allowed to warm to rt and stirred for 4 h. After completion of the reaction, the pH was adjusted to 4 with 1 M aqueous HCl, the precipitated solid was filtered to afford a crude product. The crude product was purified by column chromatography using 5% MeOH in DCM to afford the title compound 222A5  as a white solid. 1H NMR  δ 8.85 , 6.90 , 5.66 , 5.59 , 5.31 , 5.12 , 3.86 , 3.56 , 2.26-2.13 , 1.76-1.67 , 1.60-1.40 , 1.10-1.00 ; 13C NMR  δ 173.64, 165.23, 163.72, 132.46, 125.93, 124.49, 117.69, 59.55, 58.25, 39.58, 34.56, 32.53, 32.37, 31.98, 25.19, 25.14, 23.51; HRMS (m/z): [M + Na]+ calcd. for C17H22N2NaO4S, 373.1192; found, 373.1196; HPLC: purity 97.5%, retention time 7.662 min.To a solution of (6Further information on research design is available in the Supplementary informationReporting Summary


Upon germination, plants received six LED treatments delivering different blue fractions (from 7% to 35%) but the same total photon flux density (400 to 799 nm) of 180 μmol·m−2·s−1 under a 20 h photoperiod. The six LED treatments were: (1) warm white (WW180); (2) mint white (MW180); (3) MW100 + blue10 + red70; (4) blue20 + green60 + red100; (5) MW100 + blue50 + red30; and (6) blue60 + green60 + red60. Subscripts denote photon flux densities in μmol·m−2·s−1. Treatments 3 and 4 had similar blue, green, and red photon flux densities, as did treatments 5 and 6. At the harvest of mature plants, lettuce biomass, morphology, and color were similar under WW180 and MW180, which had different green and red fractions but similar blue fractions. As the blue fraction in broad spectra increased, shoot fresh mass, shoot dry mass, leaf number, leaf size, and plant diameter generally decreased and red leaf coloration intensified. Compared to blue + green + red LEDs, white LEDs supplemented with blue + red LEDs had similar effects on lettuce when they delivered similar blue, green, and red photon flux densities. We conclude that the blue photon flux density in broad spectra predominantly controls lettuce biomass, morphology, and coloration.For indoor crop production, blue + red light-emitting diodes (LEDs) have high photosynthetic efficacy but create pink or purple hues unsuitable for workers to inspect crops. Adding green light to blue + red light forms a broad spectrum (white light), which is created by: phosphor-converted blue LEDs that cast photons with longer wavelengths, or a combination of blue, green, and red LEDs. A broad spectrum typically has a lower energy efficiency than dichromatic blue + red light but increases color rendering and creates a visually pleasing work environment. Lettuce growth depends on the interactions of blue and green light, but it is not clear how phosphor-converted broad spectra, with or without supplemental blue and red light, influence crop growth and quality. We grew red-leaf lettuce ‘Rouxai’ in an indoor deep-flow hydroponic system at 22 °C air temperature and ambient CO Photons with wavelengths between 400 and 750 nm are essential to plant growth and development by driving photosynthetic activity, regulating morphological adaption, and modulating secondary metabolism. This wavelength range is typically divided into four 100 nm wavebands: blue 400 to 499 nm), green (500 to 599 nm), red (600 to 699 nm), and far red (700 to 750 nm). Photosynthesis is wavelength-dependent as these photons carry energy that differentially excites two photosystems in plant cell membranes in light-dependent reactions of photosynthesis . Althoug9 nm, gre−1), photon spectrum, form factor, and light responses of crops [In the last decade, lighting for controlled-environment agriculture has evolved rapidly thanks to the improved energy efficiency, spectral tuning, and design of light-emitting diode (LED) fixtures. These improvements increased the commercial adoption of LED fixtures, especially in indoor vertical farms. Growers can choose from a wide range of LED fixtures with different specifications. The selection depends, in part, on the fixture cost, photosynthetic photon efficacy , with higher indices indicating higher color fidelity. Other specialized white LEDs also exist, such as mint-white  LEDs. Developed by OSRAM Opto Semiconductors, these are blue LEDs with an efficient green phosphor coating that can be combined with red LEDs to create high-CRI white light [Although combining monochromatic blue, green, and red LEDs can create white light, commercial white LEDs are typically blue LEDs with a phosphor coating that converts most of the blue photons to photons at longer wavelengths , thereby emitting a broad range of biologically active radiation . Dependite light .−1, respectively) than blue, red, and far-red LEDs  [Lactuca sativa) ‘Rouxai’ under sole-source LED lighting with various broad spectra, sometimes with matching waveband-integrated photon flux densities. We hypothesized the broad spectra with higher ratios of blue to green or red light would lead to lower lettuce biomass, decreased extension growth, and greater red leaf coloration.Horticultural lighting companies often supplement white LEDs with monochromatic blue and/or red LEDs to create distinct broad spectra. First, this allows for spectrum customization to elicit specific plant responses  . Second,ctively) . However180, MW180, and B20G60R100, and lowest under MW100B50R30 and B60G60R60 , leaf brightness was higher under WW180 and MW180 than B20G60R100, MW100B50R30, and B60G60R60. As shown by a* , leaves were greenest under WW180 and MW180 and reddest under MW100B50R30, and B60G60R60. As shown by b* , leaves were yellower under WW180 and MW180 than the other treatments. The relative chlorophyll index  was 12–16% lower for plants grown under MW180 than the other treatments.Plants grown under WW redness . As show−2∙s−1. Several major lettuce phenotypic parameters showed dose-dependent relationships to the blue photon flux density (a*).The six treatments in this study provided a wide range of blue photon flux densities, from 12 to 62 μmol∙m density . IncreasIn this study, indoor hydroponic red-leaf lettuce ‘Rouxai’ grew similarly under warm-white and mint-white LEDs, which provided similar blue fractions of 7% and 9%, respectively, but different green, red, and far-red fractions . Low blu−2∙s−1 , respectively , decreased leaf length by 8%, intensified red leaf coloration, and increased chlorophyll concentration. In a separate study with a similar blue photon flux density, substituting 60 μmol∙m−2∙s−1 of green photons with red photons also decreased lettuce shoot fresh mass by 15% and leaf number but did not influence other parameters [−2∙s−1 , respectively (Brassica oleracea var. sabellica) ‘Siberian’ [The effects of 44% substitution of mint-white light with blue + red light on lettuce growth depended on the blue-to-red ratio. The lower substitute blue-to-red ratio of 1:7 changed the blue, green, red, and far-red photon flux densities by 2.6, −49.4, 47.8, and −2.3 μmol∙mectively . At a blectively . This in−1 at full power), whereas mint-white LEDs emit green photons at a much higher efficacy (1.52 μmol∙J−1 at full power) [−1 at full power) than mint-white LEDs, although both are considered white light.We combined monochromatic blue + green + red LEDs to deliver similar 100 nm waveband flux densities of blue, green, red, and far-red light to the two mint-white + blue + red treatments. The former created three distinct peaks of narrowband radiation, whereas the latter created somewhat broader, more continuous photon distributions from the phosphor conversion of blue LEDs. Despite these spectral distribution differences, all measured lettuce phenotypes were similar when the integrated blue, green, red, and far-red photon wavebands were similar. However, narrowband green LEDs have a low efficacy (0.54 μmol∙Jl power) . In addil power) . Lastly,−2∙s−1. In general, a broad spectrum with a higher blue photon flux density (in the range of 12–62 μmol∙m−2∙s−1) progressively decreased lettuce shoot biomass, leaf size, and canopy size but increased red leaf coloration. In a previous study, increasing the blue photon flux density in blue + green + red LEDs from 0 to 100 μmol∙m−2∙s−1 at a fixed photosynthetic photon flux density (PPFD) of 180 μmol∙m−2∙s−1 produced similar results [−2∙s−1 in a broad spectrum  at a PPFD of 200 μmol∙m−2∙s−1 decreased dry mass and leaf area index of tomato (Solanum lycopersicum), but not other crops including green-leaf lettuce ‘Waldmann’s Green’ [We identified a dose-dependent inverse relationship between the blue photon flux density in a broad spectrum and the measured lettuce phenotypes in this study . The blus Green’ . The fouArabidopsis thaliana), PHYTOCHROME-INTERACTING FACTOR 4 (PIF4) and PIF5 are transcription factors that promote extension growth in the shade avoidance response [Although the effects of blue light on plant growth and morphology vary by species and spectral conditions such as the PPFD , increasresponse . Under lresponse . Howeverresponse . A simil−2∙s−1 during the first 24 h at an air temperature setpoint of 20 °C. Based on visual assessment, we achieved a uniform seed germination rate of >95%. We then grew seedlings under 180 μmol∙m−2∙s−1 of warm-white light under a 20 h photoperiod at an air temperature setpoint of 22 °C until transplant on day 13. A total photon flux density of 180 μmol∙m−2∙s−1 was within the typical range delivered in commercial indoor vertical farms, while a 20 h photoperiod led to a daily light integral of 13 mol∙m−2∙d−1, which sufficed lettuce seedling growth [We performed this experiment twice over time as a randomized complete block design in the Controlled-Environment Lighting Laboratory at Michigan State University . We rinsed and soaked rockwool cubes  with deionized water, drained excess water from plastic trays, sowed one seed of red-leaf lettuce ‘Rouxai’  per cube, and covered them with transparent humidity domes. Seeds germinated under warm-white LED fixtures  at a total photon flux density (400–799 nm) of 50 μmol∙mg growth . The see−1. We prepared it in 18.9-L buckets by dissolving a 12.0N–1.7P–13.3K base fertilizer  and magnesium sulfate  sequentially in deionized water. The nutrient solution provided seedlings with the following nutrients (in mg∙L−1): 125 N, 18 P, 139 K, 73 Ca, 49 Mg, 39 S, 1.7 Fe, 0.52 Mn, 0.56 Zn, 0.13 B, 0.47 Cu, and 0.13 Mo. We adjusted the nutrient solution pH with dry potassium bicarbonate and diluted (1:31) 95% to 98% sulfuric acid .After removing the humidity domes on day 4, we began lighting treatments and sub-irrigated the rockwool cubes as needed daily to submerge one fourth of the cube height in a nutrient solution with a pH of 5.7–5.9 and electrical conductivity of 1.2–1.4 dS∙m−2∙s−1 under a 20 h photoperiod (0200–2200 h), resulting in a daily light integral of 13 mol∙m−2∙d−1, which was sufficient to produce lettuce in the exponential growth phase [180) light, mint-white (MW180) light, mint-white partially substituted with two ratios of blue + red light (MW100B10R70 and MW100B50R30), and combined monochromatic blue + green + red light with two ratios of blue + red light (B20G60R100 and B60G60R60). The number after each LED type indicates its photon flux density in μmol∙m−2∙s−1. During the setup of each lighting treatment, we took spectral scans at nine representative locations at plant canopy (46 cm below the LED fixtures) with a spectroradiometer . We used the average photon flux density to adjust the fixture output in lighting control software  until the average was ±3 μmol∙m−2∙s−1 for each LED type. The spectral characteristics and distributions of the six lighting treatments are shown in On day 4, we transferred uniform lettuce seedlings to six broad-spectrum treatments. These treatments delivered the same total photon flux density of 180 μmol∙mth phase ,30. Tuna−2, plant centers were 20.3 cm apart horizontally and 14.6 cm apart diagonally. The nutrient solutions were constantly recirculated with water pumps and aerated with air stone discs , which were submerged in three reservoirs and connected to external air pumps . The nutrient solutions contained the same two-part fertilizers in deionized water as for seedlings and provided the following initial nutrients at transplant (in mg∙L−1): 150 N, 22 P, 166 K, 88 Ca, 58 Mg, 47 S, 2.1 Fe, 0.63 Mn, 0.68 Zn, 0.15 B, 0.56 Cu, and 0.15 Mo.On day 13, we transplanted lettuce seedlings into the 36-cell foam rafts  floating on top of nutrient solutions in flood tables  of a deep flow technique hydroponic system. At a planting density of 48.4 plants∙m−1 at transplant to 1.5 to 1.7 dS∙m−1 at harvest.From transplant to harvest, we measured the nutrient solution pH, electrical conductivity, and temperature daily with a pH and electrical conductivity meter . Each of these parameters was similar among the three reservoirs, which were used for the six lighting treatments, throughout the two replications . Wheneve2 concentration. We monitored the environment with a temperature and relative humidity sensor  and a CO2 sensor . Sensors were wired to a datalogger  that recorded hourly means of 10 s intervals  were 22.4 ± 0.6 °C, 410 ± 50 μmol∙mol−1, and 34% ± 10%, respectively, in replication 1 and 22.5 ± 0.6 °C, 398 ± 35 μmol∙mol−1, and 35% ± 7%, respectively, in replication 2.A ventilation and air-conditioning unit , which was connected to a wireless thermostat controller , maintained a constant air temperature setpoint of 22 °C. Plants were grown at the ambient COntervals . The meaL*a*b* color space values with a color reader  and the relative chlorophyll index  with a chlorophyll meter . These pigmentation measurements targeted unshaded interveinal leaf tissue. Subsequently, we placed each plant in a paper bag, dried it for ≥5 d at 60 °C in a forced air drying oven , and measured shoot dry mass. In addition, we photographed a representative plant from each lighting treatment from overhead to document crop appearance (α = 0.05). We also performed regression analysis between the blue photon flux density and plant parameters with Microsoft Excel . All data were pooled for analysis because of the non-significance of the treatment × replication interaction term (p > 0.05) and/or similar treatment trends between replications.On day 30 and 33 in the two consecutive replications, we harvested and conducted destructive measurements on eight randomly selected plants from each lighting treatment. For each plant, we measured shoot fresh mass with a top-loading balance , the number of leaves longer than 3 cm, plant diameter , and the length and width of the sixth most mature true leaf. At three random locations on recently matured leaves of each plant, we also measured the International Commission on Illumination pearance . We analWarm-white and mint-white LEDs with distinctly different broad spectra had similar effects on indoor hydroponic lettuce biomass, morphological traits, and pigmentation. When a phosphor-converted broad spectrum (mint white) was partially substituted with blue + red light, the change in the blue photon flux density primarily determined the plant phenotypic responses. Lettuce grew similarly under mint-white LEDs partially substituted with blue + red LEDs and blue + green + red LEDs that delivered similar photon flux densities of 100 nm blue, green, and red wavebands. Lastly, the blue photon flux density in the six broad spectra tested was an accurate predictor of lettuce growth and coloration.


NF1 gene in the proband was identified. This pathogenic variant of the NF1 gene produced a truncated protein that lost more than one-third of the NF1 protein at the C-terminus including half of the CRAL-TRIO lipid-binding domain and nuclear localization signal (NLS), thus leading to pathogenicity (ACMG criteria: PVS1 + PM2 + PM2). Analysis for NF1 conservation in species revealed high conservation in different species. Analysis of NF1 mRNA levels in different human tissues showed low tissue specificity, which may affect multiple organs presenting other symptoms or phenotypes. Moreover, prenatal NF1 gene diagnosis showed both alleles as wild types. Thus, this NF1 novel variant probably underlays the NF1 pathogenesis in this pedigree, which would help for the diagnosis, genetic counseling, and clinical management of this disorder.Neurofibromatosis type 1 (NF1) presents an autosomal dominant, haploinsufficient, and multisystemic disorder with patches of skin café-au-lait spots, lisch nodules in the iris, even tumors in the peripheral nervous system or fibromatous skin. In this study, a Chinese young woman who suffered from NF1 disease with first-trimester spontaneous abortion was recruited. Analysis for whole exome sequencing (WES), Sanger sequencing, short tandem repeat (STR), and co-segregation was carried out. As results, a novel, heterozygous, de novo pathogenic variant (c.4963delA:p.Thr1656Glnfs*42) of the The online version contains supplementary material available at 10.1186/s12920-023-01514-x. NF1 gene was mapped to chromosome 17q11.2 [NF1 gene region of 17q11.2 in three patients carrying NF1 disease. This large transcript was disrupted NF1 gene expression due to translocations t  and t , or a 0.5-kb insertion on NF1 region. This large transcript is the NF1 gene, called neurofibromin 1 . Latter more NF1 gene germline mutations that caused neurofibromatosis type 1 diseases have been identified [Neurofibromatosis type 1 (NF1) OMIM: 162,200), as an autosomal dominant, haploinsufficient and multisystemic disorder, presented with patches of skin café-au-lait spots, lisch nodules of the iris, tumors of the peripheral nervous system and fibromatous skin . At leas2,200, as 17q11.2  further entified , 10.NF1 gene (NM_001042492.3) is 8,517 bps in coding sequences (CDS) length and encodes 2,839 amino acids in length with predicted molecular mass 319 kDa in weight (NP_001035957.1). This large gene (60 exons and > 300 kilobases (kb) of genomic DNA) has one of the highest rates of spontaneous mutations in the entire human genome. The gene mutations of NF1 with different types vary from complete gene deletions, insertions, stop, splicing mutations, amino acid substitutions and chromosomal rearrangements, leading the loss of heterozygosity of NF1 gene function related mainly in neurons, Schwann cells, oligodendrocytes, and leukocytes, thus causing Multisystem abnormities. [NF1 gene product directly inhibits RAS activation by converting the active form of GTP-bound RAS to its inactive, GDP-bound state. The end result of RAF/MAPK inactivation is suppression of transcription and cell growth. Thus, neurofibromin deficiency leads to increased RAS signaling which is assumed to be the root cause of NF1 pathology [NF1 gene mutations are linked not only to neurofibromatosis type 1 but also to juvenile myelomonocytic leukemia , familial spinal neurofibromatosis , and Watson syndrome , etc. [NF1 gene showed a broad spectrum of clinical characteristics, making it a challenge to detect mutations in NF1, genetics and molecular testing are thus necessary [The rmities. . Althougrmities. . The “twrmities. . Splicedrmities. , 14. In athology . As a tu0), etc. , 16. MutNF1 in a Chinese patient with NF1 by whole exome sequencing (WES), Sanger sequencing, and family co-segregation analysis.In the current study, we have successfully identified a novel, de novo pathogenic variant (p. Thr1656Glnfs*42) of A Chinese female proband with NF1 was recruited and she was first diagnosed at the Dermatology Department in a local county hospital of Gansu Province, in northwest China in the M659 pedigree , and for proband parents  as described previously , 24. Aft, and forhttps://www.ncbi.nlm.nih.gov/homologene?Db=homologene&Cmd=Retrieve&list_uids=226) [https://www.proteinatlas.org/ENSG00000196712-NF1/tissue).Conserved domains from NCBI’s conserved domain database for the NF1 protein (GenBank access no. NP_001035957.1) were searched by the URL (ids=226) –33. The NF1 gene . PCR amplification was performed. The PCR conditions: 95 °C for 90 s, 33 cycles of 95 °C for 30 s, 30 s annealing at 60 °C, and 72 °C for 30 s, followed by a final extension at 72 °C for 5 min. The amplified products with 356 bp were then used for Sanger sequencing on an ABI-3500DX sequencer using primer NF1-4963 L.PCR amplification and Sanger sequencing were performed for pathogenic variant verification . Primers® ID-X 1.5 software [Using the AGCU Ex22 kit, the STR genotype was conducted in accordance with the relevant provisions of the Technical Specification for Paternity Appraisal by China (SF/Z JD0105001-2018); STR profiles and the combined paternity index (CPI) were calculated by using the GeneMappersoftware , 36.The amniotic fluid of the fetus was extracted through amniocentesis at 21 weeks of the proband’s pregnancy and then the DNA was extracted as previously reported . Sanger The proband was a 34-year-old woman from a non-consanguineous Chinese family Fig. A, II: 1.NF1 gene (NM_001042492.3)  for the proband  classification criteria, and it may be PVS1 + PM2 + PM6) [WES was first performed and then identified a heterozygous pathogenic variant c.4963delA carrying a single nucleotide heterozygous deletion in exon 37 of the  NM_00104492.3 (ch2 + PM6) .NF1 gene wild type. The proband husband also showed NF1 gene wild type   were inherited from her parents (I:1 and I:2) with a CPI (combined paternity index) of 3.9109 × 10Analysis for NF1 conservation in species revealed that it is highly conserved in chimpanzees, Rhesus monkeys, dogs, rats, mice, chickens, frogs, zebrafish, fruit flies, and mosquitoes, of different species . The Sanger sequencing of the amplified PCR products of amniotic fluid DNA of the fetus revealed the wild type alleles of the NF1 gene was reported highly conserved in chimpanzees, Rhesus monkeys, dogs, rats, mice, chickens, frogs, zebrafish, fruit flies, and mosquitoes. Moreover, all of them exhibited RAS-GAP domain and CRAL-TRIO lipid binding domain that demonstrates partial deletion of CRAL-TRIO domain in the proband leading to NF1 disease in this family. Analysis of NF1 mRNA expressions in different human tissues found that NF1 mRNA showed low tissue specificity, mainly detected in the brain, colon, peripheral nerve, lung, muscle, etc., which is not fully consistent with the previous reports [We recruited a pregnant young woman with NF1 in this study, and identified a novel, de novo, heterozygous pathogenic variant c.4963delA: (p.Thr1656Glnfs*42), which produced a truncated protein causing NF1 pathogenic in this proband of the Chinese family. The heterozygous pathogenic variant c.4963delA:(p.Thr1656Glnfs*42) causes a loss of half of the CRAL-TRIO lipid-binding domain and NLS, thus should cause NF1 disease in this proband.  reports . Thus, tDugoff and Sujansky (1996) have reported the outcomes for 247 pregnancies in 105 NF1 women . Of thesIn previous study, Santasree Banerjee and his co-workers (2017) presented a clinical molecular study of four Chinese probands with NF1 from four unrelated families, showing extreme phenotypic variation with rare phenotype, tibial pseudarthrosis, and anemia . Yao andNF1 gene, while her patents contained wild type genotype. But the paternity of this pedigree didn’t confirm. Prenatal diagnosis was carried out at the 20 weeks of gestation on her mother’s second pregnancy, and the baby also showed wild type genotype. Aurélia Gruber et al. [NF1 gene mutations between 8 and 15 weeks of gestation, which were in parallel to conventional invasive diagnosis. They designed specific hydrolysis probes to detect the paternal mutation and to assess the presence of cell-free fetal DNA by ddPCR. Despite NIPT possesses high accuracy, invasive prenatal diagnosis remains the gold standard.Prenatal diagnosis acted as an effective means of examination in NF1 families. A young girl with NF1 was recruited by Bei Liu et al. , and wasr et al. .performeAs we discussed previously, five possibilities with discordant segregation are existed, including no paternity or errors in sampling, de novo mutation, heterozygous micro-deletion, and uniparental disomy (UPD) . ThroughNF1 gene. The results demonstrate that this baby inherits her mother wild type allele, not mutant NF1. The baby showed no NF1 symptoms at five months old. However, further following up should be conducted to monitor’s the baby development.Prenatal gene diagnosis was conducted by Sanger sequencing of the DNA of amniotic fluid of the fetus that showed the wild type of the NF1 gene, which would cause NF1 disorder in the Chinese family. Next-generation sequencing (NGS) including WES [We successfully identified a novel, de novo, heterozygous frameshift pathogenic variant in the ding WES  and STR ding WES  are usefBelow is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2


Phytophthora cinnamomi is an important plant pathogen responsible for dieback diseases in plant genera including Quercus, Fagus, Castanea, Eucalyptus, and Pinus, among others, all over the world. P. cinnamomi infection exerts tremendous ecological and economic losses. Several strategies have been developed to combat this pathogenic oomycete, including the search for novel anti-oomycete compounds. In this work, a Mediterranean vascular plant, Phlomis purpurea, has been screened for secondary bioactivity against this pathogen. The genus Phlomis includes a group of herbaceous plants and shrubs described as producers of many different bioactive compounds, including several triterpenoids. Triterpenoids are well-known molecules synthesized by plants and microorganisms with potent antioxidant, antitumoral, and antimicrobial activities. We have isolated by HPLC-DAD and characterized by HPLC-MS and NMR two nortriterpenoid compounds (phlomispentaol A and phlomispurtetraolone) from the root extracts of P. purpurea. One of them (phlomispentaol A) is active against the plant pathogenic oomycete P. cinnamomi (based on in vitro inhibition bioassays). Based on their chemical structure and their relationship to other plant triterpenoids, oleanolic acid is proposed to be the common precursor for these molecules. The anti-oomycete activity shown by phlomispentaol A represents a promising alternative to counteract the worldwide-scale damage caused to forest ecosystems by this pathogen. It stara, etc.) . The lifa, etc.) Figure 1a, etc.) .P. cinnamomi infestation  (60%) (Bayer)  and some divalent cations  . Example (Bayer) . Long-te options , but the Zealand .P. purpurea, a 1- m-tall shrub that belongs to the Lamiaceae family and is endemic to Mediterranean ecosystems. This plant is well adapted to the characteristically arid ecosystem and has been described as a producer of other triterpenoid-type compounds endowed with antimicrobial activity, as well as other compounds with anti-inflammatory (useful for example against ulcerative colitis) and antitumor activities  and phlomispurtetraolone (first described in this article), possess a 28-norolean spirocyclic triterpenoid skeleton, pointing out the absence of the C28 methyl group as in the oleanane-type basic structure , sesquiterpenes (C15), diterpenes (C20), sesterterpenes (C25), triterpenes (C30), and tetraterpenes (C40). Also, regarding the nomenclature of these compounds, it is necessary to make a distinction between triterpenes and triterpenoids. Triterpenes are naturally occurring terpene compounds, whereas triterpenoids are terpene derivatives containing keto or hydroxyl groups (or any other heteroatom) of natural or synthetic origin . Triterptructure . In the 22.1Phlomis purpurea (collected during the summer season), were provided by the collaborating company Campojerez SL, located in Jerez de los Caballeros , from plants cultivated in nurseries under the environmental conditions of this area: annual average temperature: 17.1°C; maximal annual average temperature: 23.8°C; minimal annual average temperature: 10. 3°C; average annual precipitations: 447 mm; and average annual number of daylight hours: 2,860 (https://www.aemet.es). All roots were taken from 1- year-old plants.The samples, consisting of dry root fragments of The root fragments were peeled with a scalpel to obtain their bark. The dry bark, which was fragile and brittle, was micronized in an Ultra-turrax  process until a fine and homogeneous powder was obtained. The powder was stored at −20°C until its extraction.P. purpurea was mixed with 99% ethanol in a proportion of 0.1 g/ml. The mixture was shaken for 1 h and centrifuged for 5 min at 9,000 rpm  at 4° C. The ethanolic extract was separated and saved. This extraction process was repeated twice, and then both ethanolic extracts were pooled. In total, 60 g of root samples were extracted. Ethanol was removed from the extract by evaporation under vacuum , and the resulting solid was dissolved in 10 ml of 20% methanol in water. The total process took 3 days to complete.The solvent used for extracting the micronized root mass was 99% ethanol . In a Falcon tube, the dry root powder of 2.2Phytophthora cinnamomi were selected. The solvents were then removed by evaporation under vacuum and then lyophilized until dry and stored at − 20°C.The dissolved root extract was submitted to solid-phase extraction (SPE) fractionation using a Phenomenex Strata C18-E column  . The SPE chromatography was performed with methanol and water as buffers A and B, respectively. First, the column was conditioned with 50 ml of 100% methanol, followed by 20% methanol in water. The root extract was then loaded onto the column and eluted in ten 50-ml fractions (C1–C10). C1–C9 fractions consisted of eluates with progressively increasing percentages of methanol , while C10 fraction was collected with 100% acetone. The fractions were then tested in bioassays, and those that showed inhibitory action against the pathogen 2.3After the SPE fractionation experiments, the anti-oomycete molecules were further purified by high-performance liquid chromatography (HPLC)-DAD using an Agilent 1260 Infinity equipment  with a Teknochroma Mediterranea Sea18 column  . The active fractions were dissolved in 850 μl of 20% methanol in water and injected for fractionation using four sequential HPLC programs. All gradients were made with analytical-grade solvent B  or acetonitrile, 100% ) and water as solvent A. All solvents contained 0.1% formic acid. HPLC Program #1  was as follows: 0–37 min (73% B), 37–40 min (73%–100% B), 40–41 min (100% B), 41–42 min (100%–10% B), and 42–48 min (10% B). Fractions (each one collected every 0.5 min) were collected from 0 to 47 min. HPLC Program #2  was as follows: 0–5 min (50% B), 5–56 min (50%–83% B), 56–70 min (83%–90% B), 70–83 min (90%–100% B), 90–91 min (100%–50% B), and 91–96 min (50% B). Fractions were collected from 36 to 83 min. HPLC Program #3  was as follows: 0–27 min (85% B), 27–69 min (85%–92% B), 69–83 min (92%–100% B), 83–85 min (100% B), 85–86 min (100%–85% B), and 86–88 min (85% B). Fractions were collected from 0 to 34 min. HPLC Program #4  was as follows: 0–5 min (10% B), 5–30 min (10%–51% B), 30–55 min (51%–81% B), 55–70 min (81%–100% B), 70–80 min (100% B), 80–82 min (100%–10% B), and 82–84 min (10% B). Fractions were collected from 20 to 67 min. The HPLC programs were selected by estimating the solvent percentage for the elution of the anti-oomycete compound and changing the main slope of the previous program according to the quality of peak resolution.P. cinnamomi, and the inhibitory refractions were collected, pooled, dried, dissolved in 20% methanol in water, and reinjected into the next HPLC fractionation program.Between each fractionation experiment, all the fractions were tested in a bioassay against Also, after each fractionation round, the positive fractions in the bioassay were analyzed by HPLC-HRESIMS to find out the exact mass of the compounds of interest. The equipment used for these experiments was a Bruker Impact II UHPLC-MS-QTOF  with a Zorbax Eclipse Plus C18 column  , and the program (0.25 ml/min) used a gradient of water (solvent A) and acetonitrile (solvent B), both with 0.1% formic acid: 0 min at 10% acetonitrile, 1 min at 10% acetonitrile, 4 min at 35% acetonitrile, 5 min at 35% acetonitrile, 8 min at 100% acetonitrile, 10 min at 100% acetonitrile, 11 min at 10% acetonitrile, and 15 min at 10% acetonitrile.2.4P. cinnamomi on Sabouraud Dextrose Agar (SA) solid medium . In total, 2,000 μl of the HPLC fractions obtained were evaporated under vacuum, and the solid residue was resuspended in 900 μl of 20% methanol in water. In order to avoid masking the results due to the potential toxicity of the solvent used to resuspend the samples (20% methanol in water), 90 μl of this volume was then added to 4 ml of SA without exceeding a final solvent concentration of 2.25% (v/v) methanol. The SA culture medium was melted and allowed to cool to 50° C before adding the respective fraction to it. The medium was then poured into two 25 -well Petri dishes (4 ml of SA medium per well) , making duplicates for each bioassay. Subsequently, each agar well was inoculated with P. cinnamomi mycelium extracted from a grown plate using a wide-mouth pipette tip as a punch. In all the bioassays, a blank control was used, containing only the culture medium, as well as a solvent control of the culture medium with 2.25% methanol (used to rule out the possible toxic effect of the solvent on P. cinnamomi growth). Mycelium areas were measured by taking a picture of the mycelium, including an object of known size, and analyzing it with an image processing software , using the size known object as a reference for the measurement. A result was considered positive when a clear reduction of the sample mycelium area was observed compared with that of the blank control in the Image J pictures among assay samples in the microtiter wells. The minimum inhibitory concentration (MIC) experiments were carried out by a liquid bioassay performed in a sterile microtiter plaque. The sample E7F4, containing compound A (phlomispentaol A), was used to determine the MIC against P. cinnamomi. An eight-point serial dilution of the sample was elaborated from 150 μg/ml to 2.34 μg/ml, including a well with no sample (0 μg/ml). Another eight-point serial dilution was made as a solvent control from 3.75% to 0.06% methanol concentrations. The total volume for each well was 100 μl. Dilutions were made in an SA liquid culture medium.The different fractions obtained from the HPLC-DAD repurification cycles were analyzed using a bioassay against 2.51H and at 200.20 MHz for 13C . Two different HPLC fractions were analyzed (E7F4 and E5E6), in both cases dissolved in 600 μl MeOD.Nuclear magnetic resonance (NMR) analyses were performed with a Bruker AVIII-800 spectrometer equipped with a TCI cryoprobe at 800.13 MHz for 1H NMR, 1H NMR with presaturation, 1H-1H COSY, 1H-1H TOCSY (80 ms), 1H-1H ROESY (300 ms), 1H-13C-HSQC, 1H-13C-HSQC_TOCSY (60 ms), 1H-13C-HMBC, and 13C{1H}. The following tests were performed on sample E5E6: 1H NMR with presaturation, 1H-1H COSY, 1H-1H TOCSY (80 ms), 1H-1H NOSY (500 ms), 1H-13C-HSQC, 1H-13C-HSQC_TOCSY (60 ms), and 1H-13C-HMBC.The following tests were performed on HPLC sample E7F4: 2.6p-values below 0.05. Asterisks in the graphics show the level of significance. The regression analysis was carried out using the Microsoft Office Excel program.The statistical analysis for the bioassay results was made with GraphPad Prism 9.0.2 software. The results were analyzed by a one-way ANOVA comparing all the samples with the blank control and assuming a normal distribution of data. Statistically significant results were considered when obtaining 33.1The HPLC purification process of the anti-oomycete compounds from the root extracts was monitored using bioassays-guided HPLC fractionation , and this showed that the concentration of molecule with m/z ions of 521.3484 Da matched perfectly to the growth observed in the bioassay from each E4 to F4 fraction : five singlets corresponding to methyl groups attached to a quaternary aliphatic carbon, two CH signals, and nine signals corresponding to CH2  indicated the presence of signals in the aliphatic zone of the spectrum  experiments and through experiments of dipolar correlation based on the Nuclear Overhauser Effect (NOESY or ROESY). The information provided in this last type of dipole correlation experiment, together with the study of the vicinal coupling constants 1H-1H (at three bonds), allowed the assignment of the stereochemistry of the different stereogenic centers of the compound A molecule . The small amount of sample (0.7 mg) and the minor proportion of compound B in the mixture meant that it was not possible to identify and assign all the signals of this compound, but it was possible to determine some of the differences existing between both samples. In the COSY, TOCSY, and HSQC-TOCSY scalar correlation experiments, for compound B, it was observed that there was no correlation between the olefinic proton and any other proton in the CHn–O group zone. In the HMBC experiment, a new quaternary 13C signal appeared at 208.3 ppm, corresponding to a ketone group. The new signals corresponding to the CH2 group at carbon 19 (in the structure of compound B) were those that correlate with the ketone carbon  showed a 4The biosynthetic pathway that leads to terpenoid molecules, like the phlomispentaol A and phlomispurtetraolone described above, has several common steps that start when the geranyl pyrophosphate synthase (GPPS) binds dimethylallyl diphosphate (DMAPP) to isoprenyl diphosphate (IPP), generating geranyl diphosphate (GPP) Figure 6Candida albicans, exhibiting antifungal cytotoxic activity through an increase in intracellular reactive oxygen species and calcium homeostasis disturbance in this pathogenic yeast  present in its tissues. In this case, the β-amyrin synthase (a type of OSC) transforms the 2,3-oxidosqualene into β-amyrin, through the intermediates dammarenyl, baccharenyl, lupenyl, and oleanyl cations   from the CYP716A subfamily convert β-amyrin into oleanolic acid. The transformation of β-amyrin into oleanolic acid involves the intermediates erythrodiol and oleanolic aldehyde  and L929 (fibroblasts) cancer cell lines, and as moderately cytotoxic against the HL-60 (leukemia) cancer cell line . The intermediate oleanolic acid in these extracts was also found to be a moderate inhibitor of α-glucosidase   . This plavonoids . Terpenoavonoids .Phlomis viscosa plant extracts , and norviscoside heptaacetate, can be found in extracts . Knowingextracts Figure 6P. purpurea, with one of them, phlomispurtetraolone (compound B), being first described in this work. These two molecules are members of a biosynthetic family that contains various antimicrobial compounds and has oleanolic acid as the common precursor. One of these compounds, phlomispentaol A, was found to be active against the phytopathogen P. cinnamomi in this work. This information can be used to propose these molecules as a promising treatment to solve the worldwide problem caused by this plague. Further studies are needed to determine the best way to apply this treatment and detect possible side effects.Therefore, in this study, we have isolated and characterized two 28-noroleane spirocyclic triterpenoids from organic extracts of the roots of 5Phytophthora cinnamomi Rands. NCBI Taxonomy ID 4785. Phlomis purpurea NCBI Taxonomy ID 316258.The original contributions presented in the study are included in the article/LF-C and MG-D carried out the experimental analyses and interpretation of the data. BR, JM, CV, and FL designed the work. LF-C, CV, and FL drafted and revised the manuscript. BR, JM, and FL obtained the funding for carrying out the experimental work. All authors contributed to the article and approved the submitted version.


ORF8). Among the most dramatic genomic changes observed in SARS-CoV isolated from patients during the peak of the pandemic in 2003 was the acquisition of a characteristic 29-nucleotide deletion in ORF8. This deletion cause splitting of ORF8 into two smaller ORFs, namely ORF8a and ORF8b. Functional consequences of this event are not entirely clear.Accessory proteins have diverse roles in coronavirus pathobiology. One of them in SARS-CoV (the causative agent of the severe acute respiratory syndrome outbreak in 2002–2003) is encoded by the open reading frame 8 (ORF8a and ORF8b genes and documented that in both cases the frequency of synonymous mutations was greater than that of nonsynonymous ones. These results suggest that ORF8a and ORF8b are under purifying selection, thus proteins translated from these ORFs are likely to be functionally important. Comparisons with several other SARS-CoV genes revealed that another accessory gene, ORF7a, has a similar ratio of nonsynonymous to synonymous mutations suggesting that ORF8a, ORF8b, and ORF7a are under similar selection pressure.Here, we performed evolutionary analyses of ORF7a-ORF7b-ORF8 complex of accessory genes in SARS-CoV-2. A high frequency of deletions in this gene complex might reflect recurrent searches in “functional space” of various accessory protein combinations that may eventually produce more advantageous configurations of accessory proteins similar to the fixed deletion in the SARS-CoV ORF8 gene.Our results for SARS-CoV echo the known excess of deletions in the The online version contains supplementary material available at 10.1186/s12864-023-09482-3. ORFs; hereafter gene names are Italicized, protein names are capitalized) that encode 26 proteins: 16 non-structural proteins (NSP1 to NSP16), four structural proteins , and six accessory proteins  [ORF8 [ORF8  encodes a 123 amino-acid polypeptide, consisting of an N-terminal signal sequence followed by the predicted Ig-like and transmembrane domains. Notably, ORF7a and ORF8 genes have similar length and domain architecture suggesting similarities of their functions [The severe acute respiratory syndrome coronavirus (SARS-CoV) genome is a ~ 30 kb long, single-stranded, positive RNA molecule with the gene organization typical of coronaviruses including that of infamous SARS-CoV-2. There are 12 open reading frames , 8, 10) –7. Diffe, 10 –7. , 8, 10) . One of unctions . It has unctions . The cleunctions .ORF8s is about 40% implying that ORF8 is a relatively fast evolving in comparison to other viral proteins. The second fastest evolving gene is ORF6 with 70% identity. Identity for other genes varies between 72% and 95%: for example, ORF3a, N, and spike (S) genes have 72, 91, and 76% identity, respectively [ORF8 is the fastest evolving gene, there is no doubt that it evolves under strong purifying selection  [The sequence identity of SARS-CoV and SARS-CoV2 ectively . Althougtations) , 15.OFR8 gene [ORF8, which splits ORF8 into ORF8a and ORF8b (with a frameshift of 35 bp) encoding 39- and 84-residue polypeptides, respectively  that can eventually define their relevance to future therapeutics development [It has been widely speculated that the truncated products of ORF8a/b led to a modulation of pathogenicity and/or replication that favored adaptation of SARS-CoV to humans , 20, 21.ORF8a and ORF8b genes and found that in both cases the number of synonymous mutations was greater than that for nonsynonymous ones. These results suggested that both ORF8a and ORF8b are under purifying selection implying that proteins translated from these ORFs are functional.Here, we performed evolutionary analyses of ORF8” for sequences without the 29-nucleotide deletion, as well as “ORF8a” and “ORF8b” (outcomes of the 29-nucleotide deletion) according to the commonly used nomenclature [ORF8 sequences, the last sequence was “SARS coronavirus isolate HC/SZ/266/03” (AY545916), whereas for the ORF8a/b sequences, the last sequence was “SARS coronavirus Urbani isolate icSARS-C7” (MK062183). The “last sequence” means a human-associated sequence with the highest e-value compared to other sequences, in which human is listed as the host, in the BLASTN output.We used the term “nclature . We perfORF8 and ORF8a/b sequences  to analyze a significance of heterogeneity of 2 × 2 tables assuming independence of 4 variables: the raw number of nonsynonymous and synonymous mutations vs. raw numbers of nonsynonymous and synonymous sites. The right-tailed Fisher Exact Test was used because the alternative to independence in the case of ORF8 is that there is positive association between the variables . We used the PBL method as implemented in DnaSP v. 5.10.01 [ORF8 and ORF8a/b genes, we analyzed raw numbers of nonsynonymous and synonymous mutations in ORF3a, ORF6, ORF7a, ORF7b, S, and N genes ; incomplete windows at 3′ ends were not used for analyses of nonsynonymous and synonymous mutations. The ORF3a gene was split into 3 windows. The N gene was split into 4 windows. The S gene was split into 11 windows.To estimate Ka and Ks, we extracted mutations from multiple alignments of es Figs.  using an 5.10.01 , 25 and  5.10.01 , 25 to eORF8. For the ORF8a/b alignment, the number of mutations is larger compared to that of ORF8, thus, more uncertainty of root location is expected. To resolve this issue, we arbitrarily chose AY394995 as a putative outgroup (the sequence that was used as a query in BLASTN searches).To map the mutations onto phylogenetic trees, we first inferred phylogenies using the maximum parsimony approach, which is suitable for closely related sequences , 27. We status quo in terms of fitness. The Ka/Ks ratio  is commonly used to distinguish between purifying and positive selection. It is widely accepted that Ka/Ks < 1 reflects purifying selection, whereas Ka/Ks > 1 may indicate positive (Darwinian) selection [Analysis of nonsynonymous and synonymous substitutions is a powerful tool to analyze modes of natural selection and tendencies in evolution of protein-coding genes –32. Purielection , 32.ORF8 and ORF8a/b sequences. First, we estimated numbers of nonsynonymous and synonymous sites. Lists of mutations are relatively short due to the limited number of SARS-CoV sequences  consensus sequences are ancestral and (ii) all parallel mutations (if any) are the result of recombination events. In other words, we assumed no parallel (independent recurrent) mutations. We found that the number of synonymous mutations in ORF8b is greater than that for nonsynonymous ones (9 vs. 6). Considering the larger number of predicted nonsynonymous sites compared to the synonymous ones, this excess is statistically significant  . After merging ORF8a and ORF8b mutations into one set (11 synonymous vs. 8 nonsynonymous mutations), the probability value was 0.002. These results strongly suggested that at least ORF8b (or both ORF8a and ORF8b genes) is(are) under purifying selection and, subsequently, proteins translated from these ORFs are functional.To infer mutations in ORF8 gene, the number of nonsynonymous mutations is greater than that of the synonymous ones (8 vs. 1) neutrally suggesting weaker functional constrains on the protein encoded by ORF8. It is parsimonious to suggest that ORF8 and ORF8a/b experienced different evolutionary forces. For example, emergence of ORF8a/b could be a result of “unsuccessful” searches for “optimal” variants of ORF8. This could cause a “successful” fixation of the ORF8a/b variant in the SARS-CoV population.For the intact  1) Fig. A. The nuORF8 and ORF8a/b sequences  under the assumption that there are no recombination events. We were not able to define reliable outgroup sequence(s) because more distantly related sequences are unlikely to reflect a complex history of zoonotic transfer events. Therefore, it was not possible to reliably root phylogenetic trees. In other words, different locations of the root across phylogenetic trees depending on various methods of phylogenetic reconstructions should be considered. Alignments of es Figs.  and S2 wes Figs.  and S4. es Figs. .ORF8, we found one position that contains parallel A > C mutations   Fig. A. No par#9) Fig. B. Thus, #9) Fig. A: the smons Fig. , althougORF8a, ORF8b, ORF7a, and N genes, the ratio (R) of the number of nonsynonymous mutations and the number of synonymous mutations is close to or equal 1. For ORF3a, ORF6, and S genes, the R varies between 1.6 (S) and 3.1 (ORF3a)  , 4.0 (window #2), and 0.8 (window #3)  to 1.7 (window #3). For the S, we revealed a mix of windows with relatively small (0.8–2) and windows with relatively large R values (3.3–7.0)  Fig. A. These F8) Fig. B. For ORORF8 has smaller Ka compared to Ks. For example, for the SARS coronavirus HSZ-Cc vs. bat SARS-like coronavirus YNLF_34C, the Ka = 0.099 and Ks = 0.476, giving Ka/Ks of 0.21   and an additional 1-nucleotide insertion in the ORF8b gene .A high density of mutations (3 synonymous and 3 nonsynonymous) has been detected at the 3′ end of the ene Fig. . This clne Figs.  and S2. ORF8 and ORF8a/b genes  and ORF8b is the same  and “mutated” sequence  are shown below the alignment.Supplementary Material 1: Fig. S1. Intact SARS-CoV ORF8ab with the deletion, alignment and delineated mutations. A consensus sequence (reconstructed using the “majority” rule) and “mutated” sequence  are shown below the alignment.Supplementary Material 2: Fig. S2. ORF8 sequences.Supplementary Material 3: Fig. S3. Reduced matrices containing positions with mutations for the alignments of ORF8a/b sequences.Supplementary Material 4: Fig. S4. Reduced matrices containing positions with mutations for the alignments of Supplementary Material 5: Fig. S5. Numbers of differences among distantly related viral sequences.Additional file 1: Alignments of all studied SARS-CoV genes (ZIP file).Additional file 2: Python script for extraction of mutations from BLASTN multiple alignments (ZIP file).Additional file 3: Alignments of distantly related viral sequences (ZIP file).