Moreover, the catalyst optimization study showed that at catalyst loadings as low as [C=C]/Ru = 5000:1, the theoretical molecular weight is in good agreement with the experimental molecular weight and the expected diols and polyols are formed. At higher ratios than those, the difference between theoretical and experimental molecular weight is wide, and there is no control over HTPB. Therefore, the rubber/CTA molar ratio and the amount of catalyst play an important role in PB degradation and HTPB synthesis. Biobased HTPB can be used to synthesize engineering design polymers, intermediates, fine chemicals, and in the polyurethane industry, and contribute to the development of environmentally friendly raw materials.Biobased hydroxyl-terminated polybutadiene (HTPB) was successfully synthesized in a one-pot reaction via metathesis degradation of industrial rubbers. Thus, polybutadiene (PB) and poly(styrene-butadiene-styrene) (SBS) were degraded via metathesis with high yields (>94%), using the fatty alcohol 10-undecen-1-ol as a chain transfer agent (CTA) and the second-generation Grubbs–Hoveyda catalyst. The identification of the hydroxyl groups (-OH) and the formation of biobased HTPB were verified by FT-IR and NMR. Likewise, the molecular weight and properties of the HTPB were controlled by changing the molar ratio of rubber to CTA ([C=C]/CTA) from 1:1 to 100:1, considering a constant molar ratio of the catalyst ([C=C]/Ru = 500:1). The number average molecular weight ( SBS iThe tire industry is the most important market for such synthetic rubber as SBS, SBR, PB, butyl rubber (IIR) and polyisoprene (IR). Styrene butadiene rubber is the most important grade at the moment, with around 5.1 million tons consumed in 2020. In 2020, 14.4 million tons of synthetic rubber were produced. The global demand for tires will reach 3.2 billion units in 2022. Furthermore, it is reported that a total of about 223 million replacement tires were shipped to the USA in 2019 ,7.In addressing this challenge, several waste-management methods have been investigated and tested in the last few decades, including thermal (pyrolysis), mechanical , physical , chemical , and biological  processes ,12,13,14Fn close to 2). Telechelic compounds, oligomers, polymers, biobased products, and raw materials with different functional groups have been obtained via metathesis degradation reaction, including hydroxyl-terminated compounds, which represent a challenging task  in their structure and catalysts based on transition metals such as Ru, Mo, and W. Highly active catalysts based on Ru and high concentrations of these are required to achieve the successful synthesis of hydroxy-terminated compounds ,20. In tHydroxyl-terminated polybutadiene oligomers (HTPB) can be used as diols and polyols. Diols are employed in the elaboration of adhesives, coating, sealants, intermediate products, fine chemicals, and polymeric binders for both propellants and explosives, and as chain-extender oligomers in polycondensation reactions for the synthesis of copolymers, polyether polyols, and polyurethanes (PU), where they constitute the rigid segment ,24,25,26Ricinus communis), and it has been converted into diols and polyols dichloro(o-isopropoxyphenylmethylene) ruthenium (second generation Hoveyda-Grubbs) (Ru) were purchased from Sigma-Aldrich, Inc. , and used as received.The synthesis of biobased HTPB was carried out via metathesis degradation reaction from industrial rubber (SBS or PB) using the fatty alcohol 10-undecen-1-ol as chain transfer agent (CTA), chlorobenzene as solvent, and second-generation Hoveyda–Grubbs catalyst Ru . All reaFirst, the rubber SBS or PB  and CTA  were placed in the Schlenk tube charging with chlorobenzene anhydrous as solvent (10 wt.%), then the catalyst Ru  was added. The temperature was controlled using an oil bath maintained at 40 °C for 12 h with continuous stirring.The molar ratios of carbon-carbon double bonds of SBS or PB to CTA used were [SBS]/[CTA] or [PB]/[CTA] = 1:1, 10:1, 20:1, 50:1, and 100:1  in order to control the molecular weights and for the catalyst [C=C]/[Ru] = 500. During the reaction process, ethylene is generated as a by-product, which means that is possible to displace the equilibrium process toward a specific product. Thus, in order to favor the formation of HTPB, ethylene gas was removed using a vacuum line ,19. Aften = 3) for each reaction using means ± standard deviation for GPG and the yields.The structure, molecular weights and thermal properties of the biobased HTPBs were characterized by infrared spectroscopy (FT-IR), nuclear magnetic resonance (NMR), gel-permeation chromatography (GPC) and thermogravimetric analysis (TGA and DTG). The reactions were carried out in triplicate and analyzed  using the fatty alcohol 10-undecen-1-ol as CTA  and second–generation Hoveyda–Grubbs catalyst (Ru) . In all −1. Data are presented as the frequency of absorption (cm−1).Infrared spectroscopy (FT-IR) measurements of all samples were analyzed using a Perkin Elmer Frontier MIR FT-IR spectrometer  fitted with a Frontier Universal Diamond/ZnSe ATR with a single reflection top plate and pressure arm. The spectra were recorded in the region of 4000–500 cm1H-NMR) spectra were recorded using a Bruker spectrometer at 400 MHz  and deuterated solvents , and chemical shifts (δ) are reported in parts per million (ppm) relative to tetramethylsilane (TMS) as an internal standard.Nuclear magnetic resonance (nM and wM) and molecular weight distribution (PDI) were determined with reference to monodisperse polystyrene standards on a Waters 2695 ALLIANCE gel-permeation chromatography (GPC) instrument  at 30 °C with tetrahydrofuran (HPLC grade) as a solvent, a universal column, and a flow rate of 1 mL/min. At least three replicates were analyzed for each condition using means ± standard deviation for nM, wM and PDI.The number and weight average molecular weight  was carried out on a TA Instrument Q5000  under a nitrogen atmosphere from room temperature to 600 °C with a heating rate of 10 °C/min.In −1 was attributed to the stretching of the double bond (-CH=CH-), the absorption peaks at 2925–2851 cm−1 were attributed to the stretching of the aliphatic symmetric and asymmetric methylene groups (-CH2-), and the absorption peaks at 1650–1436 cm−1 were attributed to the stretching of double bonds (C=C) present in the aliphatic chain and aromatic ring in the SBS. Finally, the signals at 1000–970 and 698 cm−1 are associated with the aromatic ring present in the polystyrene (PS).FT-IR and NMR analysis confirmed the formation of HTPB. −1, which is associated with the R-OH stretching vibration of the HTPB. The signals of aromatic rings at 1000–970 and 698 cm−1 disappeared, correlated with the participation of SBS in the degradation reaction via metathesis, and the formation of the expected biobased HTPB, after isolating the microblocks of the PS. In FT-IR spectra, two features indicate the degradation in polymers: changes in signal intensity and the appearance of new signals /[Ru] = 10,000, the experimental molecular weight was practically twice the theoretical (nM) for the synthesis of biobased hydroxyl-terminated oligomers , it was possible to control the molecular weight of oligomers , and the signal at 3.52 ppm (j) corresponds to the methylene protons attached to the hydroxyl groups (-CH2-OH). This signal (j) is essential in the spectra because—in addition to confirming the formation of oligomers—it provides a measure of control in molecular weight by functional groups. Unlike the PB7 spectrum, where a decrease in the signal (j) is observed at 3.52 ppm, this is associated with the value of repeating units of PB (m different from 10) and its molecular weight. Therefore, at molar ratios greater than 10,000:1, there is no control in the molecular weight of biobased HTPB.dT at 5 and 50% were obtained by TGA for the biobased HTPB obtained by metathesis degradation reaction of PB .On the other hand, the Therefore, for metathesis reactions using the industrial rubbers SBS or PB, it is possible to use low concentrations of catalyst to carry out the reaction. It is reported that for metathesis reactions of rubbers, a ratio molar from 500:1–4000:1 of catalyst is used ,19,38. OSoon, the waste derived from SBS and PB was degraded via cross-metathesis using 10-undecen-1-ol (which can be obtained from castor oil) and other natural resources, such as CTA, optimizing and economizing the catalyst. Biobased HTPB obtained with lower and higher molecular weight  can be used to synthesize engineering design polymers, intermediates, fine chemicals, and in the polyurethane industry and contribute to developing environmentally friendly raw materials: “reuse–reduce–recycle.”nM) obtained ranged from 583 to 6580 g/mol, and decomposition temperatures (Td 5%) from 134 to 220 °C.Biobased HTPB (unsaturated diols or polyols by their molecular weight) were synthesized by metathesis degradation from PB and SBS using the fatty alcohol 10-undecen-1-ol as CTA and the second-generation Grubbs–Hoveyda catalyst with high yields (94–98%). The PB was degraded, as well as the PB microblocks of the SBS. Meanwhile, the polystyrene (PS) microblocks of such copolymer were isolated and recovered. Moreover, it was established that by changing the molar ratio of double bonds [C=C] of rubber (PB or SBS) to CTA in a wide range (1:1–100:1), the molecular weight and the properties could be controlled at a constant molar ratio of the catalyst ([C=C]/Ru = 500:1). The number average molecular weight  play an important role in PB degradation. The catalyst optimization study showed that at catalyst loadings as low as [C=C]/Ru = 5000:1, the theoretical molecular weight is in good agreement with the experimental molecular weight, and the expected diols and polyols are formed. The difference between theoretical and experimental molecular weights is wide at higher ratios than those, and there is no control over biobased HTPB.Therefore, the chosen CTA and catalyst synthesized tailor-made biobased HTPBs by metathesis degradation of industrial rubbers in a one-pot reaction. The second-generation Grubbs–Hoveyda catalyst has an internal coordination bond that makes it tolerant to air, moisture, and most functional groups (-OH).Biobased HTPB can be used to synthesize engineering design polymers, intermediates, fine chemicals and in the polyurethane industry, and contribute to the development of environmentally friendly raw materials: “reuse–reduce–recycle.”


Structure-constrained molecular generation is a promising approach to drug discovery. The goal of structure-constrained molecular generation is to produce a novel molecule that is similar to a given source molecule (e.g. hit molecules) but has enhanced chemical properties (for lead optimization). Many structure-constrained molecular generation models with superior performance in improving chemical properties have been proposed; however, they still have difficulty producing many novel molecules that satisfy both the high structural similarities to each source molecule and improved molecular properties.We propose a structure-constrained molecular generation model that utilizes contractive and margin loss terms to simultaneously achieve property improvement and high structural similarity. The proposed model has two training phases; a generator first learns molecular representation vectors using metric learning with contractive and margin losses and then explores optimized molecular structure for target property improvement via reinforcement learning.We demonstrate the superiority of our proposed method by comparing it with various state-of-the-art baselines and through ablation studies. Furthermore, we demonstrate the use of our method in drug discovery using an example of sorafenib-like molecular generation in patients with drug resistance.The online version contains supplementary material available at 10.1186/s13321-023-00679-y. Structure-constrained molecular generation is a challenging problem in goal-directed molecular optimisation studies . The goaent}1060 . To addrAI-based methods for efficient goal-directed molecular optimisation utilize various deep generative models, optimisation techniques, and molecular representation methods –9. VariaMany AI-based molecular optimisation methods have been proposed for structure-constrained molecular generation against diverse goals of chemical and pharmacological properties. For instance, generative tensorial reinforcement learning (GENTRL), which identifies potent DDR1 kinase inhibitors for the treatment of renal fibrosis, has been a great achievement in drug discovery because it succeeded in finding novel nanomolar hits with improved half-maximum inhibitory concentrations using VAE, the policy gradient algorithm of reinforcement learning, and the SMILES string representation method . JTVAE aAlthough these state-of-the-art models have shown good molecular generation performance in terms of chemical properties and structural similarity, there is still room to increase the efficiency of molecular generation with these two constraints. Furthermore, graph-based methods have been proposed more often than SMILES-based methods in structure-constrained molecular generation studies. However, a recent comprehensive study demonstrated that there are no obvious shortcomings of SMILES-based generation methods compared to graph-based generation methods, and SMILES-based molecular optimisation may be better .contractive and margin losses, to achieve high structural similarity and property improvement simultaneously in structure-constrained molecular generation tasks.We designed a COMA molecular generation model with two novel regularization terms, named We verified that COMA outperformed various molecular generation models on four benchmark datasets and demonstrated the merits of contractive and margin losses by conducting ablation studies.We demonstrated the proof-of-concept of COMA via the discovery task of novel compounds that were similar to sorafenib, but had desirable properties associated with drug resistance in HCC.To this end, we propose a novel structure-constrained molecular generation model called COMA, which utilizes reinforcement learning and metric learning techniques Fig. b. The prcontractive loss is to embed molecules with similar structures in close points to each other in a latent space, whereas the role of margin loss is to force dissimilar molecules to be placed as far as possible from each other. The decoder learns how to generate valid SMILES strings from latent vectors, whereas the encoder learns a mapping function; then, it is further trained by reinforcement learning to selectively produce SMILES strings with not only high structural similarity to source molecules but also improved target properties. Details of the training process are provided in the Additional file The generative model of the COMA is a gated recurrent unit (GRU)-based VAE for encoding and decoding SMILES strings. SMILES describes molecular structures using ASCII codes, where each ASCII code represents a component of a molecular structure, such as an atom, bond type, or branch structure. The molecular generation process of COMA is simple: for a given source molecule, the model calculates a latent vector using a pretrained encoder with contractive and margin losses; then, the model generates a molecule using the latent vector and a decoder pretrained by reinforcement learning. The training process of COMA is as follows. An encoder learns a mapping function of SMILES strings into latent vectors using contractive and margin losses to improve the structural similarity performance. The role of Dataset To evaluate the performance of the COMA, we used four benchmark datasets  provided in [vided in . The goasibility . Detailsmentclass2pt{minimBaseline methods We compared COMA with seven state-of-the-art models: JTVAE, VJTNN, VJTNN+GAN, CORE, HierG2G, HierG2G + BT, and UGMMT. JTVAE is a graph-based molecular generation model that optimizes molecular properties using a Bayesian optimisation method [n method . VJTNN in method . CORE isn method . HierG2Gn method . HierG2Gn method . The UGMn method .Evaluation metrics We evaluated the COMA and baseline models using various evaluation metrics of structure-constrained molecular generation , to enhance the therapeutic effectiveness of chemotherapy in sorafenib-resistant HCC patients.Association between Sorafenib Resistance and ABC transporters Sorafenib is an inhibitor of protein kinases in the Raf/Mek/Erk pathway that suppresses cell proliferation and angiogenesis in tumour cells [ur cells . Owing tur cells , the disur cells . Since mur cells , it appeBinding Affinity Optimisation against ABCG2 To conduct a proof-of-concept of COMA for sorafenib-like hit discovery, we designed a goal to decrease the binding affinity score against the protein of ABC subfamily G member 2 (ABCG2) while conserving the substructures of sorafenib without loss of affinity against the serine/threonine-protein kinase B-raf (BRAF), which is a target kinase of sorafenib prona 1.2.3  for ABCGna 1.2.3 . We utilWe found that 15 molecules had lower binding energies against ABCG2 than sorafenib Fig. c; hence,Synthetic accessibility evaluation Finally, we evaluated the synthesizability of the molecules generated by COMA using the retrosynthesis analysis of Scifinder-n [in silico analysis demonstrate that sorafenib derivatives generated by COMA can be alternative drug candidates to sorafenib in patients exhibiting high drug resistance.finder-n . Most mocontractive and margin losses. Our model achieved both high property improvement and high structural similarity via two training phases: a metric learning phase that enables the VAE to get the ability to generate molecules similar to the source molecule and a reinforcement learning phase that makes the VAE intensively produce molecules satisfying both the similarity constraint and property enhancement. COMA outperformed various state-of-the-art models on four benchmark datasets: DRD2, QED, plogP04, and plogP06. Ablation studies demonstrated the importance of contractive and margin losses. Furthermore, we introduced the use case of COMA in drug discovery for sorafenib resistance.AI-based generative models for structure-constrained molecular generation can not only be a solution for effective drug discovery but also a powerful and explainable tool for chemists and pharmacologists. Existing structure-constrained molecular generation models exhibit good performance in terms of generating molecules with better molecular properties than source compounds; however, they have limitations in producing molecules that satisfy molecular property improvement, novelty, and high similarity to the source molecule simultaneously. In this study, we designed a VAE-based generative model for structure-constrained molecular generation with two novel regularization terms of metric learning: COMA exploited reinforcement learning framework with heuristically customized reward functions for each benchmark task. While the importance of reward function design in reinforcement learning is well-known , there iIn this study, we used the metric learning framework for structure-constrained molecular optimisation, which allows to improve a target property with retaining some molecular structures, but recent drug design studies devote their efforts to multi-objective molecular optimisation, which might be a more challenging task. In the future, to address the multi-objective molecular optimisation tasks, we will attempt to develop new applications of our proposed contractive and margin loss terms by replacing structure similarity by biological activity similarity.COMA was implemented using Python 3.6, and several open-source tools, including PyTorch 1.10.1 and RDKit 2021.03.5. RDKit, an open source tool for chemoinformatics, was used for SMILES kekulization, SMILES validity check, Tanimoto similarity computation, and estimation of QED. PyTorch, an open-source machine learning framework, was used to construct and train the neural networks of COMA. All experiments were conducted on Ubuntu 18.04.6 LTS with 64 GB of memory and a GeForce RTX 3090.x and y with the corresponding fingerprint vectors The Tanimoto similarity, which ranges from 0 to 1, compares molecular structures such as atom pairs and topological torsions, represented by Morgan fingerprints. In this study, the Morgan fingerprints were binary vectors generated using RDKit with radii of 2 and 2048 bits. For any two SMILES strings Predicting the binding affinity scores for ABCG2 and BRAF is crucial for the application of COMA for sorafenib resistance. In this study, DeepPurpose , a PyTorIn this study, we used four benchmark datasets provided in  and the The DRD2 dataset contains 34k molecular pairs (source and target) with DRD2 activity scores derived from the ZINC database . The DRDWe constructed a dataset for the sorafenib-like molecular generation to introduce an example of COMA application. Based on the observation that the activity of ABCG2 is related to sorafenib resistance in hepatocellular carcinoma , 43, thiCOMA has the structure of VAEs. It consists of an encoder entclass1pt{minimadistance  that meaentclass1pt{minimareconstruction loss was that the encoder and decoder learned how to generate valid and diverse SMILES strings, and both the contractive loss, which was equal to the Fréchet distance between two multivariate Gaussian distributions, and the margin loss, which was similar to soft hinge loss, were aimed at improving the performance of structure-constrained molecular generation in terms of similarity. COMA was first trained using these three loss terms (Additional file Equation  can impomentclass2pt{minimx and a given reward function R, the objective function is expressed asx, and x sampled by the pretrained encoder x is within the allowable limits. In the case of DRD2 and QED, the reward function was defined asCOMA can translate an input molecule into a valid SMILES string that resembles the input via metric learning; however, the outputs of COMA are not yet guaranteed to exhibit desirable chemical properties. Reinforcement learning aims to encourage COMA to produce molecules that are structurally similar to the given input molecule, but with desirable chemical properties. To achieve this goal, we utilized the REINFORCE algorithm , where aThe details of the reinforcement learning procedure for COMA are described in the Additional file p is true and zero otherwise; x is valid and zero otherwise; x; To evaluate the performance in structure-constrained molecular generation tasks, we used the following seven metrics: validity, novelty, property, improvement, similarity, diversity, and success rate. More concretely, given the training and test datasets To verify our findings, that is, the molecules translated from sorafenib in the application example, we analysed the binding poses and evaluated the binding energy using docking simulation tools, including AutoDock Vina , ChimeraAdditional file 1. Supplementary algorithms S1–S3, tables S1–S12, and figures S1–S6.


Croton linearis Jacq. is an aromatic shrub that has been utilized in traditional medicine in the Bahamas, Jamaica, and Cuba. Recent studies have revealed the antiprotozoal potential of its leaves. The present work is aimed to identify the volatile constituents of essential oil from the stems of C. linearis (CLS-EO) and evaluate its in vitro antileishmanial activity. In addition, an in silico study of the molecular interactions was performed using molecular docking. A gas chromatographic–mass spectrometric analysis of CLS-EO identified 1,8-cineole (27.8%), α-pinene (11.1%), cis-sabinene (8.1%), p-cymene (5.7%), α-terpineol (4.4%), epi-γ-eudesmol (4.2%), linalool (3.9%), and terpinen-4-ol (2.6%) as major constituents. The evaluation of antileishmanial activity showed that CLS-EO has good activity on both parasite forms , with a CC50 of 49.0 ± 5.0 μg/mL on peritoneal macrophages from BALB/c mice (selectivity index = 2 and 3 using the promastigote and amastigote results). Molecular docking showed good binding of epi-γ-eudesmol with different target enzymes of Leishmania. This study is the first report of the chemical composition and anti-Leishmania evaluation of CLS-EO. These findings provide support for further studies of the antileishmanial effect of this product. Leishmania, which is transmitted to humans by the bite of infected female phlebotomine sandflies. This disease has wide clinical spectra, with three main forms being recognized: visceral , cutaneous (the most common), and mucocutaneous 2,5-diphenyltetrazolium bromide . After 4 h of incubation, the supernatant was eliminated, tetrazolium salt was dissolved with 100 µL of DMSO, and the microplate was read in an ELISA microplate reader  at 540 nm and 620 nm as reference wavelengths [50) was determined by a dose–response linear regression analysis. Each experiment was performed in duplicate, and the results were expressed as means ± standard deviations.A stock solution of CLS-EO was diluted in 100% DMSO at 20 mg/mL. The assay was carried out under the same methodology reported previously . Serial 6/mL in 24-well plates and incubated at 37 °C and 5% CO2 atmosphere for 2 h. Nonadherent cells were removed. Cells were infected with promastigotes (in the stationary phase) at a parasite/macrophage ratio of 4:1 and incubated for 4 h under the same conditions. Afterwards, the assay was carried out under the same methodology reported previously [50 was determined by a dose–response linear regression analysis. Each experiment was performed in duplicate, and the results were expressed as means ± standard deviations. In both experiments, Pentamidine®  at 10 mg/mL was used as a reference drug.Peritoneal macrophages from BALB/c mice were collected and seeded with a density of 10eviously . Serial 50) was determined on peritoneal macrophages from BALB/c mice, which were collected and washed with RPMI 1640 medium (SIGMA) supplemented with antibiotics (200 IU of penicillin and 200 µg of streptomycin per milliliter). The assay was carried out under the same methodology as reported previously [50 was determined by a dose–response linear regression analysis. Each experiment was performed in duplicate, and the results were expressed as means ± standard deviations. The selectivity index (SI) was calculated by the following formula: CC50/IC50.The median cytotoxic concentration  ). Molecular docking studies of the CLS-EO metabolites against Leishmania target proteins were performed using AutoDock 4.2 software [Molecular modeling was performed using the high-performance computing capabilities of the cluster of the software . The 3D software .Leishmania spp. [The crystal structures of the target proteins were obtained from the Protein Data Bank (PDB ID) with the codes sterol 14α demethylase (3L4D), trypanothione reductase (2JK6), trypanothione synthetase (2VOB), and arginase I (4ITY), which are considered relevant and interesting molecular targets in nia spp. .The assay parameter was the Lamarckian genetic algorithm (GA) with the following conditions: population amount, 100; maximum number of evals, 2,500,000; with maximum number of generations, 27,000; and other parameters were taken by default. The induced coupling geometric regions were determined with AutoDockTools 1.5.6 . AutoDocdock = ΔGvdW + ΔGelec + ΔGhbond + ΔGdesolv +ΔGtors) were evaluated by molecular mechanics (MM) in AutoDock4 [The docked conformations of each ligand were ranked into clusters based on the binding energy. During the molecular docking, interactions (ΔGutoDock4 . The toputoDock4  was usednorm = 5.71 × ΔGdock/MW1/3, where DSnorm is the normalized docking score, ΔGdock is the docking energy from the molecular docking program, and 5.71 is a scaling constant to make the average DSnorm values comparable to ΔGdock [Docking calculations were validated by redocking the co-crystallized ligands in the receptor structures . Howevero ΔGdock .L. amazonensis, with low values of SI. The biological activity can be attributed to the synergistic interactions of the EO components. A molecular docking analysis confirmed the stability of the complexes between epi-γ-eudesmol and the different target enzymes of Leishmania, which implies that the antileishmanial action could occur by different mechanisms of action whose main component would be this metabolite.This study is the first report of the chemical composition and antileishmanial evaluation of CLS-EO. The chemical composition of this oil revealed high concentrations of monoterpene hydrocarbons and oxygenated monoterpenes, with 1,8-cineole as the main compound. CLS-EO showed activity on the promastigote and amastigote forms of C. linearis essential oils. In addition, this study provides support for the further exploration of the main components from CLS-EO as antileishmanial agents and may contribute to research of new candidates for this NTD.In conclusion, these findings complete a series of studies about the pharmacological potentialities of


Cilium is a microtubule-based hair-like organelle found on almost every type of cells in human body. These cilia can be motile  or non-motile (as in the cilia in nephrons of kidneys and photoreceptors on the retina of eyes). Meanwhile, single cilium  or multiple copies of the cilia  are possible and observed in human cells. Despite of their highly complex structure, ciliary structure is conserved in vertebrate and consist of over 600 proteins  orchestrSince cilia are almost ubiquitously presence in all cell types in human body, it is not surprising that ciliopathies, which refers to any disorders that affects the ciliary function, cilia itself or its sub-structures, exhibit a large spectrum of anomalies in multiple organs and systems, for instance, brain, kidneys, liver, bone and eyes etc. Because of the complexity of proteins and signaling pathways involved in ciliopathies, the exact molecular mechanism is yet to be elucidated but it is certain that genetic variation like single nucleotide polymorphisms (SNPs), missense or nonsense mutations on cilia and/or related genes etc. played a major role in the pathogenesis. Thanks to the technology advancement in biotechnology and genetics on genome-editing, as well as the prevalence and readily available of next generation sequencing technique on exome and genome, the manuscripts included in this Research Topic can facilitate researchers to have a deeper understanding on the genetic variations in ciliopathies and provide ways for further verification of these changes with genome-editing and thus, test on the signaling pathways and molecular mechanism.This Research Topic is dedicated to including works that explore any novel identification or characterization of genes, signaling pathways, transcriptomic analyses, and mechanisms on ciliary gene(s) and/or protein(s) involved in ciliopathies. Moreover, recent advancement in diagnosis of ciliary and/or cilia-related diseases, integrative analysis of publicly available data on ciliary genes and/or proteins, articles that summarize the existing data on ciliary pathologies were also included. A total of 13 manuscripts were included in this Research Topic.Glis2:c.560_574delACCATGTCAACGATT, p. H188_Y192del)) , asthenoteratozoospermia  and nonsense variant RSPH4A:c.1774_1775del, p. (Leu592Aspfs*5) , dynein axonemal assembly factor (DNAAF) -4  , Meckel Gruber Syndrome (by splice site variant RPGRIP1L:c.776 + 1G > A/IVS6 + 1G > A) . These novel findings expanded the variants spectrum related to ciliopathies and provide some reference for others subjects who share the same variants in future.Among these works, a number of novel studies revealing the causes and potential mechanisms of ciliopathies has been included. For instance, four reports described the novel variants identifications contributing to various manifestation of ciliopathies, such as nephronophthisis and kidney failure ) , showing skeletal ciliopathies  r.2765_2768del; p. (Tyr923Leufs*28))  and primary ciliary dyskinesia (PCD) in respiratory cilia  . All these case reports in this Research Topic, together with other previously published reports in the literatures, enhance our current understanding on the genetics variants contributed to ciliopathies.While there are another three reports on variants that affect the renal and hepatic fibrocystic disease  and a subset of inner dynein arm (IDA) assembly using drosophila model. While Schultz et al. studied the role of CFAP300 in dynein complex transport in motile cilia and in dynein arm assembly. Moreover, Nazlamova et al. showed in the CRISPR mutated PRPF6 and PRPF31 cell lines, patient-derived retinal organoid cultures and siRNA-treated retinal organotypic cultures that, mutation in the pre-mRNA splicing factor would resulted in defected microtubule and centrosome, ultimately leading to retinal degeneration. These works shed light on the potential mechanisms and signaling pathways for the pathogenesis of ciliopathies.Apart from the variant case reports, another three papers illustrated the vital tasks of cilia proteins in cilia assembly and functions were also included. For instance, Chau et al. has demonstrated that genetic testing is essential in the diagnosis of early-onset bronchiectasis. While the project of Antony et al. indicated the significance of genetic diagnosis in a cohort of 37 cases laterality defect with defected left-right patterning. Their efforts have demonstrated that genetic diagnosis is key for the proper diagnosis of ciliopathies and would provide a valuable reference for the clinician.Furthermore, two articles in this Research Topic have highlighted the importance of genetic testing in the diagnosis of ciliopathies. The work of Rusterholz et al. to contribute an insightful review focusing on Joubert Syndrome, which is a rare cilia-related disorder characterized by cerebellar and brain stem malformation with other renal, retinal and skeletal manifestations. Their work has summarized and featured on what have been learnt in understanding Joubert Syndrome from different zebrafish models in the literatures.In addition, it was delighted to have In conclusion, despite many unknown and challenges in delineating the exact cause and molecular mechanism of ciliopathies, these studies in this Research Topic have greatly extended our current understanding on the genetic of ciliopathies and shed light on potential signaling pathway down to molecular level. A better understanding of the genetic and molecular mechanism of the disease is crucial for early diagnosis, appropriate treatment and improving the prognosis of patients, thus, this Research Topic would be valuable for further research and beneficial to the patients in future.


Polycarbonate is an engineering plastic used in a wide range of applications due to its excellent mechanical properties, which are closely related to its molecular structure. We performed coarse-grained molecular dynamics (CGMD) calculations to investigate the effects of topological constraints and spatial distribution on the mechanical performance of a certain range of molecular weights. The topological constraints and spatial distribution are quantified as the number of entanglements per molecule ( Polycarbonate (PC) is a kind of amorphous resin material with excellent mechanical properties, such as high strength, ductility and impact resistance as a structural material, and is widely used in a variety of fields such as electronic materials, automotive parts and construction materials ,2,3. DueOne of the reasons polymeric materials have such great potential as structural materials is that they can exhibit a wide variety of mechanical properties by modifying their molecular structure. In general, the mechanical properties of polymeric materials are strongly influenced by structure at the molecular level, and the properties of the same material can be significantly altered by changing the method of manufacture—for example, injection molding conditions . This isThere have long been attempts to systematically relate molecular structure and mechanical properties based on experimental results. For example, Wu  introducThe all-atom molecular dynamics (AAMD) analysis and coarse-grained molecular dynamics (CGMD) analysis are two major methods of molecular-level simulation for polymeric materials. These methods provide a direct relationship between molecular structure and mechanical properties. They are also useful to understand the essential mechanisms of deformation and fracture, as the evolution of molecular structures during deformation is evaluated. The influence of molecular structure on the deformation and fracture behavior of polymeric materials has been simulated using both AA and CG simulations. Fujimoto et al.  demonstrDespite this progress, the relationship between molecular structure and deformation/fracture behavior has not been yet clarified. For example, although structural parameters such as molecular weight and entanglement points were used to explain the origin of stress in the AAMD analysis by Tang et al. , the effIn this study, coarse-grained molecular dynamics simulations are performed to elucidate the effects of the molecular structure of polycarbonate on the deformation and fracture properties. Molecular structure models are created by systematically changing the radius of gyration, number of entanglement points and the molecular weight. For each of these models, a deformation analysis is performed to determine the stress–strain relationships and the effects of these molecular structure properties on the maximum stress and critical strain.In a coarse-grained molecular dynamics (CGMD) analysis, a predefined group of atoms  is represented as a coarse-grained particle, and its motion is treated as a point mass. This allows analysis in larger spatial and temporal scales than compared with all-atom molecular dynamics (AAMD) analysis, which explicitly treats the behavior of all atoms. Coarse-grained molecular dynamics are often introduced to simulate the behavior of polymers because it is often possible to reproduce the physical properties of polymers without much loss of accuracy. The mass of a coarse-grained particle is given as the sum of the masses of its constituent atoms. Physical quantities such as temperature are calculated based on particle mass and velocity, as in the all-atom model.i-j; b and We adopt the CG model by Kubo et al. , that is in ref. ,11. Note in ref. , our modN, The parameters used in this study to quantitatively describe molecular structure are the radius of gyration  Random placement of molecular chains with appropriately set Kuhn segment lengths.  (ii) MThe Kuhn segment length in this study is the length of a straight monomer when the molecular chain is composed of straight monomers of a fixed length jointed at their ends (the ends can rotate freely), as shown schematically in T = 1000 K, followed by cooling down to 300 K at 1000 K/ns, then NPT ensemble for 1 ns at T = 300 K. The time step  of eachTo evaluate the uneven distribution of loading in S1, firstly, we utilized the snapshot during loading, shown in z-axis). The distributions are evaluated at three different strains: 0.0, 2.0 and 6.0. The nominal number density of particles and nominal number density of entanglement points are the number of particles and the number of entanglement points, respectively, divided by the initial volume of the given structure. The z-axis is normalized by the length of the simulation cell αNeβ,log(σm)=lgn)αNeβ,log(σm)=lC, The critical strain is the strain of the condition in which the maximum stress is present, which is the strain that the structure can withstand before it fractures. The maximum stress can only reflect the strength of the material, not the amount of strain energy density that the material realizes without critical strain. The relationship between the critical strain and the molecular parameters is shown in follows: log(εc)=lTo clarify the relationship between the mechanical properties and molecular structure of polycarbonate, we performed CGMD analysis to investigate what kind of molecular structure features describe maximum stress (


Purpose of review: The emerging field of molecular predictive medicine is aiming to change the traditional medical approach in renal transplantation. Many studies have explored potential biomarker molecules with predictive properties in renal transplantation, issued from omics research. Herein, we review the biomarker molecules of four technologies  associated with favorable kidney transplant outcomes. Recent findings: Several panels of molecules have been associated with the outcome that the majority of markers are related to inflammation and immune response; although. other molecular ontologies are also represented, such as proteasome, growth, regeneration, and drug metabolism. Throughout this review, we highlight the lack of properly validated statistical demonstration. Indeed, the most preeminent molecular panels either remain at the limited size study stage or are not confirmed during large-scale studies. At the core of this problem, we identify the methodological shortcomings and propose a comprehensive workflow for discovery and validation of molecular biomarkers that aims to improve the relevance of these tools in the future. Summary: Overall, adopting a patient management through omics approach could bring remarkable improvement to transplantation success. An increased effort and investment between scientists, medical biologists, and clinicians seem to be the path toward a proper solution. Kidney transplantation represents the optimal therapeutic strategy for patients with end-stage renal disease (ESRD), increasing quality of life . HoweverNowadays, accepting an organ for transplantation is complex and hazardous. Tools to anticipate outcomes are based on serum creatinine and donor characteristics, including biochemistry, visual appraisal, and time zero biopsies. However, these approaches have drawbacks: serum creatinine is not specific to injury type and is a marker of advanced kidney damage; while renal biopsy, representing the gold standard in transplant evaluation, is too invasive and cannot be performed frequently . AccordiCurrently, graft evaluation relies on a limited set of molecular biomarkers, often only one parameter, closely correlated with a single functional aspect of the organ involved, or with a specific disease action monitoring. Such molecular markers would ideally be used throughout the transplantation process, from the donor, through organ procurement, preservation, and after transplantation. Donor molecular biomarkers may help in anticipating (short-) and long-term outcomes, while post-transplant, molecular markers may improve knowledge on graft adaptation and its shortcomings .Reviews have been conducted on markers issued from high-throughput omics technologies ,5. HowevHerein, we review the principal predictive models available for kidney transplantation and explore the current data on novel innovative tools for before procurement and after transplantation evaluation, discussing their advantages and limits.A biomarker is defined as “a biological molecule found in blood, other body fluids, or tissues that can be used to follow body processes and diseases in humans and animals” . In the Nowadays, graft evaluation relies on few biomarkers, and the search for a “golden molecule”, capturing the function of a transplanted organ in all its complexity, has been fruitless. Indeed, the complexity of the mechanisms underlying graft outcome can likely only be captured with a multiparametric molecular approach.High-throughput technologies have revolutionized medical research. The advent of “omics” technologies implies a comprehensive assessment of a set of molecules. There are four main types of molecular omics: genomics, transcriptomics, proteomics, and metabolomics; each with the potential to improve understanding of pathophysiological mechanisms, support diagnosis, and ultimately lead to new therapeutics and improved outcomes . GenomicTo date, the use of these approaches in renal transplantation has been mainly restricted to research, and transition to clinical practices remains a challenge. Indeed, molecular biomarker validation, identification, or verification is laborious and demands particular attention. The tools involved in exploring these biomarkers are also to be taken into consideration; indeed, as we will expose, in many publications, the validity of biomarkers is assumed where it should be evaluated and re-evaluated.The need for organs greatly exceeds donations, leading professionals to use extended criteria donor organs, more sensitive to ischemia/reperfusion injury, unavoidable during transplantation, and associated with adverse outcomes.Anticipating organ performance is a serious clinical challenge, which can improve allocation and individualize post-transplant management. To this end, several predictive models have been developed  Table 1Table 1.The leading algorithm is the Kidney Donor Profile Index (KDPI), currently the most effective scoring system to quantify graft failure risk. It includes ten variables: age, height, weight, last serum creatinine, history of diabetes, hypertension, HCV-infection, ethnicity, and the cause of death . It shouIn this section, we will review studies performed using omics and their prediction models based on “before procurement” data .APOL1 polymorphism, a molecule involved in the formation of most cholesteryl esters in plasma that also promotes the efflux of cholesterol from cells, and has been validated technologically and statistically (p < 0.0001) in large single- and multi-center studies. It is mainly expressed in African ancestry populations and associated with worse outcomes [Despite early enthusiasm in demonstrating a potential association between donor DNA polymorphisms and outcome, no study could show good predictive performance. The only exception is the outcomes ,17. It iTGF-β and CCR5 genes were associated with acute rejection (AR) [HLA gene [Other studies focused on gene coding for immune-related molecules, exploring the link between late allograft loss and genetic variation in the immune response. Authors have demonstrated that genotypic polymorphisms of ion (AR) . Howeverion (AR) . Others HLA gene .Transcriptomics quickly followed genomics , using hIGFBP5 and CSNK2A2 (cell cycle/growth); RASGRP3 ; CD83, BCL3, MX1, and TNFRSF1B (immune response); ENPP4, GBA3 (metabolism), which was significantly associated with the stratification of graft performance in correlation with the recipient’s DGF (p < 0.001) [HLA-G, HLA-E, HLA-DRB1, HLA-DRA, HLA-DPB1, HLA-DPA1, HLA-DQB1, HLA-DQA1, HLA-B, HLA-C, HLA-DMA, PSMB8, PSME1, HSP90AB1, and PRDX1). This molecular panel was able to discriminate kidneys that would later develop DGF and impaired 1-month function [A genome-wide gene expression study on human donor kidney studied 48 genes coding for molecules linked to cell communication, apoptosis, inflammation, and immune response. The authors show that these were increased in cadaveric donors vs. living donors and increased the risk of acute renal failure . An unsu< 0.001) . Later, function .n = 80) and will need to be confirmed in large transplant cohorts.Recently, a 23-transcript molecular signature associated with NK and CD8+ T-cell activation  was described in pretransplant blood to predict AR [n = 113) [n = 94) stage.Several proteomic studies have been carried out in recent years, mostly focusing on urine and perfusate , aiming n = 113) ,24. Amonp < 0.05) [Metabolomics is now acknowledged as a potential approach in transplantation, generally used for molecular biomarkers discovery and generating biological hypotheses. One such study used 266 plasma molecular metabolites to build ANOVA multiblock OPLS models; the main molecules being azelaic acid, creatinine, kynurenic acid, kynurenine, indoxyl sulfate, and tryptophan. They showed a strong association with rejection prediction ( < 0.05) .Recently, a review was conducted on metabolomics investigation during perfusion for the heart, lung, kidney, and liver. Biomarker molecules mainly associated with energy metabolism , glycogenolysis, and amino acid metabolism were discovered. There is an interesting association with graft quality, warranting larger studies . Indeed,To date, these multi-omics molecular investigations coalesce into a large panel of targets, but without coherence. One of the main challenges stems from the absence of consensus or a centralized database, inducing sensitivity and specificity for diagnostic analysis. Development of uniformized workflow for exploration and predictive model building is needed. This would open the way toward organ-tailored preservation, whereby high-risk grafts undergo an assessment by omics-conducted molecular profiling, leading to re-conditioning before transplantation .Kidney biopsy and creatinine are the main graft monitoring molecules agreed upon in the transplantation community. However, they cannot apprehend the complete phenotype of a patient. Hence, machine learning-based prediction algorithms have been explored. The iBox prognostication system was developed, estimating long-term allograft survival. iBox defines a score based on immunologic, histologic, and functional (eGFR and proteinuria) recipient’s criteria . Other wOmics molecular profiling provides new resources to improve prediction. This section aims to overview studies performed after transplantation .Genomic studies have become of great importance in kidney transplantations ,44. The MET, ST5, and KAAG1 (tumor development or suppression); RNF149, ASB15, and KLH13 (ubiquitination and proteasome); TGIF1, SPRY4, WNT9A, RXRA, and FJX1 ; and CHCHD10 and SERINC5 (energy and membrane repair). They demonstrated good predictive power for the development of fibrosis at 1 year (AUC 0.9) [p = 0.004) for polymorphisms of several genes such as CYP3A5 (involved in drug metabolization), CCR5, FOXP3, and other genes involved in inflammation and immune response ; TGF b, VEGF, and other mediators of fibrosis. However, this was performed in a small subset and requires larger studies, meta-analysis, and subsequent validation [Recently, genomic analyses have given more specific and robust results. Authors investigated a panel of 13 genes: AUC 0.9) . ResearcAUC 0.9) ; with folidation .Almost two decades ago, it was demonstrated that recipient urinary cell mRNA screening has similar diagnostic performance as biopsy histological analysis for AR . Later, DUSP1, CFLAR, ITGAX, NAMPT, MAPK9, PSEN1, RYBP, NKTR, SLC25A37, CEACAM4, RARA, RXRA, EPOR, GZMK, and RHEB) together with 18S ribosomal RNA as a housekeeping gene, a molecular signature mainly directed at defining the type and intensity of the inflammatory response. This project, called the Kidney Solid Organ Response Test (kSORT), is currently undergoing validation in prospective clinical trials [Further investigation on peripheral blood transcriptomics molecular analysis highlighted a 17-gene set to detect high risk of AR with high sensitivity (AUC = 0.93). The target molecules were l trials ,41. Howel trials .CD3ε, CXCL10 mRNA, and 18S ribosomal RNA, all associated with inflammation and immune response, to determine their predictive potential for rejection and infection in pediatric kidney transplant recipients: the VIRTUUS molecular panel [Recently, a manuscript described the design and methodology of a new clinical trial that investigated three genes: 18S-normalized ar panel .CXCL9 and CXCL10 proteins, both linked to inflammation signaling, were able to show good predictive potential for T-cell-mediated rejection (TCMR) and antibody-mediated rejection (ABMR) . However, none have been implemented in clinical practice [Proteomics investigations of molecular biomarkers have been increasingly used post-transplantation. The most promising explored urinary levels of practice .Furthermore, smaller studies found differing patterns of protein biomarkers associated with short- and long-term outcomes; however, there is no uniformly agreed molecular panel as each study; similar to before-procurement studies, has different disease definitions, sample collection, and methodologies for data acquisition and analysis ,48.Studies explored systemic metabolism-related molecular changes after kidney transplantation in serum, plasma, and urine, all with the goal of reflecting the key processes related to graft accommodation and possibly predicting rejection ,49,50.Overall, none of these studies resulted in molecular biomarkers progressing beyond the discovery stage for similar reasons to proteomics; that is, a lack of a more unambiguous identification of metabolite biomarkers and extensive validation efforts to enable these markers to be integrated into patient health care .Altogether, these omics approaches could become valuable tools in the clinic to discover and validate new molecular biomarkers and to open the possibility of preservation tailored to the needs of the organ, increasing the number and the quality of donor organs. Rather than employing a reductionist approach, these systems use a holistic and integrative approach to better capture ongoing biological processes and their related molecules.However, integration of such technologies and molecules in clinical practice requires an understanding of the dynamics underlying molecular biomarker discovery and model development ,52,53. HThe first phase is the DISCOVERY. It focuses on hypothesis generation without a priori and is generally untargeted . In shorExperimental design, defining targeted clinical issues, molecule types that are likely involved , and thus the choice of high-throughput technology to use.Defining the molecular investigation technology, next-generation sequencing, microarray, or mass-spectrometry, the major omics platforms have distinct limitations in sampling and instrumental methods that must be taken into account. Human sample collection, storage, and preparation are also critical issues, aiming at limiting internal degradation of target molecules while remaining compatible with a clinical setting. This may involve the use of stabilizer reagents  or of pre-analytical steps such as deprotonation . In terms of the instrumental method, optimal parameters selection is laborious: on the one hand, the industrial sector offers a large catalog of these tools; on the other hand, the adjustment is conditioned by the  researcher’s knowledge and the literature. To date, no consensus defines an efficient configuration [guration ; hence, Data processing, the instrument generates a large dataset containing hundreds to thousands of molecules and their respective detected level in each sample, producing enormous tables, so-called «Big Data». The process and management of these datasets are complex. The common stages of omics data processing are transformation, normalization, quality control correction, noise filtration, and imputation. All of them are essential and could be a potential source of error. Hence, it requires numerous adjustments and perfect understanding [standing ,52. Nowastanding . In mostData analysis, chemometric is a discipline that manipulates data utilizing mathematics and statistic fundamentals [amentals . This stamentals . In ordeamentals ,56,57. Tamentals ,58. The The second phase is the VALIDATION, aiming at validating the predictive model, and thus the set of molecular biomarker(s) identified in the previous phase, over a large, and new, sample set.Predicting model and validation, does the model performance reflects the distance between the predicted and observed outcome? Is the model able to distinguish patients who will experience the event from patients who will not? Does the predicting tool account for competing risks in the population of interest? What is the model power? The performances of the models are usually measured using the concordance statistics that correspond to the ROC AUC for classification and to the correlation coefficient for regression, which take into account the error risks. It is recommended to evaluate the predictive tool both through internal and external validations. Internal validation includes random split-sampling of cohort and resampling methods. External validation includes large uni- and multi-centric cohorts to generalize the tool. Finally, molecular biomarker(s) candidate(s) and these predictive models’ implementation in clinical practice can be considered [nsidered ,56.Workflow optimization is aimed at the practical adaptation of predictive tools toward easy routine implementation “at the patient’s bedside”. It is important to consider that the most sophisticated, sensitive, and specific molecular biomarker(s) will only reluctantly be accepted in clinical practice if the tool is too complex to implement. To increase speed, it is necessary to re-optimize the analytical approach with only the specific molecules in mind, in order to only target these and maximize executions of the signal acquisition and analysis. This may include for instance-specific buffers and/or adjuvants, which would maximize molecular recovery. Successful implementation into clinical practice hinges on: (i) cost-effectiveness; (ii) availability of clinically realistic sample collection and procedures; (iii) development and validation of viable bioanalytical strategies; and (iv) software tools that allow for data analysis and fast translation into clinically meaningful information [ormation ,34,57.Integration at the patient’s bedside, there are two aspects to consider in order to foster the introduction of multiparametric omics-based tools to the clinic: the analytical technology, and the end-user perspective. Regarding technology, omics relies on high-end analytical automata, requiring a high level of expertise. The enrolment of the medical biology services is thus of capital importance, and thankfully such high-end technologies such as mass spectrometry and next-generation sequencing are now well installed in the labs. It is thus possible to envision sending the patient’s sample through the regular bioanalytical route and specialized personnel to perform the molecular extraction and run of the sample, with the appropriate internal and external controls. Regarding the end-user perspective, this is where the versatility of machine learning classifiers can be an asset; while the diversity of possible outputs and overly complex molecular names can be overwhelming at first, the involvement of a medical biologist trained in biology and data science can transform it into a result tailored to the clinician’s need (on the donor of the recipient side), with either a score, a decision tree, a relative risk, or a combination of such. Help from software engineers can also be a bonus to implement the prediction algorithms on user-friendly platforms .It is hoped that this will give the reader insight into the direction, in which the medical sciences should, in our opinion, be moving over the next few years. The study of disease at the molecular level could enable us to treat many diseases more rationally; however, it is indispensable to undertake it the right way, to maximize benefits, and to avoid wasting valuable patient samples. However, this will not happen overnight; there is likely to be a long period of development and evaluation of our new technology before its clinical application can be fully assessed.In summary, we address the important issue of predicting the outcome of kidney transplants, which is becoming increasingly relevant in clinical practice. As with other techniques and tools, practical implementation of a molecular biomarker model should be performed with caution and a degree of skepticism. An increased effort and investment between scientists, medical biologists, and clinicians seem to be the path toward a proper solution.We perceived in renal transplant literature a willingness to work together. However, in the studies reviewed herein, we found substantial variability in data collection, signal processing methods, and choice of classifier to build predictive models. Moreover, the definition of graft failure was particularly variable, and the endpoint may vary depending on the clinician . About hEven though we paint a negative picture of this field, omics molecular technologies have enormous potential. Among these, metabolomics offers a low-cost option Moreover, sample and data analysis can be performed in a matter of hours, which is compatible with the timing in transplantations. The increased availability of high-definition equipment in hospital biochemistry services  can foster the use of many omics technologies toward improving transplantation, keeping the itinerary of the sample within typical lanes in medical practice, hence improving the possibility for deployment into routine use ,61. AlthAs discussed above, the first promising steps have been made and clinical molecular markers in combination with predictive models could provide valuable information on allograft outcomes. In the future, omics technologies could bring a new regard to current clinical practice and patient/graft management .


The synthesis and design of two-dimensional supramolecular assemblies with specific functionalities is one of the principal goals of the emerging field of molecule-based electronics, which is relevant for many technological applications. Although a large number of molecular assemblies have been already investigated, engineering uniform and highly ordered two-dimensional molecular assemblies is still a challenge. Here we report on a novel approach to prepare wide highly crystalline molecular assemblies with tunable structural properties. We make use of the high-reactivity of the carboxylic acid functional moiety and of the predictable structural features of non-polar alkane chains to synthesize 2D supramolecular assemblies of 4-(decyloxy)benzoic acid (4DBA;C When solid surfaces are used as supports, the molecular self-assembly can be regarded as a two-dimensional (2D) process, and the structural and electronic properties, such as molecular packing, the number and typologies of domains, and the binding energy of the molecular levels, strongly depend on the interplay between the intermolecular forces and the interaction between the adsorbate and the underlying support9.Complex molecular architectures are usually constructed by connecting molecular building blocks through non-covalent bonding, allowing for a variety of different supramolecular structures and phases to emerge13. Key steps to these accomplishments are (a) to understand how molecules self-arrange, and (b) to master the growth of homogeneous, highly ordered and defect-free films with tunable properties15.A suitable choice of molecular building blocks and supporting substrates would enable, in principle, synthesizing and designing 2D supramolecular assemblies with specific functionalities, disclosing exciting new opportunities in many areas of science and technology28.Small carboxylated molecules present an ideal testing playground for exploring and establishing strategies to design novel molecular assemblies. Owing to their functional groups, these molecules can bind to one another through strong and directional hydrogen bonding. Supported on solid surfaces, these molecular building blocks can self-assemble through electrostatic or van der Waals interaction to form complex 2D architectures32. In these assemblies the molecules adopt a lamellar structure where the alkyl chains pack side-by-side flat on the surfaces. The specific structure depends on the number of the carbon atoms in the alkyl chain through a mechanism known as “odd-even” effect: Alcohols with an even (odd) number of carbons interact weakly (strongly) with the substrate producing a nearly tilted  alcohol orientation with respect to the lamellae.Although hundreds of structures have been realized so far, predicting and designing 2D crystalline materials are still difficult tasks. One remarkable exception are alkyls, which form self-assembled monolayers that can be modulated by varying the chain lengthThe present study takes advantage of the high-reactivity of the carboxylic acid functional moiety and of the predictable structural features of non-polar alkane chains to synthesize nearly-free standing 2D supramolecular assemblies of 4-(decyloxy)benzoic acid , indicating that the 4DBA molecules are too mobile for imaging. This behavior is distinctive of weakly interacting overlayers38. This indicates that the adsorbed layer does not substantially change the surface stress of the substrate at odd with molecular overlayers that chemisorb strongly43. Furthermore, we note that the orientation of the molecules do not change on the different sides of the elbow position of the herringbone reconstruction  and benzoic head width (w = 0.5 ± 0.1 nm) of the 4DBA molecule, in excellent agreement with the predicted ones44, and strongly suggests a parallel adsorption geometry on the metallic surface. This finding is consistent with the planar geometry adopted by monocarboxylic acids on HOPG28, and by similar molecules like for example benzoic, trimesic or isophthalic acid on Au(111)45, HOPG31, and benzene on face-centered cubic {111} nobel metal surfaces50.The line profiles parallel and perpendicular to the molecular axis presented in Fig. 32 and HOPG31 revealed different 2D molecular patterns depending on the parity (even or odd) of the carbon count in the alkyl chain. Inspired by these results, we explored the possibility to modulate the periodicity of the assembly by changing the alkoxy chain length but always keeping an even number of the carbon atoms.STM investigations on alkyl molecules adsorbed on Au(111)a = 3.9 ± 0.2 nm, b = 1.8 ± 0.1 nm, a = 3.6 ± 0.2 nm, b = 1.8 ± 0.1 nm, Figure The 4DBA/Au(111) system therefore represents one of the few supramolecular surface assemblies that allows, through a proper choice of the length of the alkoxyl chain, to modulate the structural properties of the ensemble in a controlled way without altering the molecular arrangement.To gain insight on the observed molecular structure, we investigated the 4DBA assembly by means of first principles atomistic calculations. Inspection of the frontier molecular orbitals  and trimesic acids on Cu(111)22.The electronic structure of 4DBA/Au(111) was investigated by photoemission spectroscopy. The measured spectrum of C 1s core level region of a single 4DBA in gas phase, presented in Fig. s orbital of each C atom is marked by a colored bar on the horizontal axis of the plot. As expected, the C atoms of the COOH group have the highest binding energy, followed by the C atoms bonded to the O atom of the molecular tail. The remainder of the C atoms generates the main peak at the lowest binding energy. The valence band spectrum of clean Au(111), shown in Fig. d states in the energy region in between 8 and 2 eV below the Fermi level  surface is formed. High-resolution STM measurements combined with DFT calculations revealed that the 4DBA molecules self-assemble and interact via a combination of hydrogen bonds and lateral weak C–H62.STM measurements were performed in ultra-high vacuum (UHV) conditions  followed by annealing (T = 700 K for 20 min). The cleanliness quality of the pristine gold surface was verified with STM or photoemission measurements. 4DBA molecules were deposited at RT on Au(111) by organic molecular beam deposition at a pressure of The Au(111) surface  layer on mica, 99.99% purity), was cleaned by several cycles of Ar63. We carried out a full relaxation of all the coordinates, using an unrestricted double-zeta Pople’s 6-31+G basis67 and the B3LYP functional68, without imposing any condition on the symmetry of the molecules. These results on the electron structure calculations were also employed to simulate the C 1s and O 1s spectral line shapes.The electronic structure of both 4DBA conformers were first studied in gas phase using the Quantum Chemistry package Gaussian0969, as implemented in the VASP code72, using the Perdew-Burke-Ernzerhof (PBE) exchange correlation functional73 and the Tkatchenko-Scheffler74 corrections, to account for weak dispersion forces. Five different types of periodic self-assemblies were taken into account, containing either two or six 4DBA molecules in the 2D unit cell, using a vacuum region of at least 20 Å in the out-of-plane direction. We adopted a 400 eV plane wave cut-off and a total energy threshold of The molecule self-assembly calculations were carried out using density functional theory within the projector augmented wave (PAW) approach76 through the appropriate modifications reported in Supporting Information (Fig. 77. The organization of the mesophases for all three compounds are reported in Table 78.The synthesis of the 4-alkoxy-benzoic acid derivatives was performed consistently with the already reported literatureion Fig. . All thrion Fig. –S9. All mentclass2pt{minimSupplementary Information.


Mikania micrantha, recognized as one of the world’s top 10 pernicious weeds, is a rapidly spreading tropical vine that has invaded the coastal areas of South China, causing serious economic losses and environmental damage. Rapid stem growth is an important feature of M. micrantha which may be related to its greater number of genes involved in auxin signaling and transport pathways and its ability to synthesize more auxin under adverse conditions to promote or maintain stem growth. Plant growth and development is closely connected to the regulation of endogenous hormones, especially the polar transport and asymmetric distribution of auxin. The PIN-FORMED (PIN) auxin efflux carrier gene family plays a key role in the polar transport of auxin and then regulates the growth of different plant tissues, which could indicate that the rapid growth of M. micrantha is closely related to this PIN-dependent auxin regulation. In this study, 11 PIN genes were identified and the phylogenetic relationship and structural compositions of the gene family in M. micrantha were analyzed by employing multiple bioinformatic methods. The phylogenetic analysis indicated that the PIN proteins could be divided into five distinct clades. The structural analysis revealed that three putative types of PIN  exist among the proteins according to the length and the composition of the hydrophilic domain. The majority of the PINs were involved in the process of axillary bud differentiation and stem response under abiotic stress, indicating that M. micrantha may regulate its growth, development and stress response by regulating PIN expression in the axillary bud and stem, which may help explain its strong growth ability and environmental adaptability. Our study emphasized the structural features and stress response patterns of the PIN gene family and provided useful insights for further study into the molecular mechanism of auxin-regulated growth and control in M. micrantha. Auxin was the earliest hormone found to promote plant growth and exists in nature mainly as indole-3-acetic acid (IAA). Auxin affects cell elongation, division and differentiation by establishing concentration gradients in different tissues , therebyArabidopsis thaliana, and eight AtPIN proteins were identified to form the AtPIN auxin efflux protein family [Arabidopsis PIN proteins can be divided into different categories. Based on the subcellular localizations, one group is located in the plasma membrane (PM) and has a long central hydrophilic loop (AtPIN1-4 and AtPIN 7); another is located in the endoplasmic reticulum (ER) and the central hydrophilic loop is shorter (AtPIN5 and AtPIN8) [A. thaliana are different because of modifications to different phosphorylation sites on the central hydrophilic loop [The PIN family, the most widely studied auxin efflux carriers, were first cloned in n family . Accordi AtPIN8) ,17. In a AtPIN8) ,19,20. B AtPIN8) . But thi AtPIN8) ,22,23, g AtPIN8) , phototr AtPIN8) ,25; the  AtPIN8)  and part AtPIN8) ; and thelic loop .A. thaliana, Oryza sativa, and Glycine max) by genome-wide characterization [M. micrantha Kunth, is a perennial vine of the Asteraceae family and native to Central and South America [M. micrantha was introduced into Hong Kong in the late 1800s and has since spread throughout southern China, resulting in significant economic losses and environmental damage [M. micrantha grows rapidly, has a strong climbing ability [M. micrantha is urgently needed for research into prevention and control strategies.So far, PIN genes have been identified in more than 30 plant species , lettuce (Lactuca sativa), artichoke thistle (Cynara cardunculus), chrysanthemum (Dendranthema morifolium) and Artemisia annua—M. micrantha has more genes involved in auxin signaling and transport pathways. Moreover, one study showed that the auxin content in the stem of M. micrantha after a defoliation treatment was significantly higher than that of the control group [M. micrantha also ensured rapid growth by increasing the amount of auxin, which was beneficial for its invasion under the forest [M. micrantha genome and the response of auxin to abiotic stress indicate that the rapid growth of M. micrantha may be related to the regulation of auxin, which largely depends on the asymmetric distribution of the auxin polar transport system [M. micrantha PIN gene family is the primary method to further analyze the auxin regulation mechanism of M. micrantha. In this paper, members of the gene family were identified by bioinformatics at the whole genome level. The phylogenetic relationship, gene structure, chromosome location, cis-acting regulatory element composition, conserved motif, tissue expression profile and the abiotic stress response were analyzed to provide reference information for future studies on the biological functions of the M. micrantha PIN protein family. It also provided a theoretical basis to research the molecular mechanism of auxin-regulated growth and the prevention and control of M. micrantha.One of the remarkable characteristics of the rapid growth of of auxin . Studiesol group . In a she forest . Therefot system . The auxt system . TherefoM. micrantha genome, which were similar to those of A. thaliana (8), O. sativa (12), and Vitis vinifera (8). Details about the identified genes are shown in M. micrantha PIN proteins varied from 5 to 9, and the prediction of subcellular localization indicated that, except for E3N88_01311, which was located in the vascular membrane, the rest were in the plasma membrane , group II (PIN8), group III (PIN5), group IV (PIN6), group V (PIN1), and group VI (PIN3/4/7). Among these, one M. micrantha PIN belonged to group I (E3N88_36012) and group III (E3N88_01311); two belonged to group IV (E3N88_14925 and E3N88_15091) and group V (E3N88_34143 and E3N88_11787); and five belonged to group VI . No M. micrantha PIN was found in group II. Protein clustering based on sequence similarity among the PINs of the four species meant that they had similar functional or subfunctional roles during species-dependent development [M. micrantha PINs and their respective groups may have had similar functions. Meanwhile, there was one paralogue gene pair  of the M. micrantha PIN family in group IV and two pairs  in group VI. The five expanded M. micrantha PINs in group VI  clustered with other species into two clades, and it was speculated that the possible reason was the difference among species  and  were distributed on the same chromosome: (CM018686.1) and (CM018681.1), respectively. Gene duplication is a major contributor to evolutionary momentum [M. micrantha PIN family shared three pairs of paralogue genes with high sequence similarity (>85%), two of which were on the same chromosome, indicating that a duplication had occurred. The three gene pairs ,  and  belonged to segmental duplication according to amino acid sequence identity  in group I which had a gene structure quite different from that of the other three species , the other PINs in the same group tended to have similar exon structure. For example, in group VI, the number of PIN exons in M. micrantha, A. thaliana, B. oleracea and V. vinifera ranged from four to seven, indicating that the gene structure of PIN members in the same group may be conserved  was mainly due to differences in the length of the central hydrophilic loop, the structure of which has been identified as the main functional domain of the PIN protein. According to the classification of PINs by Bennett et al. (2014) [M. micrantha PINs  at the N-terminus and C-terminus regions, respectively, and two conserved motifs (4 and 8) in the central hydrophilic loop region. At the same time, there were four highly conserved regions (HC1–HC4) in the sequence alignment results, so this type was classified as canonical PINs: . M. micrantha E3N88_01311 lacked conserved motifs 4 and 8, and the conserved region of HC1–HC4 in the central hydrophilic loop, so it was classified as a noncanonical PIN. E3N88_14925 and E3N88_15091 were classified into group IV (PIN6) with A. thaliana AtPIN6 on the phylogenetic tree; thus, they were classified as semi-canonical PINs. The above classification was consistent with the classification results of the phylogenetic tree according to A. thaliana PIN characteristics. However, E3N88_36012 lacked the C-terminal transmembrane region sequence, so it cannot be classified according to the definition of a canonical PIN, but its hydrophilic region still had conserved motifs 4 and 8 and three conserved regions (HC1-HC3), which were highly similar to the A. thaliana AtPIN2 sequence. It was temporarily classified in the PIN2 subgroup, but its structure needs to be further identified.The number of predicted transmembrane domains in tructure : two hig. (2014) , a compatha PINs  showed tM. micrantha PIN genes and how they are regulated by phytohormones and defense and stress response elements, the PlantCare web server was used to search for possible cis-elements in the 2000 bp promoter region. A total of 12 common response-related CAREs were selected in the M. micrantha PIN gene family, including light, methyl jasmonate (MeJA), abscisic acid, auxin, salicylic acid, defense and stress response components   b, indicaM. micrantha PIN genes  showed relatively high expression levels in stems  was detected via transcriptome data and a qRT-PCR experiment a,b. The in stems a, sugges results b.M. micrantha PIN genes were involved in responses under different stresses and treatments at the transcriptional level, transcriptome data were used to analyze their expression under abiotic stress  and exogenous hormone (CPPU or GIC) treatment [M. micrantha stems.To understand whether the reatment ,36,40. TM. micrantha genome, this is the first time the PIN gene family of this invasive plant has been identified and its potential roles in growth and development analyzed in response to abiotic stresses and stimuli. The results indicated that the PINs may be involved in differentiating axillary buds and adapting stems under adversity, which may be closely related to the development of axillary buds and the rapid growth of stems. At the same time, it was predicted that E3N88_40533, E3N88_03858 and E3N88_03888 may be key PIN genes involved in the rapid growth of M. micrantha stems and the response to environmental stresses and stimuli.As a key regulator of plant growth and development, auxin participates in plant growth and development and in response to various environmental stresses or stimuli through polar auxin transport. Regulation of PINs at the transcriptional or post-transcriptional levels consists of spatiotemporal expression patterns, subcellular polar localization, intracellular trafficking and recycling, and degradation ,18,41,42M. micrantha genome, which were located on nine chromosomes  were divided into six subgroups based on sequence similarity, among which canonical PINs were clustered into group I (PIN2), group V (PIN1), group VI (PIN3/4/7); noncanonical PINs were grouped into group II (PIN8) and group III (PIN5); and semi-canonical PINs were grouped into group IV (PIN6)  had a pair of paralogue genes , and group VI (PIN3/4/7) had two pairs of paralogue genes   . The PIN8_03888) , in whic8_03888) . Whole-gications , and theications ; thus, sM. micrantha tolerates low temperature, flooding and salt stress [M. micrantha stem [M. micrantha PINs was higher than that of vines such as V. vinifera (0) [C. lanatus (2) [Phaseolus vulgaris (2) [Gene amplification indicated that plant species may suffer stresses that promote the richness of this gene family or generate new functions to adapt to environmental changes ,45. The t stress ,47,48, ht stress . Furthertha stem ,36,50. Tfera (0) , C. lanaatus (2)  and Phasaris (2) .M. micrantha in response to various environmental stresses and may help it to adapt and successfully invade through PIN-dependent auxin regulation. In the prediction of cis-acting regulatory elements, M. micrantha PIN genes had the highest number of light-responsive elements and were significantly higher than those of the two vines (C. lanatus and V. vinifera)  were all less than 1 , indicatinifera) b, which M. micrantha PIN proteins in the same subgroup were similar , which was different from that of the other species, the PIN gene structure of the different species in the other subgroups tended to be conserved, which was similar to the results of the gene structure in the vine C. lanatus [M. micrantha PINs all had transmembrane regions at both terminals and a central hydrophilic loop  lacked the conserved motifs 9 and 10 in the hydrophilic domain  E3N88_36012 was only highly expressed in the roots and may have a similar function to that of A. thaliana AtPIN2, which was specifically expressed in the root apical meristem and elongation zone and participated in root gravitational growth [Some studies have demonstrated the regulatory role of PIN proteins in plant growth and development. In this regard, the expression patterns of PINs in different tissues of analyzed a,b. Studormation . Rice Osormation , and A. xpansion . The PINl growth .M. micrantha are inseparable from its strong branching ability. A. thaliana AtPIN3, -4 and -7 were usually divided into the same subgroup due to their high sequence similarity and close evolutionary relationship, and as such there was functional overlap; that is, all three were involved in negative gravitropic stem growth, phototropic plant growth and early lateral root development [SlPIN4-silenced plant lines in tomatoes exhibited phenotypes such as increased apical dominance, decreased axillary buds and slender stems, indicating that SlPIN4 was involved in the establishment of plant architecture by altering auxin transport in the main stem [NtPIN4, highly expressed in stems and branches, regulated axillary bud germination, and mutations on the gene led to a stable and heritable abundant branching phenotype [A. thaliana AtPIN6-overexpressing lines increased stem branching and decreased apical dominance at the shoot tips [M. micrantha, indicating that they may be important for stem growth and branching architecture and may be closely related to rapid growth.Whether climbing and winding around other plants or covering the ground , the rapelopment ,60,61. Iain stem . Tobaccohenotype . Likewisoot tips . A totalM. micrantha PIN genes predicted that light, MeJA, abscisic acid and low temperature response elements were widely present in the promoter region  significantly reduced the level of auxin in the axillary buds, and affected auxin signal transduction pathways, thereby inhibiting the axillary bud differentiation . Howevermembrane ; therefobranches . The higM. micrantha showed a rapid stem elongation plasticity response to shading by significantly increasing auxin content and transcriptome level regulation under low-light stress [A. thaliana AtPIN3 and Medicago truncatula MtPIN3 were also found to be involved in plant shading responses [M. micrantha PINs may promote the rapid elongation of the stem under low light by regulating the auxin level of the stem. Similarly, under defoliation stress, M. micrantha maintained stem growth by improving the photosynthesis rate and increasing the amount of auxin and other phytohormones [M. micrantha PINs also showed varying degrees of expression compared to the control group, and most of the expression in group VI (PIN3/4/7) was up-regulated  were up-regulated in response to both abiotic stresses. It was speculated that M. micrantha, all of its protein sequence data were downloaded from the National Center for Biotechnology Information (NCBI) website. The gene identifier of Arabidopsis AtPINs was obtained from Yang et al. (2019), and the corresponding protein sequences were obtained as query sequences to perform a BLAST search [−8, the identity was >60%, and the candidate genes were screened. The CD-search  and SMART  databases were searched to confirm the existence of the PIN-conserved domain (Pfam 03547: Mem_trans) to verify the putative PIN gene core sequence and to screen out all PIN gene sequences of M. micrantha. Gene ID information, chromosomal location and exon number were all obtained from M. micrantha gene annotation files. PASy  was used to calculate the length, molecular weight (WM), isoelectric point (pI), and hydropathicity (GRAVY) analysis of the PIN proteins. The transmembrane helices of M. micrantha PIN proteins were predicted using TMHHMv.2.0 , and protein subcellular localization was predicted using WoLF PSORT .To identify the possible PIN genes in T search . The E vM. micrantha, A. thaliana, B. oleracea and V. vinifera PIN proteins, and 1000 bootstrap replications were performed based on multiple alignments of protein sequences encoded by the PIN genes. Accession numbers of the PIN proteins are listed in The genome visualization tool MapChart (version 1.0.0.0)  was usedhttps://meme-suite.org/meme/tools/meme, accessed on 6 September 2021) was used to analyze the conserved motifs in PIN proteins with the following settings: the maximum number of motifs was 10; minimum motif width was 6; maximum motif width was 50, and number of repetitions was arbitrary. Intron–exon structure information was included in the M. micrantha gif file downloaded from the NCBI. Both conserved motifs and gene structures were visualized using TBtools software (version 1.098696) [M. micrantha PINs was performed using DNAMAN software (version 6.0.3.99) [The online program Multiple Em for Motif Elicitation (MEME) (.098696) . The ami.0.3.99)  with defM. micrantha, C. lanatus and V. vinifera genomes by TBtools software (version 1.098696) [http://bioinformatics.psb.ugent.be/webtools/plantcare.html, accessed on 6 September 2021) for analysis. The PIN gene ID numbers of C. lanatus and V. vinifera came from Shang et al. (2021) and Hu et al. (2021), respectively [M. micrantha PIN genes, and the most common CAREs were selected in TBtools, and the numbers of various response-related elements in the M. micrantha, C. lanatus and V. vinifera PIN genes were counted.The upstream sequence (2 kb) of the PIN gene was retrieved from the .098696)  accordinectively ,52. The M. micrantha were identified from the NCBI  sequence read archive (SRA) database  to obtain the RNA-seq dataset (accession number: no. SRR8857621-RR8857640). Similarly, the response expression profiles of PIN gene family members under different stress treatments  and their respective RNA-seq datasets were obtained for further analysis . The downloaded raw data were subjected to quality control such as de-linking and redundancy removal to obtain clean data for subsequent comparison and quantitative analysis. The Fragments Per Kilobase of exon model per Million (FPKM) mapped fragments were used to estimate the gene expression level. Heatmaps of PIN gene family members in different tissues or under various stress/treatments were drawn using the pheatmap package in the R program (version 4.0.5) [Expression profiles of PIN gene family members in different tissues/organs of