Cell infiltration into the 3D nanofibers with low packing density improved compared to densely packed 2D nanofibers.Despite the unique properties of polyaniline (PANI), the processability of this smart polymer is associated with challenges. Particularly, it is very difficult to prepare PANI nanofibers due to poor solubility, high charge density, and rigid backbone. The most common approach for solving this problem is blending PANI with a carrier polymer. Furthermore, the major limitations of nanofibers for tissue engineering applications are their low porosity and two-dimensional (2D) structure. In this study, conductive nanofibers were fabricated through electrospinning of PANI/poly(ether sulfone) (PES) with different solvents including dimethyl sulfoxide (DMSO), N-methyl-2-pyrrolidone (NMP), and hexafluoroisopropanol (HFIP). The effect of solvent, carrier polymer (PES), and PANI content on formation of 3D conductive nanofibers with appropriate porosity were investigated. It was shown that a solvent with suitable properties should be selected in such a way that the composite nanofibers can be electrospun at the lowest concentration of PES. In this way, the ratio of PANI increased in the scaffold, the electrical conductivity of nanofibers enhanced, and the flat 2D structure of scaffold changed to a fluffy 3D structure. Among the three studied solvents, HFIP with the lowest boiling point and the lowest surface tension was the best solvent for the fabrication of PANI/PES nanofibers. PES could be electrospun at a concentration of 9% w/w in HFIP, while the optimum percentage of PES in DMSO and NMP was above 23% w/w to produce uniform nanofibers. 3D nanofibrous scaffold obtained from 0.5% PANI/9% PES/HFIP solution with electrical conductivity of 3.7 × 10 Electrospun PES/PANI nanofibers were fabricated with three solvents and different PES concentrations. HFIP with the lowest boiling point and surface tension was the best solvent for preparing highly conductive fluffy PANI nanofibers in comparison to the other solvents. 3D conductive nanofibers supported cellular infiltration and proliferation. The role of conductive polymers in tissue engineering became more prominent with the fabrication of conductive polymeric nanofibers due to their unique properties and their similarity to the native extracellular matrix (ECM) morphology . An ideaAmong conductive polymers, PANI has attracted the attention of many researchers due to its facile preparation, low cost, high electrical conductivity, low cytotoxicity, and environmental stability . There aThe size, microstructure, and general properties of electrospun nanofibers are controlled by different parameters. These are divided into three general groups of parameters including solution, processing, and environmental parameters. Solution parameter is the one play the most important role in the processability of the polymers . It is dThe pore size of electrospun nanofibers is less than 10 μm so that the cells cannot easily infiltrate the nanofibers and form a 3D structure such as natural ECM. The limitation of traditional electrospinning is that the nanofibers are deposited tightly on the collector, resulting in a low-porosity sheet-like scaffold. Porosity is a key property in tissue-engineered scaffolds. The porosity and pore size of the scaffold affect the cellular behaviors such as cell spreading, migration, and proliferation. They also affect the exchange of nutrients and waste between the scaffold and the surrounding environment. Numerous strategies have been introduced to overcome this challenge including tuning electrospinning parameters, using sacrificial components, manipulating collector structure, wet electrospinning, ultrasonication, and inclusion of biological factors . ChanginIn this study, the role of solvent in the preparation of highly conductive 3D PANI nanofibers is investigated. The choice of solvent is very important to prepare an electrospinning solution consist of a combination of PANI and a carrier polymer. Three important considerations to selecting an appropriate solvent are the solubility of the polymers in the solvent, the effect of solvent on the conductivity of PANI, and the properties of the solvent such as boiling point, dielectric constant, and surface tension which affect the electrospinnability of the solution. A suitable solvent for PANI is a solvent that can dissolve the highest percentage of PANI and can change the molecular conformation of the PANI from compact coil to straight chain . In otheGenerally, solvent and carrier polymer are two key factors influencing the preparation of electrospun PANI-based nanofibers. Herein, the effect of DMSO, NMP, and HFIP as the solvents on electrospinnability, morphology, and conductivity of PES/PANI nanofibers were investigated. It was also represented that a suitable solvent may lead to a 3D fluffy structure with a good cell penetration. To the best of our knowledge, this is the first report investigating the effect of solvent on the formation of 3D PANI/PES nanofibers with enhanced cell infiltration.®, Germany) was dissolved at concentrations of 9, 15, and 23% w/w in the PANI solutions for 24 h. A 21G blunt needle was selected, the constant flow rate was set to 0.5 mL/h, the distance between the needle and the steel grounded collector was 16 Cm, and the voltage was kept at 25 kV. All the electrospinning parameters were the same for the three conductive samples except the solvent type.Electrospinning was used to prepare nanofibers according to a previously reported method . For theMorphology of the PES/PANI-CPSA nanofibers (PPC) with different solvents and carrier polymer concentration was determined using scanning electron microscopy  at an accelerating voltage of 20 kV after sputter coating with Au particles. The average diameter of the nanofibers was calculated from SEM images using image processing software (ImageJ) from at least 70 randomly selected fibers and expressed as mean ± SD.The two-point probe method was used for measuring the electrical conductivity of the conductive samples  .Scaffold porosity was measured via three different methods named gravimetry, liquid intrusion, and image processing. For gravimetry, five circular samples with 15 mm diameter were used. The estimated porosity of each sample was calculated by the following equation:For liquid intrusion, the circular dry samples were weighed, sub-merged in absolute ethanol as an intruding liquid, left overnight on a shaker incubator to allow ethanol flow into the void spaces, wiped with tissue paper, and weighed again immediately. Thereafter, the porosity was calculated by the following equation:EtOH is the ratio between mass change after liquid intrusion and ethanol density and Vs is scaffold mass to density ratio. For image processing, porosity and average pore area of electrospun scaffolds were quantified from SEM micrographs using image processing software (ImageJ).where V2.3T3 fibroblast cells were cultured within T75 flasks with Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum and 1% antibiotic (penicillin/streptomycin) at 37°C with 5% humidified CO4 cells per well was suspended in 400 μL of medium and seeded onto the scaffolds. Cell adhesion and infiltration into the nanofibrous scaffolds were assessed by screening cell morphology on the scaffolds by SEM. Cell-seeded scaffolds were rinsed twice with phosphate-buffered saline (1x PBS) after 48 h to remove non-adherent cells, fixed with 2.5% glutaraldehyde for 1 h, rinsed again with PBS, dehydrated in gradient concentrations of ethanol, and finally left to dry at room temperature. The dried samples were sputter-coated with gold, and observed under SEM.The nanofibrous mats were cut into 1.5 cm diameter circular scaffolds to be placed in 24-well tissue culture polystyrene (TCP) and sterilized in 70% ethanol. An initial density of 4 × 104 cells per well and incubated at 37°C and 5% CO2. After 1, 3, and 7 days of cell seeding, 50 μL of MTT solution (5 mg/mL in DMEM) was added to each well followed by incubation at 37°C for 3.5 h. For dissolution of the intracellular formazan, the supernatant was removed and isopropanol (Merck) was added. The absorbance at 570 nm was read spectrophotometrically.The proliferation of 3T3 cells on nanofibrous scaffolds was evaluated via MTT assay. The sterilized scaffolds were placed in a 24-well culture plate. The cells were seeded on to the mats and control (TCP) with a density of 10P-value for statistical significance is defined as p < 0.05. All statistical analysis was performed using SPSS software.All experiments were conducted at least three times. Data are expressed as mean ± standard deviation (SD). One-way analysis of variance (ANOVA) was used to compare the results. The The SEM images of electrospun PPC nanofibers prepared with DMSO, NMP, and HFIP at different PES concentrations  are shown in The diameter distributions of electrospun nanofibers are also shown in The macroscopic image of 3D and 2D nanofibers are presented in The conductivity of the nanofibers is shown in A simple and easy way to check the percentage of PANI in the scaffold and its conductivity is to compare the colors of the scaffolds. PANI is green in its conductive form. Thus, the darker green scaffold contains more PANI with higher conductivity . The difp < 0.05). The former had less porosity than the latter due to the fluffy structure and larger pore area of the PPC/HFIP samples.In the mats prepared with HFIP, it was perceived that as the concentration of PES increased, the porosity and average pore area of the scaffolds decreased. The lower the PES percentage, the thinner nanofibers deposited. Thinner nanofibers offered higher porosity and larger average pore area. In the samples prepared with DMSO, the opposite results were achieved. The porosity increased by increasing the percentage of PES in the mats due to the lower beads existing in the nanofibers. Furthermore, there was a significant difference between the porosity of PPC/DMSO and PPC/HFIP samples (p < 0.05).It was not possible to measure the porosity of the nanofibers prepared with NMP in 9 and 15% w/w of PES concentrations because a large part of the scaffolds was made of polymeric film and there were very small nanofibers. The numbers obtained through image analysis were not realistic for these samples because it considered the polymeric film as pores . The porCell adhesion to scaffolds and cell infiltration into nanofibers were investigated using SEM images. The SEM micrographs of the cell-scaffold structures after 2 days of cell culture are shown in As it is shown in The ability of conductive nanofibers to support cell proliferation was evaluated via MTT assay. As it was shown in As it was mentioned earlier, solution properties play the most important role in the electrospinning of a polymer. The solutions with higher viscosity and conductivity, as well as the solvents with the lower surface tension and the boiling point assist the formation of nanofibers without beads . On the In present study, the PES concentration should be increased for increasing the solution viscosity. However, increasing the concentration of PES decreases the percentage of PANI in the final solution and results in the reduction of solution conductivity. Furthermore, the solubility of PANI is very low and it is almost impossible to make a PANI solution with a concentration above 0.5% w/v. Therefore, the percentage of carrier polymer should be reduced to result in a solution with a higher percentage of PANI. By decreasing the percentage of PES in PPC/NMP and PPC/DMSO samples, the solutions with lower viscosity are obtained and lead to the formation of beads and particles. Solution viscosity determines the amount of polymer chain molecules entanglement in the solution. The solutions with low viscosity (9 and 15% w/w PES concentration in DMSO and NMP) had low viscoelastic forces during electrospinning, which were not able to match the electrostatic and columbic repulsion forces that stretch the electrospinning jet. This led to jet partially break up. However, electrospinning was also possible at low concentrations by changing the solvent to HFIP. This was attributed to the different properties of the solvents .Polyether sulfone  can be electrospun at 25% w/w in solvents such as DMSO  and NMP Any factor contributing to the electrospinnability of a polymeric solution also plays a role in increasing the diameter of nanofibers. Since those factors prevent the electrospinning jet from rupturing, they inhibit nanofibers from stretching and reducing their diameter. As a result, HFIP with lower surface tension and boiling point led to the production of nanofibers with larger diameters. Besides, solutions obtained from the solvent with a lower dielectric constant are expected to have lower conductivity. This contributes to a lower repulsive coulombic force within the jet segments, resulting in lower stretching of the charged jet during its flight between the needle and the collector. On the other hand, the presence of PANI-CSA in the PES solution is like adding salt to the electrospinning solutions, which increases the ionic conductivity and dielectric constant of the solutions . AlthougHigh SD from the mean diameter may occur for two reasons including low or high viscosity of the polymeric solution. In the solutions prepared with DMSO and NMP, even in 23% w/w PES concentration, it was impossible to produce uniform nanofibers and some cylindrical beads in the nanofibers were formed. This caused high SD in the diameter of nanofibers of PPC/DMSO/23% and PPC/NMP/23% . Besides, 23% w/w PES concentration in PPC/HFIP/23% is too high and resulted in the formation of multi-branched jets with different diameters during electrospinning of the solution .Bead-free nanofibers with suitable diameter distribution, and a 3D structure with appropriate porosity are required to prepare nanofibers scaffolds simulating natural ECM. A major problem in electrospinning is the tendency of nanofibers to accumulate densely, resulting in a 2D mat with poor porosity and small pore size. The packing density of the nanofibrous mats could be modulated through changing electrospinning parameters such as changing the electric charge of the solution , redirecThe porosity and pore sizes in the electrospun scaffolds are mainly dependent on the diameters of nanofibers and their packing density . The effCell morphology is the first cellular behavior that changes according to the environment in which the cells are located and underlies other cellular behaviors. As a result, an easy way to determine the right conditions for the cells is to evaluate their morphology. Cell attachment, penetration, and interaction with the surface of a scaffold can be analyzed according to the cell morphology on the scaffold. In general, nano features improve cell adhesion because the extracellular matrix is composed of nanofibrillar components . On the Despite all the advantages of electrospun scaffolds, these nanofibers have a major limitation. The porosity of electrospun nanofibers is not suitable for cell infiltration into the inner areas of the scaffold . There aCell viability on a scaffold depends on the interaction between cells and the structural features and the physicochemical composition of the scaffold. Although, the number of living cells on conductive PPC scaffolds was not significantly different, the results of cell viability in day 3 and 7 draw attention to the cytotoxicity of PANI and the three-dimensionality of the PPC/HFIP/9% scaffold, respectively. Many researchers claimed that PANI shows time- or dose-dependent cytotoxicity . Li et aConsequently, it can be concluded that the 3D structure of nanofibers can support proliferation and viability of cells and is essential for tissue engineering applications.In present work, the effect of solvent on the formation of 3D electrospun PANI-based nanofibers for tissue engineering applications was studied. Three points must be considered to select a suitable solvent for the preparation of PANI/carrier polymer blend nanofibers, including the solubility of the polymers (PANI and carrier polymer) in the solvent, the effect of solvent on the conductivity of PANI, and the properties of the solvent such as boiling point and surface tension. It was shown that among the three solvents used in this study , HFIP was the best option for the fabrication of PANI/PES nanofibers. PES could be electrospun at a concentration of 9% w/w in HFIP, while the optimum percentage of PES in DMSO and NMP was above 23% w/w to produce uniform nanofibers. The solvent was selected in such a way that electrospinning was possible with the minimum carrier polymer and maximum PANI concentration because a lower amount of carrier polymer (non-conductive component) made nanofibers with higher conductivity. Furthermore, it was represented that the higher conductivity of the solution and the higher volatility of the solvent led to the formation of 3D fluffy nanofibers. The resulting 3D conductive nanofibers showed higher cell infiltration than the 2D mats on day 7th. This scaffold had potential application for tissue engineering of electroactive tissues such as nerve, muscle, heart, and bone.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.IS: supervision, project administration, funding acquisition, writing – review and editing, and visualization. ZD: conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing – original draft, and visualization. Both authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Hypertrophic scar (HTS) is a fibroproliferative skin disorder characterized by excessive cell proliferation, migration, and extracellular matrix (ECM) deposition. The CUB and Sushi multiple domains 1 (CSMD1) has previously been identified as the key regulatory gene of hypertrophic scar by a large sample GWAS study. However, further research has not yet been conducted to verify this finding in other HTS patients and to determine the underlying mechanism.In this study, we verified that CSMD1 was downregulated in both HTS tissue and HTS-derived fibroblasts. The knockdown of CSMD1 resulted in enhanced migration and fibronectin1 (FN1) secretion in fibroblasts in vitro. In addition, the upstream and downstream regulatory mechanisms of CSMD1 were also investigated through microRNA (miRNA) databases screening and RNA-sequencing (RNA-seq) respectively. The screening of four common microRNA (miRNA) databases suggested that miR-190a-3p binds to the CSMD1 and may regulate its expression. We confirmed that miR-190a-3p directly targeted the CSMD1–3′-UTR using luciferase reporter assays. Furthermore, the overexpression of miR-190a-3p showed promotion of migratory activity and FN1 secretion in fibroblasts, resembling the effect of CSMD1 knockdown; whereas the knockdown of miR-190a-3p exerted the opposite effect. Finally, transcriptomic analysis showed activation of Janus kinase-signal transducer and activator of transcription (JAK/STAT) signaling pathway in the CSMD1 knockdown fibroblasts.This study has validated the conclusions of the previous GWAS study conducted in Chinese population. In vitro experiments have provided further evidence on the function of CSMD1 in the development of HTS, and have also revealed the underlying upstream and downstream regulating mechanisms. Additionally, the JAK/STAT signaling pathway identified using RNA-seq might provide a potential treatment approach, especially for HTS.The online version contains supplementary material available at 10.1186/s12864-021-07920-8. Hypertrophic scar (HTS) is a frequently encountered fibroproliferative skin disorder characterized by excessive cell proliferation, migration, and extracellular matrix (ECM) deposition, which often severely destroys the patients’ physical appearance and function, decreasing their quality of life and delaying reintegration into society . PreviouCSMD1 gene, mapping to human chromosomal region 8p23, encodes a transmembrane protein with an extracellular region containing 14 CUB and 28 sushi domains, a transmembrane domain and a cytoplasmic domain with a putative tyrosine phosphorylation site . The losMicroRNAs (miRNAs) are a set of small non-coding RNAs less than 22 nucleotides, and usually negatively regulate the target mRNAs by binding to their 3′ -untranslated regions (3′-UTR) . TherefoIn this study, we verified significantly decreased expression of CSMD1 in both HTS tissue and HTS derived fibroblasts. The loss-of-function experiments were designed to explore the influence of CSMD1 knockdown on fibroblasts. Furthermore, databases were searched to identify microRNAs that could negatively regulate CSMD1, and was reconfirmed using luciferase reporter assay. Finally, RNA-sequencing (RNA-seq) was conducted to explore the downstream regulatory signaling pathway. Taken together, these observations have provided further evidence for the function of CSMD1particularly in the development of HTS, indicating a potential therapeutic target that may be useful for treatment of HTS.First, we explored the CSMD1 mRNA level in both human hypertrophic scar (HTS) tissue and the adjacent normal skin (NS) tissue. qRT-PCR result showed significantly down-regulated mRNA level of CSMD1 in the HTS tissue compared to the paired NS tissue , both the mRNA and protein levels of CSMD1 were significantly reduced as compared to the negative control group (shNC) Fig. A, B. TraFour universally used databases  were searched to explore the candidate microRNA targeting CSMD1 gene. In order to eliminate the influence caused the change of microRNA names and the accuracy variations of databases, the subtypes ‘3p’ and ‘5p’ were not taken into consideration during the initial screening. As a result, two candidate microRNA families (miR-10 and miR-190) were identified in four databases  group, suggesting that CSMD1–3′-UTR could be a direct target of miR-190a-3p  pathway . In hepaAdditionally, our research showed that the knockdown of CSMD1 activated the JAK/STAT signaling pathway. JAK/STAT pathway is a critical signaling pathway in multiple biological processes including proliferation and migration, especially in immunoregulation –33. FourThe inhibitors targeting JAKs (Jakinibs) were used as a therapeutic strategy for immune and inflammatory diseases , 36. NumIn conclusion, our study demonstrated that miR-190a-3p directly down-regulated CSMD1, and promoted migratory activity and FN1 secretion in fibroblast. More researches are required to clarify the exact function and mechanism of CSMD1 in HTS, which will hopefully bring about new therapeutic approaches for HTS.Normal human skin fibroblast cell line  were purchased from American Type Culture Collection (ATCC), and cultured according to the instruction. Hypertrophic scars and normal skin samples were obtained from patients undergoing surgeries in the Department of Plastic and Reconstructive Surgery in Shanghai Ninth People’s Hospital, Shanghai Jiaotong University School of Medicine. All patients were well informed about the utilization of specimens and signed written informed consent forms. This study was approved by Shanghai Ninth People’s Hospital Ethics Committee Board, Shanghai Jiaotong University School of Medicine , and followed the ethical principles of the Declaration of Helsinki 1964.2.Isolation and culture of human HTS and NS fibroblasts were conducted as previously described. Specifically, specimens obtained during surgery were cut into 5 mm × 5 mm pieces and soaked in 0.3% dispase II  at 4 °C for 12 h. Then, the epidermis was torn off and the dermis were minced and incubated in collagenase NB4  at 37 °C for 4 h to isolate the dermal fibroblasts. Primary fibroblasts were cultured in Dulbecco’s modified Eagle’s medium  and CCD-1064Sk was cultured in Iscove’s modified Dulbecco’s medium , both containing 10% Fetal Bovine Serum  and 1% penicillin-streptomycin  and then incubated at 37 °C in a humidified atmosphere with 5% CO-△△Ct method was used to calculate the relative expression levels. Specifically, in Figs. Total RNA was extracted using AxyPrep™ Multisource Total RNA Miniprep Kit  and reverse transcribed into cDNA using PrimeScript RT reagent Kit . Real-time PCR was performed with SYBR Premix EX Taq , using Glyceraldehyde-3-phosphate dehydrogenase (GAPDH)  as the endogenous reference. MicroRNA was extracted using SanPrep Column microRNA Extraction Kit , and reverse transcribed using miRNA First Strand cDNA Synthesis (Tailing Reaction) . Real-time PCR was performed with MicroRNAs qPCR Kit (SYBR Green Method) , using U6 nuclear small RNA  as the endogenous reference. 2CSMD1 forward (5′-CATAAGTTACAGCTGCATGGAC-3′),CSMD1 reverse (5′-GAAACTTTTCCCACTAAGTCGC-3′),ACTA2 forward (5′-AAAAGACAGCTACGTGGGTGA-3′),ACTA2 reverse (5′-GCCATGTTCTATCGGGTACTTC-3′),COL1 forward (5′-GCTTGGTCCACTTGCTTGAA-3′),COL1 reverse (5′-TTTGGGAAGGAGTGGAGGG-3′),FN1 forward (5′-CGGTGGCTGTCAGTCAAAG-3′),FN1 reverse (5′-AAACCTCGGCTTCCTCCATAA-3′),JAK1 forward (5′-CTTTGCCCTGTATGACGAGAAC-3′),JAK1 reverse (5′-ACCTCATCCGGTAGTGGAGC-3′),STAT5A forward (5′-GCAGAGTCCGTGACAGAGG-3′),STAT5A reverse (5′-CCACAGGTAGGGACAGAGTCT-3′),PIK3CA forward (5′-GAAACAAGACGACTTTGTGACCT-3′),PIK3CA reverse (5′-CTTCACGGTTGCCTACTGGT-3′),miR-190a-3p forward (5′-GCGCGCGCTATATATCAAACATATTCC-3′).Fibroblasts were seeded in 48-well dishes and cultured until 80% confluence. After fixed in 4% paraformaldehyde at room temperature for 20 min, washed with PBS, permeabilized and blocked, cell samples were stained with anti-CSMD1 antibody  at 4 °C for 12 h. Then, cell samples were incubated with goat anti-rabbit secondary antibody, Alexa Fluor 594 . Nuclei were stained with DAPI. Images were collected with fluorescence microscope. All experiments were conducted according to the manufacturer’s instructions.The short hairpin RNA (shRNA) targeting CSMD1 was synthetized and the recombinant lentiviral vectors (Lenti-shRNA-CSMD1) were generated by Zorin Co. Ltd. . The RNA interference target was 5′-GCATACAACCCACCTGCATTG-3′. Lentiviral vector carrying green fluorescent protein without target genes (Lenti-GFP) was constructed as negative control. CCD1064Sk cell line was transfected with Lenti-shRNA-CSMD1 (shCSMD1) and Lenti-GFP (shNC) respectively at MOI of 30 in 6-well dishes for 24 h, with the assistance of 6 μg/ml polybrene. 48 h after transfection, the transfection efficiencies were briefly evaluated by observing green fluorescence with a fluorescence microscope. Meanwhile, RNA samples were extracted from the transfected cells for qRT-PCR to further evaluate the knockdown of CSMD1 and protein levels were evaluated by IF.MiR-190a-3p mimics/inhibitors and their negative controls were purchased from Genomeditech Co. Ltd. . CCD1064Sk cell line was transfected with them in 6-well dishes for 6 h, with the assistance of Lipofectamine 2000 Transfection Reagent . 48 h after transfection, microRNA samples were extracted from the transfected cells for qRT-PCR to evaluate the overexpression or knockdown of miR-190a-3p. All experiments were conducted according to the manufacturer’s instructions.http://mirdb.org) was searched to predict the potential binding sites between miR-190a-3p and the 3′-untranslated regions (3’UTR) of CSMD1. MiR-190a-3p owned the largest number of possible binding sites and ranked first in the Target Score of miRDB with CSMD1. Therefore, luciferase reporter assays were carried out to confirm that miR-190a-3p directly targeted CSMD1–3′-UTR. Mutations were generated within CSMD1–3’UTR to disrupt the potential binding. Then, the wild type (CSMD1 WT), mutational type (CSMD1 MT) and UTR negative control (UTR NC) were cloned into pGL-CMV luciferase reporter plasmid, which was co-transfected with miR-190a-3p mimics and mimics negative control (mimics NC) using Lipofectamine 2000 Transfection Reagent . At 48 h following the transfection, luciferase activity was detected using the Dual Luciferase Reporter Assay System  according to the manufacturer’s instructions.Database miRDB  was used. The upper chamber contained 1 × 10Cells were seeded into 6-well dishes and grown until 95% confluence. A sterilized 200 μl pipette tip was used to generate a scratch. After the debris was washed away, fresh serum-free IMDM was added and images of cell migrations were taken at 5 randomly selected views per well using a microscope at 0 h and 12 h respectively. The areas of scratch at 0 h were set as 100% and the healing rates of different groups at 12 h were compared.The protein samples were collected 72 h after respective treatment. Equal amounts of proteins were resolved on 10% SDS-polyacrylamide gel and transferred to PVDF membrane  for 2 to 3 h. The membranes were then incubated with primary antibody overnight at 4 °C. The membranes were then incubated with appropriate secondary antibody for 1 h at room temperature and then developed with ECL western blotting detection reagent  following the manufacturer’s instruction. Primary antibodies used in this article are as follows: anti-GAPDH mouse monoclonal antibody-HRP conjugated , anti-αSMA , anti-FN1 .p < 0.05) to identify the differentially expressed genes.Total RNA was extracted from the fibroblast cell line (CCD1064Sk) after the knockdown of CSMD1 and its negative control using TRIzol reagent . The mRNA levels of the unigenes identified using Illumina HiSeq 4000 and were normalized by the Fragments Per Kilobase of exon model per Million mapped reads (FPKM), and the log2-fold changes between two samples were tested statistically to determine whether an individual gene’s expression was altered significantly. We used the criteria of false discovery rate (FDR) < 0.01 and fold changes > 1.5 (t test or ANOVA. P < 0.05 was considered to be statistically significant.Each experiment was replicated three times. Results are presented as means ± SD. Statistical differences among groups were assessed using a two-tailed Student’s Additional file 1.


Nanostructure-based surface-enhanced infrared absorption (SEIRA) spectroscopy has attracted tremendous interest as an ultrasensitive detection tool that supplies chemical-fingerprint information. The interactions between molecular vibrations and plasmons lead to not only the enhancement of spectral intensity, but also the distortion of spectral Lorentzian lineshapes into asymmetric Fano-type or more complicated lineshapes in the SEIRA spectra; this effect hampers the correct readout of vibrational frequencies and intensities for an accurate interpretation of the measured spectra and quantitative analysis. In this work, we investigate the Fano interference between molecular vibrations and plasmons based on exact electrodynamic simulations and theoretical models. We report that, even if the molecular vibrational energy is equal to the plasmon resonant energy, the molecule–nanostructure distance-dependent dipole–dipole interactions, the plasmon-mediated coherent intermolecular interactions and the decay rates of plasmons have a significant impact on the SEIRA lineshapes. This study paves the way for controllable Fano interference at the nanoscale and more studies on plasmon-dressed molecular electronic or vibrational excited states. From analyzing the spectral lineshape evolution in surface-enhanced infrared spectroscopy, we revealed how plasmons interacting with molecular vibrations at the nanoscale and predicted plasmon-mediated long-range intermolecular interaction. Surface plasmon resonance refers to the light-driven collective oscillations of the conduction electrons of free-electron-like nanostructures, which confine the light into deep-subwavelength volumes (known as hotspots) with strongly enhanced optical fields . When moHowever, the couplings between molecules and plasmons also inevitably induce strong modifications in the spectral lineshapes of the plasmon-enhanced spectra in addition to notably large enhancement in spectral intensities . For exaThe PSSE in SERS and SEF can be understood as the result of the plasmon-modified spontaneous emission rate and the spectral distortions to a large extent could be removed by normalizing the plasmon resonance spectroscopy ,16. For Herein, we study the nanoscale couplings between the molecular vibrations and plasmons in a model system in the zero-detuning condition and study the coupling-strength-controlled evolutions of the SEIRA spectral lineshapes. Specifically, we investigate that the distance between molecules and the surface of the plasmonic nanostructure in the model system critically determines the molecule–plasmon coupling strength, and thus impacts the spectral lineshapes. Interestingly, a new mode occurs and red shifts if the molecular density increases and exceeds the critical density. The theoretical model studies reveal that this mode originates from the plasmon-mediated intermolecular coupling. Finally, the plasmon-decay-rate-controlled lineshapes are also discussed. This work unveils the mechanism of the asymmetry lineshape in SEIRA and paves the way for controllable Fano interference at the nanoscale.2@Au), as shown in Fig. 2@Au nanoparticles have been extensively studied for their plasmonic properties with tunable resonant wavelengths from the visible to mid-infrared spectral range [2@Au@ML) as the model system for studies on the molecule–plasmon interaction and its impact on the lineshape of SEIRA spectra .The plasmonic nanostructure studied in this work consists of a dielectric silica core and a thin metallic shell  , such ass (CQED) ,39 and cs (CQED) . In the 2@Au@ML) with increased oscillation strength of the molecules, which is thus equivalent to an increase in the molecular density ′ emerges when ′ mode slightly red shifts as We consider the multi-layer particle  is minimally dispersed as the plasmon energy is tuned and slightly red shifts (∼10 meV), indicating the molecular vibrational nature of P′ mode rather than a plasmonic character. One might intuitively consider P′ mode as a result of the intrinsic uncoupled molecules. However, the P′ mode behaves distinctly differently from the uncoupled molecular absorption. As shown in Fig. 2@ML core–shell structure without the Au nanoshell  or does not depend (red and blue curves) on molecular density. Only the ′ mode, as shown in Supplementary Fig. 8. For stronger interaction strength ′ mode red shifts more rapidly as the density increases. This interesting effect can be applied to evaluate the intermolecular-interaction strength at the nanoscale. Although the coupled-harmonic-oscillators model successfully predicts the energy-shift behaviors of the three modes, it cannot be used to predict the criteria of the appearance of the P′ mode. Further work on extending the model to quantitatively predict the triggering condition of the P′ mode is promising.Using the parameters from the simulations as 2@AP@ML with the tuning of the Drude damping rate q is inversely proportional to q factor increases. An intuitive picture considers the figure of merit for couplings for which the ratio between the coupling strength and the system loss Finally, we further showed that the damping rate of the plasmonic substrate also impacts the lineshapes of SEIRA, although the molecule–plasmon coupling strength remains unchanged. For metals in the mid-infrared spectral range, the optical loss mainly originates from the intraband transitions, which is depicted by the Drude damping terms in the Drude dielectric model. Experimentally, although it is usually difficult to tune the Drude loss in traditional metals, one can consider the vast candidates of mid-infrared plasmonic materials such as doped semiconductors or graphene to achieve the loss tunings ,47. In tthe loss ,49, withectively . As \docc states ,50. In tial loss . As a reial loss . The spementclass2pt{minimIn conclusion, we studied the molecule–plasmon interactions and their spectral features based on a multilayered nanosphere model system according to analytical electrodynamic simulation and theoretical models. We show that, even if the plasmon energy is resonant with the molecular vibrations, the spectral profiles can be tuned by the molecule–plasmonic structure distance, the molecular density and the plasmonic damping rate. The detailed derivations show that the molecule–plasmon coupling strength plays a crucial role in determining the spectral lineshapes. For high molecular density, a new resonant mode is observed and is attributed to the plasmon-mediated intermolecular interactions. We prospect that the energy shift of the new mode can be applied to evaluate the nanoscale intermolecular-interaction strength in the plasmonic cavities. This work paves the way toward controllable Fano interference at the nanoscale and more studies on plasmon-dressed molecular electronic or vibrational excited states in various coupling-strength regimes.nwaa054_Supplemental_FileClick here for additional data file.


Molecular chaperones are key components of the arsenal of cellular defence mechanisms active against protein aggregation. In addition to their established role in assisting protein folding, increasing evidence indicates that molecular chaperones are able to protect against a range of potentially damaging aspects of protein behaviour, including misfolding and aggregation events that can result in the generation of aberrant protein assemblies whose formation is implicated in the onset and progression of neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. The interactions between molecular chaperones and different amyloidogenic protein species are difficult to study owing to the inherent heterogeneity of the aggregation process as well as the dynamic nature of molecular chaperones under physiological conditions. As a consequence, understanding the detailed microscopic mechanisms underlying the nature and means of inhibition of aggregate formation remains challenging yet is a key objective for protein biophysics. In this review, we discuss recent results from biophysical studies on the interactions between molecular chaperones and protein aggregates. In particular, we focus on the insights gained from current experimental techniques into the dynamics of the oligomerisation process of molecular chaperones, and highlight the opportunities that future biophysical approaches have in advancing our understanding of the great variety of biological functions of this important class of proteins. We give an overview of current biophysical methods that are being used to study molecular chaperones and their interactions with amyloid proteins. However, some proteins escape these control mechanisms and form partially folded or misfolded states which lack their intended biological function and which are particularly prone to aggregation.7Proteins take part in virtually all of the key molecular processes in living organisms by interacting with a wide range of biomolecules, including other proteins. In order to function properly, proteins generally have to fold into specific three-dimensional structures known as native states. The folding of proteins into these conformations is, at least at low concentrations, a spontaneous self-assembly process in which their free energy is minimised,9,10 whereas amyloid aggregates are highly persistent and stable assemblies of proteins. The formation of such aggregates is associated with the onset and development of a range of disorders such as Alzheimer's and Parkinson's diseases, type II diabetes, and the formation of eye lens cataracts.11–14 The mechanism by which soluble monomeric proteins assemble into fibrils involves several distinct microscopic processes.15 Typically, protein monomers first assemble into soluble low molecular weight oligomers through primary nucleation. These oligomers then elongate via monomer addition, leading to the formation of long and highly organised mature fibrils. Amyloid formation is accelerated by fragmentation of existing fibrils and other forms of secondary events, for example secondary nucleation in which the surfaces of mature fibrils catalyse the nucleation of monomers as a feedback system that results in the production of new aggregates.15,16 To counteract the numerous pathways that lead to the formation of fibrils, it is becoming increasingly clear that cells utilise molecular chaperones as a front line defense mechanism against amyloid formation.Protein aggregates can range from amorphous species to highly ordered β-sheet rich amyloid fibrils. Amorphous aggregates are largely disordered deposits and can be degraded readily within the cell,27 In eukaryotes, such molecular chaperones include the nascent chain-associated complex (NAC) and the ribosome-associated complex (RAC), which are bound directly to the ribosome and dynamically interact with nascent chains during protein biogenesis.7,17–19 NAC exists as a heterodimer of α and β components, and it is thought to shield hydrophobic residues on protein chains emerging from the ribosome.7,18,20 RAC is a multiprotein complex that stabilises nascent chains and, with the help of other downstream molecular chaperones, assists around 20% of proteins in attaining their native conformations.17 Protein chains that cannot reach their native states with the help of NAC and RAC continue to elongate, and encounter the nonribosome-binding Hsp70/Hsp40 system which acts further downstream in the protein folding process.21 In an ATP-dependent cycle, Hsp70 rapidly binds and unbinds to hydrophobic patches on nascent protein chains to prevent inappropriate interactions, and to decrease the concentration of aggregation prone unfolded species.7 During this process, Hsp40, a 40 kDa heat shock protein, acts as a cochaperone that increases the efficiency of the ATP binding and release cycle.22 Finally, upon release from the ribosome, protein chains that still need assistance in folding proceed to interact with the TriC (TCP1 ring complex) or Hsp90 chaperone system.17,23 TriC operates by encapsulating non-native protein chains in a chamber and providing a shielded environment in which they can fold undisturbed.23The cell and its extracellular environment possess a complex system of molecular chaperones that intervene in various steps along the protein folding pathways. The first molecular chaperones encountered by proteins are those associated with the ribosome.324–34 For example, the extracellular molecular chaperones α2-macroglobulin and clusterin have been shown to interact with a variety of amyloidogenic proteins to inhibit the formation of fibrils.35–37 At the same time, there exist many different molecular chaperones that act predominantly inside the cell to prevent a range of aberrant processes, including those associated with the progression of neurodegenerative diseases.38 In the following we discuss specifically the function of two ubiquitous types of intracellular eukaryotic molecular chaperones, Hsp70 and αB-crystallin (αBC), which have been popular targets of biophysical characterisation studies because of their protective role against protein aggregation and their oligomerisation properties.The role that molecular chaperones play in assisting protein folding has been studied extensively, and significant progress has been made in elucidating the mechanisms involved, as described in the previous section. However, increasing evidence indicates that assisting nascent protein chains in reaching their native conformations is just one of the many functions of molecular chaperones . Indeed,42) which are associated with Parkinson's and Alzheimer's diseases, respectively.33,39–41 Such inhibitory action has not only been observed in human cells, but also in yeast where Hsp70 family members have been shown to increase the lag time in the aggregation of Ure2p, a prion protein.42 Although it has been proposed that Hsp70 acts by binding to aggregation-prone monomeric proteins to suppress nucleation in human cells,33 this has not been experimentally confirmed and the mechanism of chaperone action still remains largely unknown.Human Hsp70 has been shown to inhibit, at substoichiometric ratios, the formation of α-synuclein and amyloid β(1–42) peptide aggregates .29 The proposed mechanism of disaggregation involves first the identification of protein aggregates by Hsp70 and Hsp40 in an ATP-dependent process. Then, Hsp70 and Hsp40 bind and extract individual misfolded protein molecules from the surface of these clusters.24,29,43 Subsequently, the extracted misfolded molecule is translocated across ClpB (an Hsp104 homologue), which allows it to unfold and exit into the cytoplasm, where it achieves its native conformation spontaneously or with the help of other molecular chaperones.24,27,43 Disaggregation by Hsp104 has been observed for both amorphous and amyloid aggregates.24,44 The variety of molecular chaperone functions that Hsp70 possesses, including preventing protein misfolding, inhibiting amyloid fibril growth, and resolubilizing aggregates, makes it a key part of the network of molecular defences in place for assisting cells in combating protein aggregation.Members of the Hsp70 family are also known for their ability to reverse the formation of protein aggregates with the assistance of co-chaperones both 45 and is expressed at particularly high concentrations within the eye lens where it serves to maintain lens transparency.46 Many studies31,32,34,47,48 have reported that αBC binds to a wide range of proteins to inhibit amyloid formation, including disease associated proteins such as α-synuclein,9,32,49 insulin,50 and Aβ.31,51 According to in vitro biophysical studies, the addition of αBC to solutions of α-synuclein suppresses protein aggregation both in its earliest growth stages and in later stages.49 The mechanism of inhibition is unclear, although the results of nuclear-magnetic resonance (NMR) spectroscopy studies suggest that αBC is able to bind α-synuclein monomers.9,49 Electron microscopy studies9 also detected the formation of amorphous α-synuclein deposits in the presence of αBC, suggesting that αBC might be able to direct pre-fibrillar α-synuclein species towards alternative pathways leading to the formation of nontoxic and biodegradable amorphous species.9,10Another crucial molecular chaperone known for its ubiquity and important biological function in preventing amyloid formation is αB-crystallin (αBC). The molecular chaperone αBC is a small heat shock protein found in almost every tissue,Although it is becoming increasingly evident that molecular chaperones play a crucial protective role in counteracting amyloid formation, the detailed molecular mechanisms underlying this inhibition process have not been elucidated owing to the inherent heterogeneity of aggregating protein systems in the presence of molecular chaperones, as well as the transient nature of the biomolecular interactions involved.452–56 The polydispersity of molecular chaperones adds to the complexity of probing molecular chaperone–substrate interactions. Generally, the determination of the specific species interacting, the quantification of their binding affinity, and the characterisation of the population distribution and binding mechanism is difficult for interacting heterogeneous systems. It is therefore of critical importance to study the dynamics of molecular chaperone systems separately to ensure that their self-assembly behaviour can be taken into account when measuring specific interactions of these molecules with their client proteins. Elucidation of the oligomers involved in the suppression of protein aggregation is likely to yield valuable information on the molecular basis of chaperone action, the key interaction mechanisms involved, and the target amyloidogenic species. These oligomeric states of molecular chaperones have, however, proved to be particularly difficult to characterise because of the continuous interconversions between a variety of higher molecular weight species, ranging typically from dimers to dodecamers.55,56Under physiological conditions, many molecular chaperones, including Hsp70s and small heat shock proteins, have a propensity to self-associate to form oligomers.in vitro, members of the Hsp70 family have been shown to exist as a series of monomeric, dimeric, trimeric, and higher order oligomeric species in reversible dynamic equilibrium.  to the study of protein complexes and amyloid oligomers in their intact, undissociated forms have provided valuable information on quaternary protein structure and topology.88,89 In addition, use of tandem MS and collision induced dissociation (CID) has allowed the quantification of the relative populations of oligomeric αBC as well as the energetics involved in their self-assembly.53,68 Typically, heterogeneous protein samples have mass spectra with overlapping peaks which arise from the different species present, making it difficult to extract quantitative information.88 However, this problem can be overcome using CID in which highly charged monomeric species are dissociated from aggregates prior to sample injection into the flight tube. This procedure allows the deconvolution of peaks, since the highly charged dissociated monomers give rise to a signal at low m/z whereas the complementary oligomer peaks occur at higher m/z.88 Although these MS techniques provide quantitative information on heterogeneous protein systems, the change of phase of the sample from solution to gas must be accounted for appropriately.A more quantitative analysis of the dynamics of polydisperse oligomeric species can be achieved using mass spectrometry (MS).76,77 A key advantage of AUC is that it is possible to characterise the binding stoichiometry and distribution of molecules in solution, including complexes of molecular chaperones and amyloidogenic proteins. For example, AUC sedimentation experiments have shown that αBC is able to bind directly to mature protein fibrils at near stoichiometric ratios.31,32In terms of probing the interactions of molecular chaperones with their client proteins in bulk , analyti84,85 This method therefore represents a powerful approach for detecting protein–protein interactions, and has been used to elucidate key complexes formed between both intra and extracellular molecular chaperones with aggregated species of the disease associated amyloid β(1–40) (Aβ40) peptide.37,38 The findings of these studies show that both αBC and clusterin are able to form stable complexes with Aβ40 oligomers spanning a range of subunit numbers, suggesting that sequestration of Aβ40 oligomers by these molecular chaperones plays a key role in the prevention of aberrant aggregation processes.37,38Single molecule techniques have recently emerged as highly effective methods for monitoring the dynamics of individual molecules in heterogeneous solutions without the need for prior separation, making it ideal for the detection of molecular chaperone interactions with prefibrillar species. Fluorescence resonance energy transfer (FRET) imaging probes the interaction of biomolecules that are spatially located at a distance shorter than 1–10 nm based on nonradiative energy transfer from a donor molecule to an acceptor molecule.92 For example, an NMR study on αBC and α-synuclein has shown that the addition of αBC to α-synuclein fibril solutions shifts the monomer–fibril equilibrium towards monomers.32 This result indicates that the binding of αBC to fibrils leads to fibril disaggregation via weakening of the interactions between adjacent α-synuclein molecules within mature fibrils.32 In addition, NMR studies are often supplemented by molecular dynamics simulations which give further insights into the structure, topology, and dynamics of proteins.92,97–99Solution- and solid-state NMR spectroscopy enable the characterisation of protein conformations and allow the detection of interactions between biomolecules.100 and allow solution-state measurements to be made without the need for a crystalline sample.96 Since the static nature of these techniques makes the detection of transient biomolecular interactions difficult,88,89 SAXS and SANS are commonly applied for probing the structure of fibrils and oligomers, rather than dynamic chaperone–fibril interactions.9,101Small-angle X-ray scattering (SAXS) and small-angle neutron scattering (SANS) are also highly effective and established methods for the structural characterisation of proteins in bulk. These methods do not impose a molecular weight limit as in solution NMR spectroscopy90 For example, immuno-electron microscopy studies show that the mode of binding between αBC and mature α-synuclein and Aβ42 fibrils involves attachment of αBC along the long axis of the fibrils as well as to the fibril ends.31,32 This binding suggests that αBC inhibits fibril growth either by capping the reactive ends of growing fibrils and preventing elongation, or by deactivating sites which may catalyse secondary nucleation along the length of the fibril.34,50 Cryo-electron microscopy studies have been used to probe the quaternary structure of αBC as well as the growth of Aβ42 fibrils in the presence of molecular chaperones.67,91The techniques described above involve indirectly obtaining the size or mass of biomolecules from the measurement of their physical and chemical parameters such as sedimentation coefficient, diffusion coefficient, elution volume, and collision cross-section. The structure of complexes can also be visualised directly by imaging. In this context, electron microscopy techniques are commonly applied to the analysis of protein samples. In particular, immuno-electron microscopy and cryo-electron microscopy allow biomolecules to be imaged and provide a qualitative analysis of the different structures present, although quantification of the relative amounts remains challenging to achieve through this approach.50,83 By applying this technique, it has been shown that αBC is able to bind directly to insulin fibrils specifically during the fibril elongation phase, thereby inhibiting the growth of protein fibrils via monomer addition  and ThT fluorescence studies have illuminated additional aspects of the binding of molecular chaperones to fibrils. In QCM studies, protein fibril seeds are deposited onto the surface of a microbalance and are then exposed to a solution of protein monomers. The addition of monomeric units to the fibril seeds induces a decrease in oscillation frequency of the quartz balance. Highly sensitive measurements on molecular chaperone binding can therefore be achieved by recording the oscillation frequency as a function of time.addition .31,32,5040 growth seeded by Aβ42 fibrils, a decrease in fluorescence signal was observed in the presence of αBC, suggesting that the molecular chaperones interact directly with fibril seeds.51 Interactions between Hsp70 and α-synuclein seeds have been revealed in a similar way.39ThT fluorescence studies are based on the enhanced quantum yield of ThT dye upon binding to fibrils. The time evolution of fibril formation can therefore be followed by recording the fluorescence signal of ThT. Experiments performed in the absence and presence of molecular chaperones provide evidence of the capacity of these molecules to inhibit aggregation processes. For instance, in a kinetic study conducted on Aβ102 This strategy consists of monitoring the time evolution of fibril formation  at different protein and chaperone concentrations. The reaction profiles typically follow a sigmoidal shape which results from a highly non-linear combination of a series of microscopic events, including primary nucleation, elongation, fragmentation, and secondary nucleation. The modification of a specific microscopic event affects the reaction profiles in a characteristic manner.102 Therefore, kinetic analysis of the changes in the reaction profiles measured in the absence and presence of different concentrations of molecular chaperones yields information on the specific protein species targeted by molecular chaperones as well as the microscopic steps which are affected by such interactions. This method has been implemented in studies on interactions between various molecular chaperones and Aβ42.91,103 Kinetic analysis reveals that the mechanism of action which best matches the change in Aβ42 aggregation profile in the absence and presence of the molecular chaperone Brichos, a protein domain of approximately 100 residues, corresponds to the inhibition of secondary nucleation processes through direct binding of Brichos to Aβ42 fibrils (91 Similarly, it has been shown that the molecular chaperone DNAJB6 inhibits Aβ42 aggregation through suppressing both primary nucleation and growth events.103In addition to the experimental techniques described above, analysis of chemical kinetics has emerged as a powerful tool for complementing experimental characterisation and for providing insights into the microscopic mechanisms of molecular chaperones acting against protein aggregation. fibrils , which t31,32,50,91,103 This discovery is a crucial step forward since, although the potent inhibitory effect that molecular chaperones have on fibril generation has been well documented, the molecular mechanisms involved were previously unknown. It is becoming increasingly recognised that analysis of such systems using quantitative techniques allows the identification of the specific aggregation events that are inhibited as well as the key interactions involved, enhancing our understanding of how molecular chaperones function in vivo. Therefore, the development of novel quantitative biophysical methods capable of detecting individual components of a heterogeneous system of biomolecules under near physiological conditions is likely to be of critical importance in advancing our knowledge of molecular chaperone–protein interactions and the roles that they play in preventing protein aggregation.It is clear that a variety of biophysical methods are currently available for probing molecular chaperone interactions with their client proteins through all stages during the formation of amyloid fibrils  as well 6The effects that molecular chaperones have on protein aggregation are increasingly well understood and evidence has emerged that they can protect against a wide variety of aberrant protein behaviour including misfolding and amyloid fibril formation processes. However, the molecular basis of chaperone action and the specific target protein species involved remain less well characterised for many systems. Elucidating the exact roles that molecular chaperones play in regulating protein aggregation is crucial for understanding the molecular events likely to be involved in the onset and progression of protein misfolding diseases. In order to address this problem and to advance our knowledge of chaperone action, it has become apparent that a detailed analysis of the oligomeric behaviour of molecular chaperones and their interactions with amyloidogenic protein species represents a key objective for biophysical studies. Progress in this direction involves the study of highly polydisperse systems in which multiple interactions occur simultaneously, a situation which arises as a consequence of the fact that molecular chaperones are commonly present as a dynamic and heterogeneous ensemble of oligomers, and are capable of interacting with a large variety of monomeric and aggregated misfolded species. Use of biophysical techniques for the characterisation of the oligomeric states of molecular chaperones, both in isolation and in the presence of their substrates, has yielded valuable insights into the molecular mechanisms underlying their function. Current and future developments in biophysical methodologies have the potential to probe increasingly complex and heterogeneous systems of aggregates of client proteins and molecular chaperone oligomers, and to open up the possibilities for their quantitative characterisation.


Since the novel coronavirus SARS-COV-2 was first identified to be circulating in the US on January 20, 2020, some of the worst outbreaks have occurred within state and federal prisons. The vulnerability of incarcerated populations, and the additional threats posed to the health of prison staff and the people they contact in surrounding communities underline the need to better understand the dynamics of transmission in the inter-linked incarcerated population/staff/community sub-populations to better inform optimal control of SARS-COV-2.We examined SARS-CoV-2 case data from 101 non-administrative federal prisons between 5/18/2020 to 01/31/2021 and examined the per capita size of outbreaks in staff and the incarcerated population compared to outbreaks in the communities in the counties surrounding the prisons during the summer and winter waves of the SARS-COV-2 pandemic. We also examined the impact of decarceration on per capita rates in the staff/incarcerated/community populations.For both the summer and winter waves we found significant inter-correlations between per capita rates in the outbreaks among the incarcerated population, staff, and the community.p = 0.03 and p < 0.001, respectively). Average per capita rates of incarcerated population outbreaks were significantly associated with prison security level, ranked from lowest per capita rate to highest: High, Minimum, Medium, and Low security.Over-all during the pandemic, per capita rates were significantly higher in the incarcerated population than in both the staff and community (paired Student’s t-test p = 0.015).Federal prisons decreased the incarcerated population by a relative factor of 96% comparing the winter to summer wave . We found no significant impact of decarceration on per capita rates of SARS-COV-2 infection in the staff community populations, but decarceration was significantly associated with a decrease in incarcerated per capita rates during the winter wave (Negative Binomial regression We found significant evidence of community/staff/incarcerated population inter-linkage of SARS-COV-2 transmission. Further study is warranted to determine which control measures aimed at the incarcerated population and/or staff are most efficacious at preventing or controlling outbreaks.The online version contains supplementary material available at 10.1186/s12889-022-12813-w. Since the novel coronavirus SARS-COV-2 was first identified to be circulating in the US on January 20, 2020, some of the worst outbreaks have occurred within institutional settings such as homes for the elderly and in prisons and jails –5. For sThe close quarters and environment of correctional facilities can also result in high incidence of SARS-COV-2 infections among incarcerated individuals. To date, over 1 in 4 incarcerated people in the US have tested positive for SARS-COV-2 and over 2,400 have died . ControlIn an attempt to control the spread of SARS-COV-2 in incarcerated populations, in May, 2020 the Federal Bureau of Prisons (BoP) instituted a halt on prison visitation. In addition, federal prisons and some state prisons used decarceration in an effort to reduce crowding in the prison populations , 17, 18.Here we examined the number of SARS-COV-2 cases in the incarcerated population and staff in 101 non-administrative federal prisons in the US, beginning May 18, 2020 (the date of the last BoP SARS-COV-2 directive in the spring) to Jan 31, 2021. We also examined the number of SARS-COV-2 cases in the counties surrounding the prisons. We compared the per capita rates of the outbreaks in the three sub-populations, over-all during the pandemic and separately during the summer and winter waves. We examined how incarcerated population sizes and prison security level  may have affected transmission rates, and also the effect of decarceration.In the following sections we describe the sources of data and the statistical methods used, followed by a presentation of results and discussion.Data on SARS-COV-2 incidence among incarcerated populations and staff in 121 federal prisons between 04/16/2020  and 1/31/2021 were obtained from the US Federal Bureau of Prisons website . We exclCounty level data on SARS-COV-2 incidence in the general population were obtained between 01/22/2020 and 1/31/2021 from the Johns Hopkins UniversityCoronavirus Resource Center ) We found significant relationship between the incarcerated population per capita rates and prison security level during the summer wave of the pandemic . In general, as security levels rise there is less contact among the incarcerated population, and incarcerated population to staff ratios go down. We found that among Low, Medium, and High security prisons, per capita rates in the incarcerated population went down by security level, in broad agreement with the findings of . HoweverTo the authors’ knowledge, ours is among the first studies to examine the impact of decarceration on rates of SARS-COV-2 transmission among not only incarcerated populations, but also prison staff and the surrounding communities. We found that decarceration did not have a significant impact on lowering rates of SARS-COV-2 infection in the incarcerated population overall or during the summer wave, but did have a significant impact during the winter wave. These results compare to those of Vest et al. (2020) who found that 15% relative decarceration in Texas state prisons was associated with significantly lower rates of SARS-COV-2 infection and death . HoweverRather than publishing total number of newly identified cases per some time-period , the BoP instead publishes the number of people who have tested positive (and are still positive), along with the number recovered and number dead, and only does so sporadically and not at evenly spaced time points. The mismatch between having to compare prevalence in the prison populations to incidence in the community populations was a relatively minor complication to this analysis because we examine relatively long time periods. But the incidence vs prevalence reporting differences seriously complicate any potential future analysis of short term temporal trends in the data because incidence data lag that of prevalence by half the average infectious period. In addition, the daily reporting of community data, and irregular reporting of BoP data would also seriously complicate such an analysis. In general, the BoP surveillance data do not exhibit good epidemiological and public health practices in infectious disease reporting standards , 33. GreWhile detailed temporal information is available on the incarcerated population per prison, estimates of staff come from one time point in mid 2020. This potentially creates some unreliability of our staff per capita rate assessments, if in fact the staff population sizes significantly varied over the time frame under consideration.Information of race and ethnicity of incarcerated individuals in the federal prisons, including those testing positive for SARS-COV-2 are not available, but are potentially an important factor to take into account in any analysis of transmission disparities.The number of SARS-COV-2 cases in the incarcerated population and staff is published at the prison level, but for prisons that are part of larger multiple-security-level complexes, the BoP only publishes staffing information for the entire complex. Thus, per capita rates for staff could not be computed for those prisons, and they had to be excluded for parts of the analysis examining staff per capita rates. Should that data be made available, it would increase the sensitivity of the analysis to detecting potential relationship of staff per capita rates of infection to factors like security level.The BoP does not publish the number of tests performed, only the total number of positive tests (and deaths), and it is unclear what surveillance protocols were in place, and whether these differed by prison . For exaThere were many issues affecting data quality in this analysis:Further study is needed to determine why just seven out of 101 non-administrative federal prisons accounted for the majority of SARS-COV-2 cases in the incarcerated population and staff, similar to the findings of a previous study of Texas prisons . While oFurther study is also needed to examine the primary directionality of transmission and determine whether or not peak times of outbreaks in communities tend to precede or lag peak times in incarcerated populations. High-risk sub-populations can form a “reservoir” of infection that can continue to re-infect the broader population even when strict control measures are in place . The higWe examined the transmission of SARS-COV-2 in incarcerated populations and staff in non-administrative federal prisons and compared the patterns of transmission to those observed in the communities in the counties surrounding the prisons. We found significant inter-correlations between per capita rates in outbreaks in incarcerated populations, staff and communities. We also found that decarceration of incarcerated individuals during the winter wave of the pandemic was associated with significantly lower rates of SARS-COV-2 infection in incarcerated populations. These studies can help inform infectious disease response policies in prisons that help to potentially mitigate rates of infection in incarcerated populations, staff, and the community at large.Additional file 1: Appendix A.


Differential gene expression and gene ontology analysis showed that hotspots were enriched in gene sets associated with DNA replication, cell cycle, and ligand receptor interaction. Genome and exome analysis suggested hotspots and coldspots to have similar mutational profiles. However, a limited number of hotspot-specific mutations and fusion transcripts indicated that hotspot tumor cells developed from coldspot cells and point at the potential role of hotspot driver genes in glioblastoma. Our findings reveal that hotspots in glioblastomas represent a more advanced stage of molecular evolution than coldspots.Glioblastoma is one of the most aggressive cancers, but the molecular evolution is still not fully understood. We used PET imaging combined with deep sequencing of glioblastoma biopsies at both the RNA and DNA levels to get a deeper insight into molecular evolution. In the clinical setting, PET imaging provides information about metabolically active tumor areas, but the molecular interpretation is unclear. Our primary objective was to perform an intratumoral spatial comparison of biopsies from potentially aggressive and less aggressive areas in glioblastomas according to PET scans. Additionally, tissue from the tumor periphery was included. We used MRI,


Uloborus plumipes represents a biomimetic paragon for this problem: these spiders integrate thousands of nanofibers into their adhesive capture threads. A comb on their hindmost legs, termed calamistrum, enables the spiders to process the nanofibers without adhering to them. This anti-adhesion is due to a rippled nanotopography on the calamistrum. Via laser-induced periodic surface structures (LIPSS), these nanostructures can be recreated on artificial surfaces, mimicking the non-stickiness of the calamistrum. In order to advance the technical implementation of these biomimetic structured foils, we investigated how climatic conditions influence the anti-adhesive performance of our surfaces. Although anti-adhesion worked well at low and high humidity, technical implementations should nevertheless be air-conditioned to regulate temperature: we observed no pronounced anti-adhesive effect at temperatures above 30 °C. This alteration between anti-adhesion and adhesion could be deployed as a temperature-sensitive switch, allowing to swap between sticking and not sticking to nanofibers. This would make handling even easier.Due to their uniquely high surface-to-volume ratio, nanofibers are a desired material for various technical applications. However, this surface-to-volume ratio also makes processing difficult as van der Waals forces cause nanofibers to adhere to virtually any surface. The cribellate spider  Nanofibers promise immense opportunities for innovation. Already today they are integrated into efficient filters, smart textiles, and biomedical applications ,2,3,4,5.These so-called cribellate spiders produce thousands of nanofibers as adhesive parts of their capture threads a,b. The Uloborus plumipes, are equipped with nanoripples . Wi. Wi21]. Consistent with gecko adhesion, nanofiber adhesion increased at high humidity . This efIn the controls, fiber deformation increased only at 90% humidity, whereas it seems in the LIPSS structured foils fiber deformation increases already at 70% humidity and does not change further when increasing the humidity above. Hence, we suspect a transition level where mechanical properties already show initial changes  and alsoAt typical room temperature and below, the anti-adhesive effect of LIPSS is pronounced. However, when increasing temperature to 30 °C and above, anti-adhesion could no longer be observed. High temperatures are known to potentiate the influence of humidity on silk fibers . TherebyThe non-reduced adhesion at 30 °C and higher does not necessarily represent a drawback: The change from anti-adhesion to adhesion and vice versa, by simply changing temperature could enable easier handling of nanofibers. For example, the setup could be slightly heated for spooling and then easily further unwind and processed after cooling.U. plumipes, can be found in various habitats, ranging from warm and dry to humid habitats [With this study, we demonstrated that biomimetic LIPSS can significantly reduce the adhesion of 10 to 30 nm thick fibers, especially at common laboratory conditions. Surprisingly, we found temperature to be a limiting factor, rather than humidity: at an ambient temperature of 30 °C, the behavior switched from anti-adhesive to normal adhesion. Choosing the most favorable of the tested ambient conditions, the reduction of fiber deflection could be further increased by about 6% (from ≈−40% to ≈−46%) compared to the previous study, where the ambient conditions were not accounted for . Furtherhabitats . They buhabitats ,22,24. T


Automated characterization of spatial data is a kind of critical geographical intelligence. As an emerging technique for characterization, spatial Representation Learning (SRL) uses deep neural networks (DNNs) to learn non-linear embedded features of spatial data for characterization. However, SRL extracts features by internal layers of DNNs, and thus suffers from lacking semantic labels. Texts of spatial entities, on the other hand, provide semantic understanding of latent feature labels, but is insensible to deep SRL models. How can we teach a SRL model to discover appropriate topic labels in texts and pair learned features with the labels? This paper formulates a new problem: feature-topic pairing, and proposes a novel Particle Swarm Optimization (PSO) based deep learning framework. Specifically, we formulate the feature-topic pairing problem into an automated alignment task between 1) a latent embedding feature space and 2) a textual semantic topic space. We decompose the alignment of the two spaces into: 1) point-wise alignment, denoting the correlation between a topic distribution and an embedding vector; 2) pair-wise alignment, denoting the consistency between a feature-feature similarity matrix and a topic-topic similarity matrix. We design a PSO based solver to simultaneously select an optimal set of topics and learn corresponding features based on the selected topics. We develop a closed loop algorithm to iterate between 1) minimizing losses of representation reconstruction and feature-topic alignment and 2) searching the best topics. Finally, we present extensive experiments to demonstrate the enhanced performance of our method. Critical infrastructures  often consist of spatially distributed entities that interact with each other, and have generated massive spatial-networked behavior data. Analyzing such data can identify trends, forecast future behavior, and detect anomalies. To enable effective analysis, it is critical to desire a new capability of automated characterization that effectively extract feature vectors from spatio-networked data.As one of the emerging techniques, representation learning can be adapted to learn non-linear embedded features of spatial network data, which we call spatial representation learning (SRL). There has been a rich body in SRL, including node embedding, autoencoder, random walk, adversarial learning, generative learning based methods with spatial data . AlthougLacking model interpretability is possible to cause damaging or controversial consequences in incomplete scenarios that are not well-studied . For insTo relieve the limitations of prior literature and expand the application scenarios of SRL approaches, a novel SRL model should understand not just which features are effective, but also what these effective features stand for. This issue relates to two tasks: 1) deep representation learning; 2) label generation and matching for latent embedded features. In response, we formulate the problem as a task of feature-topic pairing , which iThere are three unique challenges  in addre1) Formulating the feature-topic pairing problem. Motivated by lacking feature labels in SRL, we formulate and develop a new problem: feature topic pairing. In the proposed model, we propose a new strategy: we first let an optimizer to automatically select K topics; the optimizer then guides the representation learner to learn K latent features that optimally align with the K topics.2) Generating candidate topic labels. We propose a three step mining method to generate candidate topic labels. Specifically, we first extract keywords from the texts of all spatial entities. Then, we learn keyword embedding feature vectors with a pre-trained word model . Finally3) Quantifying feature-topic alignment measurement. We identify two types of feature-topic alignments: 1) point-wise alignment, and 2) pair-wise alignment. First, the point-wise alignment is to describe the correlation between an embedding feature vector and a categorical topic distribution. In particular, we maximize the correlation so that the distance between the distribution of the embedding vector space and the distribution of the topic semantic vector space can be minimized. The underlying motivation of point-wise alignment is originated from the fact that: if a topic density is high in describing a spatial entity, the topic’s corresponding feature value is expected to be large to co-vary with the topic density. In this way, we align the distribution covariance of the two spaces. Second, the pair-wise alignment is to describe the consistency between a feature-feature similarity matrix and a topic-topic similarity matrix. In particular, we use the feature-feature similarity graph to describe the topology of the latent embedding feature space, and use the topic-topic similarity graph to describe the topology of the textual semantic topic space. If the two spaces are aligned, the two graphs (represented by matrices) are similar as well.4) Optimization in the loop. We develop a Particle Swarm Optimization (PSO)-based algorithm. In this algorithm, we first simultaneously optimize the representation learning loss, point-wise alignment loss, pair-wise alignment loss, and downstream task loss as the feedback for PSO. Guided by the feedback, the PSO based algorithm selects a better K-sized topic subset for feature-topic pairing. In particular, based on the loss function value, PSO iteratively generates topic masks  to search the optimal topics for space pairing until the learning objective converges. In this way, the PSO jointly achieves topic selection, feature-topic pairing, and latent feature learning.To solve the three challenges, we develop a new PSO-based framework (named AutoFTP) that enclose the optimizations of feature learning, topic selection, and feature-topic pairing in a loop. Specifically, our contributions are:Finally, we evaluate our method using Beijing’s urban geography and mobility data. For comparison we implemented a broad range of other algorithms. Results showed that our method consistently outperformed the competing methods. We perform ablation study, interpretability, robustness check, stability, sensitivity to justify our technical insights.In this section, we introduce key definitions of AutoFTP and the problem statement.PSO is a heuristic optimization algorithm that finds an optimal solution in a dynamic environment, by imitating the social activity of a flock of birds. Similarly, solving the feature-topic pairing problem can be analogized as a task of searching the optimal matching solutions in a dynamic environment. Specifically, we can view the eagles as a set of binary topic selector, which are to select the optimized subset of topics from a candidate topic set for feature-topic pairing. The choices of these binary topic selectors are iteratively updated in order to converge into the ultimate most matched topic-feature pairs. During the iterative process, all the binary topic selectors jointly share the changes of objective function losses , so the topic selector knows how to update the topic selection next round.Definition 1: Spatial Entity. A spatial entity is a geographical concept that consists of a range (e.g. a circle area with a radius of 1 mile) and a location (i.e. the latitude and longitude of a center). The spatial entity also includes various Points-of-Interest (POIs) of different categories .Definition 2: Point-wise Alignment. To tackle feature-topic pairing, we assume there are 1) an embedding vector that describes the features of a spatial entity and 2) a corresponding topic distribution associated to a spatial entity, which are extracted by optimization. To achieve feature-topic alignment, we propose a point-wise alignment to describe the correlation between features and topics. S; 2) a feature-feature similarity matrix S′. We measure the consistency between the two matrices as the pairwise alignment.Definition 3: Pair-wise Alignment. We propose another perspective (pair-wise) to model the feature-topic alignment. For each entity-entity pair, we compute their feature-feature similarity and topic-topic similarity, and obtain: 1) a topic-topic similarity matrix N spatial entities, the n-th entity is described by multiple graphs . Then we randomly initialize the parameters of GMD. Next, we use the Expectation Maximization (EM) algorithm to find the optimal parameters of the GMD. Finally, we calculate the probability of xi , belonging to each topic based on the GMD, and select the topic with the highest probability as the label of xi. After that, we propose to construct the topic distribution vector of each spatial entity. In particular, for the n-th entity, the topic vector tn is a T dimensional vector, where each dimension indicates a topic, and is filled by the number of associated keywords.As illustrated in Gd, where POI categories are nodes and the average distances between POI categories are edge weights. 2) POI-POI mobility graph: denoted by Gm, where nodes are POI categories, and edge weights are human mobility connectivity. The number of POI categories in this paper is M, and the two graphs are extracted via the method in , and β2 = arg maxςP(ς). We calculate the visited probability of all POIs according to the formula. We sum up the probability of POIs belonging to the same POI category to calculate the visited probability of the POI category. Finally, we calculate the connectivity strength between POI categories as: Pi and Pj represent the visited probability of POI category i and POI category j respectively; i and j.In order to learn the embedding feature vectors of spatial entities, we propose to construct the graph-structured topology of each spatial entity. This is because there is inherent spatial autocorrelation between each two spatial entities, according to the geographical first law. We describe a spatial entity in terms of its POIs, by building two graphs. 1) POI-POI distance graph: denoted by ethod in . Specifin-th entity, the encoder has two GCN layers. The encoding calculation process can be formulated as follows:An is the adjacency matrix, In is the identity matrix, U is the feature dimension; H is the output dimension of the layer; zn:To learn the representations of spatial entities, we utilize the Graph Auto Encoder (GAE)  to constThe optimization objective is to minimize the reconstruction loss between the original graph, denoted by the adjacency matrix n-th spatial entity. After that, we obtain the node representations of We apply the GAE to the POI-POI distance graph n-th entity as an example to explain the calculation process.. We first select K values from the topic vector tn as the vector rn, which is equal to minimize the negative correlation between the two vectors. The formula of the minimizing process as follows:1) Point-wise Alignment Loss: δ(.) denotes the standard deviation.. We first construct the topic-topic similarity matrix S and the feature-feature similarity matrix S′. Specifically, for S and S′ by minimizing the Frobenius norm, as follows:2) Pair-wise Alignment Loss: To pair features with topics, we conduct space alignment from the point-wise and pair-wise perspectives. Referring to definitions K topics so the representation learner can learn a K-sized embedding vector in terms of K topics to achieve feature-topic alignment. However, how can the machine automatically identify the best K and select the most appropriate K topics?As introduced above, we select K topics randomly at each iteration until we traverse all topic combinations and find the best topic subset based on the objective function. The searching process, however, is time-consuming and computationally expensive. Moreover, the topic selection problem belongs to the combinatorial optimization field, which is hard to solve by derivative-based optimization algorithms. Thus, a quickly and derivative-free optimization algorithm should be selected as our optimizer. Considering the high time complexity for traversing all possible subsets to find the optimal result, we propose to formulate the joint task of feature learning, topic selection, topic and feature matching into a PSO problem.A naive idea is that we can select cn is the golden standard real estate price and The PSO-based optimization framework is as illustrated in Then, we utilize the fitness to inform all particles how far they are from the best solution. Next, each particle moves forward to the solution based on not only its current status but also all particles’ movement. After the fitness value of PSO converges, PSO identifies the best topic subset. Finally, the semantically-rich embeddings of spatial entities, given by: In this section, we present extensive experiments with real world data to answer the following research questions: Q1. How effective is our proposed AutoFTP in spatial representation learning? Q2. How effective is each loss function of AutoFTP? Are they all necessary for spatial representation learning? Q3. How well does AutoFTP work in space alignment? Do the learned spatial embeddings contain semantic meanings? Q4. How about the robustness of AutoFTP? Does it always outperform other baselines? Q5. How about the stability and sensitivity of AutoFTP?www.fang.com. In experiments, the residential regions are treated as spatial entities. The texts reflect the urban utilities and characteristics of spatial entities from multiple perspectives such as traffic condition, economic development, demographic situation, and etc. The real estate prices indicate the average value of the real estate of each spatial entity in 6 months. Thirdly, the POIs are extracted from www.dianping.com, which is a POI  review website in China. Each POI is described in a format of < POI id, POI category, latitude, longitude >.n-th spatial entity as an example to explain the regression model. The formulation of DNN is n-th spatial entity, w is the weight term, and b is the bias term. We want to minimize the difference between predicted price yn. Thus, the objective of the DNN is N is the total number of spatial entities.Our proposed method (AutoFTP) can learn a list of vectorized representations for all spatial entities. Therefore, as a downstream application, we can apply these representations to train a regression model to predict the average real estate price of these spatial entities. Specifically, we first apply AutoFTP to learn a series of representations of spatial entities based on their geographical structural information and related text descriptions. Then, we build up a deep neural network (DNN) model for predicting average real estate price of each spatial entity according to its corresponding representation. To be convenient, we take the yn with the predicted prices We evaluated our method using a real estate price prediction task . We tookp as 0.25 and in-out parameter q as 4. 7) Struc2Vec (We compared our proposed method with seven widely-used and robust representation learning (embedding) methods as follows: 1) AttentionWalk  utilizestruc2Vec  learns tAutoFTPR, which only keeps AutoFTPR+P) Core(TM) i9-9920X CPU@ 3.50GHz, 1 way SLI Titan RTX and 128GB of RAM, with the framework of Python 3.7.4, Tensorflow 2.0.0, and Pyswarm 1.3.0.In addition, another interesting observation is that among all baseline models, GatNE outperforms others in terms of all evaluation metrics. Such observation shows that GatNE considers different attributed information of nodes in spatial graphs of spatial entities. Thus, the spatial embedding features learned by GatNE are more effective compared with other baseline models. Moreover, after further observing R+P+C)( > AutoFTPR+P)( > AutoFTPR+C), AutoFTP captures more characteristics of spatial entities from representation learning, point-wise alignment, pair-wise alignment, and regression task. In addition, compared with AutoFTPR+P)( and AutoFTPR+C)( is better than AutoFTPR+C) 497, 1,043, 1,126, and 1,232 as examples to plot their corresponding embedding vectors against their corresponding topic vectors. Meanwhile, we extracted the topic names of the most significant 6 topics. To observe the pair-wise alignment, we visualized the pair-wise topic similarity matrix and pair-wise feature matrix by heat map respectively. As illustrated in The results of To evaluate the robustness of AutoFTP, we divided the embeddings into 5 groups  according to the geographical district of spatial entities. K  for the model performance and the training time. The same to K affects the model performance heavily. The observation is reasonable because K determines the information content of the final learned embeddings. The plots in K is, the longer the training time is. A potential reason for the observation is that the larger K means that we need to try more topic subsets for feature-topic pairing.In this section, we fully evaluated the stability and parameter sensitivity of AutoFTP. We first examined the stability of AutoFTP by analyzing the training losses of AutoFTP and convergence of PSO optimization part. To observe the changing trend of each loss objectively, we scaled the value of losses into [0 ∼ 1] and visualized them in Graph Representation Learning with Latent Semantics. Graph representation learning refers to techniques that preserve the structural information of a graph into a low-dimensional vector . HoweverTopic Models in Spatio-temporal Domain. Topic models aim to automatically cluster words and expressions patterns for characterizing documents . RecentlExplainable Artificial Intelligence (XAI) With artificial intelligence methods are applied in multiple scenarios successfully, how to improve the model explainability becomes a big challenge. In the traditional machine learning domain, researchers employ some simple models that own the explainability naturally such as linear models, decision trees, rule-based models, and etc to explain the modeling process . For insComparison with Prior Literature As an emerging feature extraction technique, deep SRL has demonstrated the power in automated geographic and spatial feature extraction. However, SRL inherits drawbacks of traditional DNNs, such as: the embedding feature space lacks semantic interpretation. Texts can provide more interpretation, but spatial text mining has developed separately. Now, there is cross and increasing interests in both fields to benefit from the advances of the other. Our study targets at an unexplored area at the intersection between representation learning in geospatial data and topic label mining in texts. We develop and formulate a new problem: feature-topic pairing, to address the alignment challenges of the feature embedding space and the semantic topic space. The self-optimizing solution unifies representation learning, topic label selection, feature-topic matching in a PSO framework. This framework can be generalized to other integrated tasks, such as, representation learning integrated with not just topic based selection, but also causal selection, or other constrained selection over features, in various application senarios. This is how this study differentiates from and advances prior literature.We presented a novel spatial representation learning (SRL) framework, namely AutoFTP. The spatial embeddings produced by traditional SRL models lack semantic meaning. To overcome this limitation, we formulated the feature-topic paring problem. We proposed a novel deep learning framework to unify representation learning, topic label selection, and feature-topic pairing. Specifically, we designed a segmentation-embedding-clustering method to generate candidate feature topic labels from texts. We developed an integrated measurement to measure the pointwise and pairwise alignment between topic label and embedding feature space. We devised a PSO based optimization algorithm to effectively solve the joint task of feature learning and feature-topic pairing. Our method integrated spatial graphs and associated texts to learn effective embedding features with visible labels. Extensive experiments demonstrated the effectiveness of AutoFTP by comparing it with other baseline models. The topic labels of the learned features were shown by many case studies and the feature importance analysis of a downstream task. For future work, we plan to extend our approach from geospatial networks to other applications that consist of graphs and texts, such as social media and software code safety.