Multiple Stimuli-Responsive Color-Changing Polymer Materials for Reversible Writing and Anti-Counterfeiting.

Polymer materials with multiple stimuli-responsive properties have demonstrated many potential and practical applications. By covalently introducing spiropyran (SP1) and spirothiopyran (STP) into the polyurethane backbone, photochromic, mechanochromic, and thermally discolored polymer materials have been prepared. In this work, we report for the first time that white light (violet, blue, and green light) above a certain intensity can activate STP to green color. Based on the above discovery, the polyurethane with SP1 and STP can exhibit reversible three-color changes (brown, green, and purple) in response to four stimuli: ultraviolet irradiation, white light irradiation, mechanical stress, and heat. The color-changing polymer materials have high color contrast and excellent reversibility, and can be used for reversible writing, anticounterfeiting and information encryption, etc.

Foundation models in molecular biology.

Determining correlations between molecules at various levels is an important topic in molecular biology. Large language models have demonstrated a remarkable ability to capture correlations from large amounts of data in the field of natural language processing as well as image generation, and correlations captured from data using large language models can also be applicable to solving a wide range of specific tasks, hence large language models are also referred to as foundation models. The massive amount of data that exists in the field of molecular biology provides an excellent basis for the development of foundation models, and the recent emergence of foundation models in the field of molecular biology has really pushed the entire field forward. We summarize the foundation models developed based on RNA sequence data, DNA sequence data, protein sequence data, single-cell transcriptome data, and spatial transcriptome data respectively, and further discuss the research directions for the development of foundation models in molecular biology.

The origin of exceptionally large ductility in molybdenum alloys dispersed with irregular-shaped La2O3 nano-particles.

Molybdenum and its alloys are known for their superior strength among body-centered cubic materials. However, their widespread application is hindered by a significant decrease in ductility at lower temperatures. In this study, we demonstrate the achievement of exceptional ductility in a Mo alloy containing rare-earth La2O3 nanoparticles through rotary-swaging, a rarity in Mo-based materials. Our analysis reveals that the large ductility originates from substantial variations in the electronic density of states, a characteristic intrinsic to rare-earth elements. This characteristic can accelerate the generation of oxygen vacancies, facilitating the amorphization of the oxide-matrix interface. This process promotes vacancy absorption and modification of dislocation configurations. Furthermore, by inducing irregular shapes in the La2O3 nanoparticles through rotary-swaging, incoming dislocations interact with them, creating multiple dislocation sources near the interface. These dislocation sources act as potent initiators at even reduced temperatures, fostering diverse dislocation types and intricate networks, ultimately enhancing dislocation plasticity.

Interleukin-1ß polarization in M1 macrophage mediates myocardial fibrosis in diabetes.

BACKGROUND: Diabetes is a global health problem whose common complication is diabetic cardiomyopathy, characterized by chronic inflammation of the heart muscle. Macrophages are the main white blood cells found in the resting heart. Therefore, we investigated the underling mechanism of macrophage on myocardial fibrosis in diabetes. METHODS: Here, echocardiography was utilized to evaluate cardiac function, and the degree of myocardial fibrosis was assessed using Masson's trichrome staining, followed by single-cell RNA sequencing (scRNA-seq) to analyze the phenotype, function, developmental trajectory, and interactions between immune cells, endothelial cells (ECs), and fibroblasts (FBs) in the hearts of db/db mice at different stages of diabetes. Macrophages and cardiac fibroblasts were also co-cultured in order to study the signaling between macrophages and fibroblasts. RESULTS: We found that with the development of diabetes mellitus, myocardial hypertrophy and fibrosis occurred that was accompanied by cardiac dysfunction. A significant proportion of immune cells, endothelial cells, and fibroblasts were identified by RNA sequencing. The most significant changes observed were in macrophages, which undergo M1 polarization and are critical for oxidative stress and extracellular matrix (ECM) formation. We further found that M1 macrophages secreted interleukin-1ß (IL-1ß), which interacted with the receptor on the surface of fibroblasts, to cause myocardial fibrosis. In addition, crosstalk between M1 macrophages and endothelial cells also plays a key role in fibrosis and immune response regulation through IL-1ß and corresponding receptors. CONCLUSIONS: M1 macrophages mediate diabetic myocardial fibrosis through interleukin-1ß interaction with fibroblasts.

Advances in amelioration of plasma electrolytic oxidation coatings on biodegradable magnesium and alloys.

Magnesium and its alloys are considered excellent materials for biodegradable implants because of their good biocompatibility and biodegradability as well as their mechanical properties. However, the rapid degradation rate severely limits their clinical applications. Plasma electrolytic oxidation (PEO), also known as micro-arc oxidation (MAO), is an effective surface modification technique. However, there are many pores and cracks on the coating surface under conventional PEO process. The corrosive products tend to penetrate deeply into the substrate, reducing its corrosion resistance and the biocompatibility, which makes PEO-coated Mg difficult to meet the long-term needs of in vivo implants. Hence, it is necessary to modify the PEO coating. This review discusses the formation mechanism and the influential parameters of PEO coatings on Mg. This is followed by a review of the latest research of the pretreatment and typical amelioration of PEO coating on biodegradable Mg alloys in the past 5 years, including calcium phosphate (Ca-P) coating, layered double hydroxide (LDH)-PEO coating, ZrO2 incorporated-PEO coating, antibacterial ingredients-PEO coating, drug-PEO coating, polymer-PEO composite coating, Plasma electrolytic fluorination (PEF) coating and self-healing coating. Meanwhile, the improvements of morphology, corrosion resistance, wear resistance, biocompatibility, antibacterial abilities, and drug loading abilities and the preparation methods of the modified PEO coatings are deeply discussed as well. Finally, the challenges and prospects of PEO coatings are discussed in detail for the purpose of promoting the clinical application of biodegradable Mg alloys.

The development of a novel bio-based corrosion inhibitor: using biomass-derived 5-hydroxymethylfurfural (5-HMF) as a starting material.

An environmentally friendly corrosion inhibitor was prepared from the bio-based platform 5-hydroxymethylfurfural. This corrosion inhibitor was confirmed to be an efficient mixed-type corrosion inhibitor through a weight loss experiment and electrochemical experiment. Both thermodynamic and kinetic parameters were calculated and discussed, indicating that the adsorption of this bio-based inhibitor on a steel surface is a chemisorption process. Moreover, quantum chemical calculations were performed and further confirmed the formation of an effective productive film of this bio-based inhibitor on the metal surface. It is worth noting that the synthesis route of this bio-based corrosion inhibitor is green and environmentally friendly, and does not involve toxic chemical reagents.

Dynamic Homogenization of Internal Strain in Multi-Principal Element Alloy via High-Concentration Doping of Oxygen with Large Mobility.

Internal strain and its distribution within the crystal lattice play crucial roles in modulating dislocation activities, thereby affecting mechanical properties of materials. Through the synergistic application of integrated differential phase contrast, in situ transmission electron microscopy characterizations, and computational simulations, a method is unveiled for homogenizing dislocation pinning in NiCoCr multi-principal element alloy (MPEA) through the introduction of a high concentration of oxygen atoms with high diffusion mobility. The doping of massive oxygen atoms creates a high density of strong local pinning points for dislocation motion. Notably, oxygen interstitials exhibit remarkable diffusion and mobility across different octahedral and tetrahedral sites within the distorted crystal lattice of NiCoCrO alloy, even at room temperature. The capability allows for the release of severe stress concentrations arising from dislocation entanglement and the establishment of new strong local pinning points at alternative locations in a uniform way, enabling the material with high strength and outstanding deformability. These findings suggest that interstitial atoms can exhibit significant mobility, even at ambient temperature, in complex MPEAs with spreading lattice distortion, opening new possibilities for dislocation engineering.

Construction of Host Plant Insect-Resistance Mutant Library by High-Throughput CRISPR/Cas9 System and Identification of A Broad-Spectrum Insect Resistance Gene.

Insects pose significant challenges in cotton-producing regions. Here, they describe a high-throughput CRISPR/Cas9-mediated large-scale mutagenesis library targeting endogenous insect-resistance-related genes in cotton. This library targeted 502 previously identified genes using 968 sgRNAs, generated ≈2000 T0 plants and achieved 97.29% genome editing with efficient heredity, reaching upto 84.78%. Several potential resistance-related mutants (10% of 200 lines) their identified that may contribute to cotton-insect molecular interaction. Among these, they selected 139 and 144 lines showing decreased resistance to pest infestation and targeting major latex-like protein 423 (GhMLP423) for in-depth study. Overexpression of GhMLP423 enhanced insect resistance by activating the plant systemic acquired resistance (SAR) of salicylic acid (SA) and pathogenesis-related (PR) genes. This activation is induced by an elevation of cytosolic calcium [Ca2+ ]cyt flux eliciting reactive oxygen species (ROS), which their demoted in GhMLP423 knockout (CR) plants. Protein-protein interaction assays revealed that GhMLP423 interacted with a human epidermal growth factor receptor substrate15 (EPS15) protein at the cell membrane. Together, they regulated the systemically propagating waves of Ca2+ and ROS, which in turn induced SAR. Collectively, this large-scale mutagenesis library provides an efficient strategy for functional genomics research of polyploid plant species and serves as a solid platform for genetic engineering of insect resistance.

scCURE identifies cell types responding to immunotherapy and enables outcome prediction.

A deep understanding of immunotherapy response/resistance mechanisms and a highly reliable therapy response prediction are vital for cancer treatment. Here, we developed scCURE (single-cell RNA sequencing [scRNA-seq] data-based Changed and Unchanged cell Recognition during immunotherapy). Based on Gaussian mixture modeling, Kullback-Leibler (KL) divergence, and mutual nearest-neighbors criteria, scCURE can faithfully discriminate between cells affected or unaffected by immunotherapy intervention. By conducting scCURE analyses in melanoma and breast cancer immunotherapy scRNA-seq data, we found that the baseline profiles of specific CD8+ T and macrophage cells (identified by scCURE) can determine the way in which tumor microenvironment immune cells respond to immunotherapy, e.g., antitumor immunity activation or de-activation; therefore, these cells could be predictive factors for treatment response. In this work, we demonstrated that the immunotherapy-associated cell-cell heterogeneities revealed by scCURE can be utilized to integrate the therapy response mechanism study and prediction model construction.

Chicken manure application alters microbial community structure and the distribution of antibiotic-resistance genes in rhizosphere soil of Cinnamomum camphora forests.

The distribution of antibiotic-resistance genes (ARGs) in environmental soil is greatly affected by livestock and poultry manure fertilization, the application of manure will lead to antibiotic residues and ARGs pollution, and increase the risk of environmental pollution and human health. Cinnamomum camphora is an economically significant tree species in Fujian Province, China. Here, through high-throughput sequencing analysis, significant differences in the composition of the bacterial community and ARGs were observed between fertilized and unfertilized rhizosphere soil. The application of chicken manure organic fertilizer significantly increased the relative abundance and alpha diversity of the bacterial community and ARGs. The content of organic matter, soluble organic nitrogen, available phosphorus, nitrate reductase, hydroxylamine reductase, urease, acid protease, ß-glucosidase, oxytetracycline, and tetracycline in the soil of C. camphora forests have significant effects on bacterial community and ARGs. Significant correlations between environmental factors, bacterial communities, and ARGs were observed in the rhizosphere soil of C. camphora forests according to Mantel tests. Overall, the findings of this study revealed that chicken manure organic fertilizer application has a significant effect on the bacterial community and ARGs in the rhizosphere soil of C. camphora forests, and several environmental factors that affect the bacterial community and ARGs were identified.

A chemotherapy response prediction model derived from tumor-promoting B and Tregs and proinflammatory macrophages in HGSOC.

Background: Most patients with high-grade serous ovarian cancer (HGSOC) experienced disease recurrence with cumulative chemoresistance, leading to treatment failure. However, few biomarkers are currently available in clinical practice that can accurately predict chemotherapy response. The tumor immune microenvironment is critical for cancer development, and its transcriptomic profile may be associated with treatment response and differential outcomes. The aim of this study was to develop a new predictive signature for chemotherapy in patients with HGSOC. Methods: Two HGSOC single-cell RNA sequencing datasets from patients receiving chemotherapy were reinvestigated. The subtypes of endoplasmic reticulum stress-related XBP1+ B cells, invasive metastasis-related ACTB+ Tregs, and proinflammatory-related macrophage subtypes with good predictive power and associated with chemotherapy response were identified. These results were verified in an independent HGSOC bulk RNA-seq dataset for chemotherapy. Further validation in clinical cohorts used quantitative real-time PCR (qRT-PCR). Results: By combining cluster-specific genes for the aforementioned cell subtypes, we constructed a chemotherapy response prediction model containing 43 signature genes that achieved an area under the receiver operator curve (AUC) of 0.97 (p = 2.1e-07) for the GSE156699 cohort (88 samples). A huge improvement was achieved compared to existing prediction models with a maximum AUC of 0.74. In addition, its predictive capability was validated in multiple independent bulk RNA-seq datasets. The qRT-PCR results demonstrate that the expression of the six genes has the highest diagnostic value, consistent with the trend observed in the analysis of public data. Conclusions: The developed chemotherapy response prediction model can be used as a valuable clinical decision tool to guide chemotherapy in HGSOC patients.

scSTAR reveals hidden heterogeneity with a real-virtual cell pair structure across conditions in single-cell RNA sequencing data.

Cell-state transition can reveal additional information from single-cell ribonucleic acid (RNA)-sequencing data in time-resolved biological phenomena. However, most of the current methods are based on the time derivative of the gene expression state, which restricts them to the short-term evolution of cell states. Here, we present single-cell State Transition Across-samples of RNA-seq data (scSTAR), which overcomes this limitation by constructing a paired-cell projection between biological conditions with an arbitrary time span by maximizing the covariance between two feature spaces using partial least square and minimum squared error methods. In mouse ageing data, the response to stress in CD4+ memory T cell subtypes was found to be associated with ageing. A novel Treg subtype characterized by mTORC activation was identified to be associated with antitumour immune suppression, which was confirmed by immunofluorescence microscopy and survival analysis in 11 cancers from The Cancer Genome Atlas Program. On melanoma data, scSTAR improved immunotherapy-response prediction accuracy from 0.8 to 0.96.

Analysis of the role of BrRPP1 gene in Chinese cabbage infected by Plasmodiophora brassicae.

Introduction: The clubroot disease caused by Plasmodiophora brassicae (P. brassicae) poses a serious threat to the economic value of cruciferous crops, which is a serious problem to be solved worldwide. Some resistance genes to clubroot disease in Brassica rapa L. ssp pekinensis cause by P. brassicae have been located on different chromosomes. Among them, Rcr1 and Rcr2 were mapped to the common candidate gene Bra019410, but its resistance mechanism is not clear yet. Methods: In this experiment, the differences of BrRPP1 between the resistant and susceptible material of Chinese cabbage were analyzed by gene cloning and qRT-PCR. The gene function was verified by Arabidopsis homologous mutants. The expression site of BrRPP1 gene in cells was analyzed by subcellular localization. Finally, the candidate interaction protein of BrRPP1 was screened by yeast two-hybrid library. Results: The results showed that the cDNA sequence, upstream promoter sequence and expression level of BrRPP1 were quite different between the resistant and susceptible material. The resistance investigation found that the Arabidopsis mutant rpp1 was more susceptible to clubroot disease than the wild type, which suggested that the deletion of rpp1 reduces resistance of plant to clubroot disease. Subcellular location analysis confirmed that BrRPP1 was located in the nucleus. The interaction proteins of BrRPP1 screened from cDNA Yeast Library by yeast two-hybrid are mainly related to photosynthesis, cell wall modification, jasmonic acid signal transduction and programmed cell death. Discussion: BrRPP1 gene contains TIR-NBS-LRR domain and belongs to R gene. The cDNA and promoter sequence of BrRPP1 in resistant varieties was different from that in susceptible varieties led to the significant difference of the gene expression of BrRPP1 between the resistant varieties and the susceptible varieties. The high expression of BrRPP1 gene in resistant varieties enhanced the resistance of Chinese cabbage to P. brassicae, and the interaction proteins of BrRPP1 are mainly related to photosynthesis, cell wall modification, jasmonic acid signal transduction and programmed cell death. These results provide important clues for understanding the mechanism of BrRPP1 in the resistance of B. rapa to P. brassicae.

Optimization of Alkyl Side Chain Length in Polyimide for Gate Dielectrics to Achieve High Mobility and Outstanding Operational Stability in Organic Transistors.

Alkyl chain modification strategies in both organic semiconductors and inorganic dielectrics play a crucial role in improving the performance of organic thin-film transistors (OTFTs). Polyimide (PI) and its derivatives have received extensive attention as dielectrics for application in OTFTs because of flexibility, high-temperature resistance, and low cost. However, low-temperature solution processing PI-based gate dielectric for flexible OTFTs with high mobility, low operating voltage, and high operational stability remains an enormous challenge. Furthermore, even though di-n-decyldinaphtho[2,3-b:2',3'-f]thieno[3,2-b]thiophene (C10-DNTT) is known to have very high mobility as an air-stable and high-performance organic semiconductor, the C10-DNTT-based TFTs on the PI gate dielectrics still showed relatively low mobility. Here, inspired by alkyl side chain engineering, we design and synthesize a series of PI materials with different alkyl side chain lengths and systematically investigate the PI surface properties and the evolution of organic semiconductor morphology deposited on PI surfaces during the variation of alkyl side chain lengths. It is found that the alkyl side chain length has a critical influence on the PI surface properties, as well as the grain size and molecular orientation of semiconductors. Good field-effect characteristics are obtained with high mobilities (up to 1.05 and 5.22 cm2/Vs, which are some of the best values reported to date), relatively low operating voltage, hysteresis-free behavior, and high operational stability in OTFTs. These results suggest that the strategy of optimizing alkyl side-chain lengths opens up a new research avenue for tuning semiconductor growth to enable high mobility and outstanding operational stability of PI-based OTFTs.

A two-gene random forest model to diagnose osteoarthritis based on RNA-binding protein-related genes in knee cartilage tissue.

Osteoarthritis (OA) is one of the most common diseases in the orthopedic clinic, characterized by progressive cartilage degradation. RNA-binding proteins (RBPs) are capable of binding to RNAs at transcription and translation levels, playing an important role in the pathogenesis of OA. This study aims to investigate the diagnosis values of RBP-related genes in OA. The RBPs were collected from previous studies, and the GSE114007 dataset (control = 18, OA = 20) was downloaded from the Gene Expression Omnibus (GEO) as the training cohort. Through various bioinformatical and machine learning methods, including genomic difference detection, protein-protein interaction network analyses, Lasso regression, univariate logistic regression, Boruta algorithm, and SVM-RFE, RNMT and RBM24 were identified and then included into the random forest (RF) diagnosis model. GSE117999 dataset (control = 10, OA = 10) and clinical samples collected from local hospital (control = 10, OA = 11) were used for external validation. The RF model was a promising tool to diagnose OA in the training dataset (area under curve [AUC] = 1.000, 95% confidence interval [CI] = 1.000-1.000), the GSE117999 cohort (AUC = 0.900, 95% CI = 0.769-1.000), and local samples (AUC = 0.759, 95% CI = 0.568-0.951). Besides, qPCR and Western Blotting experiments showed that RNMT (P < 0.05) and RBM24 (P < 0.01) were both down-regulated in CHON-001 cells with IL-1ß treatment. In all, an RF model to diagnose OA based on RNMT and RBM24 in cartilage tissue was constructed, providing a promising clinical tool and possible cut-in points in molecular mechanism clarification.

Contrast-enhanced ultrasound of granular cell tumor in breast: A case report with review of the literature.

Granular cell tumor is an infrequent, predominantly benign tumor originating from Schwann cells. Granular cell tumor of the breast (GCTB) can simulate breast malignant carcinoma on the clinical assessment. We herein present a rare case of GCTB which recurred in the contralateral breast. We believe the contrast-enhanced ultrasound (CEUS) findings of GCTB have never been described. The high similarity of breast malignant carcinoma makes its differential diagnosis difficult on the clinical and radiological features. In this report, we present the CEUS findings from a rare case of GCTB, explore the possible value of CEUS in differential diagnosis between benign breast lesions and malignant ones, and briefly review the literature.

Novel lncRNAs with diagnostic or prognostic value screened out from breast cancer via bioinformatics analyses.

Background: Recent studies have shown that long non-coding RNAs (lncRNAs) may play key regulatory roles in many malignant tumors. This study investigated the use of novel lncRNA biomarkers in the diagnosis and prognosis of breast cancer. Materials and Methods: The database subsets of The Cancer Genome Atlas (TCGA) by RNA-seq for comparing analysis of tissue samples between breast cancer and normal control groups were downloaded. Additionally, anticoagulant peripheral blood samples were collected and used in this cohort study. The extracellular vesicles (EVs) from the plasma were extracted and sequenced, then analyzed to determine the expressive profiles of the lncRNAs, and the cancer-related differentially expressed lncRNAs were screened out. The expressive profiles and associated downstream-mRNAs were assessed using bioinformatics (such as weighted correlation network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) enrichments, Receiver-Operating Characteristic (ROC) curve and survival analysis, etc.) to investigate the diagnostic and prognostic values of these EV lncRNAs and their effectors. Results: In this study, 41 breast cancer-related lncRNAs were screen out from two datasets of tissue and fresh collected plasma samples of breast cancer via the transcriptomic and bioinformatics techniques. A total of 19 gene modules were identified with WGCNA analysis, of which five modules were significantly correlated with the clinical stage of breast cancer, including 28 lncRNA candidates. The ROC curves of these lncRNAs revealed that the area under the curve (AUC) of all candidates were great than 70%. However, eight lncRNAs had an AUC >70%, indicating that the combined one has a good diagnostic value. In addition, the results of survival analysis suggested that two lncRNAs with low expressive levels may indicate the poor prognosis of breast cancer. By tissue sample verification, C15orf54, AL157935.1, LINC01117, and SNHG3 were determined to have good diagnostic ability in breast cancer lesions, however, there was no significant difference in the plasma EVs of patients. Moreover, survival analysis data also showed that AL355974.2 may serve as an independent prognostic factor and as a protective factor. Conclusion: A total of five lncRNAs found in this study could be developed as biomarkers for breast cancer patients, including four diagnostic markers (C15orf54, AL157935.1, LINC01117, and SNHG3) and a potential prognostic marker (AL355974.2).

Discovery of a Novel Small-Molecule Inhibitor Disrupting TRBP-Dicer Interaction against Hepatocellular Carcinoma via the Modulation of microRNA Biogenesis.

MicroRNAs (miRNAs) are key players in human hepatocellular carcinoma (HCC) tumorigenesis. Therefore, small molecules targeting components of miRNA biogenesis may provide new therapeutic means for HCC treatment. By a high-throughput screening and structural simplification, we identified a small molecule, CIB-3b, which suppresses the growth and metastasis of HCC in vitro and in vivo by modulating expression profiles of miRNAome and proteome in HCC cells. Mechanistically, CIB-3b physically binds to transactivation response (TAR) RNA-binding protein 2 (TRBP) and disrupts the TRBP-Dicer interaction, thereby altering the activity of Dicer and mature miRNA production. Structure-activity relationship study via the synthesis of 45 CIB-3b derivatives showed that some compounds exhibited a similar inhibitory effect on miRNA biogenesis to CIB-3b. These results support TRBP as a potential therapeutic target in HCC and warrant further development of CIB-3b along with its analogues as a novel therapeutic strategy for the treatment of HCC.

scCODE: an R package for data-specific differentially expressed gene detection on single-cell RNA-sequencing data.

Differential expression (DE) gene detection in single-cell ribonucleic acid (RNA)-sequencing (scRNA-seq) data is a key step to understand the biological question investigated. Filtering genes is suggested to improve the performance of DE methods, but the influence of filtering genes has not been demonstrated. Furthermore, the optimal methods for different scRNA-seq datasets are divergent, and different datasets should benefit from data-specific DE gene detection strategies. However, existing tools did not take gene filtering into consideration. There is a lack of metrics for evaluating the optimal method on experimental datasets. Based on two new metrics, we propose single-cell Consensus Optimization of Differentially Expressed gene detection, an R package to automatically optimize DE gene detection for each experimental scRNA-seq dataset.

Identification and Functional Analysis of lncRNA by CRISPR/Cas9 During the Cotton Response to Sap-Sucking Insect Infestation.

Sap-sucking insects cause severe damage to cotton production. Long non-coding RNAs (lncRNAs) play vital regulatory roles in various development processes and stress response, however, the function of lncRNAs during sap-sucking insect infection in cotton is largely unknown. In this study, the transcriptome profiles between resistant (HR) and susceptible (ZS) cotton cultivars under whitefly infestation at different time points (0, 4, 12, 24, and 48 h) were compared. A total of 6,651 lncRNAs transcript and 606 differentially expressed lncRNAs were identified from the RNA-seq data. A co-expression network indicated that lncA07 and lncD09 were potential hub genes that play a regulatory role in cotton defense against aphid infestation. Furthermore, CRISPR/Cas9 knock-out mutant of lncD09 and lncA07 showed a decrease of jasmonic acid (JA) content, which potentially lead to increased susceptibility toward insect infestation. Differentially expressed genes between wild type and lncRNA knock-out plants are enriched in modulating development and resistance to stimulus. Additionally, some candidate genes such as Ghir_A01G022270, Ghir_D04G014430, and Ghir_A01G022270 are involved in the regulation of the JA-mediated signaling pathway. This result provides a novel insight of the lncRNA role in the cotton defense system against pests.