IDR-targeting compounds suppress HPV genome replication via disruption of phospho-BRD4 association with DNA damage response factors.

Compounds binding to the bromodomains of bromodomain and extra-terminal (BET) family proteins, particularly BRD4, are promising anticancer agents. Nevertheless, side effects and drug resistance pose significant obstacles in BET-based therapeutics development. Using high-throughput screening of a 200,000-compound library, we identified small molecules targeting a phosphorylated intrinsically disordered region (IDR) of BRD4 that inhibit phospho-BRD4 (pBRD4)-dependent human papillomavirus (HPV) genome replication in HPV-containing keratinocytes. Proteomic profiling identified two DNA damage response factors-53BP1 and BARD1-crucial for differentiation-associated HPV genome amplification. pBRD4-mediated recruitment of 53BP1 and BARD1 to the HPV origin of replication occurs in a spatiotemporal and BRD4 long (BRD4-L) and short (BRD4-S) isoform-specific manner. This recruitment is disrupted by phospho-IDR-targeting compounds with little perturbation of the global transcriptome and BRD4 chromatin landscape. The discovery of these protein-protein interaction inhibitors (PPIi) not only demonstrates the feasibility of developing PPIi against phospho-IDRs but also uncovers antiviral agents targeting an epigenetic regulator essential for virus-host interaction and cancer development.

Molecular Dynamics Simulation of Dual Nanodroplet Impacts on a Cylindrical Surface.

Droplet impact behavior is ubiquitous in various fields. However, the dynamics and spreading mechanisms of micro- and nanoscale droplet impact on curved surfaces, particularly in the case of multiple droplets, have yet to be fully elucidated. In this study, molecular dynamics (MD) methods are employed to investigate the dynamic evolution of double nanodroplet impact on a nano cylindrical wall. The effects of droplet spacing, initial impact velocity, and wall wettability on droplet impact characteristics are analyzed. The results demonstrate that there are five impact modes of nanoscale double-droplet impacts with nanocylinders: spreading-partial wrapping-splitting-complete detachment (SPSC), spreading-complete wrapping-complete attachment (SCC), spreading-partial wrapping-complete attachment (SPC), spreading-partial wrapping-partial attachment (SPP), and spreading-partial wrapping-fragmentation-partial attachment (SPFP). The droplet spacing has an insignificant effect on the impact modes but affects the droplets' spreading shape in both the axial and radial directions. The initial velocity and wall wettability have significant impacts on the droplet impact modes and liquid film spreading characteristics. As the initial velocity increases, the liquid film's radial and axial spreading distances gradually increase. Under hydrophobic conditions, the spreading of the droplet is dominant in the radial direction, while under hydrophilic conditions, the spreading is dominant in the axial direction. Properly reducing the droplet spacing, increasing the impact velocity, and enhancing the wall hydrophobicity can promote detaching the droplet from the cylindrical wall.

Prediction of Human Liver Microsome Clearance with Chirality-Focused Graph Neural Networks.

In drug candidate design, clearance is one of the most crucial pharmacokinetic parameters to consider. Recent advancements in machine learning techniques coupled with the growing accumulation of drug data have paved the way for the construction of computational models to predict drug clearance. However, concerns persist regarding the reliability of data collected from public sources, and a majority of current in silico quantitative structure-property relationship models tend to neglect the influence of molecular chirality. In this study, we meticulously examined human liver microsome (HLM) data from public databases and constructed two distinct data sets with varying HLM data quantity and quality. Two baseline models (RF and DNN) and three chirality-focused GNNs (DMPNN, TetraDMPNN, and ChIRo) were proposed, and their performance on HLM data was evaluated and compared with each other. The TetraDMPNN model, which leverages chirality from 2D structure, exhibited the best performance with a test R2 of 0.639 and a test root-mean-squared error of 0.429. The applicability domain of the model was also defined by using a molecular similarity-based method. Our research indicates that graph neural networks capable of capturing molecular chirality have significant potential for practical application and can deliver superior performance.

Constructing Innovative Covalent and Noncovalent Compound Libraries: Insights from 3D Protein-Ligand Interactions.

Noncovalent interactions between small-molecule drugs and protein targets assume a pivotal role in drug design. Moreover, the design of covalent inhibitors, forming covalent bonds with amino acid residues, requires rational reactivity for their covalent warheads, presenting a key challenge as well. Understanding the intricacies of these interactions provides a more comprehensive understanding of molecular binding mechanisms, thereby guiding the rational design of potent inhibitors. In this study, we adopted the fragment-based drug design approach, introducing a novel methodology to extract noncovalent and covalent fragments according to distinct three-dimensional (3D) interaction modes from noncovalent and covalent compound libraries. Additionally, we systematically replaced existing ligands with rational fragment substitutions, based on the spatial orientation of fragments in 3D space. Furthermore, we adopted a molecular generation approach to create innovative covalent inhibitors. This process resulted in the recombination of a noncovalent compound library and several covalent compound libraries, constructed by two commonly encountered covalent amino acids: cysteine and serine. We utilized noncovalent ligands in KLIFS and covalent ligands in CovBinderInPDB as examples to recombine noncovalent and covalent libraries. These recombined compound libraries cover a substantial portion of the chemical space present in the original compound libraries and exhibit superior performance in terms of molecular scaffold diversity compared to the original compound libraries and other 11 commercial libraries. We also recombined BTK-focused libraries, and 23 compounds within our libraries have been validated by former researchers to possess potential biological activity. The establishment of these compound libraries provides valuable resources for virtual screening of covalent and noncovalent drugs targeting similar molecular targets.

M1/M4 receptors as potential therapeutic treatments for schizophrenia: A comprehensive study.

Muscarinic acetylcholine receptors (mAChRs) play a significant role in the pathophysiology of schizophrenia. Although activating mAChRs holds potential in addressing the full range of schizophrenia symptoms, clinical application of many non-selective mAChR agonists in cognitive deficits, positive and negative symptoms is hindered by peripheral side effects (gastrointestinal disturbances and cardiovascular effects) and dosage restrictions. Ligands binding to the allosteric sites of mAChRs, particularly the M1 and M4 subtypes, demonstrate activity in improving cognitive function and amelioration of positive and negative symptoms associated with schizophrenia, enhancing our understanding of schizophrenia. The article aims to critically examine current design concepts and clinical advancements in synthesizing and designing small molecules targeting M1/M4, providing theoretical insights and empirical support for future research in this field.

Discovery of novel biphenyl derivatives as androgen receptor degraders for the treatment of enzalutamide-resistant prostate cancer.

Second-generation AR antagonists, such as enzalutamide, are the primary therapeutic agents for advanced prostate cancer. However, the development of both primary and secondary drug resistance leads to treatment failures and patient mortality. Bifunctional agents that simultaneously antagonize and degrade AR block the AR signaling pathway more completely and exhibit excellent antiproliferative activity against wild-type and drug-resistant prostate cancer cells. Here, we reported the discovery and optimization of a series of biphenyl derivatives as androgen receptor antagonists and degraders. These biphenyl derivatives exhibited potent antiproliferative activity against LNCaP and 22Rv1 cells. Our discoveries enrich the diversity of small molecule AR degraders and offer insights for the development of novel AR degraders for the treatment of enzalutamide-resistant prostate cancer.

Elucidating CTLA-4's role in tumor immunity: a comprehensive overview of targeted antibody therapies and clinical developments.

Cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) emerges as a key single-chain transmembrane glycoprotein predominantly expressed in effector T cells and regulatory T cells. It plays a crucial role in tumor immunity by modulating T cell responses. Specifically, CTLA-4 dampens T cell activation and proliferation while bolstering the survival of regulatory T cell through its competitive interaction with B7 family molecules, thereby aiding tumor cells in eluding immune detection. Given CTLA-4's significant influence on tumor immune dynamics, an array of anti-CTLA-4 antibody therapeutics have been clinically developed to combat various malignancies, including melanoma, renal cell carcinoma, colorectal carcinoma, hepatocellular carcinoma, non-small cell lung carcinoma, and pleural mesothelioma, demonstrating notable clinical therapeutic effects. This paper aims to delve into CTLA-4's integral role in tumor immunity and to encapsulate the latest advancements in the clinical research of anti-CTLA-4 antibody.

Recent advances in targeted protein degraders as potential therapeutic agents.

Targeted protein degradation (TPD) technology has gradually become widespread in the past 20 years, which greatly boosts the development of disease treatment. Contrary to small inhibitors that act on protein kinases, transcription factors, ion channels, and other targets they can bind to, targeted protein degraders could target "undruggable targets" and overcome drug resistance through ubiquitin-proteasome pathway (UPP) and lysosome pathway. Nowadays, some bivalent degraders such as proteolysis-targeting chimeras (PROTACs) have aroused great interest in drug discovery, and some of them have successfully advanced into clinical trials. In this review, to better understand the mechanism of degraders, we elucidate the targeted protein degraders according to their action process, relying on the ubiquitin-proteasome system or lysosome pathway. Then, we briefly summarize the study of PROTACs employing different E3 ligases. Subsequently, the effect of protein of interest (POI) ligands, linker, and E3 ligands on PROTAC degradation activity is also discussed in detail. Other novel technologies based on UPP and lysosome pathway have been discussed in this paper such as in-cell click-formed proteolysis-targeting chimeras (CLIPTACs), molecular glues, Antibody-PROTACs (Ab-PROTACs), autophagy-targeting chimeras, and lysosome-targeting chimeras. Based on the introduction of these degradation technologies, we can clearly understand the action process and degradation mechanism of these approaches. From this perspective, it will be convenient to obtain the development status of these drugs, choose appropriate degradation methods to achieve better disease treatment and provide basis for future research and simultaneously distinguish the direction of future research efforts.

BYL719 reverses gefitinib-resistance induced by PI3K/AKT activation in non-small cell lung cancer cells.

Non-small cell lung cancer (NSCLC) patients with epidermal growth factor receptor (EGFR) mutation often obtain de novo resistance or develop secondary resistance to EGFR tyrosine kinase inhibitors (EGFR-TKIs), which restricts the clinical benefit for the patients. The activation of phosphatidylinositol 3-kinase (PI3K)/AKT signal pathway is one of the most important mechanisms for the EGFR-TKIs resistance beyond T790M mutation. There are currently no drugs simultaneously targeting EGFR and PI3K signal pathways, and combination of these two pathway inhibitors may be a possible strategy to reverse theses resistances. To test whether this combinational strategy works, we investigated the therapeutic effects and mechanisms of combining BYL719, a PI3Kα inhibitor, with gefitinib, an EGFR-TKI inhibitor in EGFR-TKIs resistance NSCLC models induced by PI3K/AKT activation. Our results demonstrated that PIK3CA mutated cells showed increased growth rate and less sensitive or even resistant to gefitinib, associated with increased PI3K/AKT expression. The combination of BYL719 and gefitinib resulted in synergistic effect compared with the single agents alone in EGFR-mutated NSCLC cells with PI3K/AKT activation. The inhibition of AKT phosphorylation by BYL719 increased the antitumor efficacy of gefitinib in these cell lines. Moreover, the combined effect and mechanism of gefitinib and BYL719 were also confirmed in the NSCLC cells and patient-derived organoids under 3D culture condition, as well as in vivo. Taken together, the data indicate that PIK3CA mutation induces more aggressive growth and gefitinib resistance in NSCLC cells, and the combination treatment with gefitinib and BYL719 is a promising therapeutic approach to overcoming EGFR-TKIs resistance induced by PI3K/AKT activation.

A novel miRNA, Cse-miR-33, functions as an immune regulator by targeting CsTRAF6 in Chinese tongue sole (Cynoglossus semilaevis).

The tumor necrosis factor receptor-associated factor 6 (TRAF6) can act as a fundamental adaptor protein in a chain reaction of signal transduction and cascade events to finish off immune defenses. However, immunomodulatory research on TRAF6 gene is still limited in fish. In this study, a novel miRNA, Cse-miR-33 was identified from the whole genome of Chinese tongue sole (Cynoglossus semilaevis). After separate infections with three different Vibrio strains (V. harveyi, V. anguillarum, V. parahemolyticus) and one virus (nervous necrosis virus, NNV), the expressions of CsTRAF6 and Cse-miR-33 displayed significant time-dependent changes in immune related tissues and the trends were opposite in general. Through target gene prediction and dual luciferase reporter assay, Cse-miR-33 was proven to regulate CsTRAF6 by combining with 3'-UTR sequence of the gene. The results of qRT-PCR and western blotting (WB) analyses showed that Cse-miR-33 blocked the translation of CsTRAF6 protein at post-transcriptional level, rather than degrading the target mRNA. Further experiment indicated that Cse-miR-33 inhibitor largely reduced the death rate of Chinese tongue sole caused by V. harveyi and NNV. The expressions of CsTRAF6-associated immune genes (such as CsIL-1R, CsMYD88, CsIRAK1, CsTNFα, CsIL6 and CsIL8) were also significantly changed in response to Cse-miR-33 agomir and inhibitor. The study suggested that Cse-miR-33 affected the immune response via targeting CsTRAF6 in C. semilaevis, which would provide us deep insights into miRNA-mediated regulatory network and help improve the immunity in fish.

An Interpretable Multitask Framework BiLAT Enables Accurate Prediction of Cyclin-Dependent Protein Kinase Inhibitors.

The cyclin-dependent protein kinases (CDKs) are protein-serine/threonine kinases with crucial effects on the regulation of cell cycle and transcription. CDKs can be a hallmark of cancer since their excessive expression could lead to impaired cell proliferation. However, the selectivity profile of most developed CDK inhibitors is not enough, which have hindered the therapeutic use of CDK inhibitors. In this study, we propose a multitask deep learning framework called BiLAT based on SMILES representation for the prediction of the inhibitory activity of molecules on eight CDK subtypes (CDK1, 2, 4-9). The framework is mainly composed of an improved bidirectional long short-term memory module BiLSTM and the encode layer of the Transformer framework. Additionally, the data enhancement method of SMILES enumeration is applied to improve the performance of the model. Compared with baseline predictive models based on three conventional machine learning methods and two multitask deep learning algorithms, BiLAT achieves the best performance with the highest average AUC, ACC, F1-score, and MCC values of 0.938, 0.894, 0.911, and 0.715 for the test set. Moreover, we constructed a targeted external data set CDK-Dec for the CDK family, which mainly contains bait values screened by 3D similarity with active compounds. This dataset was utilized in the subsequent evaluation of our model. It is worth mentioning that the BiLAT model is interpretable and can be used by chemists to design and synthesize compounds with improved activity. To further verify the generalization ability of the multitask BiLAT model, we also conducted another evaluation on three public datasets (Tox21, ClinTox, and SIDER). Compared with several currently popular models, BiLAT shows the best performance on two datasets. These results indicate that BiLAT is an effective tool for accelerating drug discovery.

RPBP: Deep Retrosynthesis Reaction Prediction Based on Byproducts.

Retrosynthesis prediction is crucial in organic synthesis and drug discovery, aiding chemists in designing efficient synthetic routes for target molecules. Data-driven deep retrosynthesis prediction has gained importance due to new algorithms and enhanced computing power. Although existing models show certain predictive power on the USPTO-50K benchmark data set, no one considers the effects of byproducts during the prediction process, which may be due to the lack of byproduct information in the benchmark data set. Here, we propose a novel two-stage retrosynthesis reaction prediction framework based on byproducts called RPBP. First, RPBP predicts the byproduct involved in the reaction based on the product molecule. Then, it handles an end-to-end prediction problem based on the prediction of reactants by product and byproduct. Unlike other methods that first identify the potential reaction center and then predict reactant molecules, RPBP considers additional information from byproducts, such as reaction reagents, conditions, and sites. Interestingly, adding byproducts reduces model learning complexity in natural language processing (NLP). Our RPBP model achieves 54.7% and 66.6% top-1 retrosynthesis prediction accuracy when the reaction class is unknown and known, respectively. It outperforms existing methods for known-class reactions, thanks to the rich chemical information in byproducts. The prediction of four kinase drugs from the literature demonstrates the model's practicality and potential to accelerate drug discovery.

Development of 3-acetylindole derivatives that selectively target BRPF1 as new inhibitors of receptor activator of NF-κB ligand (RANKL)-Induced osteoclastogenesis.

Bromodomain and PHD finger-containing (BRPF) proteins function as epigenetic readers that specifically recognize acetylated lysine residues on histone tails. The acetyl-lysine binding pocket of BRPF has emerged as an attractive target for the development of protein interaction inhibitors owing to its potential druggability. In this study, we identified 3-acetylindoles as bone antiresorptive agents with a novel scaffold by performing structure-based virtual screening and hit optimization. Among those derivatives, compound 18 exhibited potent and selective inhibitory activities against BRPF1B (IC50 = 102 nM) as well as outstanding inhibitory activity against osteoclastogenesis (73.8% @ 1 µM) and differentiation (IC50 = 0.19 µM) without cytotoxicity. Besides, cellular mechanism assays demonstrated that compound 18 exhibited a strong bone antiresorptive effect by modulating the RANKL/RANK/NFATc1 pathway. Structural and functional studies on BRPF1 inhibitors aid in making advances to understand the epigenetic mechanisms of bone cell development and create innovative therapeutics for treating bone metastases from solid tumors and other bone erosive diseases.

Electro-acupuncture for health-related quality of life and symptoms in patients with gastric cancer undergoing adjuvant chemotherapy (EAGER): a protocol for a multicenter randomized controlled trial.

BACKGROUND: Patients with local gastric cancer experience a decline of Health-related quality of life (HRQOL) during adjuvant chemotherapy following gastrectomy. Our previous pilot study has indicated the potential of acupuncture to improve HRQOL and control cancer-related symptoms burden. This full-scale trial will focus on confirming the effect of acupuncture for patients with gastric cancer. METHODS: A multicenter, open-label, three-arm randomized controlled trial with 249 patients will be conducted in China. Patients will be randomly assigned, in a ratio of 1:1:1, to receive high-dose acupuncture (HA, 7 times each chemo-cycle for 3 cycles), low-dose acupuncture (LA, 3 times each chemo-cycle for 3 cycles), or no acupuncture. The acupoints prescription consisted of bilateral ST36, PC6, SP4, DU20, EX-HN3, and selected Back-shu points. Patients-reported Functional Assessment of Cancer Therapy-Gastric (FACT-Ga) and modified Edmonton Symptom Assessment Scale (mESAS) during the therapy will be recorded. Area under curve (AUC, 21 days/cycle × 3 cycles) and average trajectory of FACT-Ga and mESAS will be calculated. The primary outcome will be the differences in AUC of the FACT-Ga Trial Outcome Index (TOI) between HA and LA versus control groups. Secondary outcomes include AUCs and average trajectory of other FACT-Ga subscales and mESAS scores. DISCUSSION: This study aims to assess the effect of acupuncture and to compare the difference between LA and HA groups on HRQOL and symptom burden controlling in gastric cancer patients by an adequately powered trial. TRIAL REGISTRATION: This study was approved by the Ethics Committee of the Guangdong Provincial Hospital of Traditional Chinese Medicine (approval number: BF2018-118) with registration at ClinicalTrials.gov (identifier: NCT04360577).

Design, synthesis, and biological evaluation of pyrido[3,2-d]pyrimidine derivatives as novel ATR inhibitors.

Targeting ataxia telangiectasia mutated and Rad3-related (ATR) kinase is being pursued as a new therapeutic strategy for the treatment of advanced solid tumor with specific DNA damage response deficiency. Herein, we report a series of pyrido[3,2-d]pyrimidine derivatives with potent ATR inhibitory activity through structure-based drug design. Among them, the representative compound 10q exhibited excellent potency against ATR in both biochemical and cellular assays. More importantly, 10q exhibited good liver microsomes stability in different species and also showed moderate inhibitory activity against HT-29 cells in combination treatment with the ATM inhibitor AZD1390. Thus, this work provides a promising lead compound against ATR for further study.

Retracted: Effect of Sham Acupuncture on Chronic Pain: A Bayesian Network Meta-Analysis.

BACKGROUND: Along with increasing research on acupuncture for chronic pain, the validity of sham acupuncture (SA) has also been argued. METHODS: Nine databases were searched for randomized controlled trials (RCTs) from the inception dates of the databases to July 5, 2022. With Markov Chain Monte Carlo methods, a Bayesian multiple-treatment network meta-analysis (NMA) with random-effects model was conducted. RESULTS: A total of 62 RCTs with 6,806 patients and four kinds of treatments (real acupuncture [RA], non-acupuncture [NA], penetrative SA [PSA], and non-penetrative SA [NPSA]) were included. The results indicated that both NPSA and PSA were not superior to NA in improving chronic pain (NPSA: mean difference [MD]= -4.77, 95% confidence interval [CI] -11.09 to 1.52; PSA: MD= -4.96, 95% CI -10.38 to 0.48). After NPSA and PSA were combined into the SA group, the weak trend of pain relief from SA was still not statistically significant (MD= -4.91, 95% CI -9.93 to 0.05). NPSA and PSA had similar effects (MD= 0.18, 95% CI -5.45 to 5.81). RA was significantly associated with pain relief, compared with NPSA and PSA (NPSA: MD= -12.03, 95% CI -16.62 to -7.41; PSA: MD= -11.85, 95% CI -15.48 to -8.23). The results were generally consistent regardless of pain phenotype, frequency, duration, acupuncture methods, analgesic intake, or detection bias. CONCLUSION: These results suggested that acupuncture was significantly associated with reduced chronic pain. The two kinds of placebo acupuncture, NPSA and PSA, have similar effects. Both NPSA and PSA, with a weak but not significant effect, are appropriate to be inert placebo controls in RCTs for chronic pain.

Multi-channel GCN ensembled machine learning model for molecular aqueous solubility prediction on a clean dataset.

This study constructed a new aqueous solubility dataset and a solubility regression model which was ensembled by GCN and machine learning models. Aqueous solubility is a key physiochemical property of small molecules in drug discovery. In the past few decades, there have been many studies about solubility prediction. However, many of these studies have high root mean squared error (RMSE). Meanwhile, their dataset always contains salt compounds and solubility data obtained from different experimental conditions. In this paper, we constructed a clean dataset with 2609 compounds, which was small but contains only solubility records without salts at the same temperatures (25 °C). Here, we applied graph convolutional neural network (GCN) to construct an aqueous solubility prediction model. To enhance the performance of the model, the molecular MACCS key fingerprints and physiochemical descriptors were also combined with the GCN model to build a multi-channel model. Additionally, the authors also built two machine learning models (support vector regression and gradient boost decision tree) and assembled them to the GCN model to improve the root mean squared error (RMSE = 0.665). Finally, comparative experiments have shown that our framework achieved the best performance on ESOL dataset (RMSEval = 0.56, RMSEtest = 0.44) and surpassed four established software on aqueous solubility prediction of new compounds.

Multimodal multi-task deep neural network framework for kinase-target prediction.

Kinase plays a significant role in various disease signaling pathways. Due to the highly conserved sequence of kinase family members, understanding the selectivity profile of kinase inhibitors remains a priority for drug discovery. Previous methods for kinase selectivity identification use biochemical assays, which are very useful but limited by the protein available. The lack of kinase selectivity can exert benefits but also can cause adverse effects. With the explosion of the dataset for kinase activities, current computational methods can achieve accuracy for large-scale selectivity predictions. Here, we present a multimodal multi-task deep neural network model for kinase selectivity prediction by calculating the fingerprint and physiochemical descriptors. With the multimodal inputs of structure and physiochemical properties information, the multi-task framework could accurately predict the kinome map for selectivity analysis. The proposed model displays better performance for kinase-target prediction based on system evaluations.

Genome-Wide Identification and Involvement in Response to Biotic and Abiotic Stresses of lncRNAs in Turbot (Scophthalmus maximus).

Long non-coding RNAs (lncRNAs) play crucial roles in a variety of biological processes, including stress response. However, the number, characteristics and stress-related expression of lncRNAs in turbot are still largely unknown. In this study, a total of 12,999 lncRNAs were identified at the genome-wide level of turbot for the first time using 24 RNA-seq datasets. Sequence characteristic analyses of transcripts showed that lncRNA transcripts were shorter in average length, lower in average GC content and in average expression level as compared to the coding genes. Expression pattern analyses of lncRNAs in 12 distinct tissues showed that lncRNAs, especially lincRNA, exhibited stronger tissue-specific expression than coding genes. Moreover, 612, 1351, 1060, 875, 420 and 1689 differentially expressed (DE) lncRNAs under Vibrio anguillarum, Enteromyxum scophthalmi, and Megalocytivirus infection and heat, oxygen, and salinity stress conditions were identified, respectively. Among them, 151 and 62 lncRNAs showed differential expression under various abiotic and biotic stresses, respectively, and 11 lncRNAs differentially expressed under both abiotic and biotic stresses were selected as comprehensive stress-responsive lncRNA candidates. Furthermore, expression pattern analysis and qPCR validation both verified the comprehensive stress-responsive functions of these 11 lncRNAs. In addition, 497 significantly co-expressed target genes (correlation coefficient (R) > 0.7 and q-value < 0.05) for these 11 comprehensive stress-responsive lncRNA candidates were identified. Finally, GO and KEGG enrichment analyses indicated that these target genes were enriched mainly in molecular function, such as cytokine activity and active transmembrane transporter activity, in biological processes, such as response to stimulus and immune response, and in pathways, such as protein families: signaling and cellular processes, transporters and metabolism. These findings not only provide valuable reference resources for further research on the molecular basis and function of lncRNAs in turbot but also help to accelerate the progress of molecularly selective breeding of stress-resistant turbot strains or varieties.

Genome-Wide Identification, Molecular Characterization, and Involvement in Response to Abiotic and Biotic Stresses of the HSP70 Gene Family in Turbot (Scophthalmus maximus).

Heat shock proteins 70 (HSP70s) are known to play essential roles in organisms' response mechanisms to various environmental stresses. However, no systematic identification and functional analysis has been conducted for HSP70s in the turbot (Scophthalmus maximus), a commercially important worldwide flatfish. Herein, 16 HSP70 genes unevenly distributed on nine chromosomes were identified in the turbot at the genome-wide level. Analyses of gene structure, motif composition, and phylogenetic relationships provided valuable data on the HSP70s regarding their evolution, classification, and functional diversity. Expression profiles of the HSP70 genes under five different stresses were investigated by examining multiple RNA-seq datasets. Results showed that 10, 6, 8, 10, and 9 HSP70 genes showed significantly up- or downregulated expression after heat-induced, salinity-induced, and Enteromyxum scophthalmi, Vibrio anguillarum, and Megalocytivirus infection-induced stress, respectively. Among them, hsp70 (hspa1a), hspa1b, and hspa5 showed significant responses to each kind of induced stress, and qPCR analyses further validated their involvement in comprehensive anti-stress, indicating their involvement in organisms' anti-stress mechanisms. These findings not only provide new insights into the biological function of HSP70s in turbot adapting to various environmental stresses, but also contribute to the development of molecular-based selective breeding programs for the production of stress-resistant turbot strains in the aquaculture industry.