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Insertions and deletions (indels) are low-frequency deleterious genomic DNA alterations. Despite their rarity, indels are common, and insertions leading to long complementarity-determining region 3 (CDR3) are vital for antigen-binding functions in broadly neutralizing and polyreactive antibodies targeting viruses. Because of challenges in detecting indels, the mechanism that generates indels during immunoglobulin diversification processes remains poorly understood. We carried out ultra-deep profiling of indels and systematically dissected the underlying mechanisms using passenger-immunoglobulin mouse models. We found that activation-induced cytidine deaminase–dependent ±1–base pair (bp) indels are the most prevalent indel events, biasing deleterious outcomes, whereas longer in-frame indels, especially insertions that can extend the CDR3 length, are rare outcomes. The ±1-bp indels are channeled by base excision repair, but longer indels require additional DNA-processing factors. Ectopic expression of a DNA exonuclease or perturbation of the balance of DNA polymerases can increase the frequency of longer indels, thus paving the way for models that can generate antibodies with long CDR3. Our study reveals the mechanisms that generate beneficial and deleterious indels during the process of antibody somatic hypermutation and has implications in understanding the detrimental genomic alterations in various conditions, including tumorigenesis.

Cardiovascular complications combined with COVID-19 (SARS-CoV-2) lead to a poor prognosis in patients. The common pathogenesis of ischemic cardiomyopathy (ICM) and COVID-19 is still unclear. Here, we explored potential molecular mechanisms and biomarkers for ICM and COVID-19. Common differentially expressed genes (DEGs) of ICM (GSE5406) and COVID-19 (GSE164805) were identified using GEO2R. We performed enrichment and protein–protein interaction analyses and screened key genes. To confirm the diagnostic performance for these hub genes, we used external datasets (GSE116250 and GSE211979) and plotted ROC curves. Transcription factor and microRNA regulatory networks were constructed for the validated hub genes. Finally, drug prediction and molecular docking validation were performed using cMAP. We identified 81 common DEGs, many of which were enriched in terms of their relation to angiogenesis. Three DEGs were identified as key hub genes (HSP90AA1, HSPA9, and SRSF1) in the protein–protein interaction analysis. These hub genes had high diagnostic performance in the four datasets (AUC > 0.7). Mir-16-5p and KLF9 transcription factor co-regulated these hub genes. The drugs vindesine and ON-01910 showed good binding performance to the hub genes. We identified HSP90AA1, HSPA9, and SRSF1 as markers for the co-pathogenesis of ICM and COVID-19, and showed that co-pathogenesis of ICM and COVID-19 may be related to angiogenesis. Vindesine and ON-01910 were predicted as potential therapeutic agents. Our findings will contribute to a deeper understanding of the comorbidity of ICM with COVID-19.

Objective: To investigate the molecular mechanism and simulated validation of Zhi Bai Di Huang Pill (ZBDHP) for the treatment of systemic lupus erythematosus (SLE) using network pharmacology and molecular docking techniques. Methods: The active ingredients of ZBDHP were obtained through the TCMSP database and the Canonical SMILES of the active ingredients were queried through Pubchem. The targets of the active ingredients were predicted in the SwissTarget database based on the SMILES. The SLE-related disease targets were obtained through the GeneCards, OMIM and DisGenets databases, and the intersection targets of ZBDHP and SLE were obtained using the Venny 2.1.0 online platform. Intersection targets build a visual protein interaction network (PPI) through the STRING database, and the core targets were identified by network topology analysis. GO analysis and KEGG pathway enrichment analysis of the intersecting targets were performed using the DAVID database. Finally, the molecular docking of the first four active ingredients and the first four core target genes were verified by Pubchem, the PDB database and CB-Dock online molecular docking technology. Results: ZBDHP screened 91 potential active ingredients and 816 potential targets. Among them, 141 genes were intersected by ZBDHP and SLE. The network topology analysis showed that the main active ingredients were Hydroxygenkwanin, Alisol B, asperglaucide, Cerevisterol, etc., and the key target genes were TNF, AKT1, EGFR, STAT3, etc. GO and KEGG enrichment analysis showed that common targets interfere with biological processes or molecular functions such as signal transduction protein phosphorylation, inflammatory response, transmembrane receptor protein tyrosine kinase activity, etc., through multiple signaling pathways, such as pathways in cancer, Kaposi sarcoma-associated herpesvirus infection, the PI3K-Akt signaling pathway, lipid and atherosclerosis, hepatitis B, etc. Molecular docking results showed that the active components of ZBDHP have good binding activity to the core targets of SLE. Conclusions: This study reveals that the ZBDHP treatment of SLE is a complex mechanistic process with multi-components, multi-targets and multi-pathways, and it may play a therapeutic role in SLE by inhibiting the production, proliferation and apoptosis of inflammatory factors. In conclusion, the present study provides a theoretical basis for further research on ZBDHP.

Background: Qianghuo Shengshi decoction (QHSSD), a traditional Chinese medicine formula, is used to treat ankylosing spondylitis (AS) in China. The pharmacological mechanism of QHSSD for AS remains to be clarified. In this study, we investigated the molecular mechanisms of QHSSD in the treatment of AS using network pharmacology and molecular docking. Methods: To obtain the chemical components and potential targets of QHSSD, we used the Traditional Chinese Medicine Systematic Pharmacology Database and Analysis Platform (TCMSP) and SwissTargetPrediction. AS potential targets were found in the GeneCards, OMIM, and DisGenets databases. A Venn diagram was used to screen QHSSD and AS common potential targets. The STRING website and Cytoscape software were used to create and analyze protein–protein interactions and component–target networks. The DAVID database was used for the gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Molecular docking was used to visualize drug–target interactions. Results: The component–target network consisted of 119 chemical components and 193 potential targets. QHSSD was implicated in various biological processes, such as inflammation and angiogenesis, and mediated multiple signaling pathways, such as the MAPK signaling pathway. Molecular docking revealed good binding ability between medicarpin, notoptol, vitetrifolin E, and cnidilin and EGFR, TNF-α, ALB, and VEGFA. Conclusions: The chemical compositions, potential targets, and pathways involved in the QHSSD treatment of AS were successfully predicted in this study. This study provides a solid foundation for the selection of drugs to treat AS.

This study aims to identify the core genes that influence the prognosis of colon cancer (CC) and analyze their relationships with clinical characteristics.The gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified. The top ten core genes were selected by bioinformatics tools and screened through the Oncomine database. The expression of core genes in CC tissues and cells was validated by immunohistochemistry, immunoblotting and quantitative real-time polymerase chain reaction. Spearman correlation was used to analyze the relationship between different parameters. Overall survival was assessed by the Kaplan-Meier method. The area under the curve (AUC) and the receiver operating curve (ROC) were applied to assess the accuracy of genes for predicting prognosis.There were 1,665 DEGs that were identified from TCGA database. Bioinformatics analysis found thatHigh levels of

ObjectiveThis study aims to identify the core genes that influence the prognosis of colon cancer, and analyze their relationships with clinical characteristics. MethodsThe gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were identified. The top ten core genes were selected by bioinformatics tools, and screened through the Oncomine database. The expressions of core genes in colon cancer cells and tissues were validated by immunohistochemistry, immunoblotting and quantitative real-time polymerase chain reaction. Results There were 1665 DEGs that have been identified from the TCGA database. Bioinformatics analysis found that GNGT1, NMU, PPBP, AGT, and GNG4 were differentially expressed in colon cancer tissue. Over-expression of NMU, PPBP, AGT, and GNG4 in colon cancer was associated with shortened survival time (pConclusionsThe high levels of NMU, PPBP, and GNG4 were associated with poor prognosis in colon cancer. The combination prognostic model of these three genes could be a new option.

Background: FCN-437c is a second-generation CDK4/6 inhibitor. Phase 1b clinical results indicated improved antitumor activity in patients (pts) with HR+, HER2– advanced breast cancer (ABC), treated with FCN-437c + letrozole. Methods: This Phase 2, multicenter, open-label clinical study evaluated the antitumor activity, pharmacokinetics (PK), and safety of FCN-437c + fulvestrant in post-menopausal pts (Cohort 1, treatment-naïve or 2L), FCN-437c + letrozole + goserelin in pre-menopausal pts (Cohort 2, treatment-naïve). Pts received FCN-437c (200 mg QD) in a 21-day-on and 7-day-off schedule either in combination with fulvestrant (500 mg D1) or letrozole (2.5 mg QD) + goserelin (3.6 mg once per cycle) in 28-day cycles. The primary endpoint was overall response rate (ORR) and secondary endpoints were progression-free survival (PFS), overall survival (OS), PK, and safety. Results: At study cutoff (Feb 7, 2022), 36 pts were enrolled in Cohort 1 and 31 pts were in Cohort 2; 42 (62.7%) pts had visceral metastases and 9 (13.4%) pts had bone-only metastases. In Cohort 1, 18 pts were treatment-naïve, 15 pts had received 1L treatment, and 3 pts had received ≥2L treatment. In Cohort 2, 25 pts were treatment-naïve and 6 pts had received 1L treatment. Overall, 27 pts in the per-protocol set achieved partial response (PR), resulting in an ORR of 40.9% (95% CI, 29.0-53.7). Median follow-up was 12.8 months, and median PFS (mPFS), OS, and DOR were not reached. However, at 12 months, the PFS rate was 67.7% (95% CI, 53.2-78.6) and the OS rate was 95.9% (95% CI, 84.5-99.0); the 6-month DOR rate was 96.0% (95% CI, 74.8-99.4). In Cohort 1 (n=35), 11 pts achieved PR: the ORR was 31.4% (16.9-49.3%) and mPFS was 12.9 months (95% CI, 9.2-NR); the 6-month DOR rate was 100%. In Cohort 2 (n=31), 16 pts achieved PR: the ORR was 51.6% (95% CI, 33.1-69.9%). mPFS, OS, and DOR were not reached; the 6-month DOR rate was 92.9% (95% CI, 59.08-98.96) (Table). Treatment-emergent adverse events (TEAEs) were observed in all pts. Majority of AE were G1 or 2 except for hematological TEAE. 58 (86.6%) pts reported grade ≥3 TEAEs, mainly neutropenia (74.6%), leukopenia (49.3%), hypertriglyceridemia (6.0%), lymphocyte count decrease (4.5%), and γ-glutamyltransferase increase (3.0%): most were reversed through dose interruption and symptomatic therapy. Steady-state PK parameters were analyzed after 15-21 days of QD administration: Cohort 1: median Tmax was 3 h, geomean T1/2 was 44.6 h, geomean Cmax was 1650.7 ng/mL, and geomean AUC0-24h was 29,148.08 h*ng/mL; the geomean accumulation ratios of AUC0-24h and RCmax were 2.18 and 1.74, respectively, compared with first dose. Cohort 2: median Tmax was 4 h, geomean T1/2 was 35.7 h, geomean Cmax was 1314.34 ng/mL, and geomean AUC0-24h was 22,889.96 h*ng/mL; the geomean accumulation ratios of AUC0-24h and RCmax were 1.95 and 1.63, respectively, compared with first dose. Conclusion: FCN-437c in combination with fulvestrant or letrozole + goserelin demonstrates antitumor activity and safety and is well tolerated in pts with HR+, HER2– ABC. This combination therapy will be further investigated in 2 ongoing Phase 3 trials (NCT05438810 and NCT05439499). Clinical trial number: NCT05004142. Research Sponsor: Avanc Pharmaceutical Co., Ltd Table. Clinical outcomes for patients in the per-protocol set. Citation Format: JiaJie Shi, Wei Li, Zhongsheng Tong, Aimin Zang, Xiaohua Zeng, Shui Wang, Tao Huang, Ying Wang, Yanqiu Song, Lihua Kang, Zheng Lv, Yehui Shi, Hua Yang, Jing Wu, Yongmei Yin, Yan Liang, Jie Tan, Jie Ming, Yaping Yang, Simin Luo, Xiujuan Gui, Ai-Min Hui, Zhuli Wu, Ling Tian, Yuchen Yang, Lei Diao, Wenjing Zhang, Yongjiao Zhang, Yunjiang Liu. Phase 2 Study of the CDK4/6 Inhibitor FCN-437c in Combination With Fulvestrant or Letrozole and Goserelin in Patients With HR+, HER2– Advanced Breast Cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-01-21.

Samples from fifty-five surface water resources and twenty-five drinking water treatment plants in Europe, Africa, Asia, and USA were used to analyse the fluorescence composition of global surface waters and predict aromaticity and treatability from fluorescence excitation emission matrices. Nine underlying fluorescence components were identified in the dataset using parallel factor analysis (PARAFAC) and differences in aromaticity and treatability could be predicted from ratios between components H