MKG-GC: A multi-task learning-based knowledge graph construction framework with personalized application to gastric cancer.

Over the past decade, information for precision disease medicine has accumulated in the form of textual data. To effectively utilize this expanding medical text, we proposed a multi-task learning-based framework based on hard parameter sharing for knowledge graph construction (MKG), and then used it to automatically extract gastric cancer (GC)-related biomedical knowledge from the literature and identify GC drug candidates. In MKG, we designed three separate modules, MT-BGIPN, MT-SGTF and MT-ScBERT, for entity recognition, entity normalization, and relation classification, respectively. To address the challenges posed by the long and irregular naming of medical entities, the MT-BGIPN utilized bidirectional gated recurrent unit and interactive pointer network techniques, significantly improving entity recognition accuracy to an average F1 value of 84.5% across datasets. In MT-SGTF, we employed the term frequency-inverse document frequency and the gated attention unit. These combine both semantic and characteristic features of entities, resulting in an average Hits@ 1 score of 94.5% across five datasets. The MT-ScBERT integrated cross-text, entity, and context features, yielding an average F1 value of 86.9% across 11 relation classification datasets. Based on the MKG, we then developed a specific knowledge graph for GC (MKG-GC), which encompasses a total of 9129 entities and 88,482 triplets. Lastly, the MKG-GC was used to predict potential GC drugs using a pre-trained language model called BioKGE-BERT and a drug-disease discriminant model based on CNN-BiLSTM. Remarkably, nine out of the top ten predicted drugs have been previously reported as effective for gastric cancer treatment. Finally, an online platform was created for exploration and visualization of MKG-GC at https://www.yanglab-mi.org.cn/MKG-GC/.

Histone lactylation inhibits RARγ expression in macrophages to promote colorectal tumorigenesis through activation of TRAF6-IL-6-STAT3 signaling.

Macrophages are phenotypically and functionally diverse in the tumor microenvironment (TME). However, how to remodel macrophages with a protumor phenotype and how to manipulate them for therapeutic purposes remain to be explored. Here, we show that in the TME, RARγ is downregulated in macrophages, and its expression correlates with poor prognosis in patients with colorectal cancer (CRC). In macrophages, RARγ interacts with tumor necrosis factor receptor-associated factor 6 (TRAF6), which prevents TRAF6 oligomerization and autoubiquitination, leading to inhibition of nuclear factor κB signaling. However, tumor-derived lactate fuels H3K18 lactylation to prohibit RARγ gene transcription in macrophages, consequently enhancing interleukin-6 (IL-6) levels in the TME and endowing macrophages with tumor-promoting functions via activation of signal transducer and activator of transcription 3 (STAT3) signaling in CRC cells. We identified that nordihydroguaiaretic acid (NDGA) exerts effective antitumor action by directly binding to RARγ to inhibit TRAF6-IL-6-STAT3 signaling. This study unravels lactate-driven macrophage function remodeling by inhibition of RARγ expression and highlights NDGA as a candidate compound for treating CRC.

Large-scale ORF screening based on LC-MS to discover novel lncRNA-encoded peptides responding to ionizing radiation and microgravity.

In the human genome, 98% of genes can be transcribed into non-coding RNAs (ncRNAs), among which lncRNAs and their encoded peptides play important roles in regulating various aspects of cellular processes and may serve as crucial factors in modulating the biological effects induced by ionizing radiation and microgravity. Unfortunately, there are few reports in space radiation biology on lncRNA-encoded peptides below 10kD due to limitations in detection techniques. To fill this gap, we integrated a variety of methods based on genomics and peptidomics, and discovered 22 lncRNA-encoded small peptides that are sensitive to space radiation and microgravity, which have never been reported before. We concurrently validated the transmembrane helix, subcellular localization, and biological function of these small peptides using bioinformatics and molecular biology techniques. More importantly, we found that these small peptides function independently of the lncRNAs that encode them. Our findings have uncovered a previously unknown human proteome encoded by 'non-coding' genes in response to space conditions and elucidated their involvement in biological processes, providing valuable strategies for individual protection mechanisms for astronauts who carry out deep space exploration missions in space radiation environments.

Discovery of selective HDAC6 inhibitors based on a multi-layer virtual screening strategy.

The abnormal enhancement of histone deacetylase 6 (HDAC6) has been demonstrated to be closely related to the occurrence and development of various malignant tumors, attracting extensive attention as a promising target for cancer therapy. Currently, only limited selective HDAC6 inhibitors have entered clinical trials, making the rapid discovery of selective HDAC6 inhibitors with safety profiles particularly urgent. In this study, a multi-layer virtual screening workflow was established, and the representative compounds screened were biologically evaluated in combination with enzyme inhibitory and anti-tumor cell proliferation experiments. The experimental results showed that the screened compounds L-25, L-32, L-45 and L-81 exhibited nanomolar inhibitory activity against HDAC6, and exerted a certain degree of anti-proliferative activities against tumor cells, especially the cytotoxicity of L-45 to A375 (IC50 = 11.23 ± 1.27 µM) and the cytotoxicity of L-81 against HCT-116 (IC50 = 12.25 ± 1.13 µM). Additionally, the molecular mechanisms underlying the subtype selective inhibitory activities of the selected compounds were further elucidated using computational approaches, and the hotspot residues on HDAC6 contributing to the ligands' binding were identified. In summary, this study established a multi-layer screening scheme to quickly and effectively screen out hit compounds with enzyme inhibitory activity and anti-tumor cell proliferation, providing novel scaffolds for the subsequent anti-tumor drug design based on HDAC6 target.

Identifying Lymph Node Metastasis-Related Factors in Breast Cancer Using Differential Modular and Mutational Structural Analysis.

Complex diseases are generally caused by disorders of biological networks and/or mutations in multiple genes. Comparisons of network topologies between different disease states can highlight key factors in their dynamic processes. Here, we propose a differential modular analysis approach that integrates protein-protein interactions with gene expression profiles for modular analysis, and introduces inter-modular edges and date hubs to identify the "core network module" that quantifies the significant phenotypic variation. Then, based on this core network module, key factors, including functional protein-protein interactions, pathways, and driver mutations, are predicted by the topological-functional connection score and structural modeling. We applied this approach to analyze the lymph node metastasis (LNM) process in breast cancer. The functional enrichment analysis showed that both inter-modular edges and date hubs play important roles in cancer metastasis and invasion, and in metastasis hallmarks. The structural mutation analysis suggested that the LNM of breast cancer may be the outcome of the dysfunction of rearranged during transfection (RET) proto-oncogene-related interactions and the non-canonical calcium signaling pathway via an allosteric mutation of RET. We believe that the proposed method can provide new insights into disease progression such as cancer metastasis.

MiR-200/183 family-mediated module biomarker for gastric cancer progression: an AI-assisted bioinformatics method with experimental functional survey.

BACKGROUND: Gastric cancer (GC) is a major cancer burden throughout the world with a high mortality rate. The performance of current predictive and prognostic factors is still limited. Integrated analysis is required for accurate cancer progression predictive biomarker and prognostic biomarkers that help to guide therapy. METHODS: An AI-assisted bioinformatics method that combines transcriptomic data and microRNA regulations were used to identify a key miRNA-mediated network module in GC progression. To reveal the module's function, we performed the gene expression analysis in 20 clinical samples by qRT-PCR, prognosis analysis by multi-variable Cox regression model, progression prediction by support vector machine, and in vitro studies to elaborate the roles in GC cells migration and invasion. RESULTS: A robust microRNA regulated network module was identified to characterize GC progression, which consisted of seven miR-200/183 family members, five mRNAs and two long non-coding RNAs H19 and CLLU1. Their expression patterns and expression correlation patterns were consistent in public dataset and our cohort. Our findings suggest a two-fold biological potential of the module: GC patients with high-risk score exhibited a poor prognosis (p-value < 0.05) and the model achieved AUCs of 0.90 to predict GC progression in our cohort. In vitro cellular analyses shown that the module could influence the invasion and migration of GC cells. CONCLUSIONS: Our strategy which combines AI-assisted bioinformatics method with experimental and clinical validation suggested that the miR-200/183 family-mediated network module as a "pluripotent module", which could be potential marker for GC progression.

Integrated multi-dimensional analysis highlights DHCR7 mutations involving in cholesterol biosynthesis and contributing therapy of gastric cancer.

BACKGROUND: Genetic background plays an important role in the occurrence and development of gastric cancer (GC). With the application of genome-wide association study (GWAS), an increasing number of tumor susceptibility genes in gastric cancer have been discovered. While little of them can be further applicated in clinical diagnosis and treatment due to the lack of in-depth analysis. METHODS: A GWAS of peripheral blood leukocytes from GC patients was performed to identify and obtain genetic background data. In combination with a clinical investigation, key SNP mutations and mutated genes were screened. Via in vitro and in vivo experiments, the function of the mutated gene was verified in GC. Via a combination of molecular function studies and amino acid network analysis, co-mutations were discovered and further identified as potential therapeutic targets. RESULTS: At the genetic level, the G allele of rs104886038 in DHCR7 was a protective factor identified by the GWAS. Clinical investigation showed that patients with the rs104886038 A/G genotype, age ≥ 60, smoking ≥ 10 cigarettes/day, heavy drinking and H. pylori infection were independent risk factors for GC, with odds ratios of 12.33 (95% CI, 2.10 ~ 72.54), 20.42 (95% CI, 2.46 ~ 169.83), and 11.39 (95% CI, 1.82 ~ 71.21), respectively. Then molecular function studies indicated that DHCR7 regulated cell proliferation, migration, and invasion as well as apoptosis resistance via cellular cholesterol biosynthesis pathway. Further amino acid network analysis based on the predicted structure of DHCR7 and experimental verification indicated that rs104886035 and rs104886038 co-mutation reduced the stability of DHCR7 and induced its degradation. DHCR7 mutation suppressed the malignant behaviour of GC cells and induced apoptosis via inhibition on cell cholesterol biosynthesis. CONCLUSION: In this work, we provided a comprehensive multi-dimensional analysis strategy which can be applied to in-depth exploration of GWAS data. DHCR7 and its mutation sites identified by this strategy are potential theratic targets of GC via inhibition of cholesterol biosynthesis.

SLC6A14 facilitates epithelial cell ferroptosis via the C/EBPß-PAK6 axis in ulcerative colitis.

Emerging evidence suggests that ferroptosis is involved in the pathogenesis of ulcerative colitis (UC). However, the key regulator of this process remains uncertain. In this study, we aimed to explore the roles of solute carrier (SLC) family 6 member 14 (SLC6A14) in regulating ferroptosis in UC. The expression of SLC6A14 was significantly increased and positively associated with that of prostaglandin-endoperoxide synthase 2 (PTGS2) in tissue samples from patients with UC. Moreover, a series of in vitro and in vivo experiments showed that SLC6A14 knockdown markedly suppressed ferroptosis. RNA sequencing revealed that SLC6A14 inhibited the expression of P21 (RAC1)-activated kinase 6 (PAK6) and that PAK6 knockdown abolished the effects of SLC6A14 on RAS-selective lethal 3 (RSL3)-induced ferroptosis in Caco-2 cells. Furthermore, chromatin immunoprecipitation (ChIP) and Western blot analysis demonstrated that SLC6A14 negatively regulated PAK6 expression in a CCAAT enhancer binding protein beta (C/EBPß)-dependent manner. Collectively, these findings indicate that SLC6A14 facilitates ferroptosis in UC by promoting C/EBPß expression and binding activity to inhibit PAK6 expression, suggesting that targeting SLC6A14-C/EBPß-PAK6 axis-mediated ferroptosis may be a promising therapeutic alternative for UC.

A SARS-CoV-2 vaccine based on conjugation of SARS-CoV-2 RBD with IC28 peptide and mannan.

SARS-CoV-2 is a particularly transmissible virus that causes a severe respiratory disease known as COVID-19. Safe and effective vaccines are urgently needed to combat the COVID-19 pandemic. The receptor-binding domain (RBD) of SARS-CoV-2 spike protein elicits most neutralizing antibodies during viral infection and is an ideal antigen for vaccine development. In particular, RBD expressed by E. coli is amenable to low cost and high-yield manufacturability. The adjuvant is necessitated to improve the immunogenicity of RBD. IC28, a TLR5-dependent adjuvant, is a peptide from bacterial flagellin. Mannan is a ligand of TLR-4 or TLR-2 and a polysaccharide adjuvant. Here, IC28 and mannan were both covalently conjugated with RBD from E. coli. The conjugate (RBD-IC28-M) elicited high RBD-specific IgG titers, and a neutralization antibody titer of 201.4. It induced high levels of Th1-type cytokines (IFN-γ) and Th2-type cytokines (IL-5 and IL-10), along with high antigenicity and no apparent toxicity to the organs. The mouse sera of the RBD-IC28-M group competitively interfered with the interaction of RBD and ACE2. Thus, conjugation with IC28 and mannan additively enhanced the humoral and cellular immunity. Our study was expected to provide the feasibility to develop an affordable, easily scalable, effective vaccine SARS-CoV-2 vaccine.

Deciphering the in vivo Dynamic Proteomics of Mesenchymal Stem Cells in Critical Limb Ischemia.

OBJECTIVE: Regenerative therapy using mesenchymal stem cells (MSC) is a promising therapeutic method for critical limb ischemia (CLI). To understand how the cells are involved in the regenerative process of limb ischemia locally, we proposed a metabolic protein labeling method to label cell proteomes in situ and then decipher the proteome dynamics of MSCs in ischemic hind limb. METHODS AND RESULTS: In this study, we overexpressed mutant methionyl-tRNA synthetase (MetRS), which could utilize azidonorleucine (ANL) instead of methionine (Met) during protein synthesis in MSCs. Fluorescent non-canonical amino-acid tagging (FUNCAT) was performed to detect the utilization of ANL in mutant MSCs. Mice with hindlimb ischemia (HLI) or Sham surgery were treated with MetRSmut MSCs or PBS, followed by i.p. administration of ANL at days 0, 2 6, and 13 after surgery. FUNCAT was also performed in hindlimb tissue sections to demonstrate the incorporation of ANL in transplanted cells in situ. At days 1, 3, 7, and 14 after the surgery, laser doppler imaging were performed to detect the blood reperfusion of ischemic limbs. Ischemic tissues were also collected at these four time points for histological analysis including HE staining and vessel staining, and processed for click reaction based protein enrichment followed by mass spectrometry and bioinformatics analysis. The MetRSmut MSCs showed strong green signal in cell culture and in HLI muscles as well, indicating efficient incorporation of ANL in nascent protein synthesis. By 14 days post-treatment, MSCs significantly increased blood reperfusion and vessel density, while reducing inflammation in HLI model compared to PBS. Proteins enriched by click reaction were distinctive in the HLI group vs. the Sham group. 34, 31, 49, and 26 proteins were significantly up-regulated whereas 28, 32, 62, and 27 proteins were significantly down-regulated in HLI vs. Sham at days 1, 3, 7, and 14, respectively. The differentially expressed proteins were more pronounced in the pathways of apoptosis and energy metabolism. CONCLUSION: In conclusion, mutant MetRS allows efficient and specific identification of dynamic cell proteomics in situ, which reflect the functions and adaptive changes of MSCs that may be leveraged to understand and improve stem cell therapy in critical limb ischemia.

H19 promotes aerobic glycolysis, proliferation, and immune escape of gastric cancer cells through the microRNA-519d-3p/lactate dehydrogenase A axis.

Long noncoding RNAs (lncRNAs) have been investigated in multiple human cancers including gastric cancer (GC). Our research aims to explore the role of H19 in aerobic glycolysis, proliferation, and immune escape of GC cells. The expression of H19 in GC samples was analyzed using Gene Expression Profiling Interactive Analysis, Gene Expression Omnibus data, and real-time quantitative PCR analysis. Relative quantification of glucose consumption and lactate production from cell supernatant were applied to assess the aerobic glycolysis of GC cells. Subcellular fractionation, luciferase reporter, and western blot assays certified the binding between genes. Cell Counting Kit-8 and colony formation assays were used to determine GC cell proliferation. Flow cytometry, ELISA, and real-time quantitative PCR assays were applied to analyze the immunosuppressive effect of H19. H19 was highly expressed in samples of patients with GC, and associated with tumor growth in vivo. H19 knockdown suppressed glucose consumption, lactate production, and proliferation of GC cells by regulating the microRNA (miR)-519d-3p/lactate dehydrogenase A (LDHA) axis. Both miR-519d-3p depletion and LDHA overexpression could reverse the H19 knockdown-induced decrease in aerobic glycolysis and proliferation. Moreover, conditioned medium from stable knockdown H19 GC cells modulated the activity of immune cells including γδT cells, Jurkat cells, and tumor-associated macrophages in a miR-519d-3p/LDHA/lactate axis-dependent manner. The H19/miR-519d-3p/LDHA axis mainly contributed to aerobic glycolysis, proliferation, and immune escape of GC cells.

Knowledge-Guided "Community Network" Analysis Reveals the Functional Modules and Candidate Targets in Non-Small-Cell Lung Cancer.

Non-small-cell lung cancer (NSCLC) represents a heterogeneous group of malignancies that are the leading cause of cancer-related death worldwide. Although many NSCLC-related genes and pathways have been identified, there remains an urgent need to mechanistically understand how these genes and pathways drive NSCLC. Here, we propose a knowledge-guided and network-based integration method, called the node and edge Prioritization-based Community Analysis, to identify functional modules and their candidate targets in NSCLC. The protein-protein interaction network was prioritized by performing a random walk with restart algorithm based on NSCLC seed genes and the integrating edge weights, and then a "community network" was constructed by combining Girvan-Newman and Label Propagation algorithms. This systems biology analysis revealed that the CCNB1-mediated network in the largest community provides a modular biomarker, the second community serves as a drug regulatory module, and the two are connected by some contextual signaling motifs. Moreover, integrating structural information into the signaling network suggested novel protein-protein interactions with therapeutic significance, such as interactions between GNG11 and CXCR2, CXCL3, and PPBP. This study provides new mechanistic insights into the landscape of cellular functions in the context of modular networks and will help in developing therapeutic targets for NSCLC.

MiR-378a-3p as a putative biomarker for hepatocellular carcinoma diagnosis and prognosis: Computational screening with experimental validation.

BACKGROUND: Hepatocellular carcinoma (HCC) is a malignant disease with high morbidity and mortality, and the molecular mechanism for the genesis and progression is complex and heterogeneous. Biomarker discovery is crucial for the personalized and precision treatment of HCC. The accumulation of reported microRNA biomarkers makes it possible to combine computational identification with experimental validation to accelerate the discovery of novel biomarker. RESULTS: In the present work, we applied a rational computer-aided biomarker discovery model to screen for the HCC diagnosis biomarker. Two HCC-associated networks were constructed based on the microRNA and mRNA expression profiles, and the potential microRNA biomarkers were identified based on their unique regulatory and influential power in the network. These putative biomarkers were then experimentally validated. One prominent example among these identified biomarkers is MiR-378a-3p: It was shown to independently regulate several important transcription factors such as PLAGL2 and ß-catenin, affecting the ß-catenin signaling. Such mechanism may indicate a potential tumor suppressor role of MiR-378a-3p and the impact of its abnormal expression on the cell growth and invasion of HCC. CONCLUSIONS: A bioinformatics model with network topological and functional characterization was successfully applied to the identification of HCC biomarkers. The predicted microRNA biomarkers were than validated with experiments using human HCC cell lines, model animal, and clinical specimens. The results confirmed the prediction by our proposed model that miR-378a-3p was a putative biomarker for diagnosis and prognosis of HCC.

Analysis of mesenchymal stem cell proteomes in situ in the ischemic heart.

Rationale: Cell therapy for myocardial infarction is promising but largely unsuccessful in part due to a lack of mechanistic understanding. Techniques enabling identification of stem cell-specific proteomes in situ in the injured heart may shed light on how the administered cells respond to the injured microenvironment and exert reparative effects. Objective: To identify the proteomes of the transplanted mesenchymal stem cells (MSCs) in the infarcted myocardium, we sought to target a mutant methionyl-tRNA synthetase (MetRSL274G) in MSCs, which charges azidonorleucine (ANL), a methionine analogue and non-canonical amino acid, to tRNA and subsequently to nascent proteins, permitting isolation of ANL-labeled MSC proteomes from ischemic hearts by ANL-alkyne based click reaction. Methods and Results: Murine MSCs were transduced with lentivirus MetRSL274G and supplemented with ANL; the ANL-tagged nascent proteins were visualized by bio-orthogonal non-canonical amino-acid tagging, spanning all molecular weights and by fluorescent non-canonical amino-acid tagging, displaying strong fluorescent signal. Then, the MetRSL274G-transduced MSCs were administered to the infarcted or Sham heart in mice receiving ANL treatment. The MSC proteomes were isolated from the left ventricular protein lysates by click reaction at days 1, 3, and 7 after cell administration, identified by LC/MS. Among all identified proteins (in Sham and MI hearts, three time-points each), 648 were shared by all 6 groups, accounting for 82±5% of total proteins in each group, and enriched under mitochondrion, extracellular exosomes, oxidation-reduction process and poly(A) RNA binding. Notably, 26, 110 and 65 proteins were significantly up-regulated and 11, 28 and 19 proteins were down-regulated in the infarcted vs. Sham heart at the three time-points, respectively; these proteins are pronounced in the GO terms of extracellular matrix organization, response to stress and regulation of apoptotic process and in the KEGG pathways of complements and coagulation cascades, apoptosis, and regulators of actin cytoskeleton. Conclusions: MetRSL274G expression allows successful identification of MSC-specific nascent proteins in the infarcted hearts, which reflect the functional states, adaptive response, and reparative effects of MSCs that may be leveraged to improve cardiac repair.

Identification and Validation of Reference Genes Selection in Ovarian Cancer Exposed to Hypoxia.

INTRODUCTION: Hypoxia-mediated tumor metastasis, progression and drug resistance are major clinical challenges in ovarian cancer. Meanwhile, the genetic basis of these traits is still not clear. RT-qPCR, as an efficient and sensitive gene expression technique, has been widely used for gene analyses, providing a basis for in-depth understanding of molecular changes in different microenvironments. However, there is currently a lack of suitable reference genes to normalize the data associated with hypoxia in ovarian cancer cells. METHODS: A systematic method is needed to select the most suitable reference gene. Here, eight candidate reference genes (GAPDH, ß-actin, 18S RNA, TUBB, PPIA, TBP, RPL13A and SDHA) from humans were selected to assess their expression levels in SKOV3 cells under hypoxia. The geNorm and NormFinder programs were utilized to evaluate the expression stabilities of these selected candidate reference genes. RESULTS: Interestingly, 18S RNA was considered to be an ideal reference gene for the normalization of target gene expression under hypoxic conditions. Furthermore, this result was confirmed in another two ovarian cancer cell line, CAOV3 and OVCAR3 cell line. Finally, these results suggest that appropriate reference genes should be selected before performing gene expression analysis during hypoxic environmental exposure. CONCLUSION: 18S RNA can be used as an appropriate reference gene for the study of gene expression in ovarian cancer samples under hypoxia by RT-qPCR.

Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling.

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related death and has an extremely poor prognosis. Thus, identifying new disease-associated genes and targets for PDAC diagnosis and therapy is urgently needed. This requires investigations into the underlying molecular mechanisms of PDAC at both the systems and molecular levels. Herein, we developed a computational method of predicting cancer genes and anticancer drug targets that combined three independent expression microarray datasets of PDAC patients and protein-protein interaction data. First, Support Vector Machine-Recursive Feature Elimination was applied to the gene expression data to rank the differentially expressed genes (DEGs) between PDAC patients and controls. Then, protein-protein interaction networks were constructed based on the DEGs, and a new score comprising gene expression and network topological information was proposed to identify cancer genes. Finally, these genes were validated by "druggability" prediction, survival and common network analysis, and functional enrichment analysis. Furthermore, two integrins were screened to investigate their structures and dynamics as potential drug targets for PDAC. Collectively, 17 disease genes and some stroma-related pathways including extracellular matrix-receptor interactions were predicted to be potential drug targets and important pathways for treating PDAC. The protein-drug interactions and hinge sites predication of ITGAV and ITGA2 suggest potential drug binding residues in the Thigh domain. These findings provide new possibilities for targeted therapeutic interventions in PDAC, which may have further applications in other cancer types.

A triply modified human adult hemoglobin with low oxygen affinity, rapid autoxidation and high tetramer stability.

The hypoxic environment of tumor may retard the efficacy of tumor therapeutic agents. Hemoglobin (Hb)-based oxygen carriers (HBOCs) could overcome the hypoxia of tumor by the oxygen delivery. However, typical HBOCs may not provide sufficient oxygen for their low oxygen transferring efficiency (OTE). In order to increase the OTE, human adult Hb (HbA) was subjected to triple modifications, i.e., αα-fumaryl crosslink at Lys-99(α), carboxymethylation at Val-1(α) and 8-arm PEG-based polymerization. Crosslink at Lys-99(α) and carboxymethylation at Val-1(α) synergistically led to a T-like quaternary structure of HbA. The dual modification significantly increased the partial oxygen pressure at 50% saturation (P50) of HbA from 14.8 mmHg to 34.6 mmHg and OTE from 9.1% to 33.1%. Eight-arm PEG-based polymerization slightly decreased the P50 of the Hb derivative to 27.8 mmHg and OTE to 30.5%. However, it can enlarge the molecular size of HbA and then prolong the serum duration of HbA. The triple modifications synergistically increased the autoxidation rate of Hb, which promote the production of reactive oxygen species (ROS). Therefore, the sufficient oxygen delivery and substantial production of ROS by the triply modified Hb may provide a potential strategy for tumor therapy.

Advances Of Chimeric Antigen Receptor T Cell Therapy In Ovarian Cancer.

Ovarian cancer, as a common gynecological tumor, is currently recognized as the most lethal gynecological malignancy. In addition to conventional treatment methods such as surgery, radiotherapy and chemotherapy, adoptive immunotherapy represented by modified immune cells also shows good curative effects and is becoming an important method in the treatment of ovarian cancer. Studies have shown that most cancer cells can avoid the recognition of the immune system, thus limiting the anticancer effect of immunotherapy. Chimeric antigen receptor T (CAR-T) cell technology has emerged and has good targeting, killing, proliferation and persistence. A large number of clinical trials also have shown that this technology has achieved great success in improving the quality of life and prolonging the survival time of patients with malignant hematological tumors. CAR-T cell technology has become a research hotspot for immunotherapy. This article mainly reviews various CAR-T cell treatments and their specific mechanisms in the field of ovarian cancer treatment to provide new ideas for the treatment of ovarian cancer.

Recent Advances on the Network Models in Target-based Drug Discovery.

With the advancement of "proteomics" data and systems biology, new techniques are needed to meet the new era of drug discovery. Network theory is increasingly applied to describe complex biological systems, thus implying its essential roles in system-based drug design. In this review, we first summarized general network parameters used in describing biological systems, and then gave some recent applications of these network parameters as topological indices in drug design in terms of Protein Structure Networks (PSNs), Protein-Protein Interaction Networks (PPINs) including related structural PPINs, and Elastic Network Models (ENMs). These network models have enabled the development of new drugs relying on allosteric effects, describing anti-cancer targets, targeting hot spots and key proteins at the protein-protein interfaces and PPINs, and helped drug design by modulating conformational flexibility. Accordingly, we highlighted the integration of network models bringing new paradigms into the next-generation target-based drug discovery.