Exploring the immune-inflammatory mechanism of Maxing Shigan Decoction in treating influenza virus A-induced pneumonia based on an integrated strategy of single-cell transcriptomics and systems biology.

BACKGROUND: Influenza is an acute respiratory infection caused by influenza virus. Maxing Shigan Decoction (MXSGD) is a commonly used traditional Chinese medicine prescription for the prevention and treatment of influenza. However, its mechanism remains unclear. METHOD: The mice model of influenza A virus pneumonia was established by nasal inoculation. After 3 days of intervention, the lung index was calculated, and the pathological changes of lung tissue were detected by HE staining. Firstly, transcriptomics technology was used to analyze the differential genes and important pathways in mouse lung tissue regulated by MXSGD. Then, real-time fluorescent quantitative PCR (RT-PCR) was used to verify the changes in mRNA expression in lung tissues. Finally, intestinal microbiome and intestinal metabolomics were performed to explore the effect of MXSGD on gut microbiota. RESULTS: The lung inflammatory cell infiltration in the MXSGD group was significantly reduced (p < 0.05). The results of bioinformatics analysis for transcriptomics results show that these genes are mainly involved in inflammatory factors and inflammation-related signal pathways mediated inflammation biological modules, etc. Intestinal microbiome showed that the intestinal flora Actinobacteriota level and Desulfobacterota level increased in MXSGD group, while Planctomycetota in MXSGD group decreased. Metabolites were mainly involved in primary bile acid biosynthesis, thiamine metabolism, etc. This suggests that MXSGD has a microbial-gut-lung axis regulation effect on mice with influenza A virus pneumonia. CONCLUSION: MXSGD may play an anti-inflammatory and immunoregulatory role by regulating intestinal microbiome and intestinal metabolic small molecules, and ultimately play a role in the treatment of influenza A virus pneumonia.

Detection of Candidatus Liberibacter asiaticus and five viruses in individual Asian citrus psyllid in China.

Introduction: Asian citrus psyllid (ACP, Diaphorina citri) is an important transmission vector of "Candidatus Liberibacter asiaticus" (CLas), the causal agent of Huanglongbing (HLB), the most destructive citrus disease in the world. As there are currently no HLB-resistant rootstocks or varieties, the control of ACP is an important way to prevent HLB. Some viruses of insect vectors can be used as genetically engineered materials to control insect vectors. Methods: To gain knowledge on viruses in ACP in China, the prevalence of five RNA and DNA viruses was successfully determined by optimizing reverse transcription polymerase chain reaction (RT-PCR) in individual adult ACPs. The five ACP-associated viruses were identified as follows: diaphorina citri bunyavirus 2, which was newly identified by high-throughput sequencing in our lab, diaphorina citri reovirus (DcRV), diaphorina citri picorna-like virus (DcPLV), diaphorina citri bunyavirus (DcBV), and diaphorina citri densovirus-like virus (DcDV). Results: DcPLV was the most prevalent and widespread ACP-associated virus, followed by DcBV, and it was detected in more than 50% of all samples tested. DcPLV was also demonstrated to propagate vertically and found more in salivary glands among different tissues. Approximately 60% of all adult insect samples were co-infected with more than one insect pathogen, including the five ACP-associated viruses and CLas. Discussion: This is the first time these viruses, including the newly identified ACP-associated virus, have been detected in individual adult ACPs from natural populations in China's five major citrus-producing provinces. These results provide valuable information about the prevalence of ACP-associated viruses in China, some of which have the potential to be used as biocontrol agents. In addition, analysis of the change in prevalence of pathogens in a single insect vector is the basis for understanding the interactions between CLas, ACP, and insect viruses.

A Novel Necroptosis-Related Signature Can Predict Prognosis and Chemotherapy Sensitivity in Multiple Myeloma.

BACKGROUND: Necroptosis is an inflammatory cell death mode, and its association with multiple myeloma (MM) remains unclear. METHODS: This prospective study first analyzed the association between necroptosis-related signature as well as prognosis and chemotherapy sensitivity in MM using the necroptosis score. Consensus clustering was used to identify necroptosis-related molecular clusters. Least absolute shrinkage and selection operator analysis and multivariate Cox regression analysis were performed to establish the prognostic model of necroptosis-related genes (NRGs). RESULTS: A high necroptosis score was associated with poor prognosis and abundant immune infiltration. Two molecular clusters (clusters A and B) significantly differed in terms of prognosis and tumor microenvironment. Cluster B had a worse prognosis and higher tumor marker pathway activity than cluster A. The risk score model based on four NRGs can accurately predict the prognosis of patients with MM, which was validated in two validation cohorts. Receiver operating characteristic curve analysis showed that the area under the curves of the risk score in predicting the 1-, 3-, and 5-year survival rates were 0.710, 0.758, and 0.834, respectively. Further, the activity of pathways related to proliferation and genetic regulation in the high-risk group significantly increased. The drug prediction results showed that the low-risk score group was more sensitive to bortezomib, cytarabine, and doxorubicin than the high-risk score group. Meanwhile, the high-risk score group was more sensitive to lenalidomide and vinblastine than the low-risk score group. Finally, the upregulation of model genes CHMP1A, FAS, JAK3, and HSP90AA1 in clinical samples collected from patients with MM was validated via real-time polymerase chain reaction. CONCLUSION: A systematic analysis of NRGs can help identify potential necroptosis-related mechanisms and provide novel biomarkers for MM prognosis prediction, tumor microenvironment evaluation, and personalized treatment planning.

CD8 + T cell-based molecular subtypes with heterogeneous immune landscapes and clinical significance in acute myeloid leukemia.

BACKGROUND: Acute myeloid leukemia (AML) is a heterogeneous hematological malignancy. Although high-dose chemotherapy is the primary treatment option, it cannot cure the disease, and new approaches need to be developed. The tumor microenvironment (TME) plays a crucial role in tumor biology and immunotherapy. CD8 + T cells are the main anti-tumor immune effector cells, and it is essential to understand their relationship with the TME and the clinicopathological characteristics of AML. METHODS: In this study, we conducted a systematic analysis of CD8 + T cell infiltration through multi-omics data and identified molecular subtypes with significant differences in CD8 + T cell infiltration and prognosis. We aimed to provide a comprehensive evaluation of the pathological factors affecting the prognosis of AML patients and to offer theoretical support for the precise treatment of AML. RESULTS: Our results indicate that CD8 + T cell infiltration is accompanied by immunosuppression, and that there are two molecular subtypes, with the C2 subtype having a significantly worse prognosis than the C1 subtype, as well as less CD8 + T cell infiltration. We developed a signature to distinguish molecular subtypes using multiple machine learning algorithms and validated the prognostic predictive power of molecular subtypes in nine AML cohorts including 2059 AML patients. Our findings suggest that there are different immunosuppressive characteristics between the two subtypes. The C1 subtype has up-regulated expression of immune checkpoints such as CTLA4, PD-1, LAG3, and TIGITD, while the C2 subtype infiltrates more immunosuppressive cells such as Tregs and M2 macrophages. The C1 subtype is more responsive to anti-PD-1 immunotherapy and induction chemotherapy, as well as having higher immune and cancer-promoting variant-related pathway activity. Patients with the C2 subtype had a higher FLT3 mutation rate, higher WBC counts, and a higher percentage of blasts, as indicated by increased activity of signaling pathways involved in energy metabolism and cell proliferation. Analysis of data from ex vivo AML cell drug assays has identified a group of drugs that differ in therapeutic sensitivity between molecular subtypes. CONCLUSIONS: Our results suggest that the molecular subtypes we constructed have potential application value in the prognosis evaluation and treatment guidance of AML patients.

Development and validation of a disulfidptosis-related scoring system to predict clinical outcome and immunotherapy response in acute myeloid leukemia by integrated analysis of single-cell and bulk RNA-sequencing.

Background: Disulfidptosis is a metabolically relevant mode of cell death, and its relationship with acute myeloid leukemia (AML) has not been clarified. In this study, disulfidptosis scores were computed to examine the potential biological mechanisms. Methods: Consensus clustering was applied to detect disulfidptosis-related molecular subtypes. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a DRG prognostic model. Results: DRGs are upregulated in AML and associated with poor prognosis. The higher the disulfidptosis activity score, the worse the clinical outcome for patients, accompanied by increased immune checkpoint expression and tumor marker pathway activity. The two molecular subtypes exhibited distinct prognoses and tumor microenvironment (TME) profiles. A prognostic risk score model was established using six DRGs, and the AML cohort was divided into high- and low-risk score groups. Patients in the high-risk group experienced significantly worse prognosis, which was validated in seven AML cohorts. Receiver Operating Characteristic (ROC) curve analysis indicated that the area under the curve values for risk score prediction of 1-, 3-, and 5-year survival were 0.779, 0.714, and 0.778, respectively. The nomogram, in conjunction with clinicopathological factors, further improved the accuracy of prognosis prediction. The high-risk score group exhibited a higher somatic mutation frequency, increased immune-related signaling pathway activity, and greater immune checkpoint expression, suggesting a certain degree of immunosuppression. Patients with advanced age and higher cytogenetic risk also had elevated risk scores. According to drug prediction and AML anti-PD-1 therapy cohort analysis, the low-risk score group displayed greater sensitivity to chemotherapy drugs like cytarabine and midostaurin, while the high-risk score group was more responsive to anti-PD-1 therapy. Finally, clinical samples were collected for sequencing analysis, confirming that the progression of myeloid leukemia was associated with a higher risk score and a negative disulfidptosis score, suggesting that the poor prognosis of AML may be associated with disulfidptosis resistance. Conclusion: In conclusion, a systematic analysis of DRGs can help to identify potential disulfidptosis-related mechanisms and provide effective new biomarkers for prognosis prediction, TME assessment, and the establishment of personalized treatment plans in AML.

Prevalence and risk factors of stroke in high-altitude areas: a systematic review and meta-analysis.

OBJECTIVE: The primary objective of this study is to investigate the prevalence and risk factors of stroke in high-altitude areas through a comprehensive systematic review and meta-analysis. DESIGN: This study adopts a systematic review and meta-analysis design. DATA SOURCES: A thorough search was conducted on databases including PubMed, Web of Science, Embase, Cochrane Library, MEDLINE and SCOPUS, covering the period up to June 2023. ELIGIBILITY CRITERIA: Studies reporting the prevalence of stroke in high-altitude areas and exploring related risk factors were included, regardless of whether they involved clinical samples or the general population. Studies with incomplete, outdated or duplicate data were excluded. DATA EXTRACTION AND SYNTHESIS: We performed eligibility screening, data extraction and quality evaluation of the retrieved articles. Meta-analysis was employed to estimate the prevalence and risk factors of stroke in high-altitude areas. The Newcastle-Ottawa Scale was used to assess the risk of bias. RESULTS: A total of 17 studies encompassing 8 566 042 participants from four continents were included, with altitudes ranging from 1500 m to nearly 5000 m. The pooled prevalence of stroke in high-altitude areas was found to be 0.5% (95% CI 0.3%-7%). Notably, the prevalence was higher in clinical samples (1.2%; 0.4%-2.5%) compared with the general population (0.3%; 95% CI 0.1%-0.6%). When considering geographic regions, the aggregated data indicated that stroke prevalence in the Eurasia plate was 0.3% (0.2%-0.4%), while in the American region, it was 0.8% (0.4%-1.3%). Age (OR, 14.891), gender (OR, 1.289), hypertension (OR, 3.158) and obesity (OR, 1.502) were identified as significant risk factors for stroke in high-altitude areas. CONCLUSIONS: The findings of this study provide insights into the pooled prevalence of stroke in high-altitude areas, highlighting variations based on geographic regions and sampling type. Moreover, age, gender, hypertension and obesity were found to be associated with the occurrence of stroke. PROSPERO REGISTRATION NUMBER: CRD42022381541.

Bioinformatic analysis of the expression profile and identification of RhoGDI2 as a biomarker in imatinib-resistant K562 cells.

OBJECTIVES: Chronic myeloid leukemia (CML) is an aggressive malignancy originating from hematopoietic stem cells. Imatinib (IM), the first-generation tyrosine kinase inhibitor, has greatly improved theliving quality of CML patients. However, owing to the recurrence and treatment failure coming from tyrosine kinase inhibitor (TKIs) resistance, some CML patients still bear poor prognosis. Therefore, we aimed to seek potential signaling pathways and specific biomarkers for imatinib resistance. METHODS: We performed mRNA and miRNA expression profiling in imatinib-sensitive K562 cells (IS-K562) and imatinib-resistant K562 cells (IR-K562). Differentially expressed genes (DEGs) were identified and pathway enrichment analyses were performed to explore the potential mechanism. The protein-protein interaction (PPI) network and miRNA-mRNA regulatory network were constructed to explore potential relationships among these genes. RT-qPCR, western blot and CCK8 were used for further experiments. RESULTS: A total of 623 DEGs and 61 differentially expressed miRNAs were identified. GO revealed that DEGs were mainly involved in cell adhesion, cell migration, differentiation, and inflammatory response. KEGG revealed that DEGs were typically enriched in the Rap1 signaling pathway, focal adhesion, proteoglycans and transcriptional misregulation in cancer, signaling pathways regulating pluripotency of stem cells and some immune-related pathways. The protein-protein interaction (PPI) network and miRNA-mRNA regulatory network revealed a web of diverse connections among genes. Finally, we proved that RHoGDI2 played a critical role in imatinib resistance. CONCLUSION: The dynamic interplay between genes and signaling pathways is associated with TKIs resistance and RHoGDI2 is identified as a biomarker in IR-K562.

Comparing 5G mobile stroke unit and emergency medical service in patients acute ischemic stroke eligible for t-PA treatment: A prospective, single-center clinical trial in Ya'an, China.

BACKGROUND: This study aims to assess and compare the functional outcomes of patients with acute ischemic stroke (AIS) eligible for tissue plasminogen activator (t-PA) treatment who received care from either a fifth-generation(5G) mobile stroke unit (MSU) or traditional emergency medical service (EMS). METHOD: The study recruited patients between February 2020 and January 2022, with the final 90-day follow-up concluded in April 2022. Prior to enrollment, patients were assigned to either EMS or MSU care based on predetermined rules. The primary outcome measure was the Modified Rankin Scale (mRS) score at 90 days, with secondary outcome measures including time metrics, mRS and National Institutes of Health Stroke Scale scores at 7-day follow-up, and hospitalization costs. RESULTS: Of the 2281 enrolled patients, 207 were eligible for t-PA treatment, with 101 allocated to MSU care and 106 to EMS care. The percentage of patients achieving a favorable mRS score (0-2) at 90 days was 82.2% in the MSU group compared to 72.6% in the EMS group (p < .05). Median times from symptom onset to thrombolysis were 146 min in the MSU group and 204 min in the EMS group, while median times from ambulance alert to computed tomography (CT) completion were 53 and 128 min, respectively. Hospitalization charges averaged approximately $3592 in the MSU group and $4800 in the EMS group. CONCLUSIONS: Our findings indicate that 5G MSU care significantly reduces the time from symptom onset to stroke diagnosis and intravenous thrombolysis in patients with AIS, resulting in improved functional outcomes compared to EMS care. As China continues its deployment of 5G technology and other digital infrastructures, the adoption of 5G MSU care on a broader scale may eventually supplant traditional stroke treatment approaches.

Molecular subtypes predict therapeutic responses and identifying and validating diagnostic signatures based on machine learning in chronic myeloid leukemia.

Chronic myeloid leukemia (CML) is a hematological tumor derived from hematopoietic stem cells. The aim of this study is to analyze the biological characteristics and identify the diagnostic markers of CML. We obtained the expression profiles from the Gene Expression Omnibus (GEO) database and identified 210 differentially expressed genes (DEGs) between CML and normal samples. These DEGs are mainly enriched in immune-related pathways such as Th1 and Th2 cell differentiation, primary immunodeficiency, T cell receptor signaling pathway, antigen processing and presentation pathways. Based on these DEGs, we identified two molecular subtypes using a consensus clustering algorithm. Cluster A was an immunosuppressive phenotype with reduced immune cell infiltration and significant activation of metabolism-related pathways such as reactive oxygen species, glycolysis and mTORC1; Cluster B was an immune activating phenotype with increased infiltration of CD4 + and CD8 + T cells and NK cells, and increased activation of signaling pathways such as interferon gamma (IFN-γ) response, IL6-JAK-STAT3 and inflammatory response. Drug prediction results showed that patients in Cluster B had a higher therapeutic response to anti-PD-1 and anti-CTLA4 and were more sensitive to imatinib, nilotinib and dasatinib. Support Vector Machine Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage Selection Operator (LASSO) and Random Forest (RF) algorithms identified 4 CML diagnostic genes (HDC, SMPDL3A, IRF4 and AQP3), and the risk score model constructed by these genes improved the diagnostic accuracy. We further validated the diagnostic value of the 4 genes and the risk score model in a clinical cohort, and the risk score can be used in the differential diagnosis of CML and other hematological malignancies. The risk score can also be used to identify molecular subtypes and predict response to imatinib treatment. These results reveal the characteristics of immunosuppression and metabolic reprogramming in CML patients, and the identification of molecular subtypes and biomarkers provides new ideas and insights for the clinical diagnosis and treatment.

Splicing factor-mediated regulation patterns reveals biological characteristics and aid in predicting prognosis in acute myeloid leukemia.

BACKGROUND: Alternative splicing (AS) of RNA is a fundamental biological process that shapes protein diversity. Many non-characteristic AS events are involved in the onset and development of acute myeloid leukemia (AML). Abnormal alterations in splicing factors (SFs), which regulate the onset of AS events, affect the process of splicing regulation. Hence, it is important to explore the relationship between SFs and the clinical features and biological processes of patients with AML. METHODS: This study focused on SFs of the classical heterogeneous nuclear ribonucleoprotein (hnRNP) family and arginine and serine/arginine-rich (SR) splicing factor family. We explored the relationship between the regulation patterns associated with the expression of SFs and clinicopathological factors and biological behaviors of AML based on a multi-omics approach. The biological functions of SRSF10 in AML were further analyzed using clinical samples and in vitro experiments. RESULTS: Most SFs were upregulated in AML samples and were associated with poor prognosis. The four splicing regulation patterns were characterized by differences in immune function, tumor mutation, signaling pathway activity, prognosis, and predicted response to chemotherapy and immunotherapy. A risk score model was constructed and validated as an independent prognostic factor for AML. Overall survival was significantly shorter in the high-risk score group. In addition, we confirmed that SRSF10 expression was significantly up-regulated in clinical samples of AML, and knockdown of SRSF10 inhibited the proliferation of AML cells and promoted apoptosis and G1 phase arrest during the cell cycle. CONCLUSION: The analysis of splicing regulation patterns can help us better understand the differences in the tumor microenvironment of patients with AML and guide clinical decision-making and prognosis prediction. SRSF10 can be a potential therapeutic target and biomarker for AML.

Optimization of C50 Carotenoids Production by Open Fermentation of Halorubrum sp. HRM-150.

C50 carotenoids, as unique bioactive molecules, have many biological properties, including antioxidant, anticancer, and antibacterial activity, and have a wide range of potential uses in the food, cosmetic, and biomedical industries. The majority of C50 carotenoids are produced by the sterile fermentation of halophilic archaea. This study aims to look at more cost-effective and manageable ways of producing C50 carotenoids. The basic medium, carbon source supplementation, and optimal culture conditions for Halorubrum sp. HRM-150 C50 carotenoids production by open fermentation were examined in this work. The results indicated that Halorubrum sp. HRM-150 grown in natural brine medium grew faster than artificial brine medium. The addition of glucose, sucrose, and lactose (10 g/L) enhanced both biomass and carotenoids productivity, with the highest level reaching 4.53 ± 0.32 µg/mL when glucose was added. According to the findings of orthogonal studies based on the OD600 and carotenoids productivity, the best conditions for open fermentation were salinity 20-25%, rotation speed 150-200 rpm, and pH 7.0-8.2. The up-scaled open fermentation was carried out in a 7 L medium under optimum culture conditions. At 96 h, the OD600 and carotenoids productivity were 9.86 ± 0.51 (dry weight 10.40 ± 1.27 g/L) and 7.31 ± 0.65 µg/mL (701.40 ± 21.51 µg/g dry weight, respectively). When amplified with both universal bacterial primer and archaeal primer in the open fermentation, Halorubrum remained the dominating species, indicating that contamination was kept within an acceptable level. To summarize, open fermentation of Halorubrum is a promising method for producing C50 carotenoids.

Genomic Shift in Population Dynamics of mcr-1-positive Escherichia coli in Human Carriage.

Emergence of the colistin resistance gene, mcr-1, has attracted worldwide attention. Despite the prevalence of mcr-1-positive Escherichia coli (MCRPEC) strains in human carriage showing a significant decrease between 2016 and 2019, genetic differences in MCRPEC strains remain largely unknown. We therefore conducted a comparative genomic study on MCRPEC strains from fecal samples of healthy human subjects in 2016 and 2019. We identified three major differences in MCRPEC strains between these two time points. First, the insertion sequence ISApl1 was often deleted and the percentage of mcr-1-carrying IncI2 plasmids was increased in MCRPEC strains in 2019. Second, the antibiotic resistance genes (ARGs), aac(3)-IVa and blaCTX-M-1, emerged and coexisted with mcr-1 in 2019. Third, MCRPEC strains in 2019 contained more virulence genes, resulting in an increased proportion of extraintestinal pathogenic E. coli (ExPEC) strains (36.1%) in MCRPEC strains in 2019 compared to that in 2016 (10.5%), implying that these strains could occupy intestinal ecological niches by competing with other commensal bacteria. Our results suggest that despite the significant reduction in the prevalence of MCRPEC strains in humans from 2016 to 2019, MCRPEC exhibits increased resistance to other clinically important ARGs and contains more virulence genes, which may pose a potential public health threat.

Ferroptosis-related molecular patterns reveal immune escape, inflammatory development and lipid metabolism characteristics of the tumor microenvironment in acute myeloid leukemia.

Background: An increasing number of studies have revealed the influencing factors of ferroptosis. The influence of immune cell infiltration, inflammation development and lipid metabolism in the tumor microenvironment (TME) on the ferroptosis of tumor cells requires further research and discussion. Methods: We explored the relationship between ferroptosis-related genes and acute myeloid leukemia (AML) from the perspective of large sample analysis and multiomics, used multiple groups to identify and verify ferroptosis-related molecular patterns, and analyzed the sensitivity to ferroptosis and the state of immune escape between different molecular pattern groups. The single-sample gene set enrichment analysis (ssGSEA) algorithm was used to quantify the phenotypes of ferroptosis-related molecular patterns in individual patients. HL-60 and THP-1 cells were treated with ferroptosis inducer RSL3 to verify the therapeutic value of targeted inhibition of GPX4. Results: Three ferroptosis-related molecular patterns and progressively worsening phenotypes including immune activation, immune exclusion and immunosuppression were found with the two different sequencing approaches. The FSscore we constructed can quantify the development of ferroptosis-related phenotypes in individual patients. The higher the FSscore is, the worse the patient's prognosis. The FSscore is also highly positively correlated with pathological conditions such as inflammation development, immune escape, lipid metabolism, immunotherapy resistance, and chemotherapy resistance and is negatively correlated with tumor mutation burden. Moreover, RSL3 can induce ferroptosis of AML cells by reducing the protein level of GPX4. Conclusions: This study revealed the characteristics of immunity, inflammation, and lipid metabolism in the TME of different AML patients and differences in the sensitivity of tumor cells to ferroptosis. The FSscore can be used as a biomarker to provide a reference for the clinical evaluation of the pathological characteristics of AML patients and the design of personalized treatment plans. And GPX4 is a potential target for AML treatment.

IReNA: Integrated regulatory network analysis of single-cell transcriptomes and chromatin accessibility profiles.

Recently, single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) have been developed to separately measure transcriptomes and chromatin accessibility profiles at the single-cell resolution. However, few methods can reliably integrate these data to perform regulatory network analysis. Here, we developed integrated regulatory network analysis (IReNA) for network inference through the integrated analysis of scRNA-seq and scATAC-seq data, network modularization, transcription factor enrichment, and construction of simplified intermodular regulatory networks. Using public datasets, we showed that integrated network analysis of scRNA-seq data with scATAC-seq data is more precise to identify known regulators than scRNA-seq data analysis alone. Moreover, IReNA outperformed currently available methods in identifying known regulators. IReNA facilitates the systems-level understanding of biological regulatory mechanisms and is available at https://github.com/jiang-junyao/IReNA.

A novel fatty acid metabolism-related signature identifies features of the tumor microenvironment and predicts clinical outcome in acute myeloid leukemia.

BACKGROUND: Acute myeloid leukemia (AML) is the most common malignancy of the hematological system, and there are currently a number of studies regarding abnormal alterations in energy metabolism, but fewer reports related to fatty acid metabolism (FAM) in AML. We therefore analyze the association of FAM and AML tumor development to explore targets for clinical prognosis prediction and identify those with potential therapeutic value. METHODS: The identification of AML patients with different fatty acid metabolism characteristics was based on a consensus clustering algorithm. The CIBERSORT algorithm was used to calculate the proportion of infiltrating immune cells. We used Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis to construct a signature for predicting the prognosis of AML patients. The Genomics of Drug Sensitivity in Cancer database was used to predict the sensitivity of patient samples in high- and low-risk score groups to different chemotherapy drugs. RESULTS: The consensus clustering approach identified three molecular subtypes of FAM that exhibited significant differences in genomic features such as immunity, metabolism, and inflammation, as well as patient prognosis. The risk-score model we constructed accurately predicted patient outcomes, with area under the receiver operating characteristic curve values of 0.870, 0.878, and 0.950 at 1, 3, and 5 years, respectively. The validation cohort also confirmed the prognostic evaluation performance of the risk score. In addition, higher risk scores were associated with stronger fatty acid metabolisms, significantly higher expression levels of immune checkpoints, and significantly increased infiltration of immunosuppressive cells. Immune functions, such as inflammation promotion, para-inflammation, and type I/II interferon responses, were also significantly activated. These results demonstrated that immunotherapy targeting immune checkpoints and immunosuppressive cells, such as myeloid-derived suppressor cells (MDSCs) and M2 macrophages, are more suitable for patients with high-risk scores. Finally, the prediction results of chemotherapeutic drugs showed that samples in the high-risk score group had greater treatment sensitivity to four chemotherapy drugs in vitro. CONCLUSIONS: The analysis of the molecular patterns of FAM effectively predicted patient prognosis and revealed various tumor microenvironment (TME) characteristics.

LRLoop: a method to predict feedback loops in cell-cell communication.

MOTIVATION: Intercellular communication (i.e. cell-cell communication) plays an essential role in multicellular organisms coordinating various biological processes. Previous studies discovered that feedback loops between two cell types are a widespread and vital signaling motif regulating development, regeneration and cancer progression. While many computational methods have been developed to predict cell-cell communication based on gene expression datasets, these methods often predict one-directional ligand-receptor interactions from sender to receiver cells and are not suitable to identify feedback loops. RESULTS: Here, we describe ligand-receptor loop (LRLoop), a new method for analyzing cell-cell communication based on bi-directional ligand-receptor interactions, where two pairs of ligand-receptor interactions are identified that are responsive to each other and thereby form a closed feedback loop. We first assessed LRLoop using bulk datasets and found our method significantly reduces the false positive rate seen with existing methods. Furthermore, we developed a new strategy to assess the performance of these methods in single-cell datasets. We used the between-tissue interactions as an indicator of potential false-positive prediction and found that LRLoop produced a lower fraction of between-tissue interactions than traditional methods. Finally, we applied LRLoop to the single-cell datasets obtained from retinal development. We discovered many new bi-directional ligand-receptor interactions among individual cell types that potentially control proliferation, neurogenesis and/or cell fate specification. AVAILABILITY AND IMPLEMENTATION: An R package is available at https://github.com/Pinlyu3/LRLoop. The source code can be found at figshare (https://doi.org/10.6084/m9.figshare.20126138.v1). The datasets can be found at figshare (https://doi.org/10.6084/m9.figshare.20126021.v1). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Relationship between tool-like receptor 4 gene polymorphism and the susceptibility to pulmonary tuberculosis.

OBJECTIVE: To evaluate the susceptibility of pulmonary tuberculosis based on the single nucleotide polymorphism (SNP) of Toll like receptor 4 (TLR4) gene. METHODS: We searched PubMed, Web of science, EMBASE, and Chinese National Knowledge Infrastructure (CNKI) databases using mesh words: "tuberculosis", "pulmonary", "TLR4", "SNP", "Toll like receptor 4", "nucleotide polymorphism" for studies on the relations between TLR4 SNP polymorphism and the risk of pulmonary tuberculosis that were published before September 1st, 2021. Papers were selected according to the inclusion and exclusion criteria established in advance. The allele and genotype data of the four most widely studied SNP loci (rs4986791, rs4986790, rs11536889, rs10759932) in TLR4 gene were extracted and analyzed by Review Manager 5.3 software. RESULTS: 20 studies including a total of 24727 patients were included in the final meta-analysis. Results of the meta-analysis showed that the C allele of rs10759932 increased the risk of pulmonary tuberculosis (odds ratio - OR: 1.144; 95% confidence interval (CI) 1.043-1.254, P = 0.004). Compared with TT genotype, CC+CT genotype of rs10759932 and CT alone genotype significantly increased the risk of pulmonary tuberculosis (OR = 1.218, 95% CI 1.084-1.369, P = 0.001; OR = 1.227, 95% CI 1.085-1.387, P = 0.001). However, rs4986790, rs4986791 and rs11536889 had no significant correlation with the susceptibility of pulmonary tuberculosis (P > 0.05). CONCLUSION: G allele GG+GA genotype, and the GA genotype of rs4986790; C allele, CC+CT genotype, and the CC genotype of rs10759932 increased the risk of pulmonary tuberculosis, and may potentially be used as a marker for pulmonary tuberculosis diagnosis and monitoring.

Prediction of Sacral Screw Loosening after Lumbosacral Surgeries Involving Rigid Fixation of Sacral Bone Using Preoperative Computed Tomography Scans.

Objective: To find a preoperative computed tomography-based method to predict the incidence of sacral screw loosening and assist surgical planning. Methods: Surgically treated patients for degenerative lumbosacral disorders with rigid pedicle screw fixation of patients with L5-S1 vertebra in our center from January 2016 to January 2021 were retrospectively included in the current study. CT scan attenuation of the horizontal plane of the sacrum was measured with Hounsfield units (HU). Postoperative X-ray tests were used to diagnose screw loosening. The data was analyzed by independent sample t-tests, X 2 analysis, Pearson correlation analysis, and ROC curve analysis. Results: A total of 162 (114 male, 48 female, average age 63.7 ± 7.3 years) patients were included in the final analysis. Significant differences were found between the screw loosening group and nonloosening group concerning the HU value of the sacrum at the horizontal plane (P < 0.01). In ROC curve analysis, AUC was 0.674 (95% CI: 0.592-0.756). A cutoff of 200 HU provided 64.8% sensitivity and 62.4% specificity, and a cutoff of 150 HU provided 90.2% sensitivity. Conclusions: Analyzing 162 patients with at least 12 months of follow-up, we propose cutoff CT attenuation values of 200 HU and 150 HU to take moderate and radical measures of screw augmentation to prevent screw loosening in the sacral bone.

DNA hypermethylation modification promotes the development of hepatocellular carcinoma by depressing the tumor suppressor gene ZNF334.

DNA methylation plays a pivotal role in the development and progression of tumors. However, studies focused on the dynamic changes of DNA methylation in the development of hepatocellular carcinoma (HCC) are rare. To systematically illustrate the dynamic DNA methylation alternation from premalignant to early-stage liver cancer with the same genetic background, this study enrolled 5 HBV-related patients preceded with liver cirrhosis, pathologically identified as early-stage HCC with dysplastic nodules. Liver fibrosis tissues, dysplastic nodules and early HCC tissues from these patients were used to measure DNA methylation. Here, we report significant differences in the DNA methylation spectrum among the three types of tissues. In the early stage of HCC, DNA hypermethylation of tumor suppressor genes is predominant. Additionally, DNA hypermethylation in the early stage of HCC changes the binding ability of transcription factor TP53 to the promoter of tumor suppressor gene ZNF334, and inhibits the expression of ZNF334 at the transcription level. Furthermore, through a series of in vivo and in vitro experiments, we have clarified the exacerbation effect of tumor suppressor gene ZNF334 deletion in the occurrence of HCC. Combined with clinical data, we found that the overall survival and relapse-free survival of patients with high ZNF334 expression are significantly longer. Thus, we partly elucidated a sequential alternation of DNA methylation modification during the occurrence of HCC, and clarified the biological function and regulatory mechanism of the tumor suppressor gene ZNF334, which is regulated by related DNA methylation sites. Our study provides a new target and clinical evidence for the early diagnosis and sheds light on the precise treatment of liver cancer.

Inflammatory response mediates cross-talk with immune function and reveals clinical features in acute myeloid leukemia.

Accumulated genetic mutations are an important cause for the development of acute myeloid leukemia (AML), but abnormal changes in the inflammatory microenvironment also have regulatory effects on AML. Exploring the relationship between inflammatory response and pathological features of AML has implications for clinical diagnosis, treatment and prognosis evaluation. We analyzed the expression variation landscape of inflammatory response-related genes (IRRGs) and calculated an inflammatory response score for each sample using the gene set variation analysis (GSVA) algorithm. The differences in clinical- and immune-related characteristics between high- and low-inflammatory response groups were further analyzed. We found that most IRRGs were highly expressed in AML samples, and patients with high inflammatory response had poor prognosis and were accompanied with highly activated chemokine-, cytokine- and adhesion molecule-related signaling pathways, higher infiltration ratios of monocytes, neutrophils and M2 macrophages, high activity of type I/II interferon (IFN) response, and higher expression of immune checkpoints. We also used the Genomics of Drug Sensitivity in Cancer (GDSC) database to predict the sensitivity of AML samples with different inflammatory responses to common drugs, and found that AML samples with low inflammatory response were more sensitive to cytarabine, doxorubicin and midostaurin. SubMap algorithm also demonstrated that high-inflammatory response patients are more suitable for anti-PD-1 immunotherapy. Finally, we constructed a prognostic risk score model to predict the overall survival (OS) of AML patients. Patients with higher risk score had significantly shorter OS, which was confirmed in two validation cohorts. The analysis of inflammatory response patterns can help us better understand the differences in tumor microenvironment (TME) of AML patients, and guide clinical medication and prognosis prediction.