Machine-learning developed an iron, copper, and sulfur-metabolism associated signature predicts lung adenocarcinoma prognosis and therapy response.

BACKGROUND: Previous studies have largely neglected the role of sulfur metabolism in LUAD, and no study has combine iron, copper, and sulfur-metabolism associated genes together to create prognostic signatures. METHODS: This study encompasses 1564 LUAD patients, 1249 NSCLC patients, and over 10,000 patients with various cancer types from diverse cohorts. We employed the R package ConsensusClusterPlus to separate patients into different ICSM (Iron, Copper, and Sulfur-Metabolism) subtypes. Various machine-learning methods were utilized to develop the ICSMI. Enrichment analyses were conducted using ClusterProfiler and GSVA, while IOBR quantified immune cell infiltration. GISTIC2.0 and maftools were utilized for CNV and SNV data analysis. The Oncopredict package predicted drug information based on GDSC1. TIDE algorithm and cohorts GSE91061 and IMvigor210 evaluated patient response to immunotherapy. Single-cell data was processed using the Seurat package, AUCell package calculated cells geneset activity scores, and the Scissor algorithm identified ICSMI-associated cells. In vitro experiments was conducted to explore the role of ICSMRGs in LUAD. RESULTS: Unsupervised clustering identified two distinct ICSM subtypes of LUAD, each with unique clinical characteristics. The ICSMI, comprising 10 genes, was constructed using integrated machine-learning methods. Its prognostic power was validated in 10 independent datasets, revealing that LUAD patients with higher ICSMI levels had poorer prognoses. Furthermore, ICSMI demonstrated superior predictive abilities compared to 102 previously published signatures. A nomogram incorporating ICSMI and clinical features exhibited high predictive performance. ICSMI positively correlated with patients gene mutations, and integrated analysis of bulk and single-cell transcriptome data revealed its association with TME modulators. Cells representing the high-ICSMI phenotype exhibited more malignant features. LUAD patients with high ICSMI levels exhibited sensitivity to chemotherapy and targeted therapy but displayed resistance to immunotherapy. In a comprehensive analysis across various cancers, ICSMI retained significant prognostic value and emerged as a risk factor for the majority of cancer patients. CONCLUSIONS: ICSMI provides critical prognostic insights for LUAD patients, offering valuable insights into the tumor microenvironment and predicting treatment responsiveness.

Identification of a novel ADCC-related gene signature for predicting the prognosis and therapy response in lung adenocarcinoma.

BACKGROUND: Previous studies have largely neglected the role of ADCC in LUAD, and no study has systematically compiled ADCC-associated genes to create prognostic signatures. METHODS: In this study, 1564 LUAD patients, 2057 NSCLC patients, and more than 5000 patients with various cancer types from diverse cohorts were included. R package ConsensusClusterPlus was utilized to classify patients into different subtypes. A number of machine-learning algorithms were used to construct the ADCCRS. GSVA and ClusterProfiler were used for enrichment analyses, and IOBR was used to quantify immune cell infiltration level. GISTIC2.0 and maftools were used to analyze the CNV and SNV data. The Oncopredict package was used to predict drug information based on the GDSC1. Three immunotherapy cohorts were used to evaluate patient response to immunotherapy. The Seurat package was used to process single-cell data, the AUCell package was used to calculate cells' geneset activity scores, and the Scissor algorithm was used to identify ADCCRS-associated cells. RESULTS: Through unsupervised clustering, two distinct subtypes of LUAD were identified, each exhibiting distinct clinical characteristics. The ADCCRS, consisted of 16 genes, was constructed by integrated machine-learning methods. The prognostic power of ADCCRS was validated in 28 independent datasets. Further, ADCCRS shows better predictive abilities than 102 previously published signatures in predicting LUAD patients' survival. A nomogram incorporating ADCCRS and clinical features was constructed, demonstrating high predictive performance. ADCCRS positively correlates with patients' gene mutation, and integrated analysis of bulk and single-cell transcriptome data revealed the association of ADCCRS with TME modulators. Cells representing high-ADCCRS phenotype exhibited more malignant features. LUAD patients with high ADCCRS levels exhibited sensitivity to chemotherapy and targeted therapy, while displaying resistance to immunotherapy. In pan-cancer analysis, ADCCRS still exhibited significant prognostic value and was found to be a risk factor for most cancer patients. CONCLUSIONS: ADCCRS offers a critical prognostic insight for patients with LUAD, shedding light on the tumor microenvironment and forecasting treatment responsiveness.

A degron-based strategy reveals new insights into Aurora B function in C. elegans.

The widely conserved kinase Aurora B regulates important events during cell division. Surprisingly, recent work has uncovered a few functions of Aurora-family kinases that do not require kinase activity. Thus, understanding this important class of cell cycle regulators will require strategies to distinguish kinase-dependent from independent functions. Here, we address this need in C. elegans by combining germline-specific, auxin-induced Aurora B (AIR-2) degradation with the transgenic expression of kinase-inactive AIR-2. Through this approach, we find that kinase activity is essential for AIR-2's major meiotic functions and also for mitotic chromosome segregation. Moreover, our analysis revealed insight into the assembly of the ring complex (RC), a structure that is essential for chromosome congression in C. elegans oocytes. AIR-2 localizes to chromosomes and recruits other components to form the RC. However, we found that while kinase-dead AIR-2 could load onto chromosomes, other components were not recruited. This failure in RC assembly appeared to be due to a loss of RC SUMOylation, suggesting that there is crosstalk between SUMOylation and phosphorylation in building the RC and implicating AIR-2 in regulating the SUMO pathway in oocytes. Similar conditional depletion approaches may reveal new insights into other cell cycle regulators.

HOTAIR knockdown impairs metastasis of cervical cancer cells by down-regulating metastasis-related genes.

This study investigated the role of LncRNA HOTAIR knockdown in the biological impacts on cervical cancer cells. The HOTAIR gene in two human cervical cancer cell lines was silenced with small interfering (si) RNA siHOTAIR. Proliferation, apoptosis, migration and invasion of cells were assessed following the knockdown. The expressions of Notch1, EpCAM, E-cadherin, vimentin and STAT3 were assessed using qRT-PCR and Western blotting analysis. Compared with controls, HOTAIR levels were reduced significantly, the OD values of cells were significantly decreased in proliferation assays, cell apoptosis was significantly increased, cell migration and invasion were significantly reduced after HOTAIR knockdown. Molecular analysis showed that Notch1, EpCAM, vimentin and STAT3 expressions were decreased significantly, while the expression of E-cadherin was significantly increased after HOTAIR knockdown. Rescue experiments further confirmed that Notch1 and STAT3 were involved in siHOTAIR-mediated reduction of migration and invasion of cervical cancer cells.IMPACT STATEMENTWhat is already known on this subject? Long non-coding RNAs including HOTAIR, is implicated in occurrence and development of cancer and have been explored to develop new therapeutic options for cancer.What do the results of this study add? HOTAIR silencing significantly reduces the viability and migration ability of cells and induces cell apoptosis, adding experimental data supporting the potential use of HOTAIR specific-siRNA as a therapeutic avenue for the cancer.What are the implications of these findings for clinical practice and/or further research? The finding from this study would help develop clinically applicable therapeutic avenues for the cancer and identify new treatment targets in the relevant pathways leading to new drugs or treatments.

The auxin-inducible degradation (AID) system enables versatile conditional protein depletion in C. elegans.

Experimental manipulation of protein abundance in living cells or organisms is an essential strategy for investigation of biological regulatory mechanisms. Whereas powerful techniques for protein expression have been developed in Caenorhabditis elegans, existing tools for conditional disruption of protein function are far more limited. To address this, we have adapted the auxin-inducible degradation (AID) system discovered in plants to enable conditional protein depletion in C. elegans. We report that expression of a modified Arabidopsis TIR1 F-box protein mediates robust auxin-dependent depletion of degron-tagged targets. We document the effectiveness of this system for depletion of nuclear and cytoplasmic proteins in diverse somatic and germline tissues throughout development. Target proteins were depleted in as little as 20-30 min, and their expression could be re-established upon auxin removal. We have engineered strains expressing TIR1 under the control of various promoter and 3' UTR sequences to drive tissue-specific or temporally regulated expression. The degron tag can be efficiently introduced by CRISPR/Cas9-based genome editing. We have harnessed this system to explore the roles of dynamically expressed nuclear hormone receptors in molting, and to analyze meiosis-specific roles for proteins required for germ line proliferation. Together, our results demonstrate that the AID system provides a powerful new tool for spatiotemporal regulation and analysis of protein function in a metazoan model organism.

SRPX2 Enhances the Epithelial-Mesenchymal Transition and Temozolomide Resistance in Glioblastoma Cells.

Glioblastoma (GBM) is the most common and most aggressive central nervous system tumor in adults. Due to GBM cell invasiveness and resistance to chemotherapy, current medical interventions are not satisfactory, and the prognosis for GBM is poor. It is necessary to investigate the underlying mechanism of GBM metastasis and drug resistance so that more effective treatments can be developed for GBM patients. sushi repeat-containing protein, X-linked 2 (SRPX2) is a prognostic biomarker in many different cancer cell lines and is associated with poor prognosis in cancer patients. SRPX2 overexpression promotes interactions between tumor and endothelial cells, leading to tumor progression and metastasis. We hypothesize that SRPX2 also contributes to GBM chemotherapy resistance and metastasis. Our results revealed that GBM tumor samples from 42 patients expressed higher levels of SRPX2 than the control normal brain tissue samples. High-SRPX2 expression levels are correlated with poor prognosis in those patients, as well as resistance to temozolomide in cultured GBM cells. Up-regulating SRPX2 expression in cultured GBM cell lines facilitated invasiveness and migration of GBM cells, while down-regulating SRPX2 through RNA interference was inhibitory. These results suggest that SRPX2 plays an important role in GBM metastasis. Epithelial to mesenchymal transition (EMT) is one of the processes that facilitate GBM metastasis and resistance to chemotherapy. EMT marker expression was decreased in SRPX2 down-regulated GBM cells, and MAPK signaling pathway marker expression was also decreased when SRPX2 is knocked down in GBM-cultured cells. Blocking the MAPK signaling pathway inhibited GBM metastasis but did not inhibit cell invasion and migration in SRPX2 down-regulated cells. Our results indicate that SRPX2 facilitates GBM metastasis by enhancing the EMT process via the MAPK signaling pathway.

EB1 acetylation by P300/CBP-associated factor (PCAF) ensures accurate kinetochore-microtubule interactions in mitosis.

In eukaryotes, microtubules are essential for cellular plasticity and dynamics. Here we show that P300/CBP-associated factor (PCAF), a kinetochore-associated acetyltransferase, acts as a negative modulator of microtubule stability through acetylation of EB1, a protein that controls the plus ends of microtubules. PCAF acetylates EB1 on K220 and disrupts the stability of a hydrophobic cavity on the dimerized EB1 C terminus, which was previously reported to interact with plus-end tracking proteins (TIPs) containing the SxIP motif. As determined with an EB1 acetyl-K220-specific antibody, K220 acetylation is dramatically increased in mitosis and localized to the spindle microtubule plus ends. Surprisingly, persistent acetylation of EB1 delays metaphase alignment, resulting in impaired checkpoint silencing. Consequently, suppression of Mad2 overrides mitotic arrest induced by persistent EB1 acetylation. Thus, our findings identify dynamic acetylation of EB1 as a molecular mechanism to orchestrate accurate kinetochore-microtubule interactions in mitosis. These results establish a previously uncharacterized regulatory mechanism governing localization of microtubule plus-end tracking proteins and thereby the plasticity and dynamics of cells.

NHE1 gene associated with avian leukosis virus subgroup J infection in chicken.

As a kind of binding protein, the type 1 Na(+)/H(+) exchanger (NHE1) is a receptor for the highly pathogenic Avian leukosis viruses-J subgroup (ALV-J) in chicken. In order to investigate the potential effect of chicken NHE1 gene on leukosis, we compared its expression between ALV-J-affected and -unaffected chicken, screened variations across the whole gene, and then performed association analysis with ALV-J affected/unaffected trait in three un-related chicken populations. We found that the NHE1 gene expressed in four immune tissues including spleen, bursa fabricius, liver, and thymus, and its expression was significantly up-regulated in liver and thymus of ALV-J-affected chickens (with leukosis phenotype) compared to -unaffected ones (ALV-J-negative controls). Thirty-six single nucleotide polymorphisms (SNP) were identified in a 6,105 bp region of the chicken NHE1 gene, giving rise to every 170 bp per SNP. Two SNP of g.4405A>G and g.5886C>G were genotyped with PCR-RFLP method. Results showed that g.4405A>G was significantly associated (P < 0.05) with ALV-J infection in all of the three chicken populations, including White Recessive Rock (WRR), Dwarf Yellow (DY) and Shiki Yellow (SY), while g.5886C>G was significantly associated (P < 0.05) with ALV-J infection in SY. These results indicated that the NHE1 gene was related to ALV-J infection in chicken.

Semi-Infinitely Constrained Markov Decision Processes and Provably Efficient Reinforcement Learning.

We propose a novel generalization of constrained Markov decision processes (CMDPs) that we call the semi-infinitely constrained Markov decision process (SICMDP). Particularly, we consider a continuum of constraints instead of a finite number of constraints as in the case of ordinary CMDPs. We also devise two reinforcement learning algorithms for SICMDPs that we refer to as SI-CMBRL and SI-CPO. SI-CMBRL is a model-based reinforcement learning algorithm. Given an estimate of the transition model, we first transform the reinforcement learning problem into a linear semi-infinitely programming (LSIP) problem and then use the dual exchange method in the LSIP literature to solve it. SI-CPO is a policy optimization algorithm. Borrowing ideas from the cooperative stochastic approximation approach, we make alternative updates to the policy parameters to maximize the reward or minimize the cost. To the best of our knowledge, we are the first to apply tools from semi-infinitely programming (SIP) to solve constrained reinforcement learning problems. We present theoretical analysis for SI-CMBRL and SI-CPO, identifying their iteration complexity and sample complexity. We also conduct extensive numerical experiments to illustrate the SICMDP model and demonstrate that our proposed algorithms are able to solve complex control tasks leveraging modern deep reinforcement learning techniques.

Performance of public drinking water purifiers in control of trihalomethanes, antibiotics and antibiotic resistance genes.

Point-of-use water purifiers are widely applied as a terminal treatment device to produce drinking water with high quality. However, concerns are raised regarding low efficiency in eliminating emerging organic pollutants. To enhance our understanding of the reliability and potential risks of water purifiers, the removal of trihalomethanes, antibiotics, and antibiotic resistance genes (ARGs) in four public water purifiers was investigated. In the four public water purifiers in October and November, the removal efficiencies of trichloromethane (TCM) and bromodichloromethane (BDCM) were 15%-69% (averagely 37%) and 6%-44% (averagely 23%). The levels of TCM and BDCM were lowered by all water purifiers in October and November, but accelerated in effluent compared to the influent in one public water purifier in December. The removal efficiencies of twelve antibiotics greatly varied with species and time. Out of twelve sampling cases, the removal efficiencies of total antibiotics were 25%-75% in ten cases. In the other two cases, very low removal efficiency (6%) or higher levels of antibiotics present in effluent compared to the influent were observed. Two public water purifiers effectively remove ARGs from water, with log removal rates of 0.45 log-3.89 log. However, in the other two public water purifiers, the ARG abundance accidently increased in the effluents. Overall, public water purifiers were more effective in removing antibiotics and ARGs compared to household water purifiers, but less or equally effective in removing trihalomethanes. Both public and household water purifiers could be contaminated and release the accumulated micro-pollutants or biofilm-related pollutants into effluent. The production frequency and standing time of water within water purifiers can impact the internal contamination and purification efficacy.

Enzymatic characterization and application of soybean hull peroxidase as an efficient and renewable biocatalyst for degradation of zearalenone.

Zearalenone (ZEN) is a notorious mycotoxin commonly found in Fusarium-contaminated crops, which causes great loss in livestock farming and serious health problems to humans. In the present work, we found that crude peroxidase extraction from soybean hulls could use H2O2 as a co-substate to oxidize ZEN. Molecular docking and dynamic simulation also supported that ZEN could bind to the active site of soybean hull peroxidase (SHP). Subsequently, SHP extracted from soybean hulls was purified using a combined purification protocol involving ammonium sulfate precipitation, ion exchange chromatography and size exclusion chromatography. The purified SHP showed wide pH resistance and high thermal stability. This peroxidase could degrade 95 % of ZEN in buffer with stepwise addition of 100 µM H2O2 in 1 h. The two main ZEN degradation products were identified as 13-OH-ZEN and 13-OH-ZEN-quinone. Moreover, SHP-catalyzed ZEN degradation products displayed much less cytotoxicity to human liver cells than ZEN. The application of SHP in various food matrices obtained 54 % to 85 % ZEN degradation. The findings in this study will promote the utilization of SHP as a cheap and renewable biocatalyst for degrading ZEN in food.

PLK1 phosphorylation of ZW10 guides accurate chromosome segregation in mitosis.

Stable transmission of genetic information during cell division requires faithful chromosome segregation. Mounting evidence has demonstrated that PLK1 dynamics at kinetochores control correct kinetochore-microtubule attachments and subsequent silencing of the spindle checkpoint. However, the mechanisms underlying PLK1-mediated silencing of the spindle checkpoint remain elusive. Here, we identified a regulatory mechanism by which PLK1-elicited ZW10 phosphorylation regulates spindle checkpoint silencing in mitosis. ZW10 is a cognate substrate of PLK1, and the phosphorylation of ZW10 at Ser12 enables dynamic ZW10-Zwint1 interactions. Inhibition of ZW10 phosphorylation resulted in misaligned chromosomes, while persistent expression of phospho-mimicking ZW10 mutant caused premature anaphase, in which sister chromatids entangled as cells entered anaphase. These findings reveal the previously uncharacterized PLK1-ZW10 interaction through which dynamic phosphorylation of ZW10 fine-tunes accurate chromosome segregation in mitosis.

Biodegradation of ochratoxin A by Brevundimonas diminuta HAU429: Characterized performance, toxicity evaluation and functional enzymes.

Ochratoxin A (OTA) is a notorious mycotoxin commonly contaminating food products worldwide. In this study, an OTA-degrading strain Brevundimonas diminuta HAU429 was isolated by using hippuryl-L-phenylalanine as the sole carbon source. The biodegradation of OTA by strain HAU429 was a synergistic effect of intracellular and extracellular enzymes, which transformed OTA into ochratoxin α (OTα) through peptide bond cleavage. Cytotoxicity tests and cell metabolomics confirmed that the transformation of OTA into OTα resulted in the detoxification of its hepatotoxicity since OTA but not OTα disturbed redox homeostasis and induced oxidative damage to hepatocytes. Genome mining identified nine OTA hydrolase candidates in strain HAU429. They were heterologously expressed in Escherichia coli, and three novel amidohydrolase BT6, BT7 and BT9 were found to display OTA-hydrolyzing activity. BT6, BT7 and BT9 showed less than 45 % sequence identity with previously identified OTA-degrading amidohydrolases. BT6 and BT7 shared 60.9 % amino acid sequence identity, and exhibited much higher activity towards OTA than BT9. BT6 and BT7 could completely degrade 1 µg mL-1 of OTA within 1 h and 50 min, while BT9 hydrolyzed 100 % of OTA in the reaction mixture by 12 h. BT6 was the most thermostable retaining 38 % of activity after incubation at 70 °C for 10 min, while BT7 displayed the highest tolerance to ethanal remaining 76 % of activity in the presence of 6 % ethanol. This study could provide new insights towards microbial OTA degradation and promote the development of enzyme-catalyzed OTA detoxification during food processing.

Effective Degradation of Free Gossypol in Defatted Cottonseed Meal by Bacterial Laccases: Performance and Toxicity Analysis.

Cottonseed meal (CSM) is the major by-product of the cottonseed oil extraction process with high protein content. However, the presence of free gossypol (FG) in CSM severely restricts its utilization in the food and animal feed industries. The development of a biological strategy for the effective removal of FG in CSM has become an urgent need. In this study, three bacterial laccases including CotA from Bacillus licheniformis, CueO from Escherichia coli, and LcLac from Loigolactobacillus coryniformis were heterologously expressed and investigated for their FG degradation ability. The results showed that CotA laccase displayed the highest FG-degrading capacity among the three laccases, achieving 100% FG degradation at 37 °C and pH 7.0 in 1 h without the addition of a redox mediator. Moreover, in vitro and in vivo studies confirmed that the hepatotoxicity of FG was effectively eliminated after oxidative degradation by CotA laccase. Furthermore, the addition of CotA laccase could achieve 87% to 98% FG degradation in defatted CSM within 2 h. In conclusion, CotA laccase can be developed as an effective biocatalyst for the detoxification of FG in CSM.

Electroacupuncture improves low-grade duodenal inflammation in FD rats by reshaping intestinal flora through the NF-κB p65/NLRP3 pyroptosis pathway.

Electroacupuncture (EA) is an effective alternative for the treatment of functional dyspepsia (FD). It reduces low-grade duodenal inflammation and improves the symptoms of FD by downregulating the expression of NF-κB p65 and NLRP3, but its mechanism needs to be elucidated. To examine the regulatory effect of electroacupuncture (EA) on intestinal flora and NF-κB p65/NLRP3 pyroptosis pathway in FD rats. The FD rat model was established via multi-factor stress intervention for two weeks. The rats were randomly divided into the NC group, model group, NF-kB inhibitor group (NF-κB inhibitor BAY 11-7082 was administered), EA group, and EA + NF-kB inhibitor group. After 14 days of treatment, the rats were sacrificed, and the protein and mRNA levels of NF-κB p65, IκB, and NLRP3 in the duodenum were evaluated by Western blotting assays and real-time fluorescent quantitative PCR. The Illumina MiSeq sequencing platform was used to analyze the V4 region of the 16S rRNA gene of intestinal flora and predict functional genes. The concentration of short-chain fatty acids (SCFAs) in feces was assessed by metabolomics. EA can decrease low-grade duodenal inflammation and promote gastrointestinal motility in FD rats. This effect is mediated by inhibition of the NF-κB p65/NLRP3 pyroptosis pathway, an increase in the alpha and beta diversity of gut microbiota in the duodenum, an increase in the abundance of beneficial bacteria at the phylum and genus levels, and an increase in the content of SCFAs. The protective effect of EA against FD might involve multiple hierarchy and pathways. EA may remodel intestinal flora by inhibiting the NF-κB p65/NLRP3 pyroptosis pathway, thereby improving low-grade duodenal inflammation in FD rats.

MJL-1 is a nuclear envelope protein required for homologous chromosome pairing and regulation of synapsis during meiosis in C. elegans.

Interactions between chromosomes and LINC (linker of nucleoskeleton and cytoskeleton) complexes in the nuclear envelope (NE) promote homolog pairing and synapsis during meiosis. By tethering chromosomes to cytoskeletal motors, these connections lead to processive chromosome movements along the NE. This activity is usually mediated by telomeres, but in the nematode Caenorhabditis elegans, special chromosome regions called "pairing centers" (PCs) have acquired this meiotic function. Here, we identify a previously uncharacterized meiosis-specific NE protein, MJL-1 (MAJIN-Like-1), that is essential for interactions between PCs and LINC complexes in C. elegans. Mutations in MJL-1 eliminate active chromosome movements during meiosis, resulting in nonhomologous synapsis and impaired homolog pairing. Fission yeast and mice also require NE proteins to connect chromosomes to LINC complexes. Extensive similarities in the molecular architecture of meiotic chromosome-NE attachments across eukaryotes suggest a common origin and/or functions of this architecture during meiosis.

In-depth single-cell and bulk-RNA sequencing developed a NETosis-related gene signature affects non-small-cell lung cancer prognosis and tumor microenvironment: results from over 3,000 patients.

Background: Cell death caused by neutrophil extracellular traps (NETs) is known as NETosis. Despite the increasing importance of NETosis in cancer diagnosis and treatment, its role in Non-Small-Cell Lung Cancer (NSCLC) remains unclear. Methods: A total of 3298 NSCLC patients from different cohorts were included. The AUCell method was used to compute cells' NETosis scores from single-cell RNA-sequencing data. DEGs in sc-RNA dataset were obtained by the Seurat's "FindAllMarkers" function, and DEGs in bulk-RNA dataset were acquired by the DESeq2 package. ConsensusClusterPlus package was used to group patients into different NETosis subtypes, and the Enet algorithm was used to construct the NETosis-Related Riskscore (NETRS). Enrichment analyses were conducted using the GSVA and ClusterProfiler packages. Six distinct algorithms were utilized to evaluate patients' immune cell infiltration level. Patients' SNV and CNV data were analyzed by maftools and GISTIC2.0, respectively. Drug information was obtained from the GDSC1, and predicted by the Oncopredict package. Patient response to immunotherapy was evaluated by the TIDE algorithm in conjunction with the phs000452 immunotherapy cohort. Six NRGs' differential expression was verified using qRT-PCR and immunohistochemistry. Results: Among all cell types, neutrophils had the highest AUCell score. By Intersecting the DEGs between high and low NETosis classes, DEGs between normal and LUAD tissues, and prognostic related genes, 61 prognostic related NRGs were identified. Based on the 61 NRGs, all LUAD patients can be divided into two clusters, showing different prognostic and TME characteristics. Enet regression identified the NETRS composed of 18 NRGs. NETRS significantly associated with LUAD patients' clinical characteristics, and patients at different NETRS groups showed significant differences on prognosis, TME characteristics, immune-related molecules' expression levels, gene mutation frequencies, response to immunotherapy, and drug sensitivity. Besides, NETRS was more powerful than 20 published gene signatures in predicting LUAD patients' survival. Nine independent cohorts confirmed that NETRS is also valuable in predicting the prognosis of all NSCLC patients. Finally, six NRGs' expression was confirmed using three independent datasets, qRT-PCR and immunohistochemistry. Conclusion: NETRS can serves as a valuable prognostic indicator for patients with NSCLC, providing insights into the tumor microenvironment and predicting the response to cancer therapy.

Machine-learning and combined analysis of single-cell and bulk-RNA sequencing identified a DC gene signature to predict prognosis and immunotherapy response for patients with lung adenocarcinoma.

BACKGROUND: Innate immune effectors, dendritic cells (DCs), influence cancer prognosis and immunotherapy significantly. As such, dendritic cells are important in killing tumors and influencing tumor microenvironment, whereas their roles in lung adenocarcinoma (LUAD) are largely unknown. METHODS: In this study, 1658 LUAD patients from different cohorts were included. In addition, 724 cancer patients who received immunotherapy were also included. To identify DC marker genes in LUAD, we used single-cell RNAsequencing data for analysis and determined 83 genes as DC marker genes. Following that, integrative machine learning procedure was developed to construct a signature for DC marker genes. RESULTS: Using TCGA bulk-RNA sequencing data as the training set, we developed a signature consisting of seven genes and classified patients by their risk status. Another six independent cohorts demonstrated the signature' s prognostic power, and multivariate analysis demonstrated it was an independent prognostic factor. LUAD patients in the high-risk group displayed more advanced features, discriminatory immune-cell infiltrations and immunosuppressive states. Cell-cell communication analysis indicates that tumor cells with lower risk scores communicate more actively with the tumor microenvironment. Eight independent immunotherapy cohorts revealed that patients with low-risk had better immunotherapy responses. Drug sensitivity analysis indicated that targeted therapy agents exhibited greater sensitivity to low-risk patients, while chemotherapy agents displayed greater sensitivity to high-risk patients. In vitro experiments confirmed that CTSH is a novel protective factor for LUAD. CONCLUSIONS: An unique signature based on DC marker genes that is highly predictive of LUAD patients' prognosis and response to immunotherapy. CTSH is a new biomarker for LUAD.

A deep learning network based on CNN and sliding window LSTM for spike sorting.

Spike sorting plays an essential role to obtain electrophysiological activity of single neuron in the fields of neural signal decoding. With the development of electrode array, large numbers of spikes are recorded simultaneously, which rises the need for accurate automatic and generalization algorithms. Hence, this paper proposes a spike sorting model with convolutional neural network (CNN) and a spike classification model with combination of CNN and Long-Short Term Memory (LSTM). The recall rate of our detector could reach 94.40% in low noise level dataset. Although the recall declined with the increasing noise level, our model still presented higher feasibility and better robustness than other models. In addition, the results of our classification model presented an accuracy of greater than 99% in simulated data and an average accuracy of about 95% in experimental data, suggesting our classifier outperforms the current "WMsorting" and other deep learning models. Moreover, the performance of our whole algorithm was evaluated through simulated data and the results shows that the accuracy of spike sorting reached about 97%. It is noteworthy to say that, this proposed algorithm could be used to achieve accurate and robust automated spike detection and spike classification.

A cooperative network at the nuclear envelope counteracts LINC-mediated forces during oogenesis in C. elegans.

Oogenesis involves transduction of mechanical forces from the cytoskeleton to the nuclear envelope (NE). In Caenorhabditis elegans, oocyte nuclei lacking the single lamin protein LMN-1 are vulnerable to collapse under forces mediated through LINC (linker of nucleoskeleton and cytoskeleton) complexes. Here, we use cytological analysis and in vivo imaging to investigate the balance of forces that drive this collapse and protect oocyte nuclei. We also use a mechano-node-pore sensing device to directly measure the effect of genetic mutations on oocyte nuclear stiffness. We find that nuclear collapse is not a consequence of apoptosis. It is promoted by dynein, which induces polarization of a LINC complex composed of Sad1 and UNC-84 homology 1 (SUN-1) and ZYGote defective 12 (ZYG-12). Lamins contribute to oocyte nuclear stiffness and cooperate with other inner nuclear membrane proteins to distribute LINC complexes and protect nuclei from collapse. We speculate that a similar network may protect oocyte integrity during extended oocyte arrest in mammals.