KRAS Dimerization Impacts MEK Inhibitor Sensitivity and Oncogenic Activity of Mutant KRAS.

The mechanism by which the wild-type KRAS allele imparts a growth inhibitory effect to oncogenic KRAS in various cancers, including lung adenocarcinoma (LUAD), is poorly understood. Here, using a genetically inducible model of KRAS loss of heterozygosity (LOH), we show that KRAS dimerization mediates wild-type KRAS-dependent fitness of human and murine KRAS mutant LUAD tumor cells and underlies resistance to MEK inhibition. These effects are abrogated when wild-type KRAS is replaced by KRASD154Q, a mutant that disrupts dimerization at the α4-α5 KRAS dimer interface without changing other fundamental biochemical properties of KRAS, both in vitro and in vivo. Moreover, dimerization has a critical role in the oncogenic activity of mutant KRAS. Our studies provide mechanistic and biological insights into the role of KRAS dimerization and highlight a role for disruption of dimerization as a therapeutic strategy for KRAS mutant cancers.

scGMAAE: Gaussian mixture adversarial autoencoders for diversification analysis of scRNA-seq data.

The progress of single-cell RNA sequencing (scRNA-seq) has led to a large number of scRNA-seq data, which are widely used in biomedical research. The noise in the raw data and tens of thousands of genes pose a challenge to capture the real structure and effective information of scRNA-seq data. Most of the existing single-cell analysis methods assume that the low-dimensional embedding of the raw data belongs to a Gaussian distribution or a low-dimensional nonlinear space without any prior information, which limits the flexibility and controllability of the model to a great extent. In addition, many existing methods need high computational cost, which makes them difficult to be used to deal with large-scale datasets. Here, we design and develop a depth generation model named Gaussian mixture adversarial autoencoders (scGMAAE), assuming that the low-dimensional embedding of different types of cells follows different Gaussian distributions, integrating Bayesian variational inference and adversarial training, as to give the interpretable latent representation of complex data and discover the statistical distribution of different types of cells. The scGMAAE is provided with good controllability, interpretability and scalability. Therefore, it can process large-scale datasets in a short time and give competitive results. scGMAAE outperforms existing methods in several ways, including dimensionality reduction visualization, cell clustering, differential expression analysis and batch effect removal. Importantly, compared with most deep learning methods, scGMAAE requires less iterations to generate the best results.

scDCCA: deep contrastive clustering for single-cell RNA-seq data based on auto-encoder network.

The advances in single-cell ribonucleic acid sequencing (scRNA-seq) allow researchers to explore cellular heterogeneity and human diseases at cell resolution. Cell clustering is a prerequisite in scRNA-seq analysis since it can recognize cell identities. However, the high dimensionality, noises and significant sparsity of scRNA-seq data have made it a big challenge. Although many methods have emerged, they still fail to fully explore the intrinsic properties of cells and the relationship among cells, which seriously affects the downstream clustering performance. Here, we propose a new deep contrastive clustering algorithm called scDCCA. It integrates a denoising auto-encoder and a dual contrastive learning module into a deep clustering framework to extract valuable features and realize cell clustering. Specifically, to better characterize and learn data representations robustly, scDCCA utilizes a denoising Zero-Inflated Negative Binomial model-based auto-encoder to extract low-dimensional features. Meanwhile, scDCCA incorporates a dual contrastive learning module to capture the pairwise proximity of cells. By increasing the similarities between positive pairs and the differences between negative ones, the contrasts at both the instance and the cluster level help the model learn more discriminative features and achieve better cell segregation. Furthermore, scDCCA joins feature learning with clustering, which realizes representation learning and cell clustering in an end-to-end manner. Experimental results of 14 real datasets validate that scDCCA outperforms eight state-of-the-art methods in terms of accuracy, generalizability, scalability and efficiency. Cell visualization and biological analysis demonstrate that scDCCA significantly improves clustering and facilitates downstream analysis for scRNA-seq data. The code is available at https://github.com/WJ319/scDCCA.

ExosomePurity: tumour purity deconvolution in serum exosomes based on miRNA signatures.

Exosomes cargo tumour-characterized biomolecules secreted from cancer cells and play a pivotal role in tumorigenesis and cancer progression, thus providing their potential for non-invasive cancer monitoring. Since cancer cell-derived exosomes are often mixed with those from healthy cells in liquid biopsy of tumour patients, accurately measuring the purity of tumour cell-derived exosomes is not only critical for the early detection but also essential for unbiased identification of diagnosis biomarkers. Here, we propose 'ExosomePurity', a tumour purity deconvolution model to estimate tumour purity in serum exosomes of cancer patients based on microribonucleic acid (miRNA)-Seq data. We first identify the differently expressed miRNAs as signature to distinguish cancer cell- from healthy cell-derived exosomes. Then, the deconvolution model was developed to estimate the proportions of cancer exosomes and normal exosomes in serum. The purity predicted by the model shows high correlation with actual purity in simulated data and actual data. Moreover, the model is robust under the different levels of noise background. The tumour purity was also used to correct differential expressed gene analysis. ExosomePurity empowers the research community to study non-invasive early diagnosis and to track cancer progression in cancers more efficiently. It is implemented in R and is freely available from GitHub (https://github.com/WangHYLab/ExosomePurity).

TISCH2: expanded datasets and new tools for single-cell transcriptome analyses of the tumor microenvironment.

The Tumor Immune Single Cell Hub 2 (TISCH2) is a resource of single-cell RNA-seq (scRNA-seq) data from human and mouse tumors, which enables comprehensive characterization of gene expression in the tumor microenvironment (TME) across multiple cancer types. As an increasing number of datasets are generated in the public domain, in this update, TISCH2 has included 190 tumor scRNA-seq datasets covering 6 million cells in 50 cancer types, with 110 newly collected datasets and almost tripling the number of cells compared with the previous release. Furthermore, TISCH2 includes several new functions that allow users to better utilize the large-scale scRNA-seq datasets. First, in the Dataset module, TISCH2 provides the cell-cell communication results in each dataset, facilitating the analyses of interacted cell types and the discovery of significant ligand-receptor pairs between cell types. TISCH2 also includes the transcription factor analyses for each dataset and visualization of the top enriched transcription factors of each cell type. Second, in the Gene module, TISCH2 adds functions for identifying correlated genes and providing survival information for the input genes. In summary, TISCH2 is a user-friendly, up-to-date and well-maintained data resource for gene expression analyses in the TME. TISCH2 is freely available at http://tisch.comp-genomics.org/.

Impact of body mass index on pathological response after neoadjuvant chemotherapy: results from the I-SPY 2 trial.

PURPOSE: Increased body mass index (BMI) has been associated with poor outcomes in women with breast cancer. We evaluated the association between BMI and pathological complete response (pCR) in the I-SPY 2 trial. METHODS: 978 patients enrolled in the I-SPY 2 trial 3/2010-11/2016 and had a recorded baseline BMI prior to treatment were included in the analysis. Tumor subtypes were defined by hormone receptor and HER2 status. Pretreatment BMI was categorized as obese (BMI ≥ 30 kg/m2), overweight (25 ≤ BMI < 30 kg/m2), and normal/underweight (< 25 kg/m2). pCR was defined as elimination of detectable invasive cancer in the breast and lymph nodes (ypT0/Tis and ypN0) at the time of surgery. Logistic regression analysis was used to determine associations between BMI and pCR. Event-free survival (EFS) and overall survival (OS) between different BMI categories were examined using Cox proportional hazards regression. RESULTS: The median age in the study population was 49 years. pCR rates were 32.8% in normal/underweight, 31.4% in overweight, and 32.5% in obese patients. In univariable analysis, there was no significant difference in pCR with BMI. In multivariable analysis adjusted for race/ethnicity, age, menopausal status, breast cancer subtype, and clinical stage, there was no significant difference in pCR after neoadjuvant chemotherapy for obese compared with normal/underweight patients (OR = 1.1, 95% CI 0.68-1.63, P = 0.83), and for overweight compared with normal/underweight (OR = 1, 95% CI 0.64-1.47, P = 0.88). We tested for potential interaction between BMI and breast cancer subtype; however, the interaction was not significant in the multivariable model (P = 0.09). Multivariate Cox regression showed there was no difference in EFS (P = 0.81) or OS (P = 0.52) between obese, overweight, and normal/underweight breast cancer patients with a median follow-up time of 3.8 years. CONCLUSION: We found no difference in pCR rates by BMI with actual body weight-based neoadjuvant chemotherapy in this biologically high-risk breast cancer population in the I-SPY2 trial.

VILLIN2 regulates cotton defense against Verticillium dahliae by modulating actin cytoskeleton remodeling.

The active structural change of actin cytoskeleton is a general host response upon pathogen attack. This study characterized the function of the cotton (Gossypium hirsutum) actin-binding protein VILLIN2 (GhVLN2) in host defense against the soilborne fungus Verticillium dahliae. Biochemical analysis demonstrated that GhVLN2 possessed actin-binding, -bundling, and -severing activities. A low concentration of GhVLN2 could shift its activity from actin bundling to actin severing in the presence of Ca2+. Knockdown of GhVLN2 expression by virus-induced gene silencing reduced the extent of actin filament bundling and interfered with the growth of cotton plants, resulting in the formation of twisted organs and brittle stems with a decreased cellulose content of the cell wall. Upon V. dahliae infection, the expression of GhVLN2 was downregulated in root cells, and silencing of GhVLN2 enhanced the disease tolerance of cotton plants. The actin bundles were less abundant in root cells of GhVLN2-silenced plants than in control plants. However, upon infection by V. dahliae, the number of actin filaments and bundles in the cells of GhVLN2-silenced plants was raised to a comparable level as those in control plants, with the dynamic remodeling of the actin cytoskeleton appearing several hours in advance. GhVLN2-silenced plants exhibited a higher incidence of actin filament cleavage in the presence of Ca2+, suggesting that pathogen-responsive downregulation of GhVLN2 could activate its actin-severing activity. These data indicate that the regulated expression and functional shift of GhVLN2 contribute to modulating the dynamic remodeling of the actin cytoskeleton in host immune responses against V. dahliae.

Measuring the orbital angular momentum of generalized higher-order twisted partially coherent beams.

Recently a new family of partially coherent fields incorporating generalized inseparable cross-coupled phases named generalized higher-order twisted partially coherent beams (GHTPCBs) have been introduced. The twist factor u is a key parameter that not only quantifies the strength of the generalized cross-coupled phase for a given order, but also determines the amount of the concomitant orbital angular momentum (OAM). In this paper, we propose a simple and reliable method to measure the factor u using a two-pinhole mask. Without need of complicated optical system, it only requires to capture the far-field diffraction intensity distribution of the GHTPCB passing through the mask. By analyzing the Fourier spectrum of the intensity distribution, the value of twist factor can be derived nearly in real time. The influence of the separation distance between two pinholes and the pinholes' diameter and position on the measurement accuracy are thoroughly studied both in theory and experiment. The experimental results agree well with the theoretical results. Our methodology can also be extended to measure the sole factor of similar position dependent phases such as the topological charge of a vortex phase.

Breaking the symmetric spiral spectrum distribution of a Laguerre-Gaussian beam propagating in moderate-to-strong isotropic atmospheric turbulence.

We demonstrate that the spiral spectrum (also known as orbital angular momentum spectrum) of a Laguerre-Gaussian (LG) beam with topological charge (TC) l is asymmetrically broadened propagating through moderate-to-strong atmospheric turbulence, even the statistics of turbulence is isotropic. This phenomenon is quite different from that predicted in weak turbulence where the spiral spectrum of a disturbed LG beam is symmetric with respect to its TC number l. An explicit analytical expression of the spiral spectrum of the LG beam with l = 1 is derived based on the extend Huygens-Fresnel integral and quadratic approximation, which is used to illustrate the transition scenarios of the spiral spectrum from symmetry to asymmetry in weak-to-strong turbulence. The physical mechanism for the asymmetric spiral spectrum in moderate-to-strong turbulence is thoroughly discussed. Our results are confirmed by the multi-phase screen numerical simulations and are consistent with the experimental results reported in Phys. Rev. A105, 053513 (2022)10.1103/PhysRevA.105.053513 and Opt. Lett.38, 4062 (2013)10.1364/OL.38.004062.

Telemedicine network latency management system in 5G telesurgery: a feasibility and effectiveness study.

BACKGROUND: Network latency is the most important factor affecting the performance of telemedicine. The aim of the study is to assess the feasibility and efficacy of a novel network latency management system in 5G telesurgery. METHODS: We conducted 20 telesurgery simulation trials (hitching rings to columns) and 15 remote adrenalectomy procedures in the 5G network environment. Telemedicine Network Latency Management System and the traditional "Ping command" method (gold standard) were used to monitor network latency during preoperative simulated telesurgery and formal telesurgery. We observed the working status of the Telemedicine Network Latency Management System and calculated the difference between the network latency data and packet loss rate detected by the two methods. In addition, due to the lower latency of the 5G network, we tested the alert function of the system using the 4G network with relatively high network latency. RESULTS: The Telemedicine Network Latency Management System showed no instability during telesurgery simulation trials and formal telesurgery. After 20 telesurgery simulation trials and 15 remote adrenalectomy procedures, the p-value for the difference between the network latency data monitored by the Telemedicine Network Latency Management System and the "Ping command" method was greater than 0.05 in each case. Meanwhile, the surgeons reported that the Telemedicine Network Latency Management System had a friendly interface and was easy to operate. Besides, when the network latency exceeded a set threshold, a rapid alarm sounded in the system. CONCLUSION: The Telemedicine Network Latency Management System was simple and easy to operate, and it was feasible and effective to use it to monitor network latency in telesurgery. The system had an intuitive and concise interface, and its alarm function increased the safety of telesurgery. The system's own multidimensional working ability and information storage capacity will be more suitable for telemedicine work.

Changes in serum sodium concentration during hemodialysis is a predictor of mortality and cardio-cerebrovascular event.

BACKGROUND: Previous study consistently showed that lower serum sodium (SNa) was associated with a greater risk of mortality in hemodialysis (HD) patients. However, few studies have focused on the change in SNa (ΔSNa = post-HD SNa - pre-HD SNa) during an HD session. METHODS: In a retrospective cohort of maintenance HD adults, all-cause mortality and cardio-cerebrovascular event (CCVE) were followed up for a medium of 82 months. Baseline pre-HD SNa and ΔSNa were collected; time-averaged pre-HD SNa and ΔSNa were computed as the mean values within 1-year, 2-year and 3-year intervals after enrollment. Cox proportional hazards models were used to evaluate the relationships of pre-HD and ΔSNa with outcomes. RESULTS: Time-averaged pre-HD SNa were associated with all-cause mortality (2-year pre-HD SNa: HR [95% CI] 0.86 [0.74-0.99], p = 0.042) and CCVE (3-year pre-HD SNa: HR [95% CI] 0.83 [0.72-0.96], p = 0.012) with full adjustment. Time-averaged ΔSNa also demonstrated an association with all-cause mortality (3-year ΔSNa: HR [95% CI] 1.26 [1.03-1.55], p = 0.026) as well as with CCVE (3-year ΔSNa: HR [95% CI] 1.51 [1.21-1.88], p = <0.001) when fully adjusted. Baseline pre-HD SNa and ΔSNa didn't exhibit association with both outcomes. CONCLUSIONS: Lower time-averaged pre-HD SNa and higher time-averaged ΔSNa were associated with a greater risk of all-cause mortality and CCVE in HD patients.

Effects of minimally invasive surgery combined with specialized pain management nursing care on postoperativepain improvement and life quality after spinal injury.

Objective: To determine the impacts to research the impacts of pain's Specialized Pain Management Nursing Care in the perioperative period on pain symptoms and life quality of patients experiencing minimally invasive surgery for spinal injury. Method: Eighty patients with a spinal injury who underwent minimally invasive surgery in the Department of Orthopedics of Baoding No.1 Hospital from January 2018 to December 2021 were retrospectively analyzed. They were split into two groups following different nursing methods (n=40 each group). Specialized Pain Management Nursing Care were given to patients in the observation group. Those in the control group were given treated with routine care. Their pain score and nursing effect were compared, after which their quality of life, daily living ability and complication rate compared and analyzed. Results: The pain degree in the control group was considerably more than that in the observation group in the 1st postoperative period. The pain degree, which decreased in both groups, slumped more significantly in the observation group on the 2nd and 3rd postoperative days. The postoperative hospital stays and pain duration in the observation group were shorter than those in the control group (P<0.05), and the nursing effect was significantly better than that in the control group (P<0.05). After postoperative nursing intervention. Conclusion: Minimally invasive surgery integrated with the Specialized Pain Management Nursing Care can remarkably ameliorate pain after spinal injury surgery, reducing complications' incidence, and improving the life quality for patients.

scSemiAAE: a semi-supervised clustering model for single-cell RNA-seq data.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) strives to capture cellular diversity with higher resolution than bulk RNA sequencing. Clustering analysis is critical to transcriptome research as it allows for further identification and discovery of new cell types. Unsupervised clustering cannot integrate prior knowledge where relevant information is widely available. Purely unsupervised clustering algorithms may not yield biologically interpretable clusters when confronted with the high dimensionality of scRNA-seq data and frequent dropout events, which makes identification of cell types more challenging. RESULTS: We propose scSemiAAE, a semi-supervised clustering model for scRNA sequence analysis using deep generative neural networks. Specifically, scSemiAAE carefully designs a ZINB adversarial autoencoder-based architecture that inherently integrates adversarial training and semi-supervised modules in the latent space. In a series of experiments on scRNA-seq datasets spanning thousands to tens of thousands of cells, scSemiAAE can significantly improve clustering performance compared to dozens of unsupervised and semi-supervised algorithms, promoting clustering and interpretability of downstream analyses. CONCLUSION: scSemiAAE is a Python-based algorithm implemented on the VSCode platform that provides efficient visualization, clustering, and cell type assignment for scRNA-seq data. The tool is available from https://github.com/WHang98/scSemiAAE .

scCNC: a method based on capsule network for clustering scRNA-seq data.

MOTIVATION: A large number of studies have shown that clustering is a crucial step in scRNA-seq analysis. Most existing methods are based on unsupervised learning without the prior exploitation of any domain knowledge, which does not utilize available gold-standard labels. When confronted by the high dimensionality and general dropout events of scRNA-seq data, purely unsupervised clustering methods may not produce biologically interpretable clusters, which complicate cell type assignment. RESULTS: In this article, we propose a semi-supervised clustering method based on a capsule network named scCNC that integrates domain knowledge into the clustering step. Significantly, we also propose a Semi-supervised Greedy Iterative Training method used to train the whole network. Experiments on some real scRNA-seq datasets show that scCNC can significantly improve clustering performance and facilitate downstream analyses. AVAILABILITY AND IMPLEMENTATION: The source code of scCNC is freely available at https://github.com/WHY-17/scCNC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Orbital angular momentum spectra of twisted Laguerre-Gaussian Schell-model beams propagating in weak-to-strong Kolmogorov atmospheric turbulence.

The presence of atmospheric turbulence in a beam propagation path results in the spread of orbital angular momentum (OAM) modes of laser beams, limiting the performance of free-space optical communications with the utility of vortex beams. The knowledge of the effects of turbulence on the OAM spectrum (also named as spiral spectrum) is thus of utmost importance. However, most of the existing studies considering this effect are limited to the weak turbulence that is modeled as a random complex "screen" in the receiver plane. In this paper, the behavior of the OAM spectra of twisted Laguerre-Gaussian Schell-model (TLGSM) beams propagation through anisotropic Kolmogorov atmospheric turbulence is examined based on the extended Huygens-Fresnel integral which is considered to be applicable in weak-to-strong turbulence. The discrepancies of the OAM spectra between weak and strong turbulence are studied comparatively. The influences of the twist phase and the anisotropy of turbulence on the OAM spectra during propagation are investigated through numerical examples. Our results reveal that the twist phase plays a crucial role in determining the OAM spectra in turbulence, resisting the degeneration of the detection mode weight by appropriately choosing the twist factor, while the effects of the anisotropic factors of turbulence on the OAM spectra seem to be not obvious. Our findings can be applied to the analysis of OAM spectra of laser beams both in weak and strong turbulence.

Clinical significance of genetic profiling based on different anatomic sites in patients with mucosal melanoma who received or did not receive immune checkpoint inhibitors.

BACKGROUND: To date, data on the efficacy of targeted therapies for mucosal melanoma (MM) are limited. In this study, we analyzed genetic alterations according to the primary site of origin, which could provide clues for targeted therapy for MM. METHODS: We conducted a retrospective cohort study of 112 patients with MM. Targeted sequencing was performed to analyze genetic aberrations. Kaplan-Meier analysis was conducted with the log-rank test to compare the significance among subgroups. RESULTS: In total, 112 patients with MM were included according to the anatomic sites: 38 (33.9%) in the head and neck, 22 (19.6%) in the genitourinary tract, 21 (18.8%) in the anorectum, 19 (17.0%) in the esophagus, 10 (8.9%) in the uvea, and 2 (1.8%) in the small bowel. The most significantly mutated genes included BRAF (17%), KIT (15%), RAS (15%), TP53 (13%), NF1 (12%), SF3B1 (11%), GNA11 (7%), GNAQ (5%), and FBXW7 (4%). A large number of chromosomal structural variants was found. The anatomic sites of esophagus and small bowel were independent risk factors for progression-free survival (PFS, hazard ratio [HR] 4.78, 95% confidence interval [CI] 2.42-9.45, P < 0.0001) and overall survival (OS, HR 5.26, 95% CI 2.51-11.03, P < 0.0001). Casitas B-lineage lymphoma (CBL) mutants showed significantly poorer PFS and OS. In contrast, MM patients who received immune checkpoint inhibitors (ICIs) had a significantly more favorable OS (HR 0.39, 95% CI 0.20-0.75, P = 0.008). CONCLUSIONS: Our findings reveal the genetic features of patients with MM, mainly across six anatomic sites, offering a potential avenue for targeted therapies.

scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data.

Single cell RNA sequencing (scRNA-seq) enables researchers to characterize transcriptomic profiles at the single-cell resolution with increasingly high throughput. Clustering is a crucial step in single cell analysis. Clustering analysis of transcriptome profiled by scRNA-seq can reveal the heterogeneity and diversity of cells. However, single cell study still remains great challenges due to its high noise and dimension. Subspace clustering aims at discovering the intrinsic structure of data in unsupervised fashion. In this paper, we propose a deep sparse subspace clustering method scDSSC combining noise reduction and dimensionality reduction for scRNA-seq data, which simultaneously learns feature representation and clustering via explicit modelling of scRNA-seq data generation. Experiments on a variety of scRNA-seq datasets from thousands to tens of thousands of cells have shown that scDSSC can significantly improve clustering performance and facilitate the interpretability of clustering and downstream analysis. Compared to some popular scRNA-deq analysis methods, scDSSC outperformed state-of-the-art methods under various clustering performance metrics.

Drosophila as a model for studying cystic fibrosis pathophysiology of the gastrointestinal system.

Cystic fibrosis (CF) is a recessive disease caused by mutations in the CF transmembrane conductance regulator (CFTR) gene. The most common symptoms include progressive lung disease and chronic digestive conditions. CF is the first human genetic disease to benefit from having five different species of animal models. Despite the phenotypic differences among the animal models and human CF, these models have provided invaluable insight into understanding disease mechanisms at the organ-system level. Here, we identify a member of the ABCC4 family, CG5789, that has the structural and functional properties expected for encoding the Drosophila equivalent of human CFTR, and thus refer to it as Drosophila CFTR (Dmel\CFTR). We show that knockdown of Dmel\CFTR in the adult intestine disrupts osmotic homeostasis and displays CF-like phenotypes that lead to intestinal stem cell hyperplasia. We also show that expression of wild-type human CFTR, but not mutant variants of CFTR that prevent plasma membrane expression, rescues the mutant phenotypes of Dmel\CFTR Furthermore, we performed RNA sequencing (RNA-Seq)-based transcriptomic analysis using Dmel\CFTR fly intestine and identified a mucin gene, Muc68D, which is required for proper intestinal barrier protection. Altogether, our findings suggest that Drosophila can be a powerful model organism for studying CF pathophysiology.

Tumor regression and resistance mechanisms upon CDK4 and RAF1 inactivation in KRAS/P53 mutant lung adenocarcinomas.

KRAS mutant lung adenocarcinomas remain intractable for targeted therapies. Genetic interrogation of KRAS downstream effectors, including the MAPK pathway and the interphase CDKs, identified CDK4 and RAF1 as the only targets whose genetic inactivation induces therapeutic responses without causing unacceptable toxicities. Concomitant CDK4 inactivation and RAF1 ablation prevented tumor progression and induced complete regression in 25% of KRAS/p53-driven advanced lung tumors, yet a significant percentage of those tumors that underwent partial regression retained a population of CDK4/RAF1-resistant cells. Characterization of these cells revealed two independent resistance mechanisms implicating hypermethylation of several tumor suppressors and increased PI3K activity. Importantly, these CDK4/RAF1-resistant cells can be pharmacologically controlled. These studies open the door to new therapeutic strategies to treat KRAS mutant lung cancer, including resistant tumors.

Comparing cadmium uptake kinetics, xylem translocation, chemical forms, and subcellular distribution of two tobacco (Nicotiana tabacum L.) cultivars.

Tobacco (Nicotiana tabacum L.) is a potential phytoremediator that can reduce soil cadmium (Cd) contamination. Pot and hydroponic experiments were conducted to investigate the difference in absorption kinetics, translocation patterns, accumulation capacity, and extraction amounts between two leading tobacco cultivars in China. We studied the chemical forms and subcellular distribution of Cd in the plants to understand the diversity of the detoxification mechanism of the cultivars. The concentration-dependent kinetics of Cd accumulation in leaves, stems, roots, and xylem sap for cultivars Zhongyan 100 (ZY100) and K326, fitted well with the Michaelis-Menten equation. K326 exhibited high biomass, Cd tolerance, Cd translocation, and phytoextraction abilities. The acetic acid, sodium chloride, and water-extractable fractions accounted for > 90% of Cd in all ZY100 tissues but only in K326 roots and stems. Moreover, the acetic acid and NaCl fractions were the predominant storage forms, while the water fraction was the transport form. The ethanol fraction also contributed significantly to Cd storage in K326 leaves. As the Cd treatment increased, more NaCl and water fractions were found in K326 leaves, while only NaCl fractions increased in ZY100 leaves. For subcellular distribution, > 93% Cd proportions were primarily stored in both cultivars' soluble or cell wall fraction. The proportion of Cd in the cell wall fraction of ZY100 roots was less than that of K326, while that proportion in the soluble fraction in ZY100 leaves was higher than in K326 leaves. These findings demonstrate that Cd accumulation patterns, detoxification, and storage strategies differ between the cultivars, providing a deeper understanding of Cd tolerance and accumulation mechanism in tobacco plants. It also guides the screening of germplasm resources or gene modification to improve the Cd phytoextraction efficiency of tobacco.