Targeting SHP2 Cryptic Allosteric Sites for Effective Cancer Therapy.

SHP2, a pivotal component downstream of both receptor and non-receptor tyrosine kinases, has been underscored in the progression of various human cancers and neurodevelopmental disorders. Allosteric inhibitors have been proposed to regulate its autoinhibition. However, oncogenic mutations, such as E76K, convert SHP2 into its open state, wherein the catalytic cleft becomes fully exposed to its ligands. This study elucidates the dynamic properties of SHP2 structures across different states, with a focus on the effects of oncogenic mutation on two known binding sites of allosteric inhibitors. Through extensive modeling and simulations, we further identified an alternative allosteric binding pocket in solution structures. Additional analysis provides insights into the dynamics and stability of the potential site. In addition, multi-tier screening was deployed to identify potential binders targeting the potential site. Our efforts to identify a new allosteric site contribute to community-wide initiatives developing therapies using multiple allosteric inhibitors to target distinct pockets on SHP2, in the hope of potentially inhibiting or slowing tumor growth associated with SHP2.

Understanding and fine tuning the propensity of ATP-driven liquid-liquid phase separation with oligolysine.

Liquid-liquid phase separation (LLPS) plays a pivotal role in the organization and functionality of living cells. It is imperative to understand the underlying driving forces behind LLPS and to control its occurrence. In this study, we employed coarse-grained (CG) simulations as a research tool to investigate systems comprising oligolysine and adenosine triphosphate (ATP) under conditions of various ionic concentrations and oligolysine lengths. Consistent with experimental observations, our CG simulations captured the formation of LLPS upon the addition of ATP and tendency of dissociating under high ionic concentration. The electrostatic interaction between oligolysine and ATP is of great importance in forming LLPS. An in-depth analysis on the structural properties of LLPS was conducted, where the oligolysine structure remained unchanged with increased ionic concentration and the addition of ATP led to a more pronounced curvature, aligning with the observed enhancement of α-helical secondary structure in experiments. In terms of the dynamic properties, the introduction of ATP led to a significant reduction in translational and vibrational degrees of freedom but not rotational degrees of freedom. Through keeping the total number of charged residues constant and varying their entropic effects, we constructed two systems of similar biochemical significance and further validated the entropy effects on the LLPS formation. These findings provide a deeper understanding of LLPS formation and shed lights on the development of novel bioreactor and primitive artificial cells for synthesizing key chemicals for certain diseases.

Clustering single-cell multi-omics data via graph regularized multi-view ensemble learning.

MOTIVATION: Single-cell clustering plays a crucial role in distinguishing between cell types, facilitating the analysis of cell heterogeneity mechanisms. While many existing clustering methods rely solely on gene expression data obtained from single-cell RNA sequencing techniques to identify cell clusters, the information contained in mono-omic data is often limited, leading to suboptimal clustering performance. The emergence of single-cell multi-omics sequencing technologies enables the integration of multiple omics data for identifying cell clusters, but how to integrate different omics data effectively remains challenging. In addition, designing a clustering method that performs well across various types of multi-omics data poses a persistent challenge due to the data's inherent characteristics. RESULTS: In this paper, we propose a graph-regularized multi-view ensemble clustering (GRMEC-SC) model for single-cell clustering. Our proposed approach can adaptively integrate multiple omics data and leverage insights from multiple base clustering results. We extensively evaluate our method on five multi-omics datasets through a series of rigorous experiments. The results of these experiments demonstrate that our GRMEC-SC model achieves competitive performance across diverse multi-omics datasets with varying characteristics. AVAILABILITY AND IMPLEMENTATION: Implementation of GRMEC-SC, along with examples, can be found on the GitHub repository: https://github.com/polarisChen/GRMEC-SC.

Ultrasound-based deep learning radiomics nomogram for risk stratification of testicular masses: a two-center study.

OBJECTIVE: To develop an ultrasound-driven clinical deep learning radiomics (CDLR) model for stratifying the risk of testicular masses, aiming to guide individualized treatment and minimize unnecessary procedures. METHODS: We retrospectively analyzed 275 patients with confirmed testicular lesions (January 2018 to April 2023) from two hospitals, split into training (158 cases), validation (68 cases), and external test cohorts (49 cases). Radiomics and deep learning (DL) features were extracted from preoperative ultrasound images. Following feature selection, we utilized logistic regression (LR) to establish a deep learning radiomics (DLR) model and subsequently derived its signature. Clinical data underwent univariate and multivariate LR analyses, forming the "clinic signature." By integrating the DLR and clinic signatures using multivariable LR, we formulated the CDLR nomogram for testicular mass risk stratification. The model's efficacy was gauged using the area under the receiver operating characteristic curve (AUC), while its clinical utility was appraised with decision curve analysis(DCA). Additionally, we compared these models with two radiologists' assessments (5-8 years of practice). RESULTS: The CDLR nomogram showcased exceptional precision in distinguishing testicular tumors from non-tumorous lesions, registering AUCs of 0.909 (internal validation) and 0.835 (external validation). It also excelled in discerning malignant from benign testicular masses, posting AUCs of 0.851 (internal validation) and 0.834 (external validation). Notably, CDLR surpassed the clinical model, standalone DLR, and the evaluations of the two radiologists. CONCLUSION: The CDLR nomogram offers a reliable tool for differentiating risks associated with testicular masses. It augments radiological diagnoses, facilitates personalized treatment approaches, and curtails unwarranted medical procedures.

Grid-Robust Efficient Neural Interface Model for Universal Molecule Surface Construction from Point Clouds.

Molecular surfaces play a pivotal role in elucidating the properties and functions of biological complexes. While various surfaces have been proposed for specific scenarios, their widespread adoption faces challenges due to limited efficiency stemming from hand-crafted modeling designs. In this work, we proposed a general framework that incorporates both the point cloud concept and neural networks. The use of matrix multiplication in this framework enables efficient implementation across diverse platforms and libraries. We applied this framework to develop the GENIUSES (Grid-robust Efficient Neural Interface for Universal Solvent-Excluded Surface) model for constructing SES. GENIUSES demonstrates high accuracy and efficiency across data sets with varying conformations and complexities. Compared to the classical implementation of SES in the AMBER software package, our framework achieved a 26-fold speedup while retaining ∼95% accuracy when ported to the GPU platform using CUDA. Greater speedups can be obtained in large-scale systems. Importantly, our model exhibits robustness against variations in the grid spacing. We have integrated this infrastructure into AMBER to enhance accessibility for research in drug screening and related fields, where efficiency is of paramount importance.

AmberTools.

AmberTools is a free and open-source collection of programs used to set up, run, and analyze molecular simulations. The newer features contained within AmberTools23 are briefly described in this Application note.

Streamlining and Optimizing Strategies of Electrostatic Parameterization.

Accurate characterization of electrostatic interactions is crucial in molecular simulation. Various methods and programs have been developed to obtain electrostatic parameters for additive or polarizable models to replicate electrostatic properties obtained from experimental measurements or theoretical calculations. Electrostatic potentials (ESPs), a set of physically well-defined observables from quantum mechanical (QM) calculations, are well suited for optimization efforts due to the ease of collecting a large amount of conformation-dependent data. However, a reliable set of QM ESP computed at an appropriate level of theory and atomic basis set is necessary. In addition, despite the recent development of the PyRESP program for electrostatic parameterizations of induced dipole-polarizable models, the time-consuming and error-prone input file preparation process has limited the widespread use of these protocols. This work aims to comprehensively evaluate the quality of QM ESPs derived by eight methods, including wave function methods such as Hartree-Fock (HF), second-order Møller-Plesset (MP2), and coupled cluster-singles and doubles (CCSD), as well as five hybrid density functional theory (DFT) methods, used in conjunction with 13 different basis sets. The highest theory levels CCSD/aug-cc-pV5Z (a5z) and MP2/aug-cc-pV5Z (a5z) were selected as benchmark data over two homemade data sets. The results show that the hybrid DFT method, ωB97X-D, combined with the aug-cc-pVTZ (a3z) basis set, performs well in reproducing ESPs while taking both accuracy and efficiency into consideration. Moreover, a flexible and user-friendly program called PyRESP_GEN was developed to streamline input file preparation. The restraining strengths, along with strategies for polarizable Gaussian multipole (pGM) model parameterizations, were also optimized. These findings and the program presented in this work facilitate the development and application of induced dipole-polarizable models, such as pGM models, for molecular simulations of both chemical and biological significance.

Long non-coding RNAs mediate the association between short-term PM2.5 exposure and circulating biomarkers of systemic inflammation.

Although short-term fine particulate matter (PM2.5) exposure is associated with systemic inflammation, the effect of lncRNA on these association remains unknown. This study aims to investigate whether the plasma lncRNA mediate the effect of short-term PM2.5 exposure on systemic inflammation. In this cross-sectional study, plasma Clara cell protein 16 (CC16), interleukin 6 (IL-6), IL-8, tumor necrosis factor-α (TNF-α) and lncRNA expression levels were measured in 161 adults between March and April in 2018 in Shijiazhuang, China. PM2.5 concentrations were estimated 0-3 days prior to the examination date and the moving averages were calculated. Multiple linear regressions were used to evaluate the associations between PM2.5, the four biomarkers and lncRNA expression levels. Mediation analyses were performed to explore the potential roles of lncRNA expression in these associations. The median concentration of PM2.5 ranged from 39.65 to 60.91 mg/m3 across different lag days. The most significant effects on IL-6 and TNF-α per interquartile range increase in PM2.5 were observed at lag 0-3 days, with increases of 0.70 pg/mL (95% CI: 0.33, 1.07) and 0.21 pg/mL (95% CI: 0.06, 0.36), respectively. While the associations between PM2.5 and IL-8 (0.68 pg/mL, 95% CI: 0.34, 1.02) and CC16 (3.86 ng/mL, 95% CI: 1.60, 6.13) were stronger at lag 0 day. Interestingly, a negative association between PM2.5 and the expression of four novel lncRNAs (lnc-ACAD11-1:1, lnc-PRICKLE1-4:1, lnc-GPR39-7:2, and lnc-MTRNR2L12-3:6) were observed at each lag days. Furthermore, these lncRNAs mediated the effects of PM2.5 on the four biomarkers, with proportions of mediation ranged from 2.27% (95% CI: 1.19%, 9.82%) for CC16 to 35.60% (95% CI: 17.16%, 175.45%) for IL-6. Our findings suggested that plasma lncRNA expression mediat the acute effects of PM2.5 exposure on systematic inflammation. These highlight a need to consider circulating lncRNA expression as biomarkers to reduce health risks associated with PM2.5.

Ferroptosis in calcium oxalate kidney stone formation and the possible regulatory mechanism of ANKRD1.

The objective of this study was to explore the role of ferroptosis in the formation of calcium oxalate (CaOx) kidney stones and the regulatory mechanism of the ankyrin repeat domain 1 (ANKRD1) gene. The study found that the Nrf2/HO-1 and p53/SLC7A11 signaling pathways were activated in the kidney stone model group, and the expression of the ferroptosis marker proteins SLC7A11 and GPX4 was significantly reduced, while the expression of ACSL4 was significantly increased. The expression of the iron transport-related proteins CP and TF increased significantly, and Fe2+ accumulated in the cell. The expression of HMGB1 increased significantly. In addition, the level of intracellular oxidative stress was increased. The gene with the most significant difference caused by CaOx crystals in HK-2 cells was ANKRD1. Silencing or overexpression of ANKRD1 by lentiviral infection technology regulated the expression of the p53/SLC7A11 signaling pathway, which regulated the ferroptosis induced by CaOx crystals. In conclusion, CaOx crystals can mediate ferroptosis through the Nrf2/HO-1 and p53/SLC7A11 pathways, thereby weakening the resistance of HK-2 cells to oxidative stress and other unfavorable factors, enhancing cell damage, and increasing crystal adhesion and CaOx crystal deposition in the kidney. ANKRD1 participates in the formation and development of CaOx kidney stones by activating ferroptosis mediated by the p53/SLC7A11 pathway.

Novel Blood Indicators of Progression and Prognosis in Renal Cell Carcinoma: Red Cell Distribution Width-to-Lymphocyte Ratio and Albumin-to-Fibrinogen Ratio.

OBJECTIVE: To evaluate the value of preoperative red cell distribution width-to-lymphocyte ratio (RLR) and albumin-to-fibrinogen ratio (AFR) to the prognosis of patients after renal cell carcinoma (RCC). METHODS: From 2012 to 2016, a total of 273 RCC patients underwent radical nephrectomy or partial nephrectomy. This study retrospectively analyzed this group of patients. X-tile software was used to determine the optimal values of RLR and AFR in the peripheral blood. The nomogram constructed with independent factors was used to predict the survival outcome of the patients after RCC. RESULTS: The RLR of the RCC group was higher than that of the normal control group (P=0.002), whereas the AFR of the RCC group was lower than that of the normal control group (P < 0.001). RLR and AFR are related to tumour type and tumour-node-metastasis (TNM) stage (P < 0.05 for all). Cox regression analysis showed that the independent prognostic factors affecting overall survival and disease-free survival in the RCC group were symptom, tumour type, TNM stage, Fuhrman grade, RLR, and AFR (P < 0.05 for all). The nomogram constructed by multiple factors has better predictive power for patients after RCC. CONCLUSION: Preoperative RLR and AFR can serve as potential biomarkers to predict the prognosis of postoperative RCC patients and improve the predictability of patient recurrence and survival.

Distinct microbial communities among different tissues of citrus tree Citrus reticulata cv. Chachiensis.

Plant microbiota colonize all organs of a plant and play crucial roles including supplying nutrients to plants, stimulating seed germination, promoting plant growth, and defending plants against biotic and abiotic stress. Because of the economic importance, interactions between citrus and microbes have been studied relatively extensively, especially citrus-pathogen interactions. However, the spatial distribution of microbial taxa in citrus trees remains under-studied. In this study, Citrus reticulata cv. Chachiensis was examined for the spatial distribution of microbes by sequencing 16S rRNA genes. More than 2.5 million sequences were obtained from 60 samples collected from soil, roots, leaves, and phloem. The dominant microbial phyla from all samples were Proteobacteria, Actinobacteria and Acidobacteria. The composition and structure of microbial communities in different samples were analyzed by PCoA, CAP, Anosim and MRPP methods. Variation in microbial species between samples were analyzed and the indicator microbes of each sample group were identified. Our results suggested that the microbial communities from different tissues varied significantly and the microenvironments of tree tissues could affect the composition of its microbial community.

Prognostic role of pretreatment blood lymphocyte count in patients with solid tumors: a systematic review and meta-analysis.

BACKGROUND: To evaluate the prognostic value of pretreatment lymphocyte counts with respect to clinical outcomes in patients with solid tumors. METHODS: Systematic literature search of electronic databases (Pubmed, Embase and Web of Science) up to May 1, 2018 was carried out by two independent reviewers. We included Eligible studies assessed the prognostic impact of pretreatment lymphocytes and had reported hazard ratios (HR) with 95% confidence intervals (CIs) for endpoints including overall survival (OS) and progression-free survival (PFS). Only English publications were included. RESULTS: A total of 42 studies comprising 13,272 patients were included in this systematic review and meta-analysis. Low pretreatment lymphocyte count was associated with poor OS (HR = 1.27, 95% CI 1.16-1.39, P < 0.001, I2 = 58.5%) and PFS (HR = 1.27, 95% CI 1.15-1.40, P < 0.001, I2 = 25.7%). Subgroup analysis disaggregated by cancer type indicated that low pretreatment lymphocytes were most closely associated with poor OS in colorectal cancer followed by breast cancer and renal cancer. CONCLUSIONS: Low pretreatment lymphocyte count may represent an unfavorable prognostic factor for clinical outcomes in patients with solid tumors.

Comparative transcriptomic analyses revealed genes and pathways responsive to heat stress in Diaphorina citri.

The Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera: Liviidae) is a serious pest that feeds on plant phloem sap of citrus trees, and transmits Candidatus Liberibacter asiaticus, a bacterium that induces the destructive disease called Huanglongbing. Increasing evidence suggests that high temperatures could affect various biological traits, including size, longevity, mortality, behavior and metabolism of D. citri. However, the relevant mechanisms of heat stress remain unclear. In this study, a large set of transcriptomic data derived from D. citri adults were generated and differentially expressed genes (DEGs) after heat stress were identified by RNA sequencing. A total of 118, 399 unigenes were obtained, from which 37, 665 were mapped to sequences from at least one database. Seven hundreds and twenty-two unigenes were affected by high temperature of 40 °C for 4 h, in which 486 up-regulated and 236 down-regulated, and part of heat shock proteins, antioxidant and detoxification genes and cathepsins were identified as the DEGs. KEGG pathway enrichment analysis demonstrated that part of genes involved in protein synthesis and processing, metabolism, immunity, and signal transduction were differentially expressed under heat stress. Furthermore, the mRNA expression levels of 20 DEGs were confirmed by qRT-PCR, which verified the accuracy of high-throughput sequencing. Our results revealed that the response of D. citri adults to high temperatures is associated with a range of changes involved in various physiological and biochemical processes. Our data provide a basis for future research to improve our understanding on the molecular mechanism for heat responses in D. citri adults.

Association between insulin-like growth factor 1 gene rs35767 polymorphisms and cancer risk: A meta-analysis.

BACKGROUND: Several studies have been conducted on the relationship between insulin-like growth factor 1 gene (IGF-1) rs35767 polymorphisms and cancer risk, but the results are conflicting. We performed a meta-analysis to investigate the relationship between IGF-1 rs35767 polymorphisms and cancer risk. METHODS: Eight studies (5 for IGF-1 rs35767 C>T and 3 for IGF-1 rs35767 A>G) with a total of 11,257 cases and 16,213 controls were included. The studies were about the association between IGF-1 rs35767 polymorphisms and cancer risk and acquired by searching PubMed, Embase, and Web of Science databases for articles published before January 20, 2019. STATA software was used to analyze the data and identify the strength of the association by using pooled-odds ratios (ORs) with corresponding 95% confidence intervals (CIs). RESULTS: No significant associations were observed between the IGF-1 rs35767 C>T polymorphism and cancer risk in all genetic models. However, the IGF-1 rs35767 A>G polymorphism was significantly associated with increased cancer risk for all genetic models (G vs A: OR = 1.087, 95% CI: 1.036-1.141, Ph = .338; GG vs AA: OR = 1.272, 95% CI: 1.121-1.442, Ph = .359; AG vs AA: OR = 1.187, 95% CI: 1.043-1.351, Ph = .695; AG+GG vs AA: OR = 1.187, 95% CI: 1.043-1.351, Ph = .695; GG vs AA+AG: OR = 1.086, 95% CI: 1.025-1.151, Ph = .275). Begg and Egger tests showed that no publication bias existed. CONCLUSION: Our findings indicated that the IGF-1 rs35767 A>G polymorphism might be a risk factor for cancer development. However, additional well-designed studies with sample sizes larger than ours need to be conducted in the future to verify our findings.