DLTKcat: deep learning-based prediction of temperature-dependent enzyme turnover rates

The enzyme turnover rate, kcat, quantifies enzyme kinetics by indicating the maximum efficiency of enzyme catalysis. Despite its importance, kcat values remain scarce in databases for most organisms, primarily because of the cost of experimental measurements. To predict kcat and account for its strong temperature dependence, DLTKcat was developed in this study and demonstrated superior performance (log10-scale root mean squared error = 0.88, R-squared = 0.66) than previously published models. Through two case studies, DLTKcat showed its ability to predict the effects of protein sequence mutations and temperature changes on kcat values. Although its quantitative accuracy is not high enough yet to model the responses of cellular metabolism to temperature changes, DLTKcat has the potential to eventually become a computational tool to describe the temperature dependence of biological systems

Systematic elucidation of independently modulated genes in Lactiplantibacillus plantarum reveals a trade-off between secondary and primary metabolism

Lactiplantibacillus plantarum is a probiotic bacterium widely used in food and health industries, but its gene regulatory information is limited in existing databases, which impedes the research of its physiology and its applications. To obtain a better understanding of the transcriptional regulatory network of L. plantarum, independent component analysis of its transcriptomes was used to derive 45 sets of independently modulated genes (iModulons). Those iModulons were annotated for associated transcription factors and functional pathways, and active iModulons in response to different growth conditions were identified and characterized in detail. Eventually, the analysis of iModulon activities reveals a trade-off between regulatory activities of secondary and primary metabolism in L. plantarum

Oral cancer cells may rewire alternative metabolic pathways to survive from siRNA silencing of metabolic enzymes.

BackgroundCancer cells may undergo metabolic adaptations that support their growth as well as drug resistance properties. The purpose of this study is to test if oral cancer cells can overcome the metabolic defects introduced by using small interfering RNA (siRNA) to knock down their expression of important metabolic enzymes.MethodsUM1 and UM2 oral cancer cells were transfected with siRNA to transketolase (TKT) or siRNA to adenylate kinase (AK2), and Western blotting was used to confirm the knockdown. Cellular uptake of glucose and glutamine and production of lactate were compared between the cancer cells with either TKT or AK2 knockdown and those transfected with control siRNA. Statistical analysis was performed with student T-test.ResultsDespite the defect in the pentose phosphate pathway caused by siRNA knockdown of TKT, the survived UM1 or UM2 cells utilized more glucose and glutamine and secreted a significantly higher amount of lactate than the cells transferred with control siRNA. We also demonstrated that siRNA knockdown of AK2 constrained the proliferation of UM1 and UM2 cells but similarly led to an increased uptake of glucose/glutamine and production of lactate by the UM1 or UM2 cells survived from siRNA silencing of AK2.ConclusionsOur results indicate that the metabolic defects introduced by siRNA silencing of metabolic enzymes TKT or AK2 may be compensated by alternative feedback metabolic mechanisms, suggesting that cancer cells may overcome single defective pathways through secondary metabolic network adaptations. The highly robust nature of oral cancer cell metabolism implies that a systematic medical approach targeting multiple metabolic pathways may be needed to accomplish the continued improvement of cancer treatment

Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments

In the domain of causal inference research, the prevalent potential outcomes framework, notably the Rubin Causal Model (RCM), often overlooks individual interference and assumes independent treatment effects. This assumption, however, is frequently misaligned with the intricate realities of real-world scenarios, where interference is not merely a possibility but a common occurrence. Our research endeavors to address this discrepancy by focusing on the estimation of direct and spillover treatment effects under two assumptions: (1) network-based interference, where treatments on neighbors within connected networks affect one's outcomes, and (2) non-random treatment assignments influenced by confounders. To improve the efficiency of estimating potentially complex effects functions, we introduce an novel active learning approach: Active Learning in Causal Inference with Interference (ACI). This approach uses Gaussian process to flexibly model the direct and spillover treatment effects as a function of a continuous measure of neighbors' treatment assignment. The ACI framework sequentially identifies the experimental settings that demand further data. It further optimizes the treatment assignments under the network interference structure using genetic algorithms to achieve efficient learning outcome. By applying our method to simulation data and a Tencent game dataset, we demonstrate its feasibility in achieving accurate effects estimations with reduced data requirements. This ACI approach marks a significant advancement in the realm of data efficiency for causal inference, offering a robust and efficient alternative to traditional methodologies, particularly in scenarios characterized by complex interference patterns.Comment: conference pape

RL-ViGen: A Reinforcement Learning Benchmark for Visual Generalization

Visual Reinforcement Learning (Visual RL), coupled with high-dimensional observations, has consistently confronted the long-standing challenge of generalization. Despite the focus on algorithms aimed at resolving visual generalization problems, we argue that the devil is in the existing benchmarks as they are restricted to isolated tasks and generalization categories, undermining a comprehensive evaluation of agents' visual generalization capabilities. To bridge this gap, we introduce RL-ViGen: a novel Reinforcement Learning Benchmark for Visual Generalization, which contains diverse tasks and a wide spectrum of generalization types, thereby facilitating the derivation of more reliable conclusions. Furthermore, RL-ViGen incorporates the latest generalization visual RL algorithms into a unified framework, under which the experiment results indicate that no single existing algorithm has prevailed universally across tasks. Our aspiration is that RL-ViGen will serve as a catalyst in this area, and lay a foundation for the future creation of universal visual generalization RL agents suitable for real-world scenarios. Access to our code and implemented algorithms is provided at https://gemcollector.github.io/RL-ViGen/

A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis

Time series data, often characterized by unique composition and complex multi-scale temporal variations, requires special consideration of decomposition and multi-scale modeling in its analysis. Existing deep learning methods on this best fit to only univariate time series, and have not sufficiently accounted for sub-series level modeling and decomposition completeness. To address this, we propose MSD-Mixer, a Multi-Scale Decomposition MLP-Mixer which learns to explicitly decompose the input time series into different components, and represents the components in different layers. To handle multi-scale temporal patterns and inter-channel dependencies, we propose a novel temporal patching approach to model the time series as multi-scale sub-series, i.e., patches, and employ MLPs to mix intra- and inter-patch variations and channel-wise correlations. In addition, we propose a loss function to constrain both the magnitude and autocorrelation of the decomposition residual for decomposition completeness. Through extensive experiments on various real-world datasets for five common time series analysis tasks (long- and short-term forecasting, imputation, anomaly detection, and classification), we demonstrate that MSD-Mixer consistently achieves significantly better performance in comparison with other state-of-the-art task-general and task-specific approaches

Screening ANLN and ASPM as bladder urothelial carcinoma-related biomarkers based on weighted gene co-expression network analysis

Introduction: Bladder cancer (BLCA) is one of the most common malignancies in the urinary system with a poor prognosis and high treatment costs. Identifying potential prognostic biomarkers is significant for exploring new therapeutic and predictive targets of BLCA.Methods: In this study, we screened differentially expressed genes using the GSE37815 dataset. We then performed a weighted gene co‐expression network analysis (WGCNA) to identify the genes correlated with the histologic grade and T stage of BLCA using the GSE32548 dataset. Subsequently, Kaplan Meier survival analysis and Cox regression were used to further identify prognosis‐related hub genes using the datasets GSE13507 and TCGA‐BLCA. Moreover, we detected the expression of the hub genes in 35 paired samples, including BLCA and paracancerous tissue, from the Shantou Central Hospital by qRT‐polymerase chain reaction.Results: This study showed that Anillin (ANLN) and Abnormal spindle-like microcephaly-associated gene (ASPM) were prognostic biomarkers for BLCA. High expression of ANLN and ASPM was associated with poor overall survival.The qRT‐PCR results revealed that ANLN and ASPM genes were upregulated in BLCA, and there was a correlation between the expression of ANLN and ASPM in cancer tissues and paracancerous tissue. Additionally, the increasing multiples in the ANLN gene was obvious in high-grade BLCA.Discussion: In summary, this preliminary exploration indicated a correlation between ANLN and ASPM expression. These two genes, serving as the risk factors for BLCA progression, might be promising targets to improve the occurrence and progression of BLCA

PgtE Enzyme of Salmonella enterica Shares the Similar Biological Roles to Plasminogen Activator (Pla) in Interacting With DEC-205 (CD205), and Enhancing Host Dissemination and Infectivity by Yersinia pestis

Yersinia pestis, the cause of plague, is a newly evolved Gram-negative bacterium. Through the acquisition of the plasminogen activator (Pla), Y. pestis gained the means to rapidly disseminate throughout its mammalian hosts. It was suggested that Y. pestis utilizes Pla to interact with the DEC-205 (CD205) receptor on antigen-presenting cells (APCs) to initiate host dissemination and infection. However, the evolutionary origin of Pla has not been fully elucidated. The PgtE enzyme of Salmonella enterica, involved in host dissemination, shows sequence similarity with the Y. pestis Pla. In this study, we demonstrated that both Escherichia coli K-12 and Y. pestis bacteria expressing the PgtE-protein were able to interact with primary alveolar macrophages and DEC-205-transfected CHO cells. The interaction between PgtE-expressing bacteria and DEC-205-expressing transfectants could be inhibited by the application of an anti-DEC-205 antibody. Moreover, PgtE-expressing Y. pestis partially re-gained the ability to promote host dissemination and infection. In conclusion, the DEC-205-PgtE interaction plays a role in promoting the dissemination and infection of Y. pestis, suggesting that Pla and the PgtE of S. enterica might share a common evolutionary origin.Peer reviewe