Constructing efficient bacterial cell factories to enable one-carbon utilization based on quantitative biology:A review

Developing methylotrophic cell factories that can efficiently catalyze organic one-carbon(C1)feedstocks derived from electrocatalytic reduction of carbon dioxide into bio-based chemicals and biofuels is of strategic significance for building a carbon-neutral,sustainable economic and industrial system.With the rapid advancement of RNA sequencing technology and mass spectrometer analysis,researchers have used these quantitative microbiology methods extensively,especially isotope-based metabolic flux analysis,to study the metabolic processes initiating from C1 feedstocks in natural C1-utilizing bacteria and synthetic C1 bacteria.This paper reviews the use of advanced quantitative analysis in recent years to understand the metabolic network and basic principles in the metabolism of natural C1-utilizing bacteria grown on methane,methanol,or formate.The acquired knowledge serves as a guide to rewire the central methylotrophic metabolism of natural C1-utilizing bacteria to improve the carbon conversion efficiency,and to engineer non-C1-utilizing bacteria into synthetic strains that can use C1 feedstocks as the sole carbon and energy source.These progresses ultimately enhance the design and construction of highly efficient C1-based cell factories to synthesize diverse high value-added products.The integration of quantitative biology and synthetic biology will advance the iterative cycle of understand–design–build–testing–learning to enhance C1-based biomanufacturing in the future.

Proteomics techniques in protein biomarker discovery

Protein biomarkers represent specific biological activities and processes, so they have had a critical role in cancer diagnosis and medical care for more than 50 years. With the recent improvement in proteomics technologies, thousands of protein biomarker candidates have been developed for diverse disease states. Studies have used different types of samples for proteomics diagnosis. Samples were pretreated with appropriate techniques to increase the selectivity and sensitivity of the downstream analysis and purified to remove the contaminants. The purified samples were analyzed by several principal proteomics techniques to identify the specific protein. In this study, recent improvements in protein biomarker discovery, verification, and validation are investigated. Furthermore, the advantages, and disadvantages of conventional techniques, are discussed. Studies have used mass spectroscopy (MS) as a critical technique in the identification and quantification of candidate biomarkers. Nevertheless, after protein biomarker discovery, verification and validation have been required to reduce the false-positive rate where there have been higher number of samples. Multiple reaction monitoring (MRM), parallel reaction monitoring (PRM), and selected reaction monitoring (SRM), in combination with stable isotope-labeled internal standards, have been examined as options for biomarker verification, and enzyme-linked immunosorbent assay (ELISA) for validation.

Deep learning for drug-drug interaction prediction:A comprehensive review

The prediction of drug-drug interactions(DDIs)is a crucial task for drug safety research,and identifying potential DDIs helps us to explore the mechanism behind combinatorial therapy.Traditional wet chemical experiments for DDI are cumbersome and time-consuming,and are too small in scale,limiting the efficiency of DDI predictions.Therefore,it is particularly crucial to develop improved computational methods for detecting drug interactions.With the development of deep learning,several computational models based on deep learning have been proposed for DDI prediction.In this review,we summarized the high-quality DDI prediction methods based on deep learning in recent years,and divided them into four categories:neural network-based methods,graph neural network-based methods,knowledge graph-based methods,and multimodal-based methods.Furthermore,we discuss the challenges of existing methods and future potential perspectives.This review reveals that deep learning can significantly improve DDI prediction performance compared to traditional machine learning.Deep learning models can scale to large-scale datasets and accept multiple data types as input,thus making DDI predictions more efficient and accurate.

Measuring drug similarity using drug–drug interactions

Combination therapy is a promising approach to address the challenge of antimicrobial resistance,and computational models have been proposed for predicting drug–drug interactions.Most existing models rely on drug similarity measures based on characteristics such as chemical structure and the mechanism of action.In this study,we focus on the network structure itself and propose a drug similarity measure based on drug–drug interaction networks.We explore the potential applications of this measure by combining it with unsupervised learning and semi-supervised learning approaches.In unsupervised learning,drugs can be grouped based on their interactions,leading to almost monochromatic group–group interactions.In addition,drugs within the same group tend to have similar mechanisms of action(MoA).In semi-supervised learning,the similarity measure can be utilized to construct affinity matrices,enabling the prediction of unknown drug–drug interactions.Our method exceeds existing approaches in terms of performance.Overall,our experiments demonstrate the effectiveness and practicability of the proposed similarity measure.On the one hand,when combined with clustering algorithms,it can be used for functional annotation of compounds with unknown MoA.On the other hand,when combined with semi-supervised graph learning,it enables the prediction of unknown drug–drug interactions.

SimHOEPI:A resampling simulator for generating single nucleotide polymorphism data with a high-order epistasis model

Epistasis is a ubiquitous phenomenon in genetics,and is considered to be one of main factors in current efforts to unveil missing heritability of complex diseases.Simulation data is crucial for evaluating epistasis detection tools in genome-wide association studies(GWAS).Existing simulators normally suffer from two limitations:absence of support for high-order epistasis models containing multiple single nucleotide polymorphisms(SNPs),and inability to generate simulation SNP data independently.In this study,we proposed a simulator SimHOEPI,which is capable of calculating penetrance tables of high-order epistasis models depending on either prevalence or heritability,and uses a resampling strategy to generate simulation data independently.Highlights of SimHOEPI are the preservation of realistic minor allele frequencies in sampling data,the accurate calculation and embedding of high-order epistasis models,and acceptable simulation time.A series of experiments were carried out to verify these properties from different aspects.Experimental results show that SimHOEPI can generate simulation SNP data independently with high-order epistasis models,implying that it might be an alternative simulator for GWAS.

Toward predictable universal genetic circuit design

In thepast 2decades,synthetic biologists have applied systematic engineering principles to genetic circuit design to devise biological systems with bespoke behaviors,such as Boolean logic gates,signal filters,oscillators,state machines,perceptrons,and genetic controllers[1,2].Following a bottom-up strategy,the genetic circuits are designed by assembling a set of well-characterized biological components,or genetic parts[3],and optimized through the iterative Design-Build-Test-Learn(DBTL)cycles.

Accurate cell type annotation for single-cell chromatin accessibility data via contrastive learning and reference guidance

Recent advances in single-cell chromatin accessibility sequencing(scCAS)technologies have resulted in new insights into the characterization of epigenomic heterogeneity and have increased the need for automatic cell type annotation.However,existing automatic annotation methods for scCAS data fail to incorporate the reference data and neglect novel cell types,which only exist in a test set.Here,we propose RAINBOW,a reference-guided automatic annotation method based on the contrastive learning framework,which is capable of effectively identifying novel cell types in a test set.By utilizing contrastive learning and incorporating reference data,RAINBOW can effectively characterize the heterogeneity of cell types,thereby facilitating more accurate annotation.With extensive experiments on multiple scCAS datasets,we show the advantages of RAINBOW over state-of-the-art methods in known and novel cell type annotation.We also verify the effectiveness of incorporating reference data during the training process.In addition,we demonstrate the robustness of RAINBOW to data sparsity and number of cell types.Furthermore,RAINBOW provides superior performance in newly sequenced data and can reveal biological implication in downstream analyses.All the results demonstrate the superior performance of RAINBOW in cell type annotation for scCAS data.We anticipate that RAINBOW will offer essential guidance and great assistance in scCAS data analysis.The source codes are available at the GitHub website(BioX-NKU/RAINBOW).

Functional predictability of universal gene circuits in diverse microbial hosts

Although the principles of synthetic biology were initially established in model bacteria,microbial producers,extremophiles and gut microbes have now emerged as valuable prokaryotic chassis for biological engineering.Extending the host range in which designed circuits can function reliably and predictably presents a major challenge for the concept of synthetic biology to materialize.In this work,we systematically characterized the cross-species universality of two transcriptional regulatory modules—the T7 RNA polymerase activator module and the repressors module—in three non-model microbes.We found striking linear relationships in circuit activities among different organisms for both modules.Parametrized model fitting revealed host non-specific parameters defining the universality of both modules.Lastly,a genetic NOT gate and a band-pass filter circuit were constructed from these modules and tested in non-model organisms.Combined models employing host non-specific parameters were successful in quantitatively predicting circuit behaviors,underscoring the potential of universal biological parts and predictive modeling in synthetic bioengineering.

Hierarchical learning of gastric cancer molecular subtypes by integrating multi-modal DNA-level omics data and clinical stratification

Molecular subtyping of gastric cancer(GC)aims to comprehend its genetic landscape.However,the efficacy of current subtyping methods is hampered by their mixed use of molecular features,a lack of strategy optimization,and the limited availability of public GC datasets.There is a pressing need for a precise and easily adoptable subtyping approach for early DNA-based screening and treatment.Based on TCGA subtypes,we developed a novel DNA-based hierarchical classifier for gastric cancer molecular subtyping(HCG),which employs gene mutations,copy number aberrations,and methylation patterns as predictors.By incorporating the closely related esophageal adenocarcinomas dataset,we expanded the TCGA GC dataset for the training and testing of HCG(n=453).The optimization of HCG was achieved through three hierarchical strategies using Lasso-Logistic regression,evaluated by their overall the area under receiver operating characteristic curve(auROC),accuracy,F1 score,the area under precision-recall curve(auPRC)and their capability for clinical stratification using multivariate survival analysis.Subtype-specific DNA alteration biomarkers were discerned through difference tests based on HCG defined subtypes.Our HCG classifier demonstrated superior performance in terms of overall auROC(0.95),accuracy(0.88),F1 score(0.87)and auPRC(0.86),significantly improving the clinical stratification of patients(overall p-value=0.032).Difference tests identified 25 subtype-specific DNA alterations,including a high mutation rate in the SYNE1,ITGB4,and COL22A1 genes for the MSI subtype,and hypermethylation of ALS2CL,KIAA0406,and RPRD1B genes for the EBV subtype.HCG is an accurate and robust classifier for DNA-based GC molecular subtyping with highly predictive clinical stratification performance.The training and test datasets,along with the analysis programs of HCG,are accessible on the GitHub website(github.com/LabxSCUT).

A clinical trial termination prediction model based on denoising autoencoder and deep survival regression

Effective clinical trials are necessary for understanding medical advances but early termination of trials can result in unnecessary waste of resources.Survival models can be used to predict survival probabilities in such trials.However,survival data from clinical trials are sparse,and DeepSurv cannot accurately capture their effective features,making the models weak in generalization and decreasing their prediction accuracy.In this paper,we propose a survival prediction model for clinical trial completion based on the combination of denoising autoencoder(DAE)and DeepSurv models.The DAE is used to obtain a robust representation of features by breaking the loop of raw features after autoencoder training,and then the robust features are provided to DeepSurv as input for training.The clinical trial dataset for training the model was obtained from the ClinicalTrials.gov dataset.A study of clinical trial completion in pregnant women was conducted in response to the fact that many current clinical trials exclude pregnant women.The experimental results showed that the denoising autoencoder and deep survival regression(DAE-DSR)model was able to extract meaningful and robust features for survival analysis;the C-index of the training and test datasets were 0.74 and 0.75 respectively.Compared with the Cox proportional hazards model and DeepSurv model,the survival analysis curves obtained by using DAE-DSR model had more prominent features,and the model was more robust and performed better in actual prediction.

A feature extraction framework for discovering pan-cancer driver genes based on multi-omics data

The identification of tumor driver genes facilitates accurate cancer diagnosis and treatment,playing a key role in precision oncology,along with gene signaling,regulation,and their interaction with protein complexes.To tackle the challenge of distinguishing driver genes from a large number of genomic data,we construct a feature extraction framework for discovering pan-cancer driver genes based on multi-omics data(mutations,gene expression,copy number variants,and DNA methylation)combined with protein–protein interaction(PPI)networks.Using a network propagation algorithm,we mine functional information among nodes in the PPI network,focusing on genes with weak node information to represent specific cancer information.From these functional features,we extract distribution features of pan-cancer data,pan-cancer TOPSIS features of functional features using the ideal solution method,and SetExpan features of pan-cancer data from the gene functional features,a method to rank pan-cancer data based on the average inverse rank.These features represent the common message of pan-cancer.Finally,we use the lightGBM classification algorithm for gene prediction.Experimental results show that our method outperforms existing methods in terms of the area under the check precision-recall curve(AUPRC)and demonstrates better performance across different PPI networks.This indicates our framework’s effectiveness in predicting potential cancer genes,offering valuable insights for the diagnosis and treatment of tumors.

NeoHunter:Flexible software for systematically detecting neoantigens from sequencing data

Complicated molecular alterations in tumors generate various mutant peptides.Some of these mutant peptides can be presented to the cell surface and then elicit immune responses,and such mutant peptides are called neoantigens.Accurate detection of neoantigens could help to design personalized cancer vaccines.Although some computational frameworks for neoantigen detection have been proposed,most of them can only detect SNV-and indel-derived neoantigens.In addition,current frameworks adopt oversimplified neoantigen prioritization strategies.These factors hinder the comprehensive and effective detection of neoantigens.We developed NeoHunter,flexible software to systematically detect and prioritize neoantigens from sequencing data in different formats.NeoHunter can detect not only SNV-and indel-derived neoantigens but also gene fusion-and aberrant splicing-derived neoantigens.NeoHunter supports both direct and indirect immunogenicity evaluation strategies to prioritize candidate neoantigens.These strategies utilize binding characteristics,existing biological big data,and T-cell receptor specificity to ensure accurate detection and prioritization.We applied NeoHunter to the TESLA dataset,cohorts of melanoma and non-small cell lung cancer patients.NeoHunter achieved high performance across the TESLA cancer patients and detected 79%(27 out of 34)of validated neoantigens in total.SNV-and indel-derived neoantigens accounted for 90%of the top 100 candidate neoantigens while neoantigens from aberrant splicing accounted for 9%.Gene fusion-derived neoantigens were detected in one patient.NeoHunter is a powerful tool to‘catch all’neoantigens and is available for free academic use on Github(XuegongLab/NeoHunter).

DDI-Transform:A neural network for predicting drug-drug interaction events

Drug-drug interaction(DDI)event prediction is a challenging problem,and accurate prediction of DDI events is critical to patient health and new drug development.Recently,many machine learning-based techniques have been proposed for predicting DDI events.However,most of the existing methods do not effectively integrate the multidimensional features of drugs and provide poor mitigation of noise to get effective feature information.To address these limitations,we propose a DDI-Transform neural network framework for DDI event prediction.In DDI-Transform,we design a drug structure information feature extraction module and a drug bind-protein feature extraction module to obtain multidimensional feature information.A stack of DDI-Transform layers in the DDI-Transform network module are then used for adaptive learning,thus adaptively selecting the effective feature information for prediction.The results show that DDI-Transform can accurately predict DDI events and outperform the state-of-the-art models.Results on different scale datasets confirm the robustness of the method.

Role of ACLY in the development of gastric cancer under hyperglycemic conditions

To investigate the impact of hyperglycemia on the prognosis of patients with gastric cancer and identify key molecules associated with high glucose levels in gastric cancer development,RNA sequencing data and clinical features of gastric cancer patients were obtained from The Cancer Genome Atlas(TCGA)database.High glucose-related genes strongly associated with gastric cancer were identified using weighted gene co-expression network and differential analyses.A gastric cancer prognosis signature was constructed based on these genes and patients were categorized into high-and low-risk groups.The immune statuses of the two patient groups were compared.ATP citrate lyase(ACLY),a gene significantly related to the prognosis,was found to be upregulated upon high-glucose stimulation.Immunohistochemistry and molecular analyses confirmed high ACLY expression in gastric cancer tissues and cells.Gene Set Enrichment Analysis(GSEA)revealed the involvement of ACLY in cell cycle and DNA replication processes.Inhibition of ACLY affected the proliferation and migration of gastric cancer cells induced by high glucose levels.These findings suggest that ACLY,as a high glucose-related gene,plays a critical role in gastric cancer progression.

Re-examination of statistical relationships between dietary fats and other risk factors,and cardiovascular disease,based on two crucial datasets

Cardiovascular disease(CVD)is the major cause of death in many regions around the world,and several of its risk factors might be linked to diets.To improve public health and the understanding of this topic,we look at the recent Minnesota Coronary Experiment(MCE)analysis that used t-test and Cox model to evaluate CVD risks.However,these parametric methods might suffer from three problems:small sample size,right-censored bias,and lack of long-term evidence.To overcome the first of these challenges,we utilize a nonparametric permutation test to examine the relationship between dietary fats and serum total cholesterol.To address the second problem,we use a resampling-based rank test to examine whether the serum total cholesterol level affects CVD deaths.For the third issue,we use some extra-Framingham Heart Study(FHS)data with an A/B test to look for meta-relationship between diets,risk factors,and CVD risks.We show that,firstly,the link between low saturated fat diets and reduction in serum total cholesterol is strong.Secondly,reducing serum total cholesterol does not robustly have an impact on CVD hazards in the diet group.Lastly,the A/B test result suggests a more complicated relationship regarding abnormal diastolic blood pressure ranges caused by diets and how these might affect the associative link between the cholesterol level and heart disease risks.This study not only helps us to deeply analyze the MCE data but also,in combination with the long-term FHS data,reveals possible complex relationships behind diets,risk factors,and heart disease.

Plasma proteome profiling reveals biomarkers of chemotherapy resistance in patients with advanced colorectal cancer

Colorectal cancer(CRC)is one of the most common cancers.Patients with advanced CRC can only rely on chemotherapy to improve outcomes.However,primary drug resistance frequently occurs and is difficult to predict.Changes in plasma protein composition have shown potential in clinical diagnosis.Thus,it is urgent to identify potential protein biomarkers for primary resistance to chemotherapy for patients with CRC.Automatic sample preparation and high-throughput analysis were used to explore potential plasma protein biomarkers.Drug susceptibility testing of circulating tumor cells(CTCs)has been investigated,and the relationship between their values and protein expressions has been discussed.In addition,the differential proteins in different chemotherapy outcomes have been analyzed.Finally,the potential biomarkers have been detected via enzyme-linked immunosorbent assay(ELISA).Plasma proteome of 60 CRC patients were profiled.The correlation between plasma protein levels and the results of drug susceptibility testing of CTCs was performed,and 85 proteins showed a significant positive or negative correlation with chemotherapy resistance.Forty-four CRC patients were then divided into three groups according to their chemotherapy outcomes(objective response,stable disease,and progressive disease),and 37 differential proteins were found to be related to chemotherapy resistance.The overlapping proteins were further investigated in an additional group of 79 patients using ELISA.Protein levels of F5 and PROZ significantly increased in the progressive disease group compared to other outcome groups.Our study indicated that F5 and PROZ proteins could represent potential biomarkers of resistance to chemotherapy in advanced CRC patients.

GCARDTI:Drug–target interaction prediction based on a hybrid mechanism in drug SELFIES

The prediction of the interaction between a drug and a target is the most critical issue in the fields of drug development and repurposing.However,there are still two challenges in current deep learning research:(i)the structural information of drug molecules is not fully explored in most drug target studies,and the previous drug SMILES does not correspond well to effective drug molecules and(ii)exploration of the potential relationship between drugs and targets is in need of improvement.In this work,we use a new and better representation of the effective molecular graph structure,SELFIES.We propose a hybrid mechanism framework based on convolutional neural network and graph attention network to capture multi-view feature information of drug and target molecular structures,and we aim to enhance the ability to capture interaction sites between a drug and a target.In this study,our experiments using two different datasets show that the GCARDTI model outperforms a variety of different model algorithms on different metrics.We also demonstrate the accuracy of our model through two case studies.

A comprehensive review of molecular optimization in artificial intelligence-based drug discovery

Drug discovery is aimed to design novel molecules with specific chemical properties for the treatment of targeting diseases. Generally, molecular optimization is one important step in drug discovery, which optimizes the physical and chemical properties of a molecule. Currently, artificial intelligence techniques have shown excellent success in drug discovery, which has emerged as a new strategy to address the challenges of drug design including molecular optimization, and drastically reduce the costs and time for drug discovery. We review the latest advances of molecular optimization in artificial intelligence-based drug discovery, including data resources, molecular properties, optimization methodologies, and assessment criteria for molecular optimization. Specifically, we classify the optimization methodologies into molecular mapping-based, molecular distribution matching-based, and guided search-based methods, respectively, and discuss the principles of these methods as well as their pros and cons. Moreover, we highlight the current challenges in molecular optimization and offer a variety of perspectives, including interpretability, multidimensional optimization, and model generalization, on potential new lines of research to pursue in future. This study provides a comprehensive review of molecular optimization in artificial intelligence-based drug discovery, which points out the challenges as well as the new prospects. This review will guide researchers who are interested in artificial intelligence molecular optimization.

Towards applications of genome-scale metabolic model-based approaches in designing synthetic microbial communities

Background:Synthetic microbial communities,with different strains brought together by balancing their nutrition and promoting their interactions,demonstrate great advantages for exploring complex performance of communities and for further biotechnology applications.The potential of such microbial communities has not been explored,due to our limited knowledge of the extremely complex microbial interactions that are involved in designing and controlling effective and stable communities.Results:Genome-scale metabolic models(GEM)have been demonstrated as an effective tool for predicting and guiding the investigation and design of microbial communities,since they can explicitly and efficiently predict the phenotype of organisms from their genotypic data and can be used to explore the molecular mechanisms of microbehabitats and microbe-microbe interactions.In this work,we reviewed two main categories of GEM-based approaches and three uses related to design of synthetic microbial communities:predicting multi-species interactions,exploring environmental impacts on microbial phenotypes,and optimizing community-level performance.Conclusions:Although at the infancy stage,GEM-based approaches exhibit an increasing scope of applications in designing synthetic microbial communities.Compared to other methods,especially the use of laboratory cultures,GEM-based approaches can greatly decrease the trial-and-error cost of various procedures for designing synthetic communities and improving their functionality,such as identifying community members,determining media composition,evaluating microbial interaction potential or selecting the best community configuration.Future efforts should be made to overcome the limitations of the approaches,ranging from quality control of GEM reconstructions to community-level modeling algorithms,so that more applications of GEMs in studying phenotypes of microbial communities can be expected.

Empowering beginners in bioinformatics with ChatGPT

The impressive conversational and programming abilities of ChatGPT make it an attractive tool for facilitating the education of bioinformatics data analysis for beginners.In this study,we proposed an iterative model to fine-tune instructions for guiding a chatbot in generating code for bioinformatics data analysis tasks.We demonstrated the feasibility of the model by applying it to various bioinformatics topics.Additionally,we discussed practical considerations and limitations regarding the use of the model in chatbot-aided bioinformatics education.