Omics-based biomarkers as useful tools in metabolic dysfunctionassociated steatotic liver disease clinical practice:How far are we?

Unmet needs exist in metabolic dysfunction-associated steatotic liver disease(MASLD)risk stratification.Our ability to identify patients with MASLD with advanced fibrosis and at higher risk for adverse outcomes is still limited.Incorporating novel biomarkers could represent a meaningful improvement to current risk predictors.With this aim,omics technologies have revolutionized the process of MASLD biomarker discovery over the past decades.While the research in this field is thriving,much of the publication has been haphazard,often using single-omics data and specimen sets of convenience,with many identified candidate biomarkers but lacking clinical validation and utility.If we incorporate these biomarkers to direct patients’management,it should be considered that the roadmap for translating a newly discovered omics-based signature to an actual,analytically valid test useful in MASLD clinical practice is rigorous and,therefore,not easily accomplished.This article presents an overview of this area’s current state,the conceivable opportunities and challenges of omics-based laboratory diagnostics,and a roadmap for improving MASLD biomarker research.

Recent omics progress in nanobiotechnology for plant abiotic stress tolerance improvement

Use of nanomaterials(NMs)to improve plant abiotic stress tolerance(AST)is a hot topic in NM-enabled agriculture.Previous studies mainly focused on the physiological and biochemical responses of plants treated with NMs under abiotic stress.To use NMs for improving plant AST,it is necessary to understand how they act on this tolerance at the omics and epigenetics levels.In this review,we summarized the knowledge of NM-improved abiotic stress tolerance in relation to omics(such as metabolic,transcriptomic,proteomic,and microRNA),DNA methylation,and histone modifications.Overall,NMs can improve plant abiotic stress tolerance through the modulation at omics and epigenetics levels.

New perspectives on biology,disease progression,and therapy response of head and neck cancer gained from single cell RNA sequencing and spatial transcriptomics

Head and neck squamous cell carcinoma(HNSCC)is one of the most frequent cancers worldwide.The main risk factors are consumption of tobacco products and alcohol,as well as infection with human papilloma virus.Approved therapeutic options comprise surgery,radiation,chemotherapy,targeted therapy through epidermal growth factor receptor inhibition,and immunotherapy,but outcome has remained unsatisfactory due to recurrence rates of~50%and the frequent occurrence of second primaries.The availability of the human genome sequence at the beginning of the millennium heralded the omics era,in which rapid technological progress has advanced our knowledge of the molecular biology of malignant diseases,including HNSCC,at an unprecedented pace.Initially,microarray-based methods,followed by approaches based on next-generation sequencing,were applied to study the genetics,epigenetics,and gene expression patterns of bulk tumors.More recently,the advent of single-cell RNA sequencing(scRNAseq)and spatial transcriptomics methods has facilitated the investigation of the heterogeneity between and within different cell populations in the tumor microenvironment(e.g.,cancer cells,fibroblasts,immune cells,endothelial cells),led to the discovery of novel cell types,and advanced the discovery of cell-cell communication within tumors.This review provides an overview of scRNAseq,spatial transcriptomics,and the associated bioinformatics methods,and summarizes how their application has promoted our understanding of the emergence,composition,progression,and therapy responsiveness of,and intercellular signaling within,HNSCC.

The advantages of multi-level omics research on stem cell-based therapies for ischemic stroke

Stem cell transplantation is a potential therapeutic strategy for ischemic stroke. However, despite many years of preclinical research, the application of stem cells is still limited to the clinical trial stage. Although stem cell therapy can be highly beneficial in promoting functional recovery, the precise mechanisms of action that are responsible for this effect have yet to be fully elucidated. Omics analysis provides us with a new perspective to investigate the physiological mechanisms and multiple functions of stem cells in ischemic stroke. Transcriptomic, proteomic, and metabolomic analyses have become important tools for discovering biomarkers and analyzing molecular changes under pathological conditions. Omics analysis could help us to identify new pathways mediated by stem cells for the treatment of ischemic stroke via stem cell therapy, thereby facilitating the translation of stem cell therapies into clinical use. In this review, we summarize the pathophysiology of ischemic stroke and discuss recent progress in the development of stem cell therapies for the treatment of ischemic stroke by applying multi-level omics. We also discuss changes in RNAs, proteins, and metabolites in the cerebral tissues and body fluids under stroke conditions and following stem cell treatment, and summarize the regulatory factors that play a key role in stem cell therapy. The exploration of stem cell therapy at the molecular level will facilitate the clinical application of stem cells and provide new treatment possibilities for the complete recovery of neurological function in patients with ischemic stroke.

Exploring the Unknown: The Application and Prospects of Artificial Intelligence in Genomics and Bioinformatics

This review comprehensively explores the core application of artificial intelligence (AI) in the fields of genomics and bioinformatics, and deeply analyzes how it leads the innovative progress of science. In the cutting-edge fields of genomics and bioinformatics, the application of AI is propelling a deeper understanding of complex genetic mechanisms and the development of innovative therapeutic approaches. The precision of AI in genomic sequence analysis, coupled with breakthroughs in precise gene editing, such as AI-designed gene editors, significantly enhances our comprehension of gene functions and disease associations . Moreover, AI’s capabilities in disease prediction, assessing individual disease risks through genomic data analysis, provide robust support for personalized medicine. AI applications extend beyond gene identification, gene expression pattern prediction, and genomic structural variant analysis, encompassing key areas such as epigenetics, multi-omics data integration, genetic disease diagnosis, evolutionary genomics, and non-coding RNA function prediction. Despite challenges including data privacy, algorithm transparency, and bioethical issues, the future of AI is expected to continue revolutionizing genomics and bioinformatics, ushering in a new era of personalized medicine and precision treatments.

Metabolomics and gene expression levels reveal the positive effects of teaseed oil on lifespan and aging process in Caenorhabditis elegans

As an irreplaceable dietary constituent,lipids play a vital important role in health,but their effects on aging process and longevity are still not well known yet.In this paper,the metabolic profiling and gene expression levels of Caenorhabditis elegans were investigated to explore the effects of different edible oils on senescence and lifespan.The results showed that teaseed oil(TO)could prolong the life expectancy and slow down the aging process of C.elegans.Compared to the control group,the intake of lard oil(LO)and TO increased the expression levels of genes related to inhibition of protein aggregation(akt-1,daf-16,hsf-1,hsp-16.2)and lipid metabolism(daf-7,daf-1,mdt-15,lipl-4,fat-5,fat-6,fat-7),with a more significant alteration in TO group.Metabolomics revealed that palm oil can upregulated plenty of fatty acids(palmitic acid,stearic acid,tetracosanoic acid),together with some amino acids(tryptophan,L-aspartate,L-valine)and carbohydrate(D-glucose),while the trend was opposite in TO group.Besides,moderate-to-strong correlations were found between differential metabolites and changed genes.In general,this paper claimed that TO could prolong lifespan and slow down aging process via regulating the lipids,amino acids and carbohydrates metabolism.

Progress in research on tumor microenvironment-based spatial omics technologies

Spatial omics technology integrates the concept of space into omics research and retains the spatial information of tissues or organs while obtaining molecular information.It is characterized by the ability to visualize changes in molecular information and yields intuitive and vivid visual results.Spatial omics technologies include spatial transcriptomics,spatial proteomics,spatial metabolomics,and other technologies,the most widely used of which are spatial transcriptomics and spatial proteomics.The tumor microenvironment refers to the surrounding microenvironment in which tumor cells exist,including the surrounding blood vessels,immune cells,fibroblasts,bone marrow-derived inflammatory cells,various signaling molecules,and extracellular matrix.A key issue in modern tumor biology is the application of spatial omics to the study of the tumor microenvironment,which can reveal problems that conventional research techniques cannot,potentially leading to the development of novel therapeutic agents for cancer.This paper summarizes the progress of research on spatial transcriptomics and spatial proteomics technologies for characterizing the tumor immune microenvironment.

Application of single-cell omics in inflammatory bowel disease

Over the past decade,the advent of single cell RNA-sequencing has revolutionized the approach in cellular transcriptomics research.The current technology offers an unbiased platform to understand how genotype correlates to phenotype.Single-cell omics applications in gastrointestinal(GI)research namely inflammatory bowel disease(IBD)has become popular in the last few years with multiple publications as single-cell omics techniques can be applied directly to the target organ,the GI tract at the tissue level.Through examination of mucosal tissue and peripheral blood in IBD,the recent boom in single cell research has identified a myriad of key immune players from enterocytes to tissue resident memory T cells,and explored functional heterogeneity within cellular subsets previously unreported.As we begin to unravel the complex mucosal immune system in states of health and disease like IBD,the power of exploration through single-cell omics can change our approach to translational research.As novel techniques evolve through multiplexing single-cell omics and spatial transcriptomics come to the forefront,we can begin to fully comprehend the disease IBD and better design targets of treatment.In addition,hopefully these techniques can ultimately begin to identify biomarkers of therapeutic response and answer clinically relevant questions in how to tailor individual therapy to patients through personalized medicine.

High-throughput calculations combining machine learning to investigate the corrosion properties of binary Mg alloys

Magnesium(Mg)alloys have shown great prospects as both structural and biomedical materials,while poor corrosion resistance limits their further application.In this work,to avoid the time-consuming and laborious experiment trial,a high-throughput computational strategy based on first-principles calculations is designed for screening corrosion-resistant binary Mg alloy with intermetallics,from both the thermodynamic and kinetic perspectives.The stable binary Mg intermetallics with low equilibrium potential difference with respect to the Mg matrix are firstly identified.Then,the hydrogen adsorption energies on the surfaces of these Mg intermetallics are calculated,and the corrosion exchange current density is further calculated by a hydrogen evolution reaction(HER)kinetic model.Several intermetallics,e.g.Y_(3)Mg,Y_(2)Mg and La_(5)Mg,are identified to be promising intermetallics which might effectively hinder the cathodic HER.Furthermore,machine learning(ML)models are developed to predict Mg intermetallics with proper hydrogen adsorption energy employing work function(W_(f))and weighted first ionization energy(WFIE).The generalization of the ML models is tested on five new binary Mg intermetallics with the average root mean square error(RMSE)of 0.11 eV.This study not only predicts some promising binary Mg intermetallics which may suppress the galvanic corrosion,but also provides a high-throughput screening strategy and ML models for the design of corrosion-resistant alloy,which can be extended to ternary Mg alloys or other alloy systems.

Integrating artificial intelligence and high-throughput phenotyping for crop improvement

Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.

The application of omics technologies to toxicology

The application of omics technologies,including genomics,transcriptomics,proteomics and metabolomics,has the potential to revolutionize toxicology by providing a more comprehensive understanding of the molecular mechanisms of toxicity,identifying potential biomarkers of exposure or effect,and enabling personalized risk assessments for individuals.Each omics approach has its own challenges,including data analysis and interpretation,but the integration of multiple omics approaches can provide a more comprehensive understanding of toxicity.The use of omics technologies for personalized risk assessments can inform targeted interventions and improve public health outcomes.While challenges remain,the potential benefits of omics technologies in toxicology make it an exciting area of research for the future.

From the perspective of experimental practice: High-throughput computational screening in photocatalysis

Photocatalysis,a critical strategy for harvesting sunlight to address energy demand and environmental concerns,is underpinned by the discovery of high-performance photocatalysts,thereby how to design photocatalysts is now generating widespread interest in boosting the conversion effi-ciency of solar energy.In the past decade,computational technologies and theoretical simulations have led to a major leap in the development of high-throughput computational screening strategies for novel high-efficiency photocatalysts.In this viewpoint,we started with introducing the challenges of photocatalysis from the view of experimental practice,especially the inefficiency of the traditional“trial and error”method.Sub-sequently,a cross-sectional comparison between experimental and high-throughput computational screening for photocatalysis is presented and discussed in detail.On the basis of the current experimental progress in photocatalysis,we also exemplified the various challenges associated with high-throughput computational screening strategies.Finally,we offered a preferred high-throughput computational screening procedure for pho-tocatalysts from an experimental practice perspective(model construction and screening,standardized experiments,assessment and revision),with the aim of a better correlation of high-throughput simulations and experimental practices,motivating to search for better descriptors.

Development of a High-throughput Sequencing Platform for Detection of Viral Encephalitis Pathogens Based on Amplicon Sequencing

Objective Viral encephalitis is an infectious disease severely affecting human health.It is caused by a wide variety of viral pathogens,including herpes viruses,flaviviruses,enteroviruses,and other viruses.The laboratory diagnosis of viral encephalitis is a worldwide challenge.Recently,high-throughput sequencing technology has provided new tools for diagnosing central nervous system infections.Thus,In this study,we established a multipathogen detection platform for viral encephalitis based on amplicon sequencing.Methods We designed nine pairs of specific polymerase chain reaction(PCR)primers for the 12 viruses by reviewing the relevant literature.The detection ability of the primers was verified by software simulation and the detection of known positive samples.Amplicon sequencing was used to validate the samples,and consistency was compared with Sanger sequencing.Results The results showed that the target sequences of various pathogens were obtained at a coverage depth level greater than 20×,and the sequence lengths were consistent with the sizes of the predicted amplicons.The sequences were verified using the National Center for Biotechnology Information BLAST,and all results were consistent with the results of Sanger sequencing.Conclusion Amplicon-based high-throughput sequencing technology is feasible as a supplementary method for the pathogenic detection of viral encephalitis.It is also a useful tool for the high-volume screening of clinical samples.

High-throughput microfluidic production of carbon capture microcapsules:fundamentals,applications,and perspectives

In the last three decades,carbon dioxide(CO_(2)) emissions have shown a significant increase from various sources.To address this pressing issue,the importance of reducing CO_(2) emissions has grown,leading to increased attention toward carbon capture,utilization,and storage strategies.Among these strategies,monodisperse microcapsules,produced by using droplet microfluidics,have emerged as promising tools for carbon capture,offering a potential solution to mitigate CO_(2) emissions.However,the limited yield of microcapsules due to the inherent low flow rate in droplet microfluidics remains a challenge.In this comprehensive review,the high-throughput production of carbon capture microcapsules using droplet microfluidics is focused on.Specifically,the detailed insights into microfluidic chip fabrication technologies,the microfluidic generation of emulsion droplets,along with the associated hydrodynamic considerations,and the generation of carbon capture microcapsules through droplet microfluidics are provided.This review highlights the substantial potential of droplet microfluidics as a promising technique for large-scale carbon capture microcapsule production,which could play a significant role in achieving carbon neutralization and emission reduction goals.

Interpretable Machine Learning-Assisted High-Throughput Screening for Understanding NRR Electrocatalyst Performance Modulation between Active Center and C-N Coordination

Understanding the correlation between the fundamental descriptors and catalytic performance is meaningful to guide the design of high-performance electrochemical catalysts.However,exploring key factors that affect catalytic performance in the vast catalyst space remains challenging for people.Herein,to accurately identify the factors that affect the performance of N2 reduction,we apply interpretable machine learning(ML)to analyze high-throughput screening results,which is also suited to other surface reactions in catalysis.To expound on the paradigm,33 promising catalysts are screened from 168 carbon-supported candidates,specifically single-atom catalysts(SACs)supported by a BC_(3)monolayer(TM@V_(B/C)-N_(n)=_(0-3)-BC_(3))via high-throughput screening.Subsequently,the hybrid sampling method and XGBoost model are selected to classify eligible and non-eligible catalysts.Through feature interpretation using Shapley Additive Explanations(SHAP)analysis,two crucial features,that is,the number of valence electrons(N_(v))and nitrogen substitution(N_(n)),are screened out.Combining SHAP analysis and electronic structure calculations,the synergistic effect between an active center with low valence electron numbers and reasonable C-N coordination(a medium fraction of nitrogen substitution)can exhibit high catalytic performance.Finally,six superior catalysts with a limiting potential lower than-0.4 V are predicted.Our workflow offers a rational approach to obtaining key information on catalytic performance from high-throughput screening results to design efficient catalysts that can be applied to other materials and reactions.

High-throughput calculation-based rational design of Fe-doped MoS_(2) nanosheets for electrocatalytic p H-universal overall water splitting

Electrocatalytic water splitting is crucial for H2generation via hydrogen evolution reaction(HER)but subject to the sluggish dynamics of oxygen evolution reaction(OER).In this work,single Fe atomdoped MoS_(2)nanosheets(SFe-DMNs)were prepared based on the high-throughput density functional theory(DFT)calculation screening.Due to the synergistic effect between Fe atom and MoS_(2)and optimized intermediate binding energy,the SFe-DMNs could deliver outstanding activity for both HER and OER.When assembled into a two-electrode electrolytic cell,the SFe-DMNs could achieve the current density of 50 mA cm^(-2)at a low cell voltage of 1.55 V under neutral condition.These results not only confirmed the effectiveness of high-throughput screening,but also revealed the excellent activity and thus the potential applications in fuel cells of SFe-DMNs.

High-throughput computational screening and in vitro evaluation identifies 5-(4-oxo-4H-3,1-benzoxazin-2-yl)-2-[3-(4-oxo-4H-3,1-benzoxazin-2-yl)phenyl]-1H-isoindole-1,3(2H)-dione(C3),as a novel EGFR—HER2 dual inhibitor in gastric tumors

Gastric cancers are caused primarily due to the activation and amplification of the EGFR or HER2 kinases resulting in cell proliferation,adhesion,angiogenesis,and metastasis.Conventional therapies are ineffective due to the intra-tumoral heterogeneity and concomitant genetic mutations.Hence,dual inhibition strategies are recommended to increase potency and reduce cytotoxicity.In this study,we have conducted computational high-throughput screening of the ChemBridge library followed by in vitro assays and identified novel selective inhibitors that have a dual impediment of EGFR/HER2 kinase activities.Diversity-based High-throughput Virtual Screening(D-HTVS)was used to screen the whole ChemBridge small molecular library against EGFR and HER2.The atomistic molecular dynamic simulation was conducted to understand the dynamics and stability of the protein-ligand complexes.EGFR/HER2 kinase enzymes,KATOIII,and Snu-5 cells were used for in vitro validations.The atomistic Molecular Dynamics simulations followed by solvent-based Gibbs binding free energy calculation of top molecules,identified compound C3(5-(4-oxo-4H-3,1-benzoxazin-2-yl)-2-[3-(4-oxo-4H-3,1-benzoxazin-2-yl)phenyl]-1H-isoindole-1,3(2H)-dione)to have a good affinity for both EGFR and HER2.The predicted compound,C3,was promising with better binding energy,good binding pose,and optimum interactions with the EGFR and HER2 residues.C3 inhibited EGFR and HER2 kinases with IC50 values of 37.24 and 45.83 nM,respectively.The GI50 values of C3 to inhibit KATOIII and Snu-5 cells were 84.76 and 48.26 nM,respectively.Based on these findings,we conclude that the identified compound C3 showed a conceivable dual inhibitory activity on EGFR/HER2 kinase,and therefore can be considered as a plausible lead-like molecule for treating gastric cancers with minimal side effects,though testing in higher models with pharmacokinetic approach is required.

Variation of microbiological and small molecule metabolite profiles of Nuodeng ham during ripening by high-throughput sequencing and GC-TOF-MS

The internal microbial diversity and small molecular metabolites of Nuodeng ham in different processing years(the first,second and third year sample)were analyzed by high-throughput sequencing technology and gas chromatography-time of flight mass spectrography(GC-TOF-MS)to study the effects of microorganisms and small molecular metabolites on the quality of ham in different processing years.The results showed that the dominant bacteria phyla of Nuodeng ham in different processing years were Proteobacteria and Firmicutes,the dominant fungi phyla were Ascomycota and Basidiomycota,while Staphylococcus and Aspergillus were the dominant bacteria and fungi of Nuodeng ham,respectively.Totally,252 kinds of small molecular metabolites were identified from Nuodeng ham in different processing years,and 12 different metabolites were screened through multivariate statistical analysis.Further metabolic pathway analysis showed that 23 metabolic pathways were related to ham fermentation,of which 8 metabolic pathways had significant effects on ham fermentation(Impact>0.01,P<0.05).The content of L-proline,phenyllactic acid,L-lysine,carnosine,taurine,D-proline,betaine and creatine were significantly positively correlated with the relative abundance of Staphylococcus and Serratia,but negatively correlated with the relative abundance of Halomonas,Aspergillus and Yamadazyma.

High-throughput screening system of citrus bacterial cankerassociated transcription factors and its application to the regulation of citrus canker resistance

One of the main diseases that adversely impacts the global citrus industry is citrus bacterial canker(CBC),caused by the bacteria Xanthomonas citri subsp.citri(Xcc).Response to CBC is a complex process,with both proteinDNA as well as protein–protein interactions for the regulatory network.To detect such interactions in CBC resistant regulation,a citrus high-throughput screening system with 203 CBC-inducible transcription factors(TFs),were developed.Screening the upstream regulators of target by yeast-one hybrid(Y1H)methods was also performed.A regulatory module of CBC resistance was identified based on this system.One TF(CsDOF5.8)was explored due to its interactions with the 1-kb promoter fragment of CsPrx25,a resistant gene of CBC involved in reactive oxygen species(ROS)homeostasis regulation.Electrophoretic mobility shift assay(EMSA),dual-LUC assays,as well as transient overexpression of CsDOF5.8,further validated the interactions and transcriptional regulation.The CsDOF5.8–CsPrx25 promoter interaction revealed a complex pathway that governs the regulation of CBC resistance via H2O2homeostasis.The high-throughput Y1H/Y2H screening system could be an efficient tool for studying regulatory pathways or network of CBC resistance regulation.In addition,it could highlight the potential of these candidate genes as targets for efforts to breed CBC-resistant citrus varieties.

Viral Metagenomics Analysis of Planktonic Viruses in East Lake,Wuhan,China

East Lake(Lake Donghu),located in Wuhan,China,is a typical city freshwater lake that has been experiencing eutrophic conditions and algal blooming during recent years.Marine and fresh water are considered to contain a large number of viruses.However,little is known about their genetic diversity because of the limited techniques for culturing viruses.In this study,we conducted a viral metagenomic analysis using a high-throughput sequencing technique with samples collected from East Lake in Spring,Summer,Autumn,and Winter.The libraries from four samples each generated 234,669,71,837,12,820,and 34,236 contigs(>90 bp each),respectively.The genetic structure of the viral community revealed a high genetic diversity covering 23 viral families,with the majority of contigs homologous to DNA viruses,including members of Myoviridae,Podoviridae,Siphoviridae,Phycodnaviridae,and Microviridae,which infect bacteria or algae,and members of Circoviridae,which infect invertebrates and vertebrates.The highest viral genetic diversity occurred in samples collected in August,then December and June,and the least diversity in March.Most contigs have low-sequence identities with known viruses.PCR detection targeting the conserved sequences of genes(g20,psbA,psbD,and DNApol)of cyanophages further confirmed that there are novel cyanophages in the East Lake.Our viral metagenomic data provide the first preliminary understanding of the virome in one freshwater lake in China and would be helpful for novel virus discovery and the control of algal blooming in the future.