Deep learning-based approaches for multi-omics data integration and analysis.

BACKGROUND: The rapid growth of deep learning, as well as the vast and ever-growing amount of available data, have provided ample opportunity for advances in fusion and analysis of complex and heterogeneous data types. Different data modalities provide complementary information that can be leveraged to gain a more complete understanding of each subject. In the biomedical domain, multi-omics data includes molecular (genomics, transcriptomics, proteomics, epigenomics, metabolomics, etc.) and imaging (radiomics, pathomics) modalities which, when combined, have the potential to improve performance on prediction, classification, clustering and other tasks. Deep learning encompasses a wide variety of methods, each of which have certain strengths and weaknesses for multi-omics integration. METHOD: In this review, we categorize recent deep learning-based approaches by their basic architectures and discuss their unique capabilities in relation to one another. We also discuss some emerging themes advancing the field of multi-omics integration. RESULTS: Deep learning-based multi-omics integration methods were categorized broadly into non-generative (feedforward neural networks, graph convolutional neural networks, and autoencoders) and generative (variational methods, generative adversarial models, and a generative pretrained model). Generative methods have the advantage of being able to impose constraints on the shared representations to enforce certain properties or incorporate prior knowledge. They can also be used to generate or impute missing modalities. Recent advances achieved by these methods include the ability to handle incomplete data as well as going beyond the traditional molecular omics data types to integrate other modalities such as imaging data. CONCLUSION: We expect to see further growth in methods that can handle missingness, as this is a common challenge in working with complex and heterogeneous data. Additionally, methods that integrate more data types are expected to improve performance on downstream tasks by capturing a comprehensive view of each sample.

Proteogenomic reconstruction of organ-specific metabolic networks in an environmental sentinel species, the amphipod Gammarus fossarum.

Metabolic pathways are affected by the impacts of environmental contaminants underlying a large variability of toxic effects across different species. However, the systematic reconstruction of metabolic pathways remains limited in environmental sentinel species due to the lack of available genomic data in many taxa of animal diversity. In this study we used a multi-omics approach to reconstruct the most comprehensive map of metabolic pathways for a crustacean model in biomonitoring, the amphipod Gammarus fossarum in order to improve the knowledge of the metabolism of this sentinel species. We revisited the assembly of RNA-seq data by de novo approaches to reduce RNA contaminants and transcript redundancy. We also acquired extensive mass spectrometry shotgun proteomic data on several organs from a reference population of G. fossarum males and females to identify organ-specific metabolic profiles. The G. fossarum metabolic pathway reconstruction (available through the metabolic database GamfoCyc) was performed by adapting the genomic tool CycADS and we identified 377 pathways representing 7630 annotated enzymes, 2610 enzymatic reactions and the expression of 858 enzymes was experimentally validated by proteomics. To our knowledge, our analysis provides for the first time a systematic metabolic pathway reconstruction and the proteome profiles of these pathways at the organ level in this sentinel species. As an example, we show an elevated abundance in enzymes involved in ATP biosynthesis and fatty acid beta-oxidation indicative of the high-energy requirement of the gills, or the key anabolic and detoxification role of the hepatopancreatic caeca, as exemplified by the specific expression of the retinoid biosynthetic pathways and glutathione synthesis. In conclusion, the multi-omics data integration performed in this study provides new resources to investigate metabolic processes in crustacean amphipods and their role in mediating the effects of environmental contaminant exposures in sentinel species. SYNOPSIS: This study provide the first evidence that it is possible to combine multiple omics data to exhaustively describe the metabolic network of a model species in ecotoxicology, Gammarus fossarum, for which a reference genome is not yet available.

Creating and leveraging bespoke large-scale knowledge graphs for comparative genomics and multi-omics drug discovery with SocialGene.

The rapid expansion of multi-omics data has transformed biological research, offering unprecedented opportunities to explore complex genomic relationships across diverse organisms. However, the vast volume and heterogeneity of these datasets presents significant challenges for analyses. Here we introduce SocialGene, a comprehensive software suite designed to collect, analyze, and organize multi-omics data into structured knowledge graphs, with the ability to handle small projects to repository-scale analyses. Originally developed to enhance genome mining for natural product drug discovery, SocialGene has been effective across various applications, including functional genomics, evolutionary studies, and systems biology. SocialGene's concerted Python and Nextflow libraries streamline data ingestion, manipulation, aggregation, and analysis, culminating in a custom Neo4j database. The software not only facilitates the exploration of genomic synteny but also provides a foundational knowledge graph supporting the integration of additional diverse datasets and the development of advanced search engines and analyses. This manuscript introduces some of SocialGene's capabilities through brief case studies including targeted genome mining for drug discovery, accelerated searches for similar and distantly related biosynthetic gene clusters in biobank-available organisms, integration of chemical and analytical data, and more. SocialGene is free, open-source, MIT-licensed, designed for adaptability and extension, and available from github.com/socialgene.

Unveiling responses and mechanisms of spice crop chive exposure to three typical pesticides using metabolomics combined with transcriptomics, physiology and biochemistry.

Pesticides are frequently used to control target pests in the production of spice crops such as chives (Allium ascalonicum). However, little information is available on the responses and underlying mechanisms of pesticide exposure in this crop. Our findings revealed that the uptake, transportation, and subcellular distribution of three typical pesticides-the fungicide pyraclostrobin (PAL), insecticide acetamiprid (ATP), and herbicide pendimethalin (PND) in chives, as well as their physiological, biochemical, metabolic, and transcriptomic responses-were dependent on pesticide properties, especially hydrophobicity. The distribution of PAL and PND in chives decreased in the order root > stem > leaf, but the distribution order of ATP was the opposite. The proportion of PAL and PND in the solid phase of the root cells gradually increased, but ATP mainly existed in the cell-soluble component, indicating that the latter had an upward translocation ability and thus mainly accumulated in the leaves. Malondialdehyde levels in chive leaves were not significantly affected by exposure to these pesticides; however, the activities of superoxide dismutase (SOD) and catalase (CAT) in chive leaves increased significantly. Moreover, these pesticides exhibited critical differences in chive responses through the interaction of metabolites and regulation of differentially expressed genes. PAL dramatically influenced five carbohydrate metabolic pathways (34.35 %), disturbing the starch-to-sucrose balance. ATP strongly affected five amino acid (AC) metabolic pathways (33.38 %), enhancing four free amino acid levels. PND notably affected eight fatty acid (FA) metabolic pathways (25.38 %), increasing two unsaturated and decreasing one saturated FA. Simultaneously, PND, ATP, and PND accumulated in the chives could be detoxified through metabolic pathways mediated by cytochrome P450 (P450) and glycosyltransferase (GT)/glutathione S-transferase (GST), producing phase I (7, 4, and 5) and II (11, 13, and 10) metabolites, respectively. This study provides important molecular insights into the responses and underlying mechanisms of spice crop exposure to pesticides.

Multi-omics integration for both single-cell and spatially resolved data based on dual-path graph attention auto-encoder.

Single-cell multi-omics integration enables joint analysis at the single-cell level of resolution to provide more accurate understanding of complex biological systems, while spatial multi-omics integration is benefit to the exploration of cell spatial heterogeneity to facilitate more comprehensive downstream analyses. Existing methods are mainly designed for single-cell multi-omics data with little consideration of spatial information and still have room for performance improvement. A reliable multi-omics integration method designed for both single-cell and spatially resolved data is necessary and significant. We propose a multi-omics integration method based on dual-path graph attention auto-encoder (SSGATE). It can construct the neighborhood graphs based on single-cell expression profiles or spatial coordinates, enabling it to process single-cell data and utilize spatial information from spatially resolved data. It can also perform self-supervised learning for integration through the graph attention auto-encoders from two paths. SSGATE is applied to integration of transcriptomics and proteomics, including single-cell and spatially resolved data of various tissues from different sequencing technologies. SSGATE shows better performance and stronger robustness than competitive methods and facilitates downstream analysis.

Multi-Omics Integration Analysis Pinpoint Proteins Influencing Brain Structure and Function: Toward Drug Targets and Neuroimaging Biomarkers for Neuropsychiatric Disorders.

Integrating protein quantitative trait loci (pQTL) data and summary statistics from genome-wide association studies (GWAS) of brain image-derived phenotypes (IDPs) can benefit in identifying IDP-related proteins. Here, we developed a systematic omics-integration analytic framework by sequentially using proteome-wide association study (PWAS), Mendelian randomization (MR), and colocalization (COLOC) analyses to identify the potentially causal brain and plasma proteins for IDPs, followed by pleiotropy analysis, mediation analysis, and drug exploration analysis to investigate potential mediation pathways of pleiotropic proteins to neuropsychiatric disorders (NDs) as well as candidate drug targets. A total of 201 plasma proteins and 398 brain proteins were significantly associated with IDPs from PWAS analysis. Subsequent MR and COLOC analyses further identified 313 potentially causal IDP-related proteins, which were significantly enriched in neural-related phenotypes, among which 91 were further identified as pleiotropic proteins associated with both IDPs and NDs, including EGFR, TMEM106B, GPT, and HLA-B. Drug prioritization analysis showed that 6.33% of unique pleiotropic proteins had drug targets or interactions with medications for NDs. Nine potential mediation pathways were identified to illustrate the mediating roles of the IDPs in the causal effect of the pleiotropic proteins on NDs, including the indirect effect of TMEM106B on Alzheimer's disease (AD) risk via radial diffusivity (RD) of the posterior limb of the internal capsule (PLIC), with the mediation proportion being 11.18%, and the indirect effect of EGFR on AD through RD of PLIC, RD of splenium of corpus callosum (SCC), and fractional anisotropy (FA) of SCC, with the mediation proportion being 18.99%, 22.79%, and 19.91%, respectively. These findings provide novel insights into pathogenesis, drug targets, and neuroimaging biomarkers of NDs.

Application of Pan-Omics Technologies in Research on Important Economic Traits for Ruminants.

The economic significance of ruminants in agriculture underscores the need for advanced research methodologies to enhance their traits. This review aims to elucidate the transformative role of pan-omics technologies in ruminant research, focusing on their application in uncovering the genetic mechanisms underlying complex traits such as growth, reproduction, production performance, and rumen function. Pan-omics analysis not only helps in identifying key genes and their regulatory networks associated with important economic traits but also reveals the impact of environmental factors on trait expression. By integrating genomics, epigenomics, transcriptomics, metabolomics, and microbiomics, pan-omics enables a comprehensive analysis of the interplay between genetics and environmental factors, offering a holistic understanding of trait expression. We explore specific examples of economic traits where these technologies have been pivotal, highlighting key genes and regulatory networks identified through pan-omics approaches. Additionally, we trace the historical evolution of each omics field, detailing their progression from foundational discoveries to high-throughput platforms. This review provides a critical synthesis of recent advancements, offering new insights and practical recommendations for the application of pan-omics in the ruminant industry. The broader implications for modern animal husbandry are discussed, emphasizing the potential for these technologies to drive sustainable improvements in ruminant production systems.

Smccnet 2.0: a comprehensive tool for multi-omics network inference with shiny visualization.

Sparse multiple canonical correlation network analysis (SmCCNet) is a machine learning technique for integrating omics data along with a variable of interest (e.g., phenotype of complex disease), and reconstructing multi-omics networks that are specific to this variable. We present the second-generation SmCCNet (SmCCNet 2.0) that adeptly integrates single or multiple omics data types along with a quantitative or binary phenotype of interest. In addition, this new package offers a streamlined setup process that can be configured manually or automatically, ensuring a flexible and user-friendly experience. AVAILABILITY : This package is available in both CRAN: https://cran.r-project.org/web/packages/SmCCNet/index.html and Github: https://github.com/KechrisLab/SmCCNet under the MIT license. The network visualization tool is available at https://smccnet.shinyapps.io/smccnetnetwork/ .

Interactive molecular causal networks of hypertension using a fast machine learning algorithm MRdualPC.

BACKGROUND: Understanding the complex interactions between genes and their causal effects on diseases is crucial for developing targeted treatments and gaining insight into biological mechanisms. However, the analysis of molecular networks, especially in the context of high-dimensional data, presents significant challenges. METHODS: This study introduces MRdualPC, a computationally tractable algorithm based on the MRPC approach, to infer large-scale causal molecular networks. We apply MRdualPC to investigate the upstream causal transcriptomics influencing hypertension using a comprehensive dataset of kidney genome and transcriptome data. RESULTS: Our algorithm proves to be 100 times faster than MRPC on average in identifying transcriptomics drivers of hypertension. Through clustering, we identify 63 modules with causal driver genes, including 17 modules with extensive causal networks. Notably, we find that genes within one of the causal networks are associated with the electron transport chain and oxidative phosphorylation, previously linked to hypertension. Moreover, the identified causal ancestor genes show an over-representation of blood pressure-related genes. CONCLUSIONS: MRdualPC has the potential for broader applications beyond gene expression data, including multi-omics integration. While there are limitations, such as the need for clustering in large gene expression datasets, our study represents a significant advancement in building causal molecular networks, offering researchers a valuable tool for analyzing big data and investigating complex diseases.

Transcriptomics and epigenetic data integration learning module on Google Cloud.

Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell. Additionally, with the help of improved computational resources and data mining, researchers are able to integrate data from different multi-omics regimes to identify new prognostic, diagnostic, or predictive biomarkers, uncover novel therapeutic targets, and develop more personalized treatment protocols for patients. For the research community to parse the scientifically and clinically meaningful information out of all the biological data being generated each day more efficiently with less wasted resources, being familiar with and comfortable using advanced analytical tools, such as Google Cloud Platform becomes imperative. This project is an interdisciplinary, cross-organizational effort to provide a guided learning module for integrating transcriptomics and epigenetics data analysis protocols into a comprehensive analysis pipeline for users to implement in their own work, utilizing the cloud computing infrastructure on Google Cloud. The learning module consists of three submodules that guide the user through tutorial examples that illustrate the analysis of RNA-sequence and Reduced-Representation Bisulfite Sequencing data. The examples are in the form of breast cancer case studies, and the data sets were procured from the public repository Gene Expression Omnibus. The first submodule is devoted to transcriptomics analysis with the RNA sequencing data, the second submodule focuses on epigenetics analysis using the DNA methylation data, and the third submodule integrates the two methods for a deeper biological understanding. The modules begin with data collection and preprocessing, with further downstream analysis performed in a Vertex AI Jupyter notebook instance with an R kernel. Analysis results are returned to Google Cloud buckets for storage and visualization, removing the computational strain from local resources. The final product is a start-to-finish tutorial for the researchers with limited experience in multi-omics to integrate transcriptomics and epigenetics data analysis into a comprehensive pipeline to perform their own biological research.This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [16] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Omics approaches in asthma research: Challenges and opportunities.

Asthma, a chronic respiratory disease with a global prevalence of approximately 300 million individuals, presents a significant societal and economic burden. This multifaceted syndrome exhibits diverse clinical phenotypes and pathogenic endotypes influenced by various factors. The advent of omics technologies has revolutionized asthma research by delving into the molecular foundation of the disease to unravel its underlying mechanisms. Omics technologies are employed to systematically screen for potential biomarkers, encompassing genes, transcripts, methylation sites, proteins, and even the microbiome components. This review provides an insightful overview of omics applications in asthma research, with a special emphasis on genetics, transcriptomics, epigenomics, and the microbiome. We explore the cutting-edge methods, discoveries, challenges, and potential future directions in the realm of asthma omics research. By integrating multi-omics and non-omics data through advanced statistical techniques, we aspire to advance precision medicine in asthma, guiding diagnosis, risk assessment, and personalized treatment strategies for this heterogeneous condition.

Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community.

Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This has increased the quality and scope of the model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or reference conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference for nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains' growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine learning models. Based on those findings we anticipate that Yeast9 will continue to empower systems biology studies of yeast metabolism.

DeepIDA-GRU: a deep learning pipeline for integrative discriminant analysis of cross-sectional and longitudinal multiview data with applications to inflammatory bowel disease classification.

Biomedical research now commonly integrates diverse data types or views from the same individuals to better understand the pathobiology of complex diseases, but the challenge lies in meaningfully integrating these diverse views. Existing methods often require the same type of data from all views (cross-sectional data only or longitudinal data only) or do not consider any class outcome in the integration method, which presents limitations. To overcome these limitations, we have developed a pipeline that harnesses the power of statistical and deep learning methods to integrate cross-sectional and longitudinal data from multiple sources. In addition, it identifies key variables that contribute to the association between views and the separation between classes, providing deeper biological insights. This pipeline includes variable selection/ranking using linear and nonlinear methods, feature extraction using functional principal component analysis and Euler characteristics, and joint integration and classification using dense feed-forward networks for cross-sectional data and recurrent neural networks for longitudinal data. We applied this pipeline to cross-sectional and longitudinal multiomics data (metagenomics, transcriptomics and metabolomics) from an inflammatory bowel disease (IBD) study and identified microbial pathways, metabolites and genes that discriminate by IBD status, providing information on the etiology of IBD. We conducted simulations to compare the two feature extraction methods.

A Cell Cycle-Aware Network for Data Integration and Label Transferring of Single-Cell RNA-Seq and ATAC-Seq.

In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity, and confounding factors. As it is known, the cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it is not clear how it will work on the integrated single-cell multi-omics data. Here, a cell cycle-aware network (CCAN) is developed to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell type-specific variations. This is the first computational model to study the cell-cycle effects in the integration of single-cell multi-omics data. Validations on several benchmark datasets show the outstanding performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA-seq datasets from different protocols, integrating paired and unpaired scRNA-seq and scATAC-seq data, accurately transferring cell type labels from scRNA-seq to scATAC-seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data.

Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data.

The inherent heterogeneity of cancer contributes to highly variable responses to any anticancer treatments. This underscores the need to first identify precise biomarkers through complex multi-omics datasets that are now available. Although much research has focused on this aspect, identifying biomarkers associated with distinct drug responders still remains a major challenge. Here, we develop MOMLIN, a multi-modal and -omics machine learning integration framework, to enhance drug-response prediction. MOMLIN jointly utilizes sparse correlation algorithms and class-specific feature selection algorithms, which identifies multi-modal and -omics-associated interpretable components. MOMLIN was applied to 147 patients' breast cancer datasets (clinical, mutation, gene expression, tumor microenvironment cells and molecular pathways) to analyze drug-response class predictions for non-responders and variable responders. Notably, MOMLIN achieves an average AUC of 0.989, which is at least 10% greater when compared with current state-of-the-art (data integration analysis for biomarker discovery using latent components, multi-omics factor analysis, sparse canonical correlation analysis). Moreover, MOMLIN not only detects known individual biomarkers such as genes at mutation/expression level, most importantly, it correlates multi-modal and -omics network biomarkers for each response class. For example, an interaction between ER-negative-HMCN1-COL5A1 mutations-FBXO2-CSF3R expression-CD8 emerge as a multimodal biomarker for responders, potentially affecting antimicrobial peptides and FLT3 signaling pathways. In contrast, for resistance cases, a distinct combination of lymph node-TP53 mutation-PON3-ENSG00000261116 lncRNA expression-HLA-E-T-cell exclusions emerged as multimodal biomarkers, possibly impacting neurotransmitter release cycle pathway. MOMLIN, therefore, is expected advance precision medicine, such as to detect context-specific multi-omics network biomarkers and better predict drug-response classifications.

Unveiling the power of proteomics in advancing tropical animal health and production.

Proteomics, the large-scale study of proteins in biological systems has emerged as a pivotal tool in the field of animal and veterinary sciences, mainly for investigating local and rustic breeds. Proteomics provides valuable insights into biological processes underlying animal growth, reproduction, health, and disease. In this review, we highlight the key proteomics technologies, methodologies, and their applications in domestic animals, particularly in the tropical context. We also discuss advances in proteomics research, including integration of multi-omics data, single-cell proteomics, and proteogenomics, all of which are promising for improving animal health, adaptation, welfare, and productivity. However, proteomics research in domestic animals faces challenges, such as sample preparation variation, data quality control, privacy and ethical considerations relating to animal welfare. We also provide recommendations for overcoming these challenges, emphasizing the importance of following best practices in sample preparation, data quality control, and ethical compliance. We therefore aim for this review to harness the full potential of proteomics in advancing our understanding of animal biology and ultimately improve animal health and productivity in local breeds of diverse animal species in a tropical context.

Predicting type 2 diabetes via machine learning integration of multiple omics from human pancreatic islets.

Type 2 diabetes (T2D) is the fastest growing non-infectious disease worldwide. Impaired insulin secretion from pancreatic beta-cells is a hallmark of T2D, but the mechanisms behind this defect are insufficiently characterized. Integrating multiple layers of biomedical information, such as different Omics, may allow more accurate understanding of complex diseases such as T2D. Our aim was to explore and use Machine Learning to integrate multiple sources of biological/molecular information (multiOmics), in our case RNA-sequening, DNA methylation, SNP and phenotypic data from islet donors with T2D and non-diabetic controls. We exploited Machine Learning to perform multiOmics integration of DNA methylation, expression, SNPs, and phenotypes from pancreatic islets of 110 individuals, with ~ 30% being T2D cases. DNA methylation was analyzed using Infinium MethylationEPIC array, expression was analyzed using RNA-sequencing, and SNPs were analyzed using HumanOmniExpress arrays. Supervised linear multiOmics integration via DIABLO based on Partial Least Squares (PLS) achieved an accuracy of 91 ± 15% of T2D prediction with an area under the curve of 0.96 ± 0.08 on the test dataset after cross-validation. Biomarkers identified by this multiOmics integration, including SACS and TXNIP DNA methylation, OPRD1 and RHOT1 expression and a SNP annotated to ANO1, provide novel insights into the interplay between different biological mechanisms contributing to T2D. This Machine Learning approach of multiOmics cross-sectional data from human pancreatic islets achieved a promising accuracy of T2D prediction, which may potentially find broad applications in clinical diagnostics. In addition, it delivered novel candidate biomarkers for T2D and links between them across the different Omics.

Omics technology draws a comprehensive heavy metal resistance strategy in bacteria.

The rapid industrial revolution significantly increased heavy metal pollution, becoming a major global environmental concern. This pollution is considered as one of the most harmful and toxic threats to all environmental components (air, soil, water, animals, and plants until reaching to human). Therefore, scientists try to find a promising and eco-friendly technique to solve this problem i.e., bacterial bioremediation. Various heavy metal resistance mechanisms were reported. Omics technologies can significantly improve our understanding of heavy metal resistant bacteria and their communities. They are a potent tool for investigating the adaptation processes of microbes in severe conditions. These omics methods provide unique benefits for investigating metabolic alterations, microbial diversity, and mechanisms of resistance of individual strains or communities to harsh conditions. Starting with genome sequencing which provides us with complete and comprehensive insight into the resistance mechanism of heavy metal resistant bacteria. Moreover, genome sequencing facilitates the opportunities to identify specific metal resistance genes, operons, and regulatory elements in the genomes of individual bacteria, understand the genetic mechanisms and variations responsible for heavy metal resistance within and between bacterial species in addition to the transcriptome, proteome that obtain the real expressed genes. Moreover, at the community level, metagenome, meta transcriptome and meta proteome participate in understanding the microbial interactive network potentially novel metabolic pathways, enzymes and gene species can all be found using these methods. This review presents the state of the art and anticipated developments in the use of omics technologies in the investigation of microbes used for heavy metal bioremediation.

Integrated transcriptomics and metabolomics reveal the mechanism of intestinal damage upon acute patulin exposure in mice.

Mycotoxin contamination has become a major food safety issue and greatly threatens human and animal health. Patulin (PAT), a common mycotoxin in the environment, is exposed through the food chain and damages the gastrointestinal tract. However, its mechanism of enterotoxicity at the genetic and metabolic levels remains to be elucidated. Herein, the intestinal histopathological and biochemical indices, transcriptome, and metabolome of C57BL/6 J mice exposed to different doses of PAT were successively assessed, as well as the toxicokinetics of PAT in vivo. The results showed that acute PAT exposure induced damaged villi and crypts, reduced mucus secretion, decreased SOD and GSH-Px activities, and enhanced MPO activity in the small intestine and mild damage in the colon. At the transcriptional level, the genes affected by PAT were dose-dependently altered in the small intestine and fluctuated in the colon. PAT primarily affected inflammation-related signaling pathways and oxidative phosphorylation in the small intestine and immune responses in the colon. At the metabolic level, amino acids decreased, and extensive lipids accumulated in the small intestine and colon. Seven metabolic pathways were jointly affected by PAT in two intestinal sites. Moreover, changes in PAT products and GST activity were detected in the small intestinal tissue but not in the colonic tissue, explaining the different damage degrees of the two sites. Finally, the integrated results collectively explained the toxicological mechanism of PAT, which damaged the small intestine directly and the colon indirectly. These results paint a clear panorama of intestinal changes after PAT exposure and provide valuable information on the exposure risk and toxic mechanism of PAT.

Unraveling the Landscape of Pediatric Glioblastoma Biomarkers: A Comprehensive Review of Enhancing Diagnostics and Therapeutic Insights.

Glioblastoma, the most common and aggressive form of primary brain tumor, poses significant challenges to patients, caregivers, and clinicians alike. Pediatric glioblastoma is a rare and aggressive brain tumor that presents unique challenges in treatment. It differs from its adult counterpart in terms of genetic and molecular characteristics. Its incidence is relatively low, but the prognosis remains grim due to its aggressive behavior. Diagnosis relies on imaging techniques and histopathological analysis. The rarity of the disease underscores the need for effective treatment strategies. In recent years, the quest to understand and manage pediatric glioblastoma has seen a significant shift towards unraveling the intricate landscape of biomarkers. Surgery remains a cornerstone of glioblastoma management, aiming to resect as much of the tumor as possible. Glioblastoma's infiltrative nature presents challenges in achieving a complete surgical resection. This comprehensive review delves into the realm of pediatric glioblastoma biomarkers, shedding light on their potential to not only revolutionize diagnostics but also shape therapeutic strategies. From personalized treatment selection to the development of targeted therapies, the potential impact of these biomarkers on clinical outcomes is undeniable. Moreover, this review underscores the substantial implications of biomarker-driven approaches for therapeutic interventions. All advancements in targeted therapies and immunotherapy hold promise for the treatment of pediatric glioblastoma. The genetic profiling of tumors allows for personalized approaches, potentially improving treatment efficacy. The ethical dilemmas surrounding pediatric cancer treatment, particularly balancing potential benefits with risks, are complex. Ongoing clinical trials and preclinical research suggest exciting avenues for future interventions.