Kentucky bluegrass (Poa pratensis) is known for its high cadmium (Cd) tolerance and accumulation, and it is therefore considered to have the potential for phytoremediation of Cd-contaminated soil. ...However, the mechanisms underlying the accumulation and tolerance of Cd in Kentucky bluegrass are largely unknown. In this study, we examined variances in the transcriptome and metabolome of a Cd-tolerant variety (Midnight, M) and a Cd-sensitive variety (Rugby II, R) to pinpoint crucial regulatory genes and metabolites associated with Cd response. We also validated the role of the key metabolite, l-phenylalanine, in Cd transport and alleviation of Cd stress by applying it to the Cd-tolerant variety M. Metabolites of the M and R varieties under Cd stress were subjected to co-expression analysis. The results showed that shikimate-phenylpropanoid pathway metabolites (phenolic acids, phenylpropanoids, and polyketides) were highly induced by Cd treatment and were more abundant in the Cd-tolerant variety. Gene co-expression network analysis was employed to further identify genes closely associated with key metabolites. The calcium regulatory genes, zinc finger proteins (ZAT6 and PMA), MYB transcription factors (MYB78, MYB62, and MYB33), ONAC077, receptor-like protein kinase 4, CBL-interacting protein kinase 1, and protein phosphatase 2A were highly correlated with the metabolism of phenolic acids, phenylpropanoids, and polyketides. Exogenous l-phenylalanine can significantly increase the Cd concentration in the leaves (22.27%–55.00%) and roots (7.69%–35.16%) of Kentucky bluegrass. The use of 1 mg/L of l-phenylalanine has been demonstrated to lower malondialdehyde levels and higher total phenols, flavonoids, and anthocyanins levels, while also significantly enhancing the uptake of Cd and its translocation from roots to shoots. Our results provide insights into the response mechanisms to Cd stress and offer a novel l-phenylalanine-based phytoremediation strategy for Cd-containing soil.
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•Metabolites important to the response to Cd stress were identified in Kentucky bluegrass.•Transcriptional regulators of these key metabolites were explored.•l-phenylalanine was determined to play a key role in orchestrating the related metabolism.•Exogenous l-phenylalanine enhanced long-distance translocation and accumulation of Cd.
In recent years, high-throughput sequencing technologies provide unprecedented opportunity to depict cancer samples at multiple molecular levels. The integration and analysis of these multi-omics ...datasets is a crucial and critical step to gain actionable knowledge in a precision medicine framework. This paper explores recent data-driven methodologies that have been developed and applied to respond major challenges of stratified medicine in oncology, including patients' phenotyping, biomarker discovery, and drug repurposing. We systematically retrieved peer-reviewed journals published from 2014 to 2019, select and thoroughly describe the tools presenting the most promising innovations regarding the integration of heterogeneous data, the machine learning methodologies that successfully tackled the complexity of multi-omics data, and the frameworks to deliver actionable results for clinical practice. The review is organized according to the applied methods: Deep learning, Network-based methods, Clustering, Features Extraction, and Transformation, Factorization. We provide an overview of the tools available in each methodological group and underline the relationship among the different categories. Our analysis revealed how multi-omics datasets could be exploited to drive precision oncology, but also current limitations in the development of multi-omics data integration.
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•NucMap 2.0 is a database of genome-wide nucleosome positioning maps across species.•Omics data integration and visualization of samples, especially for human samples.•Integration of ...potential transcription factor binding sites.•A new online tool to identify differential nucleosome regions.•NucMap 2.0 can serve as an important resource for epigenetics.
Nucleosome dynamics plays important roles in many biological processes, such as DNA replication and gene expression. NucMap (https://ngdc.cncb.ac.cn/nucmap) is the first database of genome-wide nucleosome positioning maps across species. Here, we present an updated version, NucMap 2.0, by incorporating more species and MNase-seq samples. In addition, we integrate other related omics data for each MNase-seq sample to provide a comprehensive view of nucleosome positioning, such as gene expression, transcription factor binding sites, histone modifications and DNA methylation. In particular, NucMap 2.0 integrates and pre-analyzes RNA-seq data and ChIP-seq data of human-related samples, which facilitates the interpretation of nucleosome positioning in humans. All processed data are integrated into an in-built genome browser, and users can make comprehensive side-by-side analyses. In addition, more online analytical functions are developed, which allows researchers to identify differential nucleosome regions and explore potential gene regulatory regions. All resources are open access with a user-friendly web interface.
The numbers and diversity of microbes in ecosystems within and around us is unmatched, yet most of these microorganisms remain recalcitrant to in vitro cultivation. Various high-throughput molecular ...techniques, collectively termed multi-omics, provide insights into the genomic structure and metabolic potential as well as activity of complex microbial communities. Nonetheless, pure or defined cultures are needed to (1) decipher microbial physiology and thus test multi-omics-based ecological hypotheses, (2) curate and improve database annotations and (3) realize novel applications in biotechnology. Cultivation thus provides context. In turn, we here argue that multi-omics information awaits integration into the development of novel cultivation strategies. This can build the foundation for a new era of omics information-guided microbial cultivation technology and reduce the inherent trial-and-error search space. This review discusses how information that can be extracted from multi-omics data can be applied for the cultivation of hitherto uncultured microorganisms. Furthermore, we summarize groundbreaking studies that successfully translated information derived from multi-omics into specific media formulations, screening techniques and selective enrichments in order to obtain novel targeted microbial isolates. By integrating these examples, we conclude with a proposed workflow to facilitate future omics-aided cultivation strategies that are inspired by the microbial complexity of the environment.
Cancer cells rewire metabolism to sculpt the immune tumor microenvironment (TME) and propel tumor advancement, which intricately tied to post-translational modifications. Histone lactylation has ...emerged as a novel player in modulating protein functions, whereas little is known about its pathological role in pancreatic ductal adenocarcinoma (PDAC) progression. Employing a multi-omics approach encompassing bulk and single-cell RNA sequencing, metabolomics, ATAC-seq, and CUT&Tag methodologies, we unveiled the potential of histone lactylation in prognostic prediction, patient stratification and TME characterization. Notably, "LDHA-H4K12la-immuno-genes" axis has introduced a novel node into the regulatory framework of "metabolism-epigenetics-immunity," shedding new light on the landscape of PDAC progression. Furthermore, the heightened interplay between cancer cells and immune counterparts via Nectin-2 in liver metastasis with elevated HLS unraveled a positive feedback loop in driving immune evasion. Simultaneously, immune cells exhibited altered HLS and autonomous functionality across the metastatic cascade. Consequently, the exploration of innovative combination strategies targeting the metabolism-epigenetics-immunity axis holds promise in curbing distant metastasis and improving survival prospects for individuals grappling with challenges of PDAC.
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•Histone lactylation suggests poor prognosis and highlights a suppressive tumor immune environment in PDAC.•The axis involving LDHA-H4K12la-immuno-genes operates in immune evasion to promote the progression of PDAC.•Elevated histone lactylation in liver metastasis increases Nectin-2-mediated immune evasion through a positive feedback loop.
Multi-omics allows the systematic understanding of the information flow across different omics layers, while single omics can mainly reflect one aspect of the biological system. The advancement of ...bulk and single-cell sequencing technologies and related computational methods for multi-omics largely facilitated the development of system biology and precision medicine. Single-cell approaches have the advantage of dissecting cellular dynamics and heterogeneity, whereas traditional bulk technologies are limited to individual/population-level investigation. In this review, we first summarize the technologies for producing bulk and single-cell multi-omics data. Then, we survey the computational approaches for integrative analysis of bulk and single-cell multimodal data, respectively. Moreover, the databases and data storage for multi-omics, as well as the tools for visualizing multimodal data are summarized. We also outline the integration between bulk and single-cell data, and discuss the applications of multi-omics in precision medicine. Finally, we present the challenges and perspectives for multi-omics development.
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•LSD contains 31,740 genes and 1,209 mutants spanning 148 species.•LSD mines SAGs and mutants through manual literature curation.•LSD has comprehensively annotated SAGs through a ...variety of approaches.•LSD provides an invaluable resource for comparative studies of plant senescence.•LSD is meticulously maintained and constantly updated.
Through an extensive literature survey, we have upgraded the Leaf Senescence Database (LSD v5.0; https://ngdc.cncb.ac.cn/lsd/), a curated repository of comprehensive senescence-associated genes (SAGs) and their corresponding mutants. Since its inception in 2010, LSD undergoes frequent updates to encompass the latest advances in leaf senescence research and its current version comprises a high-quality collection of 31,740 SAGs and 1,209 mutants from 148 species, which were manually searched based on robust experimental evidence and further categorized according to their functions in leaf senescence. Furthermore, LSD was greatly enriched with comprehensive annotations for the SAGs through meticulous curation using both manual and computational methods. In addition, it was equipped with user-friendly web interfaces that facilitate text queries, BLAST searches, and convenient download of SAG sequences for localized analysis. Users can effortlessly navigate the database to access a plethora of information, including literature references, mutants, phenotypes, multi-omics data, miRNA interactions, homologs in other plants, and cross-links to various databases. Taken together, the upgraded version of LSD stands as the most comprehensive and informative plant senescence-related database to date, incorporating the largest collection of SAGs and thus bearing great utility for a wide range of studies related to plant senescence.
Schematic representation of the main strategies for multi-omics datasets integration. A) Early integration concatenates all omics datasets into a single matrix on which machine learning model can be ...applied. B) Mixed integration first independently transforms or maps each omics block into a new representation before combining them for downstream analysis. C) Intermediate integration simultaneously transforms the original datasets into common and omics-specific representations. D) Late integration analyses each omics separately and combines their final predictions. E) Hierarchical integration bases the integration of datasets on prior regulatory relationships between omics layers.
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Increased availability of high-throughput technologies has generated an ever-growing number of omics data that seek to portray many different but complementary biological layers including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. New insight from these data have been obtained by machine learning algorithms that have produced diagnostic and classification biomarkers. Most biomarkers obtained to date however only include one omic measurement at a time and thus do not take full advantage of recent multi-omics experiments that now capture the entire complexity of biological systems.
Multi-omics data integration strategies are needed to combine the complementary knowledge brought by each omics layer. We have summarized the most recent data integration methods/ frameworks into five different integration strategies: early, mixed, intermediate, late and hierarchical. In this mini-review, we focus on challenges and existing multi-omics integration strategies by paying special attention to machine learning applications.
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