Multi-omics Methods Research Articles

Activation and Identification of a Griseusin Cluster in Streptomyces sp. CA-256286 by Employing Transcriptional Regulators and Multi-Omics Methods.

Streptomyces are well-known producers of a range of different secondary metabolites, including antibiotics and other bioactive compounds. Recently, it has been demonstrated that “silent” biosynthetic gene clusters (BGCs) can be activated by heterologously expressing transcriptional regulators from other BGCs. Here, we have activated a silent BGC in Streptomyces sp. CA-256286 by overexpression of a set of SARP family transcriptional regulators. The structure of the produced compound was elucidated by NMR and found to be an N-acetyl cysteine adduct of the pyranonaphtoquinone polyketide 3′-O-α-d-forosaminyl-(+)-griseusin A. Employing a combination of multi-omics and metabolic engineering techniques, we identified the responsible BGC. These methods include genome mining, proteomics and transcriptomics analyses, in combination with CRISPR induced gene inactivations and expression of the BGC in a heterologous host strain. This work demonstrates an easy-to-implement workflow of how silent BGCs can be activated, followed by the identification and characterization of the produced compound, the responsible BGC, and hints of its biosynthetic pathway.

MPA_Pathway_Tool: User-Friendly, Automatic Assignment of Microbial Community Data on Metabolic Pathways.

Taxonomic and functional characterization of microbial communities from diverse environments such as the human gut or biogas plants by multi-omics methods plays an ever more important role. Researchers assign all identified genes, transcripts, or proteins to biological pathways to better understand the function of single species and microbial communities. However, due to the versality of microbial metabolism and a still-increasing number of newly biological pathways, linkage to standard pathway maps such as the KEGG central carbon metabolism is often problematic. We successfully implemented and validated a new user-friendly, stand-alone web application, the MPA_Pathway_Tool. It consists of two parts, called ‘Pathway-Creator’ and ‘Pathway-Calculator’. The ‘Pathway-Creator’ enables an easy set-up of user-defined pathways with specific taxonomic constraints. The ‘Pathway-Calculator’ automatically maps microbial community data from multiple measurements on selected pathways and visualizes the results. The MPA_Pathway_Tool is implemented in Java and ReactJS.

Integrative Multi-omics Landscape of Non-structural Protein 3 of Severe Acute Respiratory Syndrome Coronaviruses

The coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is currently a global pandemic. Extensive investigations have been performed to study the clinical and cellular effects of SARS-CoV-2 infection. Mass spectrometry-based proteomics studies have revealed the cellular changes due to the infection and identified a plethora of interactors for all SARS-CoV-2 components, except for the longest non-structural protein 3 (NSP3). Here, we expressed the full-length NSP3 proteins of SARS-CoV and SARS-CoV-2 to investigate their unique and shared functions using multi-omics methods. We conducted interactome, phosphoproteome, ubiquitylome, transcriptome, and proteome analyses of NSP3-expressing cells. We found that NSP3 plays essential roles in cellular functions such as RNA metabolism and immune response (e.g., NF-κB signal transduction). Interestingly, we showed that SARS-CoV-2 NSP3 has both endoplasmic reticulum and mitochondrial localizations. In addition, SARS-CoV-2 NSP3 is more closely related to mitochondrial ribosomal proteins, whereas SARS-CoV NSP3 is related to the cytosolic ribosomal proteins. In summary, our integrative multi-omics study of NSP3 improves the understanding of the functions of NSP3 and offers potential targets for the development of anti-SARS strategies.

Joint changes in RNA, RNA polymerase II, and promoter activity through the cell cycle identify non-coding RNAs involved in proliferation

Proper regulation of the cell cycle is necessary for normal growth and development of all organisms. Conversely, altered cell cycle regulation often underlies proliferative diseases such as cancer. Long non-coding RNAs (lncRNAs) are recognized as important regulators of gene expression and are often found dysregulated in diseases, including cancers. However, identifying lncRNAs with cell cycle functions is challenging due to their often low and cell-type specific expression. We present a highly effective method that analyses changes in promoter activity, transcription, and RNA levels for identifying genes enriched for cell cycle functions. Specifically, by combining RNA sequencing with ChIP sequencing through the cell cycle of synchronized human keratinocytes, we identified 1009 genes with cell cycle-dependent expression and correlated changes in RNA polymerase II occupancy or promoter activity as measured by histone 3 lysine 4 trimethylation (H3K4me3). These genes were highly enriched for genes with known cell cycle functions and included 57 lncRNAs. We selected four of these lncRNAs—SNHG26, EMSLR, ZFAS1, and EPB41L4A-AS1—for further experimental validation and found that knockdown of each of the four lncRNAs affected cell cycle phase distributions and reduced proliferation in multiple cell lines. These results show that many genes with cell cycle functions have concomitant cell-cycle dependent changes in promoter activity, transcription, and RNA levels and support that our multi-omics method is well suited for identifying lncRNAs involved in the cell cycle.

CD8+ T effector and immune checkpoint signatures predict prognosis and responsiveness to immunotherapy in bladder cancer.

Immune-checkpoint blockade (ICB) has been routinely implemented to treat bladder cancer; however, most patients have little or no clinical benefit. In this study, 348 pretreated metastatic urothelial cancer samples from the IMvigor210 cohort were used to identify important genes significantly associated with CD8+ T effector and immune checkpoint signatures. The immune checkpoint inhibitor score (IMS) scoring system was constructed to predict the immunotherapy responsiveness. Transcriptome analysis confirmed that the high IMS score group had significant immune activation with better prognosis and higher immunotherapy responsiveness, which was a powerful biomarker for predicting the prognosis and responsiveness of ICB. Tumor immune dysfunction and exclusion (TIDE) scores were calculated using 2031 external bladder cancer samples for further validation. We selected the important Hub genes as potential therapeutic targets, and validated the genes using genomic, transcriptomic, immunomic, and other multi-omics methods. In addition, we construct a risk prediction model which could stratify patients with bladder cancer and predict patient prognosis and ICB treatment responsiveness. In conclusion, this study identified effective biomarkers for the prediction of immune checkpoint inhibitor treatment responsiveness in bladder cancer patients and provided new immunotherapeutic targets.

Multiomics Method Enabled by Sequential Metabolomics and Proteomics for Human Pluripotent Stem-Cell-Derived Cardiomyocytes.

Human pluripotent stem-cell-derived cardiomyocytes (hPSC-CMs) show immense promise for patient-specific disease modeling, cardiotoxicity screening, and regenerative therapy development. However, thus far, hPSC-CMs in culture have not recapitulated the structural or functional properties of adult CMs in vivo. To gain global insight into hPSC-CM biology, we established a multiomics method for analyzing the hPSC-CM metabolome and proteome from the same cell culture, creating multidimensional profiles of hPSC-CMs. Specifically, we developed a sequential extraction to capture metabolites and proteins from the same hPSC-CM monolayer cultures and analyzed these extracts using high-resolution mass spectrometry. Using this method, we annotated 205 metabolites/lipids and 4319 proteins from 106 cells with high reproducibility. We further integrated the proteome and metabolome measurements to create network profiles of molecular phenotypes for hPSC-CMs. Out of 310 pathways identified using metabolomics and proteomics, 40 pathways were considered significantly overrepresented (false-discovery-rate-corrected p ≤ 0.05). Highly populated pathways included those involved in protein synthesis (ribosome, spliceosome), ATP generation (oxidative phosphorylation), and cardiac muscle contraction. This multiomics method achieves a deep coverage of metabolites and proteins, creating a multidimensional view of the hPSC-CM phenotype, which provides a strong technological foundation to advance the understanding of hPSC-CM biology. Raw data are available in the MassIVE repository with identifier MSV000088010.

Shaping the Trans-Scale Properties of Schizophrenia via Cerebral Alterations on Magnetic Resonance Imaging and Single-Nucleotide Polymorphisms of Coding and Non-Coding Regions.

Schizophrenia is a complex mental illness with genetic heterogeneity, which is often accompanied by alterations in brain structure and function. The neurobiological mechanism of schizophrenia associated with heredity remains unknown. Recently, the development of trans-scale and multi-omics methods that integrate gene and imaging information sheds new light on the nature of schizophrenia. In this article, we summarized the results of brain structural and functional changes related to the specific single-nucleotide polymorphisms (SNPs) in the past decade, and the SNPs were divided into non-coding regions and coding regions, respectively. It is hoped that the relationship between SNPs and cerebral alterations can be displayed more clearly and intuitively, so as to provide fresh approaches for the discovery of potential biomarkers and the development of clinical accurate individualized treatment decision-making.

Exploration of sea anemone-inspired high-performance biomaterials with enhanced antioxidant activity

Antioxidant biomaterials have attracted much attention in various biomedical fields because of their effective inhibition and elimination of reactive oxygen species (ROS) in pathological tissues. However, the difficulty in ensuring biocompatibility, biodegradability and bioavailability of antioxidant materials has limited their further development. Novel bioavailable antioxidant materials that are derived from natural resources are urgently needed. Here, an integrated multi-omics method was applied to fabricate antioxidant biomaterials. A key cysteine-rich thrombospondin-1 type I repeat-like (TSRL) protein was efficiently discovered from among 1262 adhesive components and then used to create a recombinant protein with a yield of 500 mg L−1. The biocompatible TSRL protein was able to self-assemble into either a water-resistant coating through Ca2+-mediated coordination or redox-responsive hydrogels with tunable physical properties. The TSRL-based hydrogels showed stronger 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging rates than glutathione (GSH) and ascorbic acid (Aa) and protected cells against external oxidative stress significantly more effectively. When topically applied to mice skin, TSRL alleviated epidermal hyperplasia and suppressed the degradation of collagen and elastic fibers caused by ultraviolet radiation B (UVB) irradiation, confirming that it enhanced antioxidant activity in vivo. This is the first study to successfully characterize natural antioxidant biomaterials created from marine invertebrate adhesives, and the findings indicate the excellent prospects of these biomaterials for great applications in tissue regeneration and cosmeceuticals.

Single-cell intracellular epitope and transcript detection reveals signal transduction dynamics

SummaryTo further our understanding of how biochemical information flows through cells upon external stimulation, we require single-cell multi-omics methods that concurrently map changes in (phospho)protein levels across signaling networks and the associated gene expression profiles. Here, we present quantification of RNA and intracellular epitopes by sequencing (QuRIE-seq), a droplet-based platform for single-cell RNA and intra- and extracellular (phospho)protein quantification through sequencing. We applied QuRIE-seq to quantify cell-state changes at both the signaling and the transcriptome level after 2-, 4-, 6-, 60-, and 180-min stimulation of the B cell receptor pathway in Burkitt lymphoma cells. Using the multi-omics factor analysis (MOFA+) framework, we delineated changes in single-cell (phospho)protein and gene expression patterns over multiple timescales and revealed the effect of an inhibitory drug (ibrutinib) on signaling and gene expression landscapes.

Cascade Deep Forest With Heterogeneous Similarity Measures for Drug-Target Interaction Prediction.

Drug repositioning is a method of systematically identifying potential molecular targets that known drugs may act on. Compared with traditional methods, drug repositioning has been extensively studied due to the development of multi-omics technology and system biology methods. Because of its biological network properties, it is possible to apply machine learning related algorithms for prediction. Based on various heterogeneous network model, this paper proposes a method named THNCDF for predicting drug–target interactions. Various heterogeneous networks are integrated to build a tripartite network, and similarity calculation methods are used to obtain similarity matrix. Then, the cascade deep forest method is used to make prediction. Results indicate that THNCDF outperforms the previously reported methods based on the 10-fold cross-validation on the benchmark data sets proposed by Y. Yamanishi. The area under Precision Recall curve (AUPR) value on the Enzyme, GPCR, Ion Channel, and Nuclear Receptor data sets is 0.988, 0.980, 0.938, and 0.906 separately. The experimental results well illustrate the feasibility of this method.

Arsenal of single-cell multi-omics methods expanded.

Arsenal of single-cell multi-omics methods expanded.

Heterogeneous Nuclear Ribonucleoprotein A1 Loads Batched Tumor-Promoting MicroRNAs Into Small Extracellular Vesicles With the Assist of Caveolin-1 in A549 Cells.

MicroRNAs in small extracellular vesicle (sEV-miRNAs) have been widely investigated as crucial regulated molecules secreted by tumor cells to communicate with surroundings. It is of great significance to explore the loading mechanism of sEV-miRNAs by tumor cells. Here, we comprehensively illustrated a reasoned loading pathway of batched tumor-promoting sEV-miRNAs in non-small cell lung cancer (NSCLC) cell line A549 with the application of a multi-omics method. The protein heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1) was strictly selected as a powerful sEV-miRNA loading protein from miRNA-binding proteome and further verified through small RNA sequencing after hnRNPA1 silence. In terms of the mechanism, SUMOylated hnRNPA1 in sEVs was verified to control sEV-miRNA loading. Subsequently, as a scaffolding component of caveolae, caveolin-1 (CAV1) was detailedly demonstrated to assist the loading of SUMOylated hnRNPA1 and its binding miRNAs into sEVs. Inhibition of CAV1 significantly prevented SUMOylated hnRNPA1 from encapsulating into sEVs, resulting in less enrichment of sEV-miRNAs it loaded. Finally, we confirmed that hnRNPA1-loaded sEV-miRNAs could facilitate tumor proliferation and migration based on database analysis and cytological experiments. Our findings reveal a loading mechanism of batched tumor-promoting sEV-miRNAs, which may contribute to the selection of therapeutic targets for lung cancer.

The Role of Epigenomic Regulatory Pathways in the Gut-Brain Axis and Visceral Hyperalgesia.

The gut-brain axis (GBA) is broadly accepted to describe the bidirectional circuit that links the gastrointestinal tract with the central nervous system (CNS). Interest in the GBA has grown dramatically over past two decades along with advances in our understanding of the importance of the axis in the pathophysiology of numerous common clinical disorders including mood disorders, neurodegenerative disease, diabetes mellitus, non-alcohol fatty liver disease (NAFLD) and enhanced abdominal pain (visceral hyperalgesia). Paralleling the growing interest in the GBA, there have been seminal developments in our understanding of how environmental factors such as psychological stress and other extrinsic factors alter gene expression, primarily via epigenomic regulatory mechanisms. This process has been driven by advances in next-generation multi-omics methods and bioinformatics. Recent reviews address various components of GBA, but the role of epigenomic regulatory pathways in chronic stress-associated visceral hyperalgesia in relevant regions of the GBA including the amygdala, spinal cord, primary afferent (nociceptive) neurons, and the intestinal barrier has not been addressed. Rapidly developing evidence suggests that intestinal epithelial barrier dysfunction and microbial dysbiosis play a potentially significant role in chronic stress-associated visceral hyperalgesia in nociceptive neurons innervating the lower intestine via downregulation in intestinal epithelial cell tight junction protein expression and increase in paracellular permeability. These observations support an important role for the regulatory epigenome in the development of future diagnostics and therapeutic interventions in clinical disorders affecting the GBA.

Three-in-One Simultaneous Extraction of Proteins, Metabolites and Lipids for Multi-Omics.

Elucidation of complex molecular networks requires integrative analysis of molecular features and changes at different levels of information flow and regulation. Accordingly, high throughput functional genomics tools such as transcriptomics, proteomics, metabolomics, and lipidomics have emerged to provide system-wide investigations. Unfortunately, analysis of different types of biomolecules requires specific sample extraction procedures in combination with specific analytical instrumentation. The most efficient extraction protocols often only cover a restricted type of biomolecules due to their different physicochemical properties. Therefore, several sets/aliquots of samples are needed for extracting different molecules. Here we adapted a biphasic fractionation method to extract proteins, metabolites, and lipids from the same sample (3-in-1) for liquid chromatography-tandem mass spectrometry (LC-MS/MS) multi-omics. To demonstrate utility of the improved method, we used bacteria-primed Arabidopsis leaves to generate multi-omics datasets from the same sample. In total, we were able to analyze 1849 proteins, 1967 metabolites, and 424 lipid species in single samples. The molecules cover a wide range of biological and molecular processes, and allow quantitative analyses of different molecules and pathways. Our results have shown the clear advantages of the multi-omics method, including sample conservation, high reproducibility, and tight correlation between different types of biomolecules.

Microbial Interactions in Pollution Control Ecosystems

Microbial interaction determines the performance, stability, and resilience of the microbial communities. Understanding microbial interactions benefits the development of environmental biotechnology. The purpose of this review is to summarize the recent findings of microbial interactions in pollution control ecosystems from aspects of the substrate degradation, energy utilization, electron transfer, and information exchange. Cross-feeding of substrates such as vitamins was found to be necessary for the degradation of some trace organic contaminants. Under different conditions, microorganisms can mediate various energy-utilization pathways (e.g., co-metabolism) to grow. Electroactive bacteria and cable bacteria can mediate extracellular electron transfer via conductive pili, c-type cytochrome, or filamentous structure. Quorum sensing plays an important role in the microbial aggregation and functional microbe enrichments. With all these knowledges, it will potentially benefit the development of disruptive environmental biotechnologies. This review summarized recent findings of microbial interactions, many of which have huge potentials to advance environmental biotechnologies. Multi-omics methods should be further applied for comprehensively confirming known and unknown microbial processes. The co-occurrence network should be applied to unravel the interlinks among substrate degradation, energy utilization, electron transfer, and information exchange. The proper regulation of microbial interactions in practical application should be further addressed.

Identification of a Six Gene Prognosis Signature for Papillary Thyroid Cancer Using Multi-Omics Methods and Bioinformatics Analysis.

Papillary thyroid carcinoma (PTC) is the most common subtype of thyroid cancer. PTC is typically curable with an excellent survival rate; however, some patients experience disease recurrence or death. This study aimed to discover potential key genes and signaling pathways of PTC, which could provide new insights for thyroid lesions. Four GEO microarray datasets were integrated to screen for candidate genes involved in PTC progression. A total of 164 upregulated and 168 downregulated differentially expressed genes (DEGs) were screened. Gene Ontology/Kyoto Encyclopedia of Genes and Genomes were used in pathway enrichment analyses for DEGs. A protein-protein interaction network was then built and analyzed utilizing STRING and Cytoscape, followed by the identification of 13 hub genes by cytoHubba. CDH3, CTGF, CYR61, OGN, FGF13, and CHRDL1 were selected through survival analyses. Furthermore, immune infiltration, mutations and methylation analysis indicated that these six hub genes played vital roles in immune surveillance and tumor progression. ROC and K-M plots showed that these genes had good prognostic values for PTC which was validated by TCGA dataset. Finally, GSEA for a single hub gene revealed that each candidate hub gene had close associations with PTC development. These findings provided new insights into PTC pathogenesis and identified six candidate gene prognosis signature for PTC.

Metagenomic Insight Into Patterns and Mechanism of Nitrogen Cycle During Biocrust Succession.

The successional ecology of nitrogen cycling in biocrusts and the linkages to ecosystem processes remains unclear. To explore this, four successional stages of natural biocrust with five batches of repeated sampling and three developmental stages of simulated biocrust were studied using relative and absolute quantified multi-omics methods. A consistent pattern across all biocrust was found where ammonium assimilation, mineralization, dissimilatory nitrite to ammonium (DNiRA), and assimilatory nitrate to ammonium were abundant, while denitrification medium, N-fixation, and ammonia oxidation were low. Mathematic analysis showed that the nitrogen cycle in natural biocrust was driven by dissolved organic N and NO3–. pH and SO42– were the strongest variables affecting denitrification, while C:(N:P) was the strongest variable affecting N-fixation, DNiRA, nitrite oxidation, and dissimilatory nitrate to nitrite. Furthermore, N-fixation and DNiRA were closely related to elemental stoichiometry and redox balance, while assimilatory nitrite to ammonium (ANiRA) and mineralization were related to hydrological cycles. Together with the absolute quantification and network models, our results suggest that responsive ANiRA and mineralization decreased during biocrust succession; whereas central respiratory DNiRA, the final step of denitrification, and the complexity and interaction of the whole nitrogen cycle network increased. Therefore, our study stresses the changing environmental functions in the biocrust N-cycle, which are succession-dependent.

Single-Cell Multiomics Analysis Reveals Heterogeneous Cell States Linked to Metastatic Potential in Liver Cancer Cell Lines

Liver cancer is a malignant neoplasm with a high mortality and recurrence rate. The most common liver cancer is hepatocellular carcinoma (HCC). One of the major obstacles to successful treatment of HCC is a high rate of metastasis. However, the molecular mechanisms that drive high metastasis rates remain unclear. Single-cell multi-omics methods provide deep and comprehensive insight into cell states, and may elucidate mechanisms underlying disease processes, including metastasis. We combined single-cell transcriptomic, proteomic and chromatin accessibility data to investigate how heterogeneous phenotypes contribute to metastatic potential in five HCC cell lines. We confirmed that the prevalence of a mesenchymal state is linked to the metastatic potential. Levels of cell proliferation were also linked to metastatic capacity, though this correlate was not absolute across cell lines. In addition, we identified a rare hypoxic subtype that has a higher capacity for glycolysis and exhibits dormant, invasive, and malignant characteristics. This subtype has increased metastatic potential. We further identified a robust 14-gene panel representing this hypoxia signature and verified its presence in clinical samples. Our data suggest that the presence of this hypoxia signature could serve as a prognostic index. We investigated phenotypes in HCC cell lines that include EMT capacity, cell proliferation, and hypoxia status and linked these to their metastatic potential. Our data provide a valuable data resource, facilitate a deeper understanding of metastatic mechanism, and may help diagnosis of metastatic potential in individual patients, thus supporting personalized medicine.

Advances in single-cell multi-omics methods and their applications in developmental biology

Advances in single-cell multi-omics methods and their applications in developmental biology

'There and Back Again'-Forward Genetics and Reverse Phenotyping in Pulmonary Arterial Hypertension.

Although the invention of right heart catheterisation in the 1950s enabled accurate clinical diagnosis of pulmonary arterial hypertension (PAH), it was not until 2000 when the landmark discovery of the causative role of bone morphogenetic protein receptor type II (BMPR2) mutations shed new light on the pathogenesis of PAH. Since then several genes have been discovered, which now account for around 25% of cases with the clinical diagnosis of idiopathic PAH. Despite the ongoing efforts, in the majority of patients the cause of the disease remains elusive, a phenomenon often referred to as “missing heritability”. In this review, we discuss research approaches to uncover the genetic architecture of PAH starting with forward phenotyping, which in a research setting should focus on stable intermediate phenotypes, forward and reverse genetics, and finally reverse phenotyping. We then discuss potential sources of “missing heritability” and how functional genomics and multi-omics methods are employed to tackle this problem.