scCross: a deep generative model for unifying single-cell multi-omics with seamless integration, cross-modal generation, and in silico exploration.

Single-cell multi-omics data reveal complex cellular states, providing significant insights into cellular dynamics and disease. Yet, integration of multi-omics data presents challenges. Some modalities have not reached the robustness or clarity of established transcriptomics. Coupled with data scarcity for less established modalities and integration intricacies, these challenges limit our ability to maximize single-cell omics benefits. We introduce scCross, a tool leveraging variational autoencoders, generative adversarial networks, and the mutual nearest neighbors (MNN) technique for modality alignment. By enabling single-cell cross-modal data generation, multi-omics data simulation, and in silico cellular perturbations, scCross enhances the utility of single-cell multi-omics studies.

Genomic and Cell-Specific Regulation of Benzylisoquinoline Alkaloid Biosynthesis in Opium Poppy.

Opium poppy is a crop of great commercial value as a source of several opium alkaloids for the pharmaceutical industries including morphine, codeine, thebaine, noscapine and papaverine. Most enzymes involved in benzylisoquinoline alkaloids (BIAs) biosynthesis in opium poppy have been functionally characterized, and opium poppy currently serves as a model system to study BIA metabolism in plants. BIA biosynthesis in opium poppy involves two biosynthetic gene clusters associated respectively with the morphine and noscapine branches. Recent reports have shown that genes in the same cluster are co-expressed, suggesting they might also be co-regulated. However, the transcriptional regulation of opium poppy BIA biosynthesis is not well studied. Opium poppy BIA biosynthesis involves three cell types associated with the phloem system: companion cells, sieve elements and laticifers. The transcripts and enzymes associated with BIA biosynthesis are distributed across cell types, requiring the translocation of key enzymes and pathway intermediates between cell types. Together, these suggest that the regulation of BIA biosynthesis in opium poppy is multilayered and complex, involving biochemical, genomic, and physiological mechanisms. In this review, we highlight recent advances in genome sequencing and single cell and spatial transcriptomics with a focus on how these efforts can improve our understanding of the genomic and cell-specific regulation of BIA biosynthesis. Such knowledge is vital for opium poppy genetic improvement and metabolic engineering efforts targeting the modulation of alkaloid yield and composition.

Budding mutation reprogrammed flavonoid biosynthesis in jujube by deploying MYB41 and bHLH93.

Budding mutations are known to cause metabolic changes in new jujube varieties; however, the mechanisms underlying these changes are still unclear. Here, we performed muti-omics analysis to decipher the detailed metabolic landscape of "Saimisu 1" (S1) and its budding mutation line "Saimisu 2" (S2) at all fruit stages. We found that the genes involved in the biosyntheses of flavonoids, phenylpropanoids, and amino acids were upregulated in S2 fruits at all stages, especially PAL and DFR, resulting in increased accumulation of related compounds in S2 mature fruits. Further co-expression regulatory network analysis showed that the transcription factors MYB41 and bHLH93 potentially regulated the expression of PAL and DFR, respectively, by directly binding to their promoters. Moreover, the overexpression of MYB41 or bHLH93 induced their expression levels to redirect the flux of the flavonoid biosynthetic pathway, eventually leading to high levels of related compounds in S2 fruits. Overall, this study revealed the metabolic variations between S1 and S2 and contributed to the understanding of the mechanisms underlying budding mutation-mediated metabolic variations in plants, eventually providing the basis for breeding excellent jujube varieties using budding mutation lines.

Harnessing HetHydrogel: A Universal Platform to Dropletize Single-Cell Multiomics.

A universal platform is developed for dropletizing single cell plate-based multiomic assays, consisting of three main pillars: a miniaturized open Heterogeneous Hydrogel reactor (abbreviated HetHydrogel) for multi-step biochemistry, its tunable permeability that allows Tn5 tagmentation, and single cell droplet barcoding. Through optimizing the HetHydrogel manufacturing procedure, the chemical composition, and cell permeation conditions, simultaneous high-throughput mitochondrial DNA genotyping and chromatin profiling at the single-cell level are demonstrated using a mixed-species experiment. This platform offers a powerful way to investigate the genotype-phenotype relationships of various mtDNA mutations in biological processes. The HetHydrogel platform is believed to have the potential to democratize droplet technologies, upgrading a whole range of plate-based single cell assays to high throughput format.

Multi-omics integration with weighted affinity and self-diffusion applied for cancer subtypes identification.

BACKGROUND: Characterizing cancer molecular subtypes is crucial for improving prognosis and individualized treatment. Integrative analysis of multi-omics data has become an important approach for disease subtyping, yielding better understanding of the complex biology. Current multi-omics integration tools and methods for cancer subtyping often suffer challenges of high computational efficiency as well as the problem of weight assignment on data types. RESULTS: Here, we present an efficient multi-omics integration via weighted affinity and self-diffusion (MOSD) to dissect cancer heterogeneity. MOSD first construct local scaling affinity on each data type and then integrate all affinities by weighted linear combination, followed by the self-diffusion to further improve the patients' similarities for the downstream clustering analysis. To demonstrate the effectiveness and usefulness for cancer subtyping, we apply MOSD across ten cancer types with three measurements (Gene expression, DNA methylation, miRNA). CONCLUSIONS: Our approach exhibits more significant differences in patient survival and computationally efficient benchmarking against several state-of-art integration methods and the identified molecular subtypes reveal strongly biological interpretability. The code as well as its implementation are available in GitHub: https://github.com/DXCODEE/MOSD .

Multiomics Analysis Reveals the Chemical and Genetic Bases of Pigmented Potato Tuber.

Anthocyanins and carotenoids determine the diversity of potato tuber flesh pigmentation; here, the underlying chemical and genetic bases were elucidated by multiomics analyses. A total of 31 anthocyanins and 30 carotenoids were quantified in five differently pigmented tubers. Cyanidin and pelargonidin derivatives determined the redness, while malvidin, petunidin, and delphinidin derivatives contributed to purpleness. Violaxanthin derivatives determined the light-yellow color, while zeaxanthin and antheraxanthin derivatives further enhanced the deep-yellow deposition. Integrated transcriptome and proteome analyses identified that F3'5'H highly enhanced anthocyanin biosynthesis in purple flesh and was responsible for metabolic divergence between red and purple samples. BCH2 significantly enhanced carotenoid biosynthesis in yellow samples and along with ZEP, NCED1, and CCD1 genes determined metabolic divergence between light and deep-yellow samples. The weighted correlation network analysis constructed a regulatory network revealing the central role of AN1 in regulating anthocyanin biosynthesis, and 10 new transcription factors related to anthocyanin and carotenoid metabolism regulation were identified. Our findings provide targeted genes controlling tuber pigmentation, which will be meaningful for the genetic manipulation of tuber quality improvement.

Dysregulation of the microbiota-brain axis during long-term exposure to polystyrene nanoplastics in rats and the protective role of dihydrocaffeic acid.

Polystyrene nano-plastics (PS-NPs) can be accumulated in the food chain and can penetrate biological barriers to affect multiple physiological functions. However, the adverse effects of nano-plastics on mammals and the underlying mechanism still remain unknown. To fill the gaps, our study administrated low-dose PS-NPs (50 and 100 µg/L) for 24 consecutive weeks in rats. Behavioral and morphological evaluations were performed to assess the neurobehavoirs. A combined analysis of multiple omics was used to evaluate the dysfunctions of the gut-microbe-brain axis. After dihydrochalcone(NHDC) treatment in the PS-NPs rat model, the inflammation response and apoptosis process were assessed and proteomics was used to explore the underlying mechanism. Our results indicated that long-term exposure to low-dose PS-NPs could induce abnormal neurobehaviors and amygdaloid nucleus impairment, and stimulate inflammatory responses and apoptosis. Metagenomics results revealed that four microbial phyla including Proteobacteria, Firmicutes, Defferibacteres, and Bacteroidetes changed significantly compared to the control. Targeted metabolomics analysis in the feces showed alteration of 122 metabolites induced by the PS-NPs exposure, among which the content of dihydrocaffeic acid was significantly associated with the different microbial genera and pivotal differential metabolites in the amygdaloid nucleus. And NHDC treatment significantly alleviated PS-NP-induced neuroinflammation and apoptosis and the cyclic adenosine monophosphate(cAMP)/protein kinase A(PKA)/phosphorylated cAMP-response element binding protein(p-CREB)/plasma membrane calcium-transporting ATPase 2(Atp2b2) signaling pathway was identified in the proteomics. In conclusion, long-term exposure to low-dose PS-NPs has adverse effects on emotion through the dysregulation of the gut-brain axis, and dihydrocaffeic acid can alleviate these effects via the cAMP/PKA/p-CREB/Atp2b2 signaling pathway.

Astrocytic SIRT6 is a potential anti-depression and anti-anxiety target.

Sirtuin 6 (SIRT6) is a nuclear silencing information regulator that is widely expressed in brain. Inhibition of SIRT6 in the brain induced antidepressant effects in rodents. However, SIRT6 knockout in neurons induced developmental retardation and cognitive impairments. In this study, a mouse strain of astrocyte conditional knockout SIRT6 (AKO) was constructed. Unlike whole brain SIRT6 knockout mice, AKO mice did not show growth retardation. We showed that SIRT6 knockout in astrocytes did not impair the learning and memory ability of mice. Chronic unpredictable mild stress (CUMS) was used to evaluate the anti-depression and anti-anxiety effects in mice. In tail suspension test and forced swimming test, AKO mice did not show depression like phenotype induced by CUMS. In addition, knockout of SIRT6 in astrocytes alleviated the high anxiety level induced by CUMS in light and dark box test, open field test and elevated cross maze test. Three box social test showed that the deletion of SIRT6 in astrocytes changed the social preference of mice. Re-expression of SIRT6 in astrocytes mediated by adeno-associated virus reversed the social preference of AKO mice, but the re-expression also eliminated the anti-depression and anti-anxiety effects in AKO mice. Deletion of SIRT6 in astrocytes change the purine metabolic homeostasis of medial prefrontal cortex in mice. The results of transcriptomics and metabolomics analysis showed that the deletion of SIRT6 would change the purine metabolic pathway of cultured astrocytes and increase the contents of inosine and the second messenger cyclic adenosine monophosphate in astrocytes. In conclusion, knockout of SIRT6 in astrocytes induced anti-depression and anti-anxiety effects in mice without impairing the development and cognitive ability of mice.

Immune- and Stemness-Related Genes Revealed by Comprehensive Analysis and Validation for Cancer Immunity and Prognosis and Its Nomogram in Lung Adenocarcinoma.

Objective: Lung adenocarcinoma (LUAD) is a familiar lung cancer with a very poor prognosis. This study investigated the immune- and stemness-related genes to develop model related with cancer immunity and prognosis in LUAD. Method: The Cancer Genome Atlas (TCGA) was utilized for obtaining original transcriptome data and clinical information. Differential expression, prognostic value, and correlation with clinic parameter of mRNA stemness index (mRNAsi) were conducted in LUAD. Significant mRNAsi-related module and hub genes were screened using weighted gene coexpression network analysis (WGCNA). Meanwhile, immune-related differential genes (IRGs) were screened in LUAD. Stem cell index and immune-related differential genes (SC-IRGs) were screened and further developed to construct prognosis-related model and nomogram. Comprehensive analysis of hub genes and subgroups, involving enrichment in the subgroup [gene set enrichment analysis (GSEA)], gene mutation, genetic correlation, gene expression, immune, tumor mutation burden (TMB), and drug sensitivity, used bioinformatics and reverse transcription polymerase chain reaction (RT-PCR) for verification. Results: Through difference analysis, mRNAsi of LUAD group was markedly higher than that of normal group. Clinical parameters (age, gender, and T staging) were ascertained to be highly relevant to mRNAsi. MEturquoise and MEblue were found to be the most significant modules (including positive and negative correlations) related to mRNAsi via WGCNA. The functions and pathways of the two mRNAsi-related modules were mainly enriched in tumorigenesis, development, and metastasis. Combining stem cell index-related differential genes and immune-related differential genes, 30 prognosis-related SC-IRGs were screened via Cox regression analysis. Then, 16 prognosis-related SC-IRGs were screened to construct a LASSO regression model at last. In addition, the model was successfully validated by using TCGA-LUAD and GSE68465, whereas c-index and the calibration curves were utilized to demonstrate the clinical value of our nomogram. Following the validation of the model, GSEA, immune cell correlation, TMB, clinical relevance, etc., have found significant difference in high- and low-risk groups, and 16-gene expression of the SC-IRG model also was tested by RT-PCR. ADRB2, ANGPTL4, BDNF, CBLC, CX3CR1, and IL3RA were found markedly different expression between the tumor and normal group. Conclusion: The SC-IRG model and the prognostic nomogram could accurately predict LUAD survival. Our study used mRNAsi combined with immunity that may lay a foundation for the future research studies in LUAD.

An Integrated Immune-Related Bioinformatics Analysis in Glioma: Prognostic Signature's Identification and Multi-Omics Mechanisms' Exploration.

As the traditional treatment for glioma, the most common central nervous system malignancy with poor prognosis, the efficacy of high-intensity surgery combined with radiotherapy and chemotherapy is not satisfactory. The development of individualized scientific treatment strategy urgently requires the guidance of signature with clinical predictive value. In this study, five prognosis-related differentially expressed immune-related genes (PR-DE-IRGs) (CCNA2, HMGB2, CASP3, APOBEC3C, and BMP2) highly associated with glioma were identified for a prognostic model through weighted gene co-expression network analysis, univariate Cox and lasso regression. Kaplan-Meier survival curves, receiver operating characteristic curves and other methods have shown that the model has good performance in predicting the glioma patients' prognosis. Further combined nomogram provided better predictive performance. The signature's guiding value in clinical treatment has also been verified by multiple analysis results. We also constructed a comprehensive competing endogenous RNA (ceRNA) regulatory network based on the protective factor BMP2 to further explore its potential role in glioma progression. Numerous immune-related biological functions and pathways were enriched in a high-risk population. Further multi-omics integrative analysis revealed a strong correlation between tumor immunosuppressive environment/IDH1 mutation and signature, suggesting that their cooperation plays an important role in glioma progression.

Crosstalk Between Metabolism and Immune Activity Reveals Four Subtypes With Therapeutic Implications in Clear Cell Renal Cell Carcinoma.

Clear cell renal cell carcinoma (ccRCC) is characterized by metabolic dysregulation and distinct immunological signatures. The interplay between metabolic and immune processes in the tumor microenvironment (TME) causes the complexity and heterogeneity of immunotherapy responses observed during ccRCC treatment. Herein, we initially identified two distinct metabolic subtypes (C1 and C2 subtypes) and immune subtypes (I1 and I2 subtypes) based on the occurrence of differentially expressed metabolism-related prognostic genes and immune-related components. Notably, we observed that immune regulators with upregulated expression actively participated in multiple metabolic pathways. Therefore, we further delineated four immunometabolism-based ccRCC subtypes (M1, M2, M3, and M4 subtypes) according to the results of the above classification. Generally, we found that high metabolic activity could suppress immune infiltration. Immunometabolism subtype classification was associated with immunotherapy response, with patients possessing the immune-inflamed, metabolic-desert subtype (M3 subtype) that benefits the most from immunotherapy. Moreover, differences in the shifts in the immunometabolism subtype after immunotherapy were observed in the responder and non-responder groups, with patients from the responder group transferring to subtypes with immune-inflamed characteristics and less active metabolic activity (M3 or M4 subtype). Immunometabolism subtypes could also serve as biomarkers for predicting immunotherapy response. To decipher the genomic and epigenomic features of the four subtypes, we analyzed multiomics data, including miRNA expression, DNA methylation status, copy number variations occurrence, and somatic mutation profiles. Patients with the M2 subtype possessed the highest VHL gene mutation rates and were more likely to be sensitive to sunitinib therapy. Moreover, we developed non-invasive radiomic models to reveal the status of immune activity and metabolism. In addition, we constructed a radiomic prognostic score (PRS) for predicting ccRCC survival based on the seven radiomic features. PRS was further demonstrated to be closely linked to immunometabolism subtype classification, immune score, and tumor mutation burden. The prognostic value of the PRS and the association of the PRS with immune activity and metabolism were validated in our cohort. Overall, our study established four immunometabolism subtypes, thereby revealing the crosstalk between immune and metabolic activities and providing new insights into personal therapy selection.

[Twenty years in the 21st century: research approaches and techniques in modern system biology for mechanisms of Chinese medicinal processing].

Clarifying the mechanisms of Chinese medicinal processing is pivotal to the modernization of Chinese medicine. Research on Chinese medicinal processing gives priority to the mechanisms of the processing in enhancing efficacy, reducing toxicity, and repurposing medicinals. During the past 20 years, scholars have carried out in-depth studies on the mechanisms of Chinese medicinal processing via modern system biology. They mainly focused on the changes of medicinal properties and efficacy caused by processing using techniques of modern pharmacology and molecular biology, spectrum-efficacy correlation, and biophoton emission. However, these techniques fail to reflect the holistic view of traditional Chinese medicine. With the introduction of system biology, multi-omics techno-logies(genomics, transcriptomics, proteomics, and metabolomics) have surged, which have been applied to the research on the mec-hanisms of Chinese medicinal processing. These multi-omics technologies have advantages in the research on holism. This study aims to summarize the research techniques and approaches in system biology for mechanisms of Chinese medicinal processing in the past 20 years and analyze the limitations and advantages of them. It is concluded that the multi-omics techniques of system biology can reconstruct the mechanisms of Chinese medicinal processing. This study provides a new direction for further research on the mechanisms of Chinese medicinal processing.

Study on toxicity effects of environmental pollutants based on metabolomics: A review.

In the past few decades, the toxic effects of environmental pollutants on non-target organisms have received more and more attention. As a new omics technology, metabolomics can clarify the metabolic homeostasis of the organism at the overall level by studying the changes in the relative contents of endogenous metabolites in the organism. Recently, a large number of studies have used metabolomics technology to study the toxic effects of environmental pollutants on organisms. In this review, we reviewed the analysis processes and data processes of metabolomics and its application in the study of the toxic effects of environmental pollutants including heavy metals, pesticides, polychlorinated biphenyls, polycyclic aromatic hydrocarbons, polybrominated diphenyl ethers and microplastics. In addition, we emphasized that the combination of metabolomics and other omics technologies will help to explore the toxic mechanism of environmental pollutants and provide new research ideas for the toxicological evaluation of environmental pollutants.

RNA m6A Methylation Regulators Multi-Omics Analysis in Prostate Cancer.

RNA N6-methyladenosine (m6A) methylation is known to be the most popular RNA modification in animals. Many research reports have elaborated on the effects of m6A regulators in medical practice, such as diagnosis, prognosis, and treatment. M6A modification has evident impacts on many aspects of RNA metabolism, just like RNA splicing, processing, translation, and stability. M6A also has a magnificent role in numerous types of cancers. We analyzed the prostate cancer datasets, from The Cancer Genome Atlas (TCGA) database, for every recognized m6A regulator in their gene expression, DNA methylation status and copy number variations (CNVs). We also systematically analyzed the relationship between different m6A regulators and the prognosis of prostate cancer. The results illustrated considerable differences in the expression of various m6A regulators between the prostate and normal cancer samples. At the same time, there were evident differences in the expression of various m6A regulators in prostate cancers with different Gleason scores. Subsequently, we determined CBLL1, FTO, YTHDC1, HNRNPA2B1 as crucial m6A regulators of prostate cancer. Premised on the expression of CBLL1, we also identified potential therapeutic agents for prostate cancer, and knockdown of HNRNPA2B1 prominently inhibited prostate cells migration and invasion in vitro experiment.

Corrigendum: RNA m6A Methylation Regulators Multi-Omics Analysis in Prostate Cancer.

[This corrects the article DOI: 10.3389/fgene.2021.768041.].