Using multivariate mixed-effects selection models for analyzing batch-processed proteomics data with non-ignorable missingness

Summary In quantitative proteomics, mass tag labeling techniques have been widely adopted in mass spectrometry experiments. These techniques allow peptides (short amino acid sequences) and proteins from multiple samples of a batch being detected and quantified in a single experiment, and as such greatly improve the efficiency of protein profiling. However, the batch-processing of samples also results in severe batch effects and non-ignorable missing data occurring at the batch level. Motivated by the breast cancer proteomic data from the Clinical Proteomic Tumor Analysis Consortium, in this work, we developed two tailored multivariate MIxed-effects SElection models (mvMISE) to jointly analyze multiple correlated peptides/proteins in labeled proteomics data, considering the batch effects and the non-ignorable missingness. By taking a multivariate approach, we can borrow information across multiple peptides of the same protein or multiple proteins from the same biological pathway, and thus achieve better statistical efficiency and biological interpretation. These two different models account for different correlation structures among a group of peptides or proteins. Specifically, to model multiple peptides from the same protein, we employed a factor-analytic random effects structure to characterize the high and similar correlations among peptides. To model biological dependence among multiple proteins in a functional pathway, we introduced a graphical lasso penalty on the error precision matrix, and implemented an efficient algorithm based on the alternating direction method of multipliers. Simulations demonstrated the advantages of the proposed models. Applying the proposed methods to the motivating data set, we identified phosphoproteins and biological pathways that showed different activity patterns in triple negative breast tumors versus other breast tumors. The proposed methods can also be applied to other high-dimensional multivariate analyses based on clustered data with or without non-ignorable missingness.</jats:p

Identifying cis-mediators for trans-eQTLs across many human tissues using genomic mediation analysis

ABSTRACTThe impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes (cis-eQTLs). More research is needed to identify effects of genetic variation on distant genes (trans-eQTLs) and understand their biological mechanisms. One common trans-eQTLs mechanism is “mediation” by a local (cis) transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are “cis-mediators” of trans-eQTLs, including those “cis-hubs” involved in regulation of many trans-genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying trans-eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multi-tissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study cis-mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of cis-hubs and trans-eQTL regulation across tissue types.</jats:p

Integrative Analysis of Spatial Transcriptome with Single-cell Transcriptome and Single-cell Epigenome in Mouse Lungs after Immunization

ABSTRACTImmunological memory is key to productive adaptive immunity. An unbiased, high throughput gene expression profiling of tissue-resident memory T cells at their precise anatomical locations within the lung is fundamental to understanding lung immunity, but such spatial information has yet to be characterized. In this study, using a well-established Klebsiella pneumoniae infection model, we performed an integrative analysis of spatial transcriptome with single-cell RNA-seq and single-cell ATAC-seq on lung cells from mice after immunization using the 10x Genomics Chromium and Visium platform. We employed several deconvolution algorithms and established an optimized deconvolution pipeline to accurately decipher specific cell-type composition by anatomic location. We identified and located 12 major cell types by scRNA-seq and spatial transcriptomic analysis. Integrating scATAC-seq data from the same cells processed in parallel with scRNA-seq, we found epigenomic profiles provide more robust cell type identification, especially for lineage-specific T helper cells. When combining all three data modalities, we observed a dynamic change in the location of T helper cells as well as their corresponding chemokines for chemotaxis. Furthermore, cell-cell communication analysis of spatial transcriptome provided evidence of lineage-specific T helper cells receiving designated cytokine signaling. In summary, our first-in-class study demonstrated the power of multi-omics analysis to uncover intrinsic spatial- and cell-type-dependent molecular mechanisms of lung immunity. Our data provides a rich research resource of single cell multi-omics data as a reference for understanding spatial dynamics of lung immunization.</jats:p

Polymethoxyflavones in Citrus Regulate Lipopolysaccharide-Induced Oscillating Decay of Circadian Rhythm Genes by Inhibiting Nlrp3 Expression

The aim of this study is to compare the regulatory abilities of citrus flavonoids on the oscillating expression of circadian genes. Seven varieties of citrus fruits and twenty-five citrus flavonoids were selected and evaluated. Per2 luciferase bioluminescence report system and serum shock were used to induce circadian gene expression in mouse microglia BV-2 cells. In vivo experiments were carried out using C57BL6/J mice to evaluate the regulation of flavonoids on the oscillatory expression of liver biorhythm genes. Lipopolysaccharide was used to interfere the gene oscillating expression. QRT-PCR was performed to detect the expression of circadian rhythm-related genes, including Clock, Bmal1, Per1, Per2, Per3, Cry1, Cry2, Rev-erbα, Rev-erbβ, Rorα, Dbp, and Npas2. The results show that the polymethoxyflavones (PMFs) exerted stronger circadian gene regulatory capability, while the flavonoids containing glycosides showed no biological activity. Also, all tested flavonoids decreased LPS-induced nitric oxide release, but only polymethoxyflavones inhibited circadian rhythm disorder. PMFs inhibited Nlrp3 inflammasome-related genes and proteins, including Nlrp3, IL-1β, ASC, and Caspase1, while other flavonoids only affected IL-1β and Caspase1 expression. This mechanism was preliminarily verified using the Nlrp3 inhibitor INF39.</jats:p

De novo missense variants disrupting protein–protein interactions affect risk for autism through gene co-expression and protein networks in neuronal cell types

Abstract Background Whole-exome sequencing studies have been useful for identifying genes that, when mutated, affect risk for autism spectrum disorder (ASD). Nonetheless, the association signal primarily arises from de novo protein-truncating variants, as opposed to the more common missense variants. Despite their commonness in humans, determining which missense variants affect phenotypes and how remains a challenge. We investigate the functional relevance of de novo missense variants, specifically whether they are likely to disrupt protein interactions, and nominate novel genes in risk for ASD through integrated genomic, transcriptomic, and proteomic analyses. Methods Utilizing our previous interactome perturbation predictor, we identify a set of missense variants that are likely disruptive to protein–protein interactions. For genes encoding the disrupted interactions, we evaluate their expression patterns across developing brains and within specific cell types, using both bulk and inferred cell-type-specific brain transcriptomes. Connecting all disrupted pairs of proteins, we construct an “ASD disrupted network.” Finally, we integrate protein interactions and cell-type-specific co-expression networks together with published association data to implicate novel genes in ASD risk in a cell-type-specific manner. Results Extending earlier work, we show that de novo missense variants that disrupt protein interactions are enriched in individuals with ASD, often affecting hub proteins and disrupting hub interactions. Genes encoding disrupted complementary interactors tend to be risk genes, and an interaction network built from these proteins is enriched for ASD proteins. Consistent with other studies, genes identified by disrupted protein interactions are expressed early in development and in excitatory and inhibitory neuronal lineages. Using inferred gene co-expression for three neuronal cell types—excitatory, inhibitory, and neural progenitor—we implicate several hundred genes in risk (FDR $$\le \hspace{0.17em}$$ ≤ 0.05), ~ 60% novel, with characteristics of genuine ASD genes. Across cell types, these genes affect neuronal morphogenesis and neuronal communication, while neural progenitor cells show strong enrichment for development of the limbic system. Limitations Some analyses use the imperfect guilt-by-association principle; results are statistical, not functional. Conclusions Disrupted protein interactions identify gene sets involved in risk for ASD. Their gene expression during brain development and within cell types highlights how they relate to ASD. </jats:sec

Evaluation of Antioxidant Capacity and Gut Microbiota Modulatory Effects of Different Kinds of Berries

Berries are fairly favored by consumers. Phenolic compounds are the major phytochemicals in berries, among which anthocyanins are one of the most studied. Phenolic compounds are reported to have prebiotic-like effects. In the present study, we identified the anthocyanin profiles, evaluated and compared the antioxidant capacities and gut microbiota modulatory effects of nine common berries, namely blackberry, black goji berry, blueberry, mulberry, red Chinese bayberry, raspberry, red goji berry, strawberry and white Chinese bayberry. Anthocyanin profiles were identified by UPLC-Triple-TOF/MS. In vitro antioxidant capacity was evaluated by four chemical assays (DPPH, ABTS, FRAP and ORAC). In vivo antioxidant capacity and gut microbiota modulatory effects evaluation was carried out by treating healthy mice with different berry extracts for two weeks. The results show that most berries could improve internal antioxidant status, reflected by elevated serum or colonic T-AOC, GSH, T-SOD, CAT, and GSH-PX levels, as well as decreased MDA content. All berries significantly altered the gut microbiota composition. The modulatory effects of the berries were much the same, namely by the enrichment of beneficial SCFAs-producing bacteria and the inhibition of potentially harmful bacteria. Our study shed light on the gut microbiota modulatory effect of different berries and may offer consumers useful consumption guidance

Anthocyanin Extracts From 11 Superfruits Mitigate Oxidative Stress and Inflammatory Damage in HK‐2 Cells Induced by Cadmium Exposure

ABSTRACT Cadmium (Cd) pollution has emerged as an escalating threat to human health, with its induced kidney toxicity being closely linked to oxidative stress and inflammation. In this study, we extracted and analyzed primary anthocyanins from 11 superfruits using UPLC‐Triple‐TOF/MS and evaluated their antioxidant capacity through DPPH, ABTS, FRAP, and ORAC assays. By employing a Cd‐exposed Human Kidney‐2 (HK‐2) cells injury model, we investigated the protective effects of superfruit anthocyanin extracts. The results demonstrated that these extracts enhanced HK‐2 cell viability, increased T‐SOD and GSH levels, and reduced ROS, MDA, and NO. Furthermore, they significantly regulated oxidative stress and inflammation‐related genes and proteins. Moreover, we elucidated the mechanisms by which these anthocyanins mitigate Cd‐induced cellular damage via classical oxidative and inflammatory pathways. These findings highlight the potential of specific anthocyanin‐rich fruits in reducing Cd‐related health risks and suggest practical applications for their bioactive properties in promoting human health