Integrative Multi-omics Analysis Research Articles

Correction: Tang et al. Integrated Multi-Omics Analysis Reveals Mountain-Cultivated Ginseng Ameliorates Cold-Stimulated Steroid-Resistant Asthma by Regulating Interactions among Microbiota, Genes, and Metabolites. Int. J. Mol. Sci. 2024, 25, 9110

In the original publication [...]

Multi-omics analysis reveals regime shifts in the gastrointestinal ecosystem in chickens following anticoccidial vaccination and Eimeria tenella challenge.

Coccidiosis, caused by Eimeria parasites, significantly impacts poultry farm economics and animal welfare. Beyond its direct impact on health, Eimeria infection disrupts enteric microbial populations leading to dysbiosis and increases vulnerability to secondary diseases such as necrotic enteritis, caused by Clostridium perfringens. The impact of Eimeria infection or anticoccidial vaccination on host gastrointestinal phenotypes and enteric microbiota remains understudied. In this study, the metabolomic profiles and microbiota composition of chicken caecal tissue and contents were evaluated concurrently during a controlled experimental vaccination and challenge trial. Cobb500 broilers were vaccinated with a Saccharomyces cerevisiae-vectored anticoccidial vaccine and challenged with 15,000 Eimeria tenella oocysts. Assessment of caecal pathology and quantification of parasite load revealed correlations with alterations to caecal microbiota and caecal metabolome linked to infection and vaccination status. Infection heightened microbiota richness with increases in potentially pathogenic species, while vaccination elevated beneficial Bifidobacterium. Using a multi-omics factor analysis, data on caecal microbiota and metabolome were integrated and distinct profiles for healthy, infected, and recovering chickens were identified. Healthy and recovering chickens exhibited higher vitamin B metabolism linked to short-chain fatty acid-producing bacteria, whereas essential amino acid and cell membrane lipid metabolisms were prominent in infected and vaccinated chickens. Notably, vaccinated chickens showed distinct metabolites related to the enrichment of sphingolipids, important components of nerve cells and cell membranes. Our integrated multi-omics model revealed latent biomarkers indicative of vaccination and infection status, offering potential tools for diagnosing infection, monitoring vaccination efficacy, and guiding the development of novel treatments or controls.IMPORTANCEAdvances in anticoccidial vaccines have garnered significant attention in poultry health management. However, the intricacies of vaccine-induced alterations in the chicken gut microbiome and its subsequent impact on host metabolism remain inadequately explored. This study delves into the metabolic and microbiotic shifts in chickens post-vaccination, employing a multi-omics integration analysis. Our findings highlight a notable synergy between the microbiome composition and host-microbe interacted metabolic pathways in vaccinated chickens, differentiating them from infected or non-vaccinated cohorts. These insights pave the way for more targeted and efficient approaches in poultry disease control, enhancing both the efficacy of vaccines and the overall health of poultry populations.

Ginsenoside compound K alleviates D-galactose-induced mild cognitive impairment by modulating gut microbiota-mediated short-chain fatty acid metabolism.

The occurrence and progression of mild cognitive impairment (MCI) are closely related to dysbiosis of the gut microbiota. Ginsenoside compound K (CK), a bioactive component of ginseng, has been shown to alleviate gut microbiota dysbiosis and neural damage. However, the mechanisms by which CK regulates the gut microbiota to improve MCI remain unexplored. In this study, an MCI mouse model induced by D-galactose was used, and 16S rRNA gene sequencing, metabolomics, transcriptomics, and integrative multi-omics analyses were employed to investigate the potential mechanisms by which CK alleviates MCI through modulation of the gut microbiota. The results demonstrated that CK repaired intestinal barrier dysfunction caused by MCI, improved blood-brain barrier (BBB) integrity, inhibited activation of microglial cells and astrocytes, and significantly ameliorated MCI. Furthermore, CK enhanced gut microbiota diversity, notably enriched beneficial bacteria such as Akkermansia, and modulated the levels of short-chain fatty acids (SCFAs), particularly increasing propionate, thereby alleviating gut microbiota dysbiosis caused by MCI. Germ-free experiments confirmed that gut microbiota is a key factor for ginsenoside CK in relieving MCI. Further investigation revealed that CK regulated the TLR4-MyD88-NF-κB signaling pathway through modulation of gut microbiota-mediated propionate metabolism, significantly reducing systemic inflammation and alleviating MCI. Our findings provide a new theoretical basis for using CK as a potential means of modulating the gut microbiota for the treatment of MCI.

The chromosome-level genome provides insights into the evolution and adaptation of extreme aggression.

Extremely aggressive behavior, as the special pattern, is rare in most species and characteristic as contestants severely injured or killed ending the combat. Current studies of extreme aggression are mainly from the perspectives of behavioral ecology and evolution, while lacked the aspects of molecular evolutionary biology. Here, a high-quality chromosome-level genome of the parasitoid Anastatus disparis was provided, which the males exhibit extreme mate-competition aggression. The integrated multiomics analysis highlighted that neurotransmitter dopamine overexpression, energy metabolism (especially from lipid) and antibacterial activity are likely major aspects of evolutionary formation and adaptation for extreme aggression in A. disparis. Conclusively, our study provided new perspectives for molecular evolutionary studies of extreme aggression as well as a valuable genomic resource in Hymenoptera.

Identification of potential therapeutic targets for nonischemic cardiomyopathy in European ancestry: an integrated multiomics analysis

BackgroundNonischemic cardiomyopathy (NISCM) is a clinical challenge with limited therapeutic targets. This study aims to identify promising drug targets for NISCM.MethodsWe utilized cis-pQTLs from the deCODE study, which includes data from 35,559 Icelanders, and SNPs from the FinnGen study, which includes data from 1,754 NISCM cases and 340,815 controls of Finnish ancestry. Mendelian randomization (MR) analysis was performed to estimate the causal relationship between circulating plasma protein levels and NISCM risk. Proteins with significant associations underwent false discovery rate (FDR) correction, followed by Bayesian colocalization analysis. The expression of top two proteins, LILRA5 and NELL1, was further analyzed using various NISCM datasets. Descriptions from the Human Protein Atlas (HPA) validated protein expression. The impact of environmental exposures on LILRA5 was assessed using the Comparative Toxicogenomics Database (CTD), and molecular docking identified the potential small molecule interactions.ResultsMR analysis identified 255 circulating plasma proteins associated with NISCM, with 16 remaining significant after FDR correction. Bayesian colocalization analysis identified LILRA5 and NELL1 as significant, with PP.H4 > 0.8. LILRA5 has a protective effect (OR = 0.758, 95% CI, 0.670–0.857) while NELL1 displays the risk effect (OR = 1.290, 95% CI, 1.199–1.387) in NISCM. Decreased LILRA5 expression was found in NISCM such as diabetic, hypertrophic, dilated, and inflammatory cardiomyopathy, while NELL1 expression increased in hypertrophic cardiomyopathy. HPA data indicated high LILRA5 expression in neutrophils, macrophages and endothelial cells within normal heart and limited NELL1 expression. Immune infiltration analysis revealed decreased neutrophil in diabetic cardiomyopathy. CTD analysis identified several small molecules that affect LILRA5 mRNA expression. Among these, Estradiol, Estradiol-3-benzoate, Gadodiamide, Topotecan, and Testosterone were found to stably bind to the LILRA5 protein at the conserved VAL-15 or THR-133 residues in the Ig-like C2 domain.ConclusionBased on European Ancestry Cohort, this study reveals that LILRA5 and NELL1 are potential therapeutic targets for NISCM, with LILRA5 showing particularly promising prospects in diabetic cardiomyopathy. Several small molecules interact with LILRA5, implying potential clinical implication.

Biomarkers for Health Functional Foods in Metabolic Dysfunction-Associated Steatotic Liver Disorder (MASLD) Prevention: An Integrative Analysis of Network Pharmacology, Gut Microbiota, and Multi-Omics

Biomarkers for Health Functional Foods in Metabolic Dysfunction-Associated Steatotic Liver Disorder (MASLD) Prevention: An Integrative Analysis of Network Pharmacology, Gut Microbiota, and Multi-Omics

M3NetFlow: A novel multi-scale multi-hop graph AI model for integrative multi-omic data analysis.

Multi-omic data-driven studies, characterizing complex disease signaling system from multiple levels, are at the forefront of precision medicine and healthcare. The integration and interpretation of multi-omic data are essential for identifying molecular targets and deciphering core signaling pathways of complex diseases. However, it remains an open problem due the large number of biomarkers and complex interactions among them. In this study, we propose a novel Multi-scale Multi-hop Multi-omic graph model, M3NetFlow, to facilitate generic multi-omic data analysis to rank targets and infer core signaling flows/pathways. To evaluate M3NetFlow, we applied it in two independent multi-omic case studies: 1) uncovering mechanisms of synergistic drug combination response (defined as anchor-target guided learning), and 2) identifying biomarkers and pathways of Alzheimer's disease (AD). The evaluation and comparison results showed M3NetFlow achieves the best prediction accuracy (accurate), and identifies a set of essential targets and core signaling pathways (interpretable). The model can be directly applied to other multi-omic data-driven studies. The code is publicly accessible at: https://github.com/FuhaiLiAiLab/M3NetFlow.

The non-canonical BAF chromatin remodeling complex is a novel target of spliceosome dysregulation in SF3B1-mutated chronic lymphocytic leukemia.

SF3B1 mutations are recurrent in chronic lymphocytic leukemia (CLL), particularly enriched in clinically aggressive stereotyped subset #2. To investigate their impact, we conducted RNA-sequencing of 18 SF3B1MUT and 17 SF3B1WT subset #2 cases and identified 80 significant alternative splicing events (ASEs). Notable ASEs concerned exon inclusion in the non-canonical BAF (ncBAF) chromatin remodeling complex subunit, BRD9, and splice variants in eight additional ncBAF complex interactors. Long-read RNA-sequencing confirmed the presence of splice variants, and extended analysis of 139 CLL cases corroborated their association with SF3B1 mutations. Overexpression of SF3B1K700E induced exon inclusion in BRD9, resulting in a novel splice isoform with an alternative C-terminus. Protein interactome analysis of the BRD9 splice isoform revealed augmented ncBAF complex interaction, while exhibiting decreased binding of auxiliary proteins, including SPEN, BRCA2, and CHD9. Additionally, integrative multi-omics analysis identified a ncBAF complex-bound gene quartet on chromosome 1 with higher expression levels and more accessible chromatin in SF3B1MUT CLL. Finally, Cancer Dependency Map analysis and BRD9 inhibition displayed BRD9 dependency and sensitivity in cell lines and primary CLL cells. In conclusion, spliceosome dysregulation caused by SF3B1 mutations leads to multiple ASEs and an altered ncBAF complex interactome, highlighting a novel pathobiological mechanism in SF3B1MUT CLL.

The chromatin accessibility landscape of mouse oocytes during configuration transition.

The transition of chromatin configuration in mammalian oocytes from a non-surrounded nucleolus (NSN) to a surrounded nucleolus (SN) is critical for acquiring the developmental competence. However, the genomic and epigenomic features underlying this process remain poorly understood. In the present study, we first establish the chromatin accessibility landscape of mouse oocytes from NSN to SN stage. Through the integrative analysis of multi-omics, we find that the establishment of DNA methylation in oocytes is independent of the dynamics of chromatin accessibility. In contrast, histone H3K4me3 status is closely associated with the dynamics of accessible regions during configuration transition. Furthermore, by focusing on the actively transcribed genes in NSN and SN oocytes, we discover that chromatin accessibility coupled with histone methylation (H3K4me3 and H3K27me3) participates in the transcriptional control during phase transition. In sum, our data provide a comprehensive resource for probing configuration transition in oocytes, and offer insights into the mechanisms determining chromatin dynamics and oocyte quality.

Multi-omics analysis provides new insights into mechanism of didymin on non-alcoholic fatty liver disease in rats

BackgroundNon-alcoholic fatty liver disease (NAFLD) is one of the most common liver diseases accompanied by lipid and glucose metabolism disorder. Didymin has been reported to have various hepatoprotective effects, however, its potential effects and mechanisms on NAFLD remain unclear from the perspective of the whole. PurposeTo investigate the underlying mechanism of didymin against NAFLD using multi-omics technologies. MethodsRats were fed with a high-fat diet (HFD) for 8 weeks to induce NAFLD, followed by didymin treatment for 8 weeks. Next, biochemical analysis and histopathological examinations were performed to evaluate the effects of didymin. The key regulating pathways were predicted using transcriptomics, metabolomics and proteomics, and the target pathways were then verified by detecting the key genes/proteins using various experiments. ResultsDidymin markedly mitigated liver injury and excessive lipid droplet accretion. An integrative multi-omics analysis suggested that the PPAR signaling cascade and insulin signaling pathway might serve as pivotal mechanisms underlying the modulation of lipid and glucose homeostasis by didymin. Further dissection identified five pivotal genes (PPARα, PPARβ, FABP4, ANGPTL4, and PLIN2) and four genes (HK1, HK3, GCK, and PTPN1) as potential hubs within these pathways. Subsequent validation experiments, including qPCR and Western blot, demonstrated upregulated expression of PPARα and PPARβ, indicating the activation of the PPAR pathway by didymin. Concurrently, didymin appeared to modulate the insulin signaling pathway, as evidenced by the upregulated expression of HK1 and downregulated expression of PTPN1. Notably, the manipulation of PPARα, PPARβ, and PTPN1 expression in LO2 cells through silence or overexpression confirmed that didymin significantly reduced lipid accumulation, with its molecular targets likely being the PPAR and insulin pathways. ConclusionsOur findings demonstrate that didymin has a protective effect on NAFLD, and its underlying mechanism may be associated with the regulation of the PPAR and insulin signaling pathways.

MosGraphFlow: a novel integrative graph AI model mining disease targets from multi-omic data.

Multi-omic data can better characterize complex cellular signaling pathways from multiple views compared to individual omic data. However, integrative multi-omic data analysis to rank key disease biomarkers and infer core signaling pathways remains an open problem. In this study, our novel contributions are that we developed a novel graph AI model, mosGraphFlow , for analyzing multi-omic signaling graphs (mosGraphs), 2) analyzed multi-omic mosGraph datasets of AD, and 3) identified, visualized and evaluated a set of AD associated signaling biomarkers and network. The comparison results show that the proposed model not only achieves the best classification accuracy but also identifies important AD disease biomarkers and signaling interactions. Moreover, the signaling sources are highlighted at specific omic levels to facilitate the understanding of the pathogenesis of AD. The proposed model can also be applied and expanded for other studies using multi-omic data. Model code is accessible via GitHub: https://github.com/FuhaiLiAiLab/mosGraphFlow.

Genome-wide RNA-seq, DNA methylation and small RNA-seq analysis unraveled complex gene regulatory networks in psoriasis pathogenesis

Psoriasis is a complex inflammatory skin disease characterized by reversible albeit relapsing red scaly plaques in the skin of a patient. In addition to the genetic predisposition, involvement of epigenetic and non-coding RNAs have also been liked with the disease. Nevertheless, any comprehensive study involving transcriptomic, small-RNA and DNA methylation at the genomic level from same patients is lacking. To investigate the complex regulation of molecular pathways in psoriasis, we carried out multi-omics integrative analysis of RNA-sequencing, small RNA-sequencing and DNA methylation profiling from the psoriatic and adjacent normal skin tissues. Our multi-omics analysis identified the genes and biological processes regulated either independently or in combination by DNA methylation and microRNAs. We identified miRNAs that specifically regulated keratinocyte hyper-proliferation, and cell cycle progression and checkpoint signaling in psoriasis. On contrary, DNA methylation was found to be more predominant in regulating immune and inflammatory responses, another causative factor in psoriasis pathogenesis. Many characteristic pathways in psoriasis e.g., Th17 cell differentiation and JAK-STAT signaling, were found to be regulated by both miRNAs and DNA methylation. We carried out functional characterization of a downregulated miRNA hsa-let-7c-5p, predicted to target upregulated genes in psoriasis involved in cell cycle processes, Th17 cell differentiation and JAK-STAT signaling pathways. Overexpression of hsa-let-7c-5p in keratinocytes caused the downregulation of its target genes, resulting in reduced cell proliferation and migration rates, demonstrating potential of miRNAs in regulating psoriasis pathogenesis. In conclusion, our findings identified distinct and shared gene-networks regulated by DNA methylation and miRNAs of a complex disease with reversible phenotype.

Maternal immune cell gene expression associates with maternal gut microbiome, milk composition and infant gut microbiome

Background– Pre-pregnancy overweight and obesity promote deleterious health impacts on both mothers during pregnancy and the offspring. Significant changes in the maternal peripheral blood mononuclear cells (PBMCs) gene expression due to obesity are well-known. However, the impact of pre-pregnancy overweight on immune cell gene expression during pregnancy and its association with maternal and infant outcomes is not well explored. Methods– Blood samples were collected from healthy normal weight (NW, pre-pregnancy BMI 18.5-24.9) or overweight (OW, pre-pregnancy BMI 25-29.9) 2nd parity pregnant women at 12, 24 and 36 weeks of pregnancy. PBMCs were isolated from the blood and subjected to mRNA sequencing. Maternal and infant microbiota were analyzed by 16S rRNA gene sequencing. Integrative multi-omics data analysis was performed to evaluate the association of gene expression with maternal diet, gut microbiota, milk composition, and infant gut microbiota. ResultsGene expression analysis revealed that 453 genes were differentially expressed in the OW women compared to NW women at 12 weeks of pregnancy, out of which 354 were upregulated and 99 were downregulated. Several up-regulated genes in the OW group were enriched in inflammatory, chemokine-mediated signaling and regulation of interleukin-8 production-related pathways. At 36 weeks of pregnancy healthy eating index score was positively associated with several genes that include, DTD1, ELOC, GALNT8, ITGA6-AS1, KRT17P2, NPW, POT1-AS1 and RPL26. In addition, at 36 weeks of pregnancy, genes involved in adipocyte functions, such as NG2 and SMTNL1, were negatively correlated to human milk 2’FL and total fucosylated oligosaccharides content collected at 1 month postnatally. Furthermore, infant Akkermansia was positively associated with maternal PBMC anti-inflammatory genes that include CPS1 and RAB7B, at 12 and 36 weeks of pregnancy. Conclusions– These findings suggest that prepregnancy overweight impacts the immune cell gene expression profile, particularly at 12 weeks of pregnancy. Furthermore, deciphering the complex association of PBMC’s gene expression levels with maternal gut microbiome and milk composition and infant gut microbiome may aid in developing strategies to mitigate obesity-mediated effects.

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.

Integrated Multi-Omics Analysis Reveals Mountain-Cultivated Ginseng Ameliorates Cold-Stimulated Steroid-Resistant Asthma by Regulating Interactions among Microbiota, Genes, and Metabolites.

Steroid-resistant asthma (SRA), resisting glucocorticoids such as dexamethasone (DEX), is a bottleneck in the treatment of asthma. It is characterized by a predominantly neutrophilic inflammatory subtype and is prone to developing into severe refractory asthma and fatal asthma. Currently, there is a lack of universally effective treatments for SRA. Moreover, since cold stimulation does increase the risk of asthma development and exacerbate asthma symptoms, the treatment of cold-stimulated SRA (CSRA) will face greater challenges. To find effective new methods to ameliorate CSRA, this study established a CSRA mouse model of allergic airway inflammation mimicking human asthma for the first time and evaluated the alleviating effects of 80% ethanol extract of mountain-cultivated ginseng (MCG) based on multi-omics analysis. The results indicate that cold stimulation indeed exacerbated the SRA-related symptoms in mice; the DEX individual treatment did not show a satisfactory effect; while the combination treatment of DEX and MCG could dose-dependently significantly enhance the lung function; reduce neutrophil aggregation; decrease the levels of LPS, IFN-γ, IL-1β, CXCL8, and IL-17; increase the level of IL-10; alleviate the inflammatory infiltration; and decrease the mucus secretion and the expression of MUC5AC. Moreover, the combination of DEX and high-dose (200 mg/kg) MCG could significantly increase the levels of tight junction proteins (TJs), regulate the disordered intestinal flora, increase the content of short-chain fatty acids (SCFAs), and regulate the abnormal gene profile and metabolic profile. Multi-omics integrated analysis showed that 7 gut microbes, 34 genes, 6 metabolites, and the involved 15 metabolic/signaling pathways were closely related to the pharmacological effects of combination therapy. In conclusion, integrated multi-omics profiling highlighted the benefits of MCG for CSRA mice by modulating the interactions of microbiota, genes, and metabolites. MCG shows great potential as a functional food in the adjuvant treatment of CSRA.

Multi-Omics Integrative Analysis to Reveal the Impacts of Shewanella algae on the Development and Lifespan of Marine Nematode Litoditis marina.

Understanding how habitat bacteria affect animal development, reproduction, and aging is essential for deciphering animal biology. Our recent study showed that Shewanella algae impaired Litoditis marina development and lifespan, compared with Escherichia coli OP50 feeding; however, the underlying mechanisms remain unclear. Here, multi-omics approaches, including the transcriptome of both L. marina and bacteria, as well as the comparative bacterial metabolome, were utilized to investigate how bacterial food affects animal fitness and physiology. We found that genes related to iron ion binding and oxidoreductase activity pathways, such as agmo-1, cdo-1, haao-1, and tdo-2, were significantly upregulated in L. marina grown on S. algae, while extracellular structural components-related genes were significantly downregulated. Next, we observed that bacterial genes belonging to amino acid metabolism and ubiquinol-8 biosynthesis were repressed, while virulence genes were significantly elevated in S. algae. Furthermore, metabolomic analysis revealed that several toxic metabolites, such as puromycin, were enriched in S. algae, while many nucleotides were significantly enriched in OP50. Moreover, we found that the "two-component system" was enriched in S. algae, whereas "purine metabolism" and "one-carbon pool by folate" were significantly enriched in E. coli OP50. Collectively, our data provide new insights to decipher how diet modulates animal fitness and biology.

Multi-omics integrative analysis provided new insights into alkaline stress in alfalfa

Saline-alkali stress is one of the main abiotic stresses that limits plant growth. Salt stress has been widely studied, but alkaline salt degradation caused by NaHCO3 has rarely been investigated. In the present study, the alfalfa cultivar ‘Zhongmu No. 1′ was treated with 50 mM NaHCO3 (0, 4, 8, 12 and 24 h) to study the resulting enzyme activity and changes in mRNA, miRNA and metabolites in the roots. The results showed that the enzyme activity changed significantly after alkali stress treatment. The genomic analysis revealed 14,970 differentially expressed mRNAs (DEMs), 53 differentially expressed miRNAs (DEMis), and 463 differentially accumulated metabolites (DAMs). Combined analysis of DEMs and DEMis revealed that 21 DEMis negatively regulated 42 DEMs. In addition, when combined with Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of DEMs and DAMs, we found that phenylpropanoid biosynthesis, flavonoid biosynthesis, starch and sucrose metabolism and plant hormone signal transduction played important roles in the alkali stress response. The results of this study further elucidated the regulatory mechanism underlying the plant response to alkali stress and provided valuable information for the breeding of new saline-alkaline tolerance plant varieties.

Intra-tumoral and peripheral blood TIGIT and PD-1 as immune biomarkers in nodular lymphocyte predominant Hodgkin lymphoma.

In classical Hodgkin lymphoma (cHL), responsiveness to immune-checkpoint blockade (ICB) is associated with specific tumor microenvironment (TME) and peripheral blood features. The role of ICB in nodular lymphocyte predominant Hodgkin lymphoma (NLPHL) is not established. To gain insights into its potential in NLPHL, we compared TME and peripheral blood signatures between HLs using an integrative multiomic analysis. A discovery/validation approach in 121 NLPHL and 114 cHL patients highlighted >2-fold enrichment in programmed cell death-1 (PD-1) and T-cell Ig and ITIM domain (TIGIT) gene expression for NLPHL versus cHL. Multiplex imaging showed marked increase in intra-tumoral protein expression of PD-1+ (and/or TIGIT+) CD4+ T-cells and PD-1+CD8+ T-cells in NLPHL compared to cHL. This included T-cells that rosetted with lymphocyte predominant (LP) and Hodgkin Reed-Sternberg (HRS) cells. In NLPHL, intra-tumoral PD-1+CD4+ T-cells frequently expressed TCF-1, a marker of heightened T-cell response to ICB. The peripheral blood signatures between HLs were also distinct, with higher levels of PD-1+TIGIT+ in TH1, TH2, and regulatory CD4+ T-cells in NLPHL versus cHL. Circulating PD-1+CD4+ had high levels of TCF-1. Notably, in both lymphomas, highly expanded populations of clonal TIGIT+PD-1+CD4+ and TIGIT+PD-1+CD8+ T-cells in the blood were also present in the TME, indicating that immune-checkpoint expressing T-cells circulated between intra-tumoral and blood compartments. In in vitro assays, ICB was capable of reducing rosette formation around LP and HRS cells, suggesting that disruption of rosetting may be a mechanism of action of ICB in HL. Overall, results indicate that further evaluation of ICB is warranted in NLPHL.

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.

Lipidomic and Proteomic Insights from Extracellular Vesicles in Postmortem Dorsolateral Prefrontal Cortex Reveal Substance Use Disorder-Induced Brain Changes.

Substance use disorder (SUD) significantly increases the risk of neurotoxicity, inflammation, oxidative stress, and impaired neuroplasticity. The activation of inflammatory pathways by substances may lead to glial activation and chronic neuroinflammation, potentially mediated by the release of extracellular particles (EPs), such as extracellular condensates (ECs) and extracellular vesicles (EVs). These particles, which reflect the physiological, pathophysiological, and metabolic states of their cells of origin, might carry molecular signatures indicative of SUD. In particular, our study investigated neuroinflammatory signatures in SUD by isolating EVs from the dorsolateral prefrontal cortex (dlPFC) Brodmann's area 9 (BA9) in postmortem subjects. We isolated BA9-derived EVs from postmortem brain tissues of eight individuals (controls: n=4, SUD: n=4). The EVs were analyzed for physical properties (concentration, size, zeta potential, morphology) and subjected to integrative multi-omics analysis to profile the lipidomic and proteomic characteristics. We assessed the interactions and bioactivity of EVs by evaluating their uptake by glial cells. We further assessed the effects of EVs on complement mRNA expression in glial cells as well as their effects on microglial migration. No significant differences in EV concentration, size, zeta potential, or surface markers were observed between SUD and control groups. However, lipidomic analysis revealed significant enrichment of glycerophosphoinositol bisphosphate (PIP2) in SUD EVs. Proteomic analysis indicates downregulation of SERPINB12, ACYP2, CAMK1D, DSC1, and FLNB, and upregulation of C4A, C3, and ALB in SUD EVs. Gene ontology and protein-protein interactome analyses highlight functions such as cell motility, focal adhesion, and acute phase response signaling that is associated with the identified proteins. Both control and SUD EVs increased C3 and C4 mRNA expression in microglia, but only SUD EVs upregulated these genes in astrocytes. SUD EVs also significantly enhanced microglial migration in a wound healing assay.This study successfully isolated EVs from postmortem brains and used a multi-omics approach to identify EV-associated lipids and proteins in SUD. Elevated C3 and C4 in SUD EVs and the distinct effects of EVs on glial cells suggest a crucial role in acute phase response signaling and neuroinflammation.