Maternal obesity, caused by diets rich in fats and sugars during pregnancy, can predispose offspring to metabolic diseases such as diabetes. We hypothesized that obesity during pregnancy leads to increased DNA methylation and reduced protein expression in factors regulating β-cell function and apoptosis. Female C57BL/6J mice were fed a high-fat diet (HFD; 42% fat content; n = 3) or a control diet (CON; 16% fat content; n = 3) for fourteen weeks before and during pregnancy. Offspring were euthanized at 8 weeks and pancreatic tissue was collected. Isolated DNA was analyzed using whole-genome bisulfite sequencing. Protein expression was quantified using LC-MS. No significant differences in body weight were observed between HFD and control pups (p = 0.10). Whole-genome bisulfite sequencing identified 91,703 and 88,415 differentially methylated regions (DMRs) in CON vs. HFD male and female offspring. A total of 34 and 4 proteins were determined to have changes in expression that correlated with changes in DNA methylation in CON vs. HFD males and females, respectively. The majority of these factors were grouped into the metabolic function category via pathway analyses. This study illustrates the complex relationship between epigenetics, diet, and sex-specific responses, therefore offering insights into potential therapeutic targets and areas for further research.

Sclerotinia sclerotiorum (Ss) is one of the most devastating fungal pathogens, causing huge yield loss in multiple economically important crops including oilseed rape. Plant resistance to Ss pertains to quantitative disease resistance (QDR) controlled by multiple minor genes. Genome-wide identification of genes involved in QDR to Ss is yet to be conducted. In this study, we integrated several assays including genome-wide association study (GWAS), multi-omics co-localization, and machine learning prediction to identify, on a genome-wide scale, genes involved in the oilseed rape QDR to Ss. Employing GWAS and multi-omics co-localization, we identified seven resistance-associated loci (RALs) associated with oilseed rape resistance to Ss. Furthermore, we developed a machine learning algorithm and named it Integrative Multi-Omics Analysis and Machine Learning for Target Gene Prediction (iMAP), which integrates multi-omics data to rapidly predict disease resistance-related genes within a broad chromosomal region. Through iMAP based on the identified RALs, we revealed multiple calcium signaling genes related to the QDR to Ss. Population-level analysis of selective sweeps and haplotypes of variants confirmed the positive selection of the predicted calcium signaling genes during evolution. Overall, this study has developed an algorithm that integrates multi-omics data and machine learning methods, providing a powerful tool for predicting target genes associated with specific traits. Furthermore, it makes a basis for further understanding the role and mechanisms of calcium signaling genes in the QDR to Ss.

A very low calorie ketogenic diet (VLCKD) impacts host metabolism in people marked by an excess of visceral adiposity, and it affects the microbiota composition in terms of taxa presence and relative abundances. As a matter of fact, there is little available literature dealing with microbiota differences in obese patients marked by altered intestinal permeability. With the aim of inspecting consortium members and their related metabolic pathways, we inspected the microbial community profile, together with the set of volatile organic compounds (VOCs) from untargeted fecal and urine metabolomics, in a cohort made of obese patients, stratified based on both normal and altered intestinal permeability, before and after VLCKD administration. Based on the taxa relative abundances, we predicted microbiota-derived metabolic pathways whose variations were explained in light of our cohort symptom picture. A totally different number of statistically significant pathways marked samples with altered permeability, reflecting an important shift in microbiota taxa. A combined analysis of taxa, metabolic pathways, and metabolomic compounds delineates a set of markers that is useful in describing obesity dysfunctions and comorbidities.

Globally, metabolic dysfunction-associated steatotic liver disease (MASLD), previously termed nonalcoholic fatty liver disease (NAFLD), is one of the most common liver disorders and is strongly associated with copper deficiency. To explore the potential effects and mechanisms of Lactiplantibacillus plantarum LPJZ-658, copper deficiency combined with a high-sugar diet-induced MASLD mouse model was utilized in this study. We fed 40-week-old (middle-aged) male C57BL/6 mice a copper-deficient and high-sugar diet for 16 weeks (CuDS), with supplementary LPJZ-658 for the last 6 weeks (CuDS + LPJZ-658). In this study, we measured body weight, liver weight, and serum biochemical markers. Lipid accumulation, histology, lipidomics, and sphingolipid metabolism-related enzyme expression were investigated to analyze liver function. Untargeted metabolomics was used to analyze the serum and the composition and abundance of intestinal flora. In addition, the correlation between differential liver lipid profiles, serum metabolites, and gut flora at the genus level was measured. The results show that LPJZ-658 significantly improves abnormal liver function and hepatic steatosis. The lipidomics analyses and metabolic pathway analysis identified sphingolipid, retinol, and glycerophospholipid metabolism as the most relevant metabolic pathways that characterized liver lipid dysregulation in the CuDS group. Consistently, RT-qPCR analyses revealed that the enzymes catalyzing sphingolipid metabolism that were significantly upregulated in the CuDS group were downregulated by the LPJZ-658 treatment. In addition, the serum metabolomics results indicated that the linoleic acid, taurine and hypotaurine, and ascorbate and aldarate metabolism pathways were associated with CuDS-induced MASLD. Notably, we found that treatment with LPJZ-658 partially reversed the changes in the differential serum metabolites. Finally, LPJZ-658 effectively regulated intestinal flora abnormalities and was significantly correlated with differential hepatic lipid species and serum metabolites. In conclusion, we elucidated the function and potential mechanisms of LPJZ-658 in alleviating copper deficiency combined with sugar-induced middle-aged MASLD and hope this will provide possible treatment strategies for improving MASLD.

The Knight-Alzheimer Disease Research Center (Knight-ADRC) at Washington University in St. Louis has pioneered and led worldwide seminal studies that have expanded our clinical, social, pathological, and molecular understanding of Alzheimer Disease. Over more than 40 years, research volunteers have been recruited to participate in cognitive, neuropsychologic, imaging, fluid biomarkers, genomic and multi-omic studies. Tissue and longitudinal data collected to foster, facilitate, and support research on dementia and aging. The Genetics and high throughput -omics core (GHTO) have collected of more than 26,000 biological samples from 6,625 Knight-ADRC participants. Samples available include longitudinal DNA, RNA, non-fasted plasma, cerebrospinal fluid pellets, and peripheral blood mononuclear cells. The GHTO has performed deep molecular profiling (genomic, transcriptomic, epigenomic, proteomic, and metabolomic) from large number of brain (n = 2,117), CSF (n = 2,012) and blood/plasma (n = 8,265) samples with the goal of identifying novel risk and protective variants, identify novel molecular biomarkers and causal and druggable targets. Overall, the resources available at GHTO support the increase of our understanding of Alzheimer Disease.

BACKGROUND: In recent years, omics technologies have offered an exceptional chance to gain a deeper insight into the structural and functional characteristics of microbial communities. As a result, there is a growing demand for user-friendly, reproducible, and versatile bioinformatic tools that can effectively harness multi-omics data to provide a holistic understanding of microbiomes. Previously, we introduced gNOMO, a bioinformatic pipeline tailored to analyze microbiome multi-omics data in an integrative manner. In response to the evolving demands within the microbiome field and the growing necessity for integrated multi-omics data analysis, we have implemented substantial enhancements to the gNOMO pipeline.
RESULTS: Here, we present gNOMO2, a comprehensive and modular pipeline that can seamlessly manage various omics combinations, ranging from 2 to 4 distinct omics data types, including 16S ribosomal RNA (rRNA) gene amplicon sequencing, metagenomics, metatranscriptomics, and metaproteomics. Furthermore, gNOMO2 features a specialized module for processing 16S rRNA gene amplicon sequencing data to create a protein database suitable for metaproteomics investigations. Moreover, it incorporates new differential abundance, integration, and visualization approaches, enhancing the toolkit for a more insightful analysis of microbiomes. The functionality of these new features is showcased through the use of 4 microbiome multi-omics datasets encompassing various ecosystems and omics combinations. gNOMO2 not only replicated most of the primary findings from these studies but also offered further valuable perspectives.
CONCLUSIONS: gNOMO2 enables the thorough integration of taxonomic and functional analyses in microbiome multi-omics data, offering novel insights in both host-associated and free-living microbiome research. gNOMO2 is available freely at https://github.com/muzafferarikan/gNOMO2.

Intervertebral disc degeneration (IDD) is a highly prevalent musculoskeletal disorder that is associated with considerable morbidity. However, there is currently no drug available that has a definitive therapeutic effect on IDD. In this study, we aimed to identify the molecular features and potential therapeutic targets of IDD through a comprehensive multiomics profiling approach. By integrating transcriptomics, proteomics, and ultrastructural analyses, we discovered dysfunctions in various organelles, including mitochondria, the endoplasmic reticulum, the Golgi apparatus, and lysosomes. Metabolomics analysis revealed a reduction in total phosphatidylcholine (PC) content in IDD. Through integration of multiple omics techniques with disease phenotypes, a pivotal pathway regulated by the lysophosphatidylcholine acyltransferase 1 (LPCAT1)-PC axis was identified. LPCAT1 exhibited low expression levels and exhibited a positive correlation with PC content in IDD. Suppression of LPCAT1 resulted in inhibition of PC synthesis in nucleus pulposus cells, leading to a notable increase in nucleus pulposus cell senescence and damage to cellular organelles. Consequently, PC exhibits potential as a therapeutic agent, as it facilitates the repair of the biomembrane system and alleviates senescence in nucleus pulposus cells via reversal of downregulation of the LPCAT1-PC axis.

UNASSIGNED: Renin-expressing cells are myoendocrine cells crucial for the maintenance of homeostasis. Renin is regulated by cAMP, p300 (histone acetyltransferase p300)/CBP (CREB-binding protein), and Brd4 (bromodomain-containing protein 4) proteins and associated pathways. However, the specific regulatory changes that occur following inhibition of these pathways are not clear.
UNASSIGNED: We treated As4.1 cells (tumoral cells derived from mouse juxtaglomerular cells that constitutively express renin) with 3 inhibitors that target different factors required for renin transcription: H-89-dihydrochloride, PKA (protein kinase A) inhibitor; JQ1, Brd4 bromodomain inhibitor; and A-485, p300/CBP inhibitor. We performed ATAC-seq, single-cell RNA sequencing, CUT&Tag, and chromatin immunoprecipitation sequencing for H3K27ac and p300 binding on biological replicates of treated and control As4.1 cells.
UNASSIGNED: In response to each inhibitor, Ren1 expression was significantly reduced and reversible upon washout. Chromatin accessibility at the Ren1 locus did not markedly change but was globally reduced at distal elements. Inhibition of PKA led to significant reductions in H3K27ac and p300 binding specifically within the Ren1 super-enhancer region. Further, we identified enriched TF (transcription factor) motifs shared across each inhibitory treatment. Finally, we identified a set of 9 genes with putative roles across each of the 3 renin regulatory pathways and observed that each displayed differentially accessible chromatin, gene expression, H3K27ac, and p300 binding at their respective loci.
UNASSIGNED: Inhibition of renin expression in cells that constitutively synthesize and release renin is regulated by an epigenetic switch from an active to poised state associated with decreased cell-cell communication and an epithelial-mesenchymal transition. This work highlights and helps define the factors necessary for renin cells to alternate between myoendocrine and contractile phenotypes.

Recent advances in sequencing, mass spectrometry, and cytometry technologies have enabled researchers to collect multiple \'omics data types from a single sample. These large datasets have led to a growing consensus that a holistic approach is needed to identify new candidate biomarkers and unveil mechanisms underlying disease etiology, a key to precision medicine. While many reviews and benchmarks have been conducted on unsupervised approaches, their supervised counterparts have received less attention in the literature and no gold standard has emerged yet. In this work, we present a thorough comparison of a selection of six methods, representative of the main families of intermediate integrative approaches (matrix factorization, multiple kernel methods, ensemble learning, and graph-based methods). As non-integrative control, random forest was performed on concatenated and separated data types. Methods were evaluated for classification performance on both simulated and real-world datasets, the latter being carefully selected to cover different medical applications (infectious diseases, oncology, and vaccines) and data modalities. A total of 15 simulation scenarios were designed from the real-world datasets to explore a large and realistic parameter space (e.g. sample size, dimensionality, class imbalance, effect size). On real data, the method comparison showed that integrative approaches performed better or equally well than their non-integrative counterpart. By contrast, DIABLO and the four random forest alternatives outperform the others across the majority of simulation scenarios. The strengths and limitations of these methods are discussed in detail as well as guidelines for future applications.

Improved understanding of the complex interaction between plant metabolism, environmental conditions and the plant-associated microbiome requires an interdisciplinary approach: Our hypothesis in our multiomics study posited that several environmental and biotic factors have modulating effects on the microbiome and metabolome of the roots of wild Echium vulgare plants. Furthermore, we postulated reciprocal interactions between the root metabolome and microbiome. We investigated the metabolic content, the genetic variability, and the prokaryotic microbiome in the root systems of wild E. vulgare plants at rosette and flowering stages across six distinct locations. We incorporated the assessment of soil microbiomes and the measurement of selected soil chemical composition factors. Two distinct genetic clusters were determined based on microsatellite analysis without a consistent alignment with the geographical proximity between the locations. The microbial diversity of both the roots of E. vulgare and the surrounding bulk soil exhibited significant divergence across locations, varying soil pH characteristics, and within the identified plant genetic clusters. Notably, acidophilic bacteria were characteristic inhabitants of both soil and roots under acidic soil conditions, emphasizing the close interconnectedness between these compartments. The metabolome of E. vulgare significantly differed between root samples from different developmental stages, geographical locations, and soil pH levels. The developmental stage was the dominant driver of metabolome changes, with significantly higher concentrations of sugars, pyrrolizidine alkaloids, and some of their precursors in rosette stage plant roots. Our study featured the complex dynamics between soil pH, plant development, geographical locations, plant genetics, plant metabolome and microbiome, shedding light on existing knowledge gaps.