Multi-omics analyses provide novel biological insights to distinguish lobular ductal types of invasive breast cancers.

BACKGROUND: Invasive lobular carcinoma (ILC) treatment is similar to invasive ductal carcinoma (IDC; now invasive carcinoma-no special type, IBC-NST), based on its intrinsic subtype. However, further investigation is required for an integrative understanding of differentially perturbed molecular patterns and pathways in these histotypes. METHODS: A dataset of 780 IDC and 201 ILC samples from the TCGA-BRCA project for cross-platform multi-omics was analyzed. We leveraged a consensus approach integrating different bioinformatic algorithms to analyze mutations, CNAs, mRNA, miRNA abundance, methylation, and protein abundance to understand the complex crosstalks that distinguish ILC and IDC samples. A histotype-matched comparison was performed. We performed Cox survival analyses for prognosis based on our identified 53 histotype-specific and four discordant genes. RESULTS: Approximately 90% of ILC cases were of the luminal subtype. Somatic mutations in CDH1 were higher in ILC than in IDC (FDR-adjusted p < 0.01). Fifty-three significant oncogenic or tumor-suppressive DEGs were identified in a single histotype. PPAR signaling and lipolysis regulation in adipocytes were significantly enriched in ILC tumors. CDH1 protein had the highest differential abundance (AUC: 0.85). Moreover, BTG2, GSTA2, GPR37L1, and PGBD5 amplification was associated with poorer OS in ILC compared with no alteration. RIMS2, NACA4P, MYC, ZFPM2, and POU5F1B amplification showed a lower overall survival in patients with IDC. miR-195 showed an IDC-specific downregulation, causing overexpression of CCNE1. Integrative multi-omics supervised analysis identified 296 differentially expressed genes that successfully distinguished IDC and ILC histotypes. CONCLUSIONS: Our findings identify novel molecular candidates that potentially drive and modify the disease differentially among these histotypes.

Impact of essential and optional ingredients on microbial and metabolic profiles of kimchi.

This study aimed to examine the impacts of essential and optional ingredients on the microbial and metabolic profiles of kimchi during 100 days of fermentation, using a mix-omics approach. Kimchi manufactured without essential ingredients (e.g., red pepper, garlic, ginger, green onion, and radish) had lower lactic acid content. The absence of garlic was associated with a higher proportion of Latilactobacillus and Lactococcus, while the absence of red pepper was associated with a greater proportion of Leuconostoc than the control group. In addition, red pepper and garlic served as primary determinants of the levels of organic acids and biogenic amines. Sugar was positively correlated with the levels of melibiose, and anchovy sauce was positively correlated with the levels of amino acids such as methionine, leucine, and glycine. These findings contribute to a fundamental understanding of how ingredients influence kimchi fermentation, offering valuable insights for optimizing kimchi production to meet various preferences.

A First Omics Data Integration Approach in Hass Avocados to Evaluate Rootstock-Scion Interactions: From Aerial and Root Plant Growth to Fruit Development.

Grafting, the careful selection of rootstocks and scions, has played a crucial role maintaining Chilean avocado fruit quality standards in a scenario in which climate change and drought-related issues have considerably decreased avocado fruit production in the last fifteen years. The historical use of seedling rootstocks in Chile has experienced a recent shift towards clonal rootstocks, driven by the potential to produce more consistent and predictable crops. This research aims to compare Hass avocado plants grafted on Mexicola seedling and Dusa® clonal rootstocks in a soilless and protected system using (i) a differential expression analysis of root and leaf samples and (ii) a fruit transcriptomic and metabolomic integration analysis to improve our understanding of rootstock-scion interaction and its impact on avocado tree performance and fruit quality. The results demonstrated that no significant transcriptomic and metabolomic differences were identified at fruit level in the ready-to-eat (RTE) stage for Hass avocado fruit from both rootstocks. However, Hass avocados grafted on the clonal rootstock showed greater aerial growth and slightly increased fruit size than the seedling rootstock due to the enrichment of cell wall-remodeling genes as revealed in leaves and fruit at harvest stage.

A multiomics integrative analysis of color de-synchronization with softening of 'Hass' avocado fruit: A first insight into a complex physiological disorder.

Exocarp color de-synchronization with softening of 'Hass' avocado is a relevant recurrent problem for the avocado supply chain. This study aimed to unravel the mechanisms driving this de-synchronization integrating omics datasets from avocado exocarp of different storage conditions and color phenotypes. In addition, we propose potential biomarkers to predict color synchronized/de-synchronized fruit. Integration of transcriptomics, proteomics and metabolomics and network analysis revealed eight transcription factors associated with differentially regulated genes between regular air (RA) and controlled atmosphere (CA) and twelve transcription factors related to avocado fruit color de-synchronization control in ready-to-eat stage. CA was positively correlated to auxins, ethylene, cytokinins and brassinosteroids-related genes, while RA was characterized by enrichment of cell wall remodeling and abscisic acid content associated genes. At ready-to-eat higher contents of flavonoids, abscisic acid and brassinosteroids were associated with color-softening synchronized avocados. In contrast, de-synchronized fruit revealed increases of jasmonic acid, salicylic acid and auxin levels.

Multivariate Analysis with the R Package mixOmics.

The high-dimensional nature of proteomics data presents challenges for statistical analysis and biological interpretation. Multivariate analysis, combined with insightful visualization can help to reveal the underlying patterns in complex biological data. This chapter introduces the R package mixOmics which focuses on data exploration and integration. We first introduce methods for single data sets: both Principal Component Analysis, which can identify the patterns of variance present in data, and sparse Partial Least Squares Discriminant Analysis, which aims to identify variables that can classify samples into known groups. We then present integrative methods with Projection to Latent Structures and further extensions for discriminant analysis. We illustrate each technique on a breast cancer multi-omics study and provide the R code and data as online supplementary material for readers interested in reproducing these analyses.

Multi-Omics Reveals Different Strategies in the Immune and Metabolic Systems of High-Yielding Strains of Laying Hens.

Lohmann Brown (LB) and Lohmann Selected Leghorn (LSL) are two commercially important laying hen strains due to their high egg production and excellent commercial suitability. The present study integrated multiple data sets along the genotype-phenotype map to better understand how the genetic background of the two strains influences their molecular pathways. In total, 71 individuals were analyzed (LB, n = 36; LSL, n = 35). Data sets include gut miRNA and mRNA transcriptome data, microbiota composition, immune cells, inositol phosphate metabolites, minerals, and hormones from different organs of the two hen strains. All complex data sets were pre-processed, normalized, and compatible with the mixOmics platform. The most discriminant features between two laying strains included 20 miRNAs, 20 mRNAs, 16 immune cells, 10 microbes, 11 phenotypic traits, and 16 metabolites. The expression of specific miRNAs and the abundance of immune cell types were related to the enrichment of immune pathways in the LSL strain. In contrast, more microbial taxa specific to the LB strain were identified, and the abundance of certain microbes strongly correlated with host gut transcripts enriched in immunological and metabolic pathways. Our findings indicate that both strains employ distinct inherent strategies to acquire and maintain their immune and metabolic systems under high-performance conditions. In addition, the study provides a new perspective on a view of the functional biodiversity that emerges during strain selection and contributes to the understanding of the role of host-gut interaction, including immune phenotype, microbiota, gut transcriptome, and metabolome.

Volatile fingerprint of food products with untargeted SIFT-MS data coupled with mixOmics methods for profile discrimination: Application case on cheese.

Volatile organic compounds (VOCs) emitted by food products are decisive for the perception of aroma and taste. The analysis of gaseous matrices is traditionally done by detection and quantification of few dozens of characteristic markers. Emerging direct injection mass spectrometry technologies offer rapid analysis based on a soft ionisation of VOCs without previous separation. The recent increase of selectivity offered by the use of several precursor ions coupled with untargeted analysis increases the potential power of these instruments. However, the analysis of complex gaseous matrix results in a large number of ion conflicts, making the quantification of markers difficult, and in a large volume of data. In this work, we present the exploitation of untargeted SIFT-MS volatile fingerprints of ewe PDO cheeses in a real farm model, using mixOmics methods allowing us to illustrate the typicality, the manufacturing processes reproducibility and the impact of the animals' diet on the final product.

Multi-Transcript Level Profiling Revealed Distinct mRNA, miRNA, and tRNA-Derived Fragment Bio-Signatures for Coping Behavior Linked Haplotypes in HPA Axis and Limbic System.

The molecular basis of porcine coping behavior (CB) relies on a sophisticated interplay of genetic and epigenetic features. Deep sequencing technologies allowed the identification of a plethora of new regulatory small non-coding RNA (sncRNA). We characterized mRNA and sncRNA profiles of central parts of the physiological stress response system including amygdala, hippocampus, hypothalamus and adrenal gland using systems biology for integration. Therefore, ten each of high- (HR) and low- (LR) reactive pigs (n = 20) carrying a CB associated haplotype in a prominent QTL-region on SSC12 were selected for mRNA and sncRNA expression profiling. The molecular markers related to the LR group included ATP1B2, MPDU1, miR-19b-5p, let-7g-5p, and 5'-tiRNA Leu in the adrenal gland, miR-194a-5p, miR-125a-5p, miR-7-1-5p, and miR-107-5p in the hippocampus and CBL and PVRL1 in the hypothalamus. Interestingly, amygdalae of the LR group showed 5'-tiRNA and 5'-tRF (5'-tRF Lys , 5'-tiRNA Lys , 5'-tiRNA Cys , and 5'-tiRNA Gln ) enrichment. Contrarily, molecular markers associated with the HR group encompassed miR-26b-5p, tRNA Arg , tRNA GlyiF in the adrenal gland, IGF1 and APOD in the amygdala and PBX1, TOB1, and C18orf1 in the hippocampus and miR-24 in the hypothalamus. In addition, hypothalami of the HR group were characterized by 3'-tiRNA enrichment (3'-tiRNAGln, 3'-tiRNA Asn , 3'-tiRNA Val , 3'-tRF Pro , 3'-tiRNA Cys , and 3'-tiRNA Ala ) and 3'-tRFs enrichment (3'-tRF Asn , 3'-tRF Glu , and 3'-tRF Val ). These evidence suggest that tRNA-derived fragments and their cleavage activity are a specific marker for coping behavior. Data integration revealed new bio-signatures of important molecular interactions on a multi-transcript level in HPA axis and limbic system of pigs carrying a CB-associated haplotype.

Covariate random effects on the CD4 count variation during HIV disease progression in women.

Purpose: To investigate the variation in CD4 count between HIV positive patients due to clinical covariates at each phase of the HIV disease progression. Patients and methods: The Centre for the AIDS Programme of Research in South Africa (CAPRISA) conducted different studies in which female patients were initially enrolled in HIV negative cohorts (phase 1). Seroconverts were further followed-up weekly to fortnightly visits up to 3 months (phase 2: acute infection), monthly visits from 3 to 12 months (phase 3: early infection), quarterly visits thereafter (phase 4: established infection) until antiretroviral therapy (ART) initiation (phase 5). Results: Eighteen out of the 46 CD4 count covariates investigated were significant. Low average CD4 counts at acute and early phase entry improved at a faster rate than entries at higher average CD4 count. During therapy, all the 18 covariates induced significantly different patients' average CD4 counts. The rate of change of CD4 count greatly varied in response to lactate dehydrogenase during the acute phase. Red blood cells increase resulted in the patients' CD4 counts approaching a common higher level during the early phase. During therapy, the already high CD4 counts improved faster than lower ones in response to the red blood cells increase. As the monocytes increased, patients with lower average CD4 counts became worse than those with higher average CD4 counts. Conclusion: Changes in the covariates measurements either induced no variation effects in certain phases or improved the CD4 count at a faster rate for those patients whose average CD4 was already high or worsen the CD4 level which was already low or caused the patients' CD4 counts to approach the same level - higher or lower than the general cohort. The studied covariates induced wide variations in the CD4 count between HIV positive patients during the ART phase.

Detecting Pulmonary Oxygen Toxicity Using eNose Technology and Associations between Electronic Nose and Gas Chromatography-Mass Spectrometry Data.

Exposure to oxygen under increased atmospheric pressures can induce pulmonary oxygen toxicity (POT). Exhaled breath analysis using gas chromatography-mass spectrometry (GC-MS) has revealed that volatile organic compounds (VOCs) are associated with inflammation and lipoperoxidation after hyperbaric-hyperoxic exposure. Electronic nose (eNose) technology would be more suited for the detection of POT, since it is less time and resource consuming. However, it is unknown whether eNose technology can detect POT and whether eNose sensor data can be associated with VOCs of interest. In this randomized cross-over trial, the exhaled breath from divers who had made two dives of 1 h to 192.5 kPa (a depth of 9 m) with either 100% oxygen or compressed air was analyzed, at several time points, using GC-MS and eNose. We used a partial least square discriminant analysis, eNose discriminated oxygen and air dives at 30 min post dive with an area under the receiver operating characteristics curve of 79.9% (95%CI: 61.1-98.6; p = 0.003). A two-way orthogonal partial least square regression (O2PLS) model analysis revealed an R² of 0.50 between targeted VOCs obtained by GC-MS and eNose sensor data. The contribution of each sensor to the detection of targeted VOCs was also assessed using O2PLS. When all GC-MS fragments were included in the O2PLS model, this resulted in an R² of 0.08. Thus, eNose could detect POT 30 min post dive, and the correlation between targeted VOCs and eNose data could be assessed using O2PLS.

Mixomics analysis of breast cancer: Long non-coding RNA linc01561 acts as ceRNA involved in the progression of breast cancer.

OBJECTIVE: This study aimed at finding the long non-coding RNA (lncRNA), miRNA and mRNA which played critical roles in breast cancer (BrCa) by using mixOmics R package. METHOD: The BrCa dataset were obtained from TCGA and then analyzed using "DESeq2" R package. Multivariate analyses were performed with the "mixOmics" R package and the first component of the stacked partial least-Squares discriminant analysis results were used for searching the interested lncRNA, miRNA and mRNA. qRT-PCR was applied to identify the bioinformatics results in four BrCa cell lines (MCF7, BT-20, ZR-75-1, and MX-1) and the breast epithelial cell line MCF-10 A. Then cells (MCF-1 and MX-1) were transfected with si-linc01561, miR-145-5p mimics and si-MMP11 to further investigate the effects of linc01561, miR-145-5p and MMP11 on the BrCa cells proliferation and apoptosis. RESULTS: MixOmics results showed that linc01561, miR-145-5p and MMP11 might play important roles in BrCa. qRT-PCR results identified that in BrCa cell lines, linc01561 and MMP11 were higher expressed while miR-145-5p was lower expressed compared with those in epithelial cell line. The linc01561 inhibition elevated miR-145-5p expression and then suppressed MMP11 expression. Moreover, linc01561 inhibition suppressed the BrCa cells proliferation and promoted the apoptosis, which was realized by up-regulating expression of miR-145-5p and down-regulating expression of MMP11. CONCLUSION: In summary, the findings of this study, based on ceRNA theory, combining the research foundation of miR-145-5p and MMP11, and taking linc01561 as a new study point, provide new insight into molecular-level reversing proliferation and apoptosis of BrCa.