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Multi-omics data integration is a term that refers to the process of combining and analyzing data from different omic experimental sources, such as genomics, transcriptomics, methylation assays, and microRNA sequencing, among others. Such data integration approaches have the potential to provide a more comprehensive functional understanding of biological systems and has numerous applications in areas such as disease diagnosis, prognosis and therapy. However, quantitative integration of multi-omic data is a complex task that requires the use of highly specialized methods and approaches. Here, we discuss a number of data integration methods that have been developed with multi-omics data in view, including statistical methods, machine learning approaches, and network-based approaches. We also discuss the challenges and limitations of such methods and provide examples of their applications in the literature. Overall, this review aims to provide an overview of the current state of the field and highlight potential directions for future research.
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BACKGROUND: Tumour dormancy, a resistance mechanism employed by cancer cells, is a significant challenge in cancer treatment, contributing to minimal residual disease (MRD) and potential relapse. Despite its clinical importance, the mechanisms underlying tumour dormancy and MRD remain unclear. In this study, we employed two syngeneic murine models of myeloid leukemia and melanoma to investigate the genetic, epigenetic, transcriptomic and protein signatures associated with tumour dormancy. We used a multiomics approach to elucidate the molecular mechanisms driving MRD and identify potential therapeutic targets. RESULTS: We conducted an in-depth omics analysis encompassing whole-exome sequencing (WES), copy number variation (CNV) analysis, chromatin immunoprecipitation followed by sequencing (ChIP-seq), transcriptome and proteome investigations. WES analysis revealed a modest overlap of gene mutations between melanoma and leukemia dormancy models, with a significant number of mutated genes found exclusively in dormant cells. These exclusive genetic signatures suggest selective pressure during MRD, potentially conferring resistance to the microenvironment or therapies. CNV, histone marks and transcriptomic gene expression signatures combined with Gene Ontology (GO) enrichment analysis highlighted the potential functional roles of the mutated genes, providing insights into the pathways associated with MRD. In addition, we compared "murine MRD genes" profiles to the corresponding human disease through public datasets and highlighted common features according to disease progression. Proteomic analysis combined with multi-omics genetic investigations, revealed a dysregulated proteins signature in dormant cells with minimal genetic mechanism involvement. Pathway enrichment analysis revealed the metabolic, differentiation and cytoskeletal remodeling processes involved in MRD. Finally, we identified 11 common proteins differentially expressed in dormant cells from both pathologies. CONCLUSIONS: Our study underscores the complexity of tumour dormancy, implicating both genetic and nongenetic factors. By comparing genomic, transcriptomic, proteomic, and epigenomic datasets, our study provides a comprehensive understanding of the molecular landscape of minimal residual disease. These results provide a robust foundation for forthcoming investigations and offer potential avenues for the advancement of targeted MRD therapies in leukemia and melanoma patients, emphasizing the importance of considering both genetic and nongenetic factors in treatment strategies.
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Modelos Animales de Enfermedad , Melanoma , Neoplasia Residual , Animales , Melanoma/genética , Melanoma/patología , Ratones , Leucemia/genética , Leucemia/patología , Variaciones en el Número de Copia de ADN , Secuenciación del Exoma , Ratones Endogámicos C57BL , Proteómica , Transcriptoma , Perfilación de la Expresión Génica , MultiómicaRESUMEN
Petroleum-derived substances, like industrial oils and grease, are ubiquitous in our daily lives. Comprised of petroleum hydrocarbons (PH), these substances can come into contact with our skin, potentially causing molecular disruptions and contributing to the development of chronic disease. In this pilot study, we employed mass spectrometry-based untargeted metabolomics and 16S rRNA gene sequencing analyses to explore these effects. Superficial skin samples were collected from subjects with and without chronic dermal exposure to PH at two anatomical sites: the fingers (referred to as the hand) and arms (serving as an intersubject variability control). Exposed hands exhibited higher bacterial diversity (Shannon and Simpson indices) and an enrichment of oil-degrading bacteria (ODB), including Dietzia, Paracoccus, and Kocuria. Functional prediction suggested enriched pathways associated with PH degradation in exposed hands vs non-exposed hands, while no differences were observed when comparing the arms. Furthermore, carboxylic acids, glycerophospholipids, organooxygen compounds, phenol ethers, among others, were found to be more abundant in exposed hands. We observed positive correlations among multiple ODB and xenobiotics, suggesting a chemical remodeling of the skin favorable for ODB thriving. Overall, our study offers insights into the complex dysregulation of bacterial communities and the chemical milieu induced by chronic dermal exposure to PH.
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Hidrocarburos , Metaboloma , Microbiota , Petróleo , Piel , Humanos , Proyectos Piloto , Petróleo/toxicidad , Petróleo/metabolismo , Piel/microbiología , Piel/metabolismo , Piel/efectos de los fármacos , Microbiota/efectos de los fármacos , Metaboloma/efectos de los fármacos , Hidrocarburos/metabolismo , Adulto , Masculino , Femenino , ARN Ribosómico 16S/genética , Bacterias/genética , Bacterias/clasificación , Bacterias/metabolismo , Bacterias/efectos de los fármacos , Persona de Mediana EdadRESUMEN
This review presents advances in the implementation of high - throughput se quencing and its application to the knowledge of medicinal plants. We conducted a bibliographic search of papers published in PubMed, Science Direct, Google Scholar, Scopus, and Web of Science databases and analyzed the obtained data using VOSviewer (versi on 1.6.19). Given that medicinal plants are a source of specialized metabolites with immense therapeutic values and important pharmacological properties, plant researchers around the world have turned their attention toward them and have begun to examine t hem widely. Recent advances in sequencing technologies have reduced cost and time demands and accelerated medicinal plant research. Such research leverages full genome sequencing, as well as RNA (ribonucleic acid) sequencing and the analysis of the transcr iptome, to identify molecular markers of species and functional genes that control key biological traits, as well as to understand the biosynthetic pathways of bioactive metabolites and regulatory mechanisms of environmental responses. As such, the omics ( e.g., transcriptomics, metabolomics, proteomics, and genomics, among others) have been widely applied within the study of medicinal plants, although their usage in Colombia is still few and, in some areas, scarce. (185)
El extracto de cloroformo (CE) y las fracciones obtenidas de las raíces de Aldama arenaria se evaluaron para determinar su actividad antiproliferativa in vitro contra 10 líneas ce lulares tumorales humanas [leucemia (K - 562), mama (MCF - 7), ovario que expresa un fenotipo resistente a múltiples fármacos (NCI/ADR - RES), melanoma (UACC - 62), pulmón (NCI - H460), próstata (PC - 3), colon (HT29), ovario (OVCAR - 3), glioma (U251) y riñón (786 - 0)]. CE presentó actividad antiproliferativa débil a moderada (log GI 50 medio 1.07), mientras que las fracciones 3 y 4, enriquecidas con diterpenos de tipo pimarane [ent - pimara - 8 (14), ácido 15 - dien - 19 - oico y ent - 8(14),15 - pimaradien - 3 ß - ol], presentaron activid ad moderada a potente para la mayoría de las líneas celulares, con un log GI 50 medio de 0.62 y 0.59, respectivamente. Los resultados mostraron una acción antiproliferativa in vitro prometedora de las muestras obtenidas de A. arenaria , con los mejores resul tados para NCI/ADR - RES, HT29 y OVCAR - 3, y valores de TGI que van desde 5.95 a 28.71 µg.mL - 1, demostrando que los compuestos de esta clase pueden ser prototipos potenciales para el descubrimiento de nuevos agentes terapéuticos
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Plantas Medicinales , Secuenciación de Nucleótidos de Alto Rendimiento , Multiómica , Medicina Tradicional , ColombiaRESUMEN
Trichoderma erinaceum is a filamentous fungus that was isolated from decaying sugarcane straw at a Brazilian ethanol biorefinery. This fungus shows potential as a source of plant cell wall-degrading enzymes (PCWDEs). In this study, we conducted a comprehensive multiomics investigation of T. erinaceum to gain insights into its enzymatic capabilities and genetic makeup. Firstly, we performed genome sequencing and assembly, which resulted in the identification of 10,942 genes in the T. erinaceum genome. We then conducted transcriptomics and secretome analyses to map the gene expression patterns and identify the enzymes produced by T. erinaceum in the presence of different substrates such as glucose, microcrystalline cellulose, pretreated sugarcane straw, and pretreated energy cane bagasse. Our analyses revealed that T. erinaceum highly expresses genes directly related to lignocellulose degradation when grown on pretreated energy cane and sugarcane substrates. Furthermore, our secretome analysis identified 35 carbohydrate-active enzymes, primarily PCWDEs. To further explore the enzymatic capabilities of T. erinaceum, we selected a ß-glucosidase from the secretome data for recombinant production in a fungal strain. The recombinant enzyme demonstrated superior performance in degrading cellobiose and laminaribiose compared to a well-known enzyme derived from Trichoderma reesei. Overall, this comprehensive study provides valuable insights into both the genetic patterns of T. erinaceum and its potential for lignocellulose degradation and enzyme production. The obtained genomic data can serve as an important resource for future genetic engineering efforts aimed at optimizing enzyme production from this fungus.
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GH-secreting tumors represent 15 % to 20 % of all pituitary neuroendocrine tumors (pitNETs), of which 95 % occur in a sporadic context, without an identifiable inherited cause. Recent multi-omic approaches have characterized the epigenomic, genomic, transcriptomic, proteomic and kynomic landscape of pituitary tumors. Transcriptomic analysis has allowed us to discover specific transcription factors driving the differentiation of pituitary tumors and gene expression patterns. GH-secreting, along with PRL- and TSH-secreting pitNETs are driven by POU1F1; ACTH-secreting tumors are determined by TBX19; and non-functioning tumors, which are predominantly of gonadotrope differentiation are conditioned by NR5A1. Upregulation of certain miRNAs, such as miR-107, is associated with tumor progression, while downregulation of others, like miR-15a and miR-16-1, correlates with tumor size reduction. Additionally, miRNA expression profiles are linked to treatment resistance and clinical outcomes, providing insights into potential therapeutic targets. Specific somatic mutations in GNAS, PTTG1, GIPR, HGMA2, MAST and somatic variants associated with cAMP, calcium signaling, and ATP pathways have also been associated with the development of acromegaly. This review focuses on the oncogenic mechanisms by which sporadic acromegaly can develop, covering a complex series of molecular alterations that ultimately alter the balance between proliferation and apoptosis, and dysregulated hormonal secretion.
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Acromegalia , Neoplasias Hipofisarias , Humanos , Acromegalia/genética , Neoplasias Hipofisarias/genética , Neoplasias Hipofisarias/metabolismo , Neoplasias Hipofisarias/patología , Tumores Neuroendocrinos/genética , Tumores Neuroendocrinos/patología , MicroARNs/genéticaRESUMEN
Lung tumors are a leading cause of cancer-related death worldwide. Lung cancers are highly heterogeneous on their phenotypes, both at the cellular and molecular levels. Efforts to better understand the biological origins and outcomes of lung cancer in terms of this enormous variability often require of high-throughput experimental techniques paired with advanced data analytics. Anticipated advancements in multi-omic methodologies hold potential to reveal a broader molecular perspective of these tumors. This study introduces a theoretical and computational framework for generating network models depicting regulatory constraints on biological functions in a semi-automated way. The approach successfully identifies enriched functions in analyzed omics data, focusing on Adenocarcinoma (LUAD) and Squamous cell carcinoma (LUSC, a type of NSCLC) in the lung. Valuable information about novel regulatory characteristics, supported by robust biological reasoning, is illustrated, for instance by considering the role of genes, miRNAs and CpG sites associated with NSCLC, both novel and previously reported. Utilizing multi-omic regulatory networks, we constructed robust models elucidating omics data interconnectedness, enabling systematic generation of mechanistic hypotheses. These findings offer insights into complex regulatory mechanisms underlying these cancer types, paving the way for further exploring their molecular complexity.
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The Zika virus (ZIKV) can be vertically transmitted, causing congenital Zika syndrome (CZS) in fetuses. ZIKV infection in early gestational trimesters increases the chances of developing CZS. This syndrome involves several pathologies with a complex diagnosis. In this work, we aim to identify biological processes and molecular pathways related to CZS and propose a series of putative protein and metabolite biomarkers for CZS prognosis in early pregnancy trimesters. We analyzed serum samples of healthy pregnant women and ZIKV-infected pregnant women bearing nonmicrocephalic and microcephalic fetuses. A total of 1090 proteins and 512 metabolites were identified by bottom-up proteomics and untargeted metabolomics, respectively. Univariate and multivariate statistical approaches were applied to find CZS differentially abundant proteins (DAP) and metabolites (DAM). Enrichment analysis (i.e., biological processes and molecular pathways) of the DAP and the DAM allowed us to identify the ECM organization and proteoglycans, amino acid metabolism, and arachidonic acid metabolism as CZS signatures. Five proteins and four metabolites were selected as CZS biomarker candidates. Serum multiomics analysis led us to propose nine putative biomarkers for CZS prognosis with high sensitivity and specificity.
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Complicaciones Infecciosas del Embarazo , Infección por el Virus Zika , Virus Zika , Embarazo , Femenino , Humanos , Infección por el Virus Zika/diagnóstico , Virus Zika/genética , Complicaciones Infecciosas del Embarazo/diagnóstico , Complicaciones Infecciosas del Embarazo/patología , Multiómica , BiomarcadoresRESUMEN
In this review, we provide a general overview of the current panorama of mining strategies for multi-omics data to investigate lncRNAs with an actual or potential role as biological markers in cancer. Several multi-omics studies focusing on lncRNAs have been performed in the past with varying scopes. Nevertheless, many questions remain regarding the pragmatic application of different molecular technologies and bioinformatics algorithms for mining multi-omics data. Here, we attempt to address some of the less discussed aspects of the practical applications using different study designs for incorporating bioinformatics and statistical analyses of multi-omics data. Finally, we discuss the potential improvements and new paradigms aimed at unraveling the role and utility of lncRNAs in cancer and their potential use as molecular markers for cancer diagnosis and outcome prediction.
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Neoplasias , ARN Largo no Codificante , Humanos , ARN Largo no Codificante/genética , Multiómica , Relevancia Clínica , Neoplasias/genética , Biología ComputacionalRESUMEN
Oral squamous cell carcinoma (OSCC) is the prevalent type of oral cavity cancer, requiring precise, accurate, and affordable diagnosis to identify the disease in early stages, Comprehending the differences in lipid profiles between healthy and cancerous tissues encompasses great relevance in identifying biomarker candidates and enhancing the odds of successful cancer treatment. Therefore, the present study evaluates the analytical performance of simultaneous mRNA and lipid extraction in gingiva tissue from healthy patients and patients diagnosed with OSCC preserved in TRIzol reagent. The data was analyzed by partial least-squares discriminant analysis (PLS-DA) and confirmed via matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). The lipid extraction in TRIzol solution was linear in a range from 330 to 2000 ng mL-1, r2 > 0.99, intra and interday precision and accuracy <15%, and absolute recovery values ranging from 90 to 110%. The most important lipids for tumor classification were evaluated by MALDI-MSI, revealing that the lipids responsible for distinguishing the OSCC group are more prevalent in the cancerous tissue in contrast to the healthy group. The results exhibit the possibilities to do transcriptomic and lipidomic analyses in the same sample and point out important candidates related to the presence of OSCC.
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In recent years, the popularity of fermented foods has strongly increased based on their proven health benefits and the adoption of new trends among consumers. One of these health-promoting products is water kefir, which is a fermented sugary beverage based on kefir grains (symbiotic colonies of yeast, lactic acid and acetic acid bacteria). According to previous knowledge and the uniqueness of each water kefir fermentation, the following project aimed to explore the microbial and chemical composition of a water kefir fermentation and its microbial consortium, through the integration of culture-dependent methods, compositional metagenomics, and untargeted metabolomics. These methods were applied in two types of samples: fermentation grains (inoculum) and fermentation samples collected at different time points. A strains culture collection of â¼90 strains was established by means of culture-dependent methods, mainly consisting of individuals of Pichia membranifaciens, Acetobacter orientalis, Lentilactobacillus hilgardii, Lacticaseibacillus paracasei, Acetobacter pomorum, Lentilactobacillus buchneri, Pichia kudriavzevii, Acetobacter pasteurianus, Schleiferilactobacillus harbinensis, and Kazachstania exigua, which can be further studied for their use in synthetic consortia formulation. In addition, metabarcoding of each fermentation time was done by 16S and ITS sequencing for bacteria and yeast, respectively. The results show strong population shifts of the microbial community during the fermentation time course, with an enrichment of microbial groups after 72 h of fermentation. Metataxonomics results revealed Lactobacillus and Acetobacter as the dominant genera for lactic acid and acetic acid bacteria, whereas, for yeast, P. membranifaciens was the dominant species. In addition, correlation and systematic analyses of microbial growth patterns and metabolite richness allowed the recognition of metabolic enrichment points between 72 and 96 h and correlation between microbial groups and metabolite abundance (e.g., Bile acid conjugates and Acetobacter tropicalis). Metabolomic analysis also evidenced the production of bioactive compounds in this fermented matrix, which have been associated with biological activities, including antimicrobial and antioxidant. Interestingly, the chemical family of Isoschaftosides (C-glycosyl flavonoids) was also found, representing an important finding since this compound, with hepatoprotective and anti-inflammatory activity, had not been previously reported in this matrix. We conclude that the integration of microbial biodiversity, cultured species, and chemical data enables the identification of relevant microbial population patterns and the detection of specific points of enrichment during the fermentation process of a food matrix, which enables the future design of synthetic microbial consortia, which can be used as targeted probiotics for digestive and metabolic health.
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In this narrative review, we aim to point out the close relationship between mpox virus (MPXV) infection and the role of saliva as a diagnostic tool for mpox, considering the current molecular approach and in the perspective of OMICs application. The MPXV uses the host cell's rough endoplasmic reticulum, ribosomes, and cytoplasmic proteins to replicate its genome and synthesize virions for cellular exit. The presence of oral mucosa lesions associated with mpox infection is one of the first signs of infection; however, current diagnostic tools find it difficult to detect the virus before the rashes begin. MPXV transmission occurs through direct contact with an infected lesion and infected body fluids, including saliva, presenting a potential use of this fluid for diagnostic purposes. Currently available diagnostic tests for MPXV detection are performed either by real-time quantitative PCR (RT-qPCR) or ELISA, which presents several limitations since they are invasive tests. Despite current clinical trials with restricted sample size, MPXV DNA was detected in saliva with a sensitivity of 85%-100%. In this context, the application of transcriptomics, metabolomics, lipidomics, or proteomics analyses coupled with saliva can identify novel disease biomarkers. Thus, it is important to note that the identification and quantification of salivary DNA, RNA, lipid, protein, and metabolite can provide novel non-invasive biomarkers through the use of OMICs platforms aiding in the early detection and diagnosis of MPXV infection. Untargeted mass spectrometry (MS)-based proteomics reveals that some proteins also expressed in saliva were detected with greater expression differences in blood plasma when comparing mpox patients and healthy subjects, suggesting a promising alternative to be applied in screening or diagnostic platforms for mpox salivary diagnostics coupled to OMICs.
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Líquidos Corporales , Enfermedades Transmisibles , Mpox , Humanos , Patología Bucal , SalivaRESUMEN
Breast cancer is a complex disease that is influenced by the concurrent influence of multiple genetic and environmental factors. Recent advances in genomics and other high throughput biomolecular techniques (-omics) have provided numerous insights into the molecular mechanisms underlying breast cancer development and progression. A number of these mechanisms involve multiple layers of regulation. In this review, we summarize the current knowledge on the role of multiple omics in the regulation of breast cancer, including the effects of DNA methylation, non-coding RNA, and other epigenomic changes. We comment on how integrating such diverse mechanisms is envisioned as key to a more comprehensive understanding of breast carcinogenesis and cancer biology with relevance to prognostics, diagnostics and therapeutics. We also discuss the potential clinical implications of these findings and highlight areas for future research. Overall, our understanding of the molecular mechanisms of multi-omic regulation in breast cancer is rapidly increasing and has the potential to inform the development of novel therapeutic approaches for this disease.
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An oil palm (Elaeis guineensis Jacq.) bud rod disorder of unknown etiology, named Fatal Yellowing (FY) disease, is regarded as one of the top constraints with respect to the growth of the palm oil industry in Brazil. FY etiology has been a challenge embraced by several research groups in plant pathology throughout the last 50 years in Brazil, with no success in completing Koch's postulates. Most recently, the hypothesis of having an abiotic stressor as the initial cause of FY has gained ground, and oxygen deficiency (hypoxia) damaging the root system has become a candidate for stress. Here, a comprehensive, large-scale, single- and multi-omics integration analysis of the metabolome and transcriptome profiles on the leaves of oil palm plants contrasting in terms of FY symptomatology-asymptomatic and symptomatic-and collected in two distinct seasons-dry and rainy-is reported. The changes observed in the physicochemical attributes of the soil and the chemical attributes and metabolome profiles of the leaves did not allow the discrimination of plants which were asymptomatic or symptomatic for this disease, not even in the rainy season, when the soil became waterlogged. However, the multi-omics integration analysis of enzymes and metabolites differentially expressed in asymptomatic and/or symptomatic plants in the rainy season compared to the dry season allowed the identification of the metabolic pathways most affected by the changes in the environment, opening an opportunity for additional characterization of the role of hypoxia in FY symptom intensification. Finally, the initial analysis of a set of 56 proteins/genes differentially expressed in symptomatic plants compared to the asymptomatic ones, independent of the season, has presented pieces of evidence suggesting that breaks in the non-host resistance to non-adapted pathogens and the basal immunity to adapted pathogens, caused by the anaerobic conditions experienced by the plants, might be linked to the onset of this disease. This set of genes might offer the opportunity to develop biomarkers for selecting oil palm plants resistant to this disease and to help pave the way to employing strategies to keep the safety barriers raised and strong.
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Arecaceae , Olea , Arecaceae/genética , Brasil , Hipoxia , Industrias , MetabolomaRESUMEN
Interactions between communities of the gut microbiome and with the host could affect the onset and progression of metabolic associated fatty liver disease (MAFLD), and can be useful as new diagnostic and prognostic biomarkers. In this study, we performed a multi-omics approach to unravel gut microbiome signatures from 32 biopsy-proven patients (10 simple steatosis -SS- and 22 steatohepatitis -SH-) and 19 healthy volunteers (HV). Human and microbial transcripts were differentially identified between groups (MAFLD vs. HV/SH vs. SS), and analyzed for weighted correlation networks together with previously detected metabolites from the same set of samples. We observed that expression of Desulfobacteraceae bacterium, methanogenic archaea, Mushu phage, opportunistic pathogenic fungi Fusarium proliferatum and Candida sorbophila, protozoa Blastocystis spp. and Fonticula alba were upregulated in MAFLD and SH. Desulfobacteraceae bacterium and Mushu phage were hub species in the onset of MAFLD, whereas the activity of Fonticula alba, Faecalibacterium prausnitzii, and Mushu phage act as key regulators of the progression to SH. A combination of clinical, metabolomic, and transcriptomic parameters showed the highest predictive capacity for MAFLD and SH (AUC = 0.96). In conclusion, faecal microbiome markers from several community members contribute to the switch in signatures characteristic of MAFLD and its progression towards SH.