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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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The Religious Order Study and Memory and Aging Project (ROSMAP) is an initiative that integrates two longitudinal cohort studies, which have been collecting clinicopathological and molecular data since the early 1990s. This extensive dataset includes a wide array of omic data, revealing the complex interactions between molecular levels in neurodegenerative diseases (ND) and aging. Neurodegenerative diseases (ND) are frequently associated with morbidity and cognitive decline in older adults. Omics research, in conjunction with clinical variables, is crucial for advancing our understanding of the diagnosis and treatment of neurodegenerative diseases. This summary reviews the extensive omics research-encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and multiomics-conducted through the ROSMAP study. It highlights the significant advancements in understanding the mechanisms underlying neurodegenerative diseases, with a particular focus on Alzheimer's disease.

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The study of extremophilic microorganisms has sparked interest in understanding extraterrestrial microbial life. Such organisms are fundamental for investigating life forms on Saturn's icy moons, such as Enceladus, which is characterized by potentially habitable saline and alkaline niches. Our study focused on the salt-alkaline soil of the Al Wahbah crater in Saudi Arabia, where we identified microorganisms that could be used as biological models to understand potential life on Enceladus. The search involved isolating 48 bacterial strains, sequencing the genomes of two thermo-haloalkaliphilic strains, and characterizing them for astrobiological application. A deeper understanding of the genetic composition and functional capabilities of the two novel strains of Halalkalibacterium halodurans provided valuable insights into their survival strategies and the presence of coding genes and pathways related to adaptations to environmental stressors. We also used mass spectrometry with a molecular network approach, highlighting various classes of molecules, such as phospholipids and nonproteinogenic amino acids, as potential biosignatures. These are essential features for understanding life's adaptability under extreme conditions and could be used as targets for biosignatures in upcoming missions exploring Enceladus' orbit. Furthermore, our study reinforces the need to look at new extreme environments on Earth that might contribute to the astrobiology field.

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Bell pepper plants are sensitive to environmental changes and are significantly affected by abiotic factors such as UV-B radiation and cold, which reduce their yield and production. Various approaches, including omics data integration, have been employed to understand the mechanisms by which this crop copes with abiotic stress. This study aimed to find metabolic changes in bell pepper stems caused by UV-B radiation and cold by integrating omic data. Proteome and metabolome profiles were generated using liquid chromatography coupled with mass spectrometry, and data integration was performed in the plant metabolic pathway database. The combined stress of UV-B and cold induced the accumulation of proteins related to photosynthesis, mitochondrial electron transport, and a response to a stimulus. Further, the production of flavonoids and their glycosides, as well as affecting carbon metabolism, tetrapyrrole, and scopolamine pathways, were identified. We have made the first metabolic regulatory network map showing how bell pepper stems respond to cold and UV-B stress. We did this by looking at changes in proteins and metabolites that help with respiration, photosynthesis, and the buildup of photoprotective and antioxidant compounds.

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In light of the post-genomic era, epigenetics brings about an opportunity to better understand how the molecular machinery works and is led by a complex dynamic set of mechanisms, often intricate and complementary in many aspects. In particular, epigenetics links developmental biology and genetics, as well as many other areas of knowledge. The present work highlights substantial scopes and relevant discoveries related to the development of the term from its first notions. To our understanding, the concept of epigenetics needs to be revisited, as it is one of the most relevant and multifaceted terms in human knowledge. To redirect future novel experimental or theoretical efforts, it is crucial to compile all significant issues that could impact human and ecological benefit in the most precise and accurate manner. In this paper, the reader can find one of the widest compilations of the landmarks and epistemic considerations of the knowledge of epigenetics across the history of biology from the earliest epigenetic formulation to genetic determinism until the present. In the present work, we link the current body of knowledge and earlier pre-genomic concepts in order to propose a new definition of epigenetics that is faithful to its regulatory nature.

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High-sensitivity nanoflow liquid chromatography (nLC) is seldom employed in untargeted metabolomics because current sample preparation techniques are inefficient at preventing nanocapillary column performance degradation. Here, we describe an nLC-based tandem mass spectrometry workflow that enables seamless joint analysis and integration of metabolomics (including lipidomics) and proteomics from the same samples without instrument duplication. This workflow is based on a robust solid-phase micro-extraction step for routine sample cleanup and bioactive molecule enrichment. Our method, termed proteomic and nanoflow metabolomic analysis (PANAMA), improves compound resolution and detection sensitivity without compromising the depth of coverage as compared with existing widely used analytical procedures. Notably, PANAMA can be applied to a broad array of specimens, including biofluids, cell lines, and tissue samples. It generates high-quality, information-rich metabolite-protein datasets while bypassing the need for specialized instrumentation.

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The beef cattle industry has experienced a shift driven by a market demand for healthier meat, cost efficiency and environmental sustainability in recent years. Consequently, there has been a growing focus on the fatty acids content and functions of meat in cattle breeding programmes. Besides, a deeper understanding of the biological mechanisms influencing the expression of different phenotypes related to fatty acid profiles is crucial. In this study, we aimed to identify Single-Nucleotide Variants (SNV) and Insertion/Deletion (InDels) DNA variants in candidate genes related to fatty acid profiles described in genomic, transcriptomic and proteomic studies conducted in beef cattle breeds. Utilizing whole-genome re-sequencing data from Brazilian locally adapted bovine breeds, namely Caracu and Pantaneiro, we identified SNVs and InDels associated with 23,947 genes. From these, we identified 318 candidate genes related to fatty acid profiles that contain variants. Subsequently, we select only genes with SNVs and InDels in their promoter, 5' UTR and coding region. Through the gene-biological process network, approximately 19 genes were highlighted. Furthermore, considering the studied trait and a literature review, we selected the main transcription factors (TF). Functional analysis via gene-TF network allowed us to identify the 30 most likely candidate genes for meat fatty acid profile in cattle. LIPE, MFSD2A and SREBF1 genes were highlighted in networks due to their biological importance. Further dissection of these genes revealed 15 new variants found in promoter regions of Caracu and Pantaneiro sequences. The gene networks facilitated a better functional understanding of genes and TF, enabling the identification of variants potentially related to the expression of candidate genes for meat fatty acid profiles in cattle.

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Soil pollution by TNT(2,4,6-trinitrotoluene), RDX(hexahydro-1,3,5-trinitro-1,3,5-triazacyclohexane), and HMX(octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine), resulting from the use of explosives, poses significant challenges, leading to adverse effects such as toxicity and alteration of microbial communities. Consequently, there is a growing need for effective bioremediation strategies to mitigate this damage. This review focuses on Microbial and Bio-omics perspectives within the realm of soil pollution caused by explosive compounds. A comprehensive analysis was conducted, reviewing 79 articles meeting bibliometric criteria from the Web of Science and Scopus databases from 2013 to 2023. Additionally, relevant patents were scrutinized to establish a comprehensive research database. The synthesis of these findings serves as a critical resource, enhancing our understanding of challenges such as toxicity, soil alterations, and microbial stress, as well as exploring bio-omics techniques like metagenomics, transcriptomics, and proteomics in the context of environmental remediation. The review underscores the importance of exploring various remediation approaches, including mycorrhiza remediation, phytoremediation, bioaugmentation, and biostimulation. Moreover, an examination of patented technologies reveals refined and efficient processes that integrate microorganisms and environmental engineering. Notably, China and the United States are pioneers in this field, based on previous successful bioremediation endeavors. This review underscores research's vital role in soil pollution via innovative, sustainable bioremediation for explosives.

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Unmet needs exist in metabolic dysfunction-associated steatotic liver disease (MASLD) risk stratification. Our ability to identify patients with MASLD with advanced fibrosis and at higher risk for adverse outcomes is still limited. Incorporating novel biomarkers could represent a meaningful improvement to current risk predictors. With this aim, omics technologies have revolutionized the process of MASLD biomarker discovery over the past decades. While the research in this field is thriving, much of the publication has been haphazard, often using single-omics data and specimen sets of convenience, with many identified candidate biomarkers but lacking clinical validation and utility. If we incorporate these biomarkers to direct patients' management, it should be considered that the roadmap for translating a newly discovered omics-based signature to an actual, analytically valid test useful in MASLD clinical practice is rigorous and, therefore, not easily accomplished. This article presents an overview of this area's current state, the conceivable opportunities and challenges of omics-based laboratory diagnostics, and a roadmap for improving MASLD biomarker research.

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Purpose: Essential oils from various plants have diverse therapeutic properties and are researched extensively. They have applications in medicine, aromatherapy, microbiology, agriculture, livestock, and the food industry, benefiting the population. Methods: This systematic review followed the PRISMA verification protocol. The study focused on the anti-inflammatory effects, nutraceutical properties, antioxidant and antibacterial activity of essential oils in lemon, orange, cumin, cinnamon, coriander, rosemary, thyme, and parsley. We also looked at their presence in the diet, their effect, their mechanism of action on health, and the most important active compounds. The search was conducted in the PubMed database for the last 12 years of publications, including in vitro, in vivo, and online cell model tests. Results: Essential oils have been shown to have multiple health benefits, primarily due to their antimicrobial and anti-inflammatory effects. The mechanism of action of cinnamon oil alters bacterial membranes, modifies lipid profiles, and inhibits cell division, giving a potential benefit in protection against colitis. On the other hand, a significant improvement was observed in the diastolic pressure of patients with metabolic syndrome when supplementing them with cumin essential oil. The antimicrobial properties of coriander essential oil, especially its application in seafood like tilapia, demonstrate efficacy in improving health and resistance to bacterial infections. Cumin essential oil treats inflammation. Parsley essential oil is an antioxidant. Orange peel oil is antibacterial, antifungal, antiparasitic, and pro-oxidative. Lemon essential oil affects mouse intestinal microbiota. Thyme essential oil protects the colon against damage and DNA methylation. Carnosic acid in rosemary oil can reduce prostate cancer cell viability by modifying the endoplasmic reticulum function. Conclusion and discussion: Essential oils have many therapeutic and antiparasitic properties. They are beneficial to human health in many ways. However, to understand their potential benefits, more research is needed regarding essential oils such as coriander, parsley, rosemary, cumin, and thyme. These research gaps are relevant since they restrict understanding of the possible benefits of these crucial oils for health-related contexts.

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Innovative techniques such as the "omics" can be a powerful tool for the understanding of intracellular pathways involved in homeostasis maintenance and identification of new potential therapeutic targets against endocrine-metabolic disorders. Over the last decades, proteomics has been extensively applied in the study of a wide variety of human diseases, including those involving the endocrine system. Among the most endocrine-related disorders investigated by proteomics in humans are diabetes mellitus and thyroid, pituitary, and reproductive system disorders. In diabetes, proteins implicated in insulin signaling, glucose metabolism, and ß-cell activity have been investigated. In thyroid diseases, protein expression alterations were described in thyroid malignancies and autoimmune thyroid illnesses. Additionally, proteomics has been used to investigate the variations in protein expression in adrenal cancers and conditions, including Cushing's syndrome and Addison's disease. Pituitary tumors and disorders including acromegaly and hypopituitarism have been studied using proteomics to examine changes in protein expression. Reproductive problems such as polycystic ovarian syndrome and endometriosis are two examples of conditions where alterations in protein expression have been studied using proteomics. Proteomics has, in general, shed light on the molecular underpinnings of many endocrine-related illnesses and revealed promising biomarkers for both their detection and treatment. The capacity of proteomics to thoroughly and objectively examine complex protein mixtures is one of its main benefits. Mass spectrometry (MS) is a widely used method that identifies and measures proteins based on their mass-to-charge ratio and their fragmentation pattern. MS can perform the separation of proteins according to their physicochemical characteristics, such as hydrophobicity, charge, and size, in combination with liquid chromatography. Other proteomics techniques include protein arrays, which enable the simultaneous identification of several proteins in a single assay, and two-dimensional gel electrophoresis (2D-DIGE), which divides proteins depending on their isoelectric point and molecular weight. This chapter aims to summarize the most relevant proteomics data from targeted tissues, as well as the daily rhythmic variation of relevant biomarkers in both physiological and pathophysiological conditions within the involved endocrine system, especially because the actual modern lifestyle constantly imposes a chronic unentrained condition, which virtually affects all the circadian clock systems within human's body, being also correlated with innumerous endocrine-metabolic diseases.

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Diabetes mellitus (DM) is a chronic metabolic disorder characterized by elevated blood sugar levels, resulting from either body's inability to produce or effectively utilize insulin. There are several types of DM, but the most common are type 1 diabetes (T1D), type 2 diabetes (T2D), and gestational diabetes mellitus (GDM). DM is a complex disease and a global health concern, and the current clinical markers, such as fasting glucose, are helpful in the diagnosis of DM, but are not specific and sensitive, especially when measured on the beginning of the pathogenesis. Therefore, there is a pressing need to discover new early biomarkers that can provide an early diagnosis. Omics is an important field for the discovery of potential new biomarkers, especially proteomics, metabolomics, and lipidomics, where techniques such as liquid chromatography, mass spectrometry, and nuclear magnetic resonance are utilized to identify novel DM biomarkers and their pathways. In this review, we report papers that applied omics in the context of DM to identify new markers and their relationship with this disease, with the aim of elucidating new diagnostic techniques for the main types of DM.

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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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The host-microbiome associations occurring on the skin of vertebrates significantly influence hosts' health. However, the factors mediating their interactions remain largely unknown. Herein, we used integrated technical and ecological frameworks to investigate the skin metabolites sustaining a beneficial symbiosis between tree frogs and bacteria. We characterize macrocyclic acylcarnitines as the major metabolites secreted by the frogs' skin and trace their origin to an enzymatic unbalance of carnitine palmitoyltransferases. We found that these compounds colocalize with bacteria on the skin surface and are mostly represented by members of the Pseudomonas community. We showed that Pseudomonas sp. MPFS isolated from frogs' skin can exploit acylcarnitines as its sole carbon and nitrogen source, and this metabolic capability is widespread in Pseudomonas. We summarize frogs' multiple mechanisms to filter environmental bacteria and highlight that acylcarnitines likely evolved for another function but were co-opted to provide nutritional benefits to the symbionts.