Multi-omics approaches to disease

Investigation on the mechanism of hepatotoxicity of dictamnine on juvenile zebrafish by integrating metabolomics and transcriptomics

Dictamnine(DIC), as the key pharmacological component of the classical Chinese herbal medicine cortex dictamni, possesses multiple pharmacological activities such as anti-microbial, anti-allergic, anti-cancer, and anti-inflammatory activities, however it is also the main toxicant of cortex dictamni induced hepatic damage, yet the underlying molecular mechanisms causing hepatic damage are still largely unknown. With the purpose of explore possibilities hepatotoxicity of dictamnine in zebrafish and to identify the key regulators and metabolites involved in the biological process, we administered zebrafish to dictamnine at a sub-lethal dose ( Collapse

Key Words

• Dictamnine
• Hepatotoxicity
• Metabolomics
• Transcriptome sequencing
• Zebrafish

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MESH Headings

• Animals
• Zebrafish/genetics
• Chemical and Drug Induced Liver Injury/genetics
• Chemical and Drug Induced Liver Injury/metabolism
• Metabolomics/methods
• Liver/drug effects
• Liver/metabolism
• Liver/pathology
• Transcriptome/drug effects
• Gene Expression Profiling/methods
• Drugs, Chinese Herbal/toxicity
• Apoptosis/drug effects

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Affiliation(s)

Xiaomin Xu Institute of Traditional Chinese Medicine, Heilongjiang University of Chinese Medicine, Harbin 150040, Heilongjiang, China
Fang Lu Institute of Traditional Chinese Medicine, Heilongjiang University of Chinese Medicine, Harbin 150040, Heilongjiang, China
Yu Wang Institute of Traditional Chinese Medicine, Heilongjiang University of Chinese Medicine, Harbin 150040, Heilongjiang, China
Shumin Liu Institute of Traditional Chinese Medicine, Heilongjiang University of Chinese Medicine, Harbin 150040, Heilongjiang, China.

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Systematic data analysis pipeline for quantitative morphological cell phenotyping

Quantitative morphological phenotyping (QMP) is an image-based method used to capture morphological features at both the cellular and population level. Its interdisciplinary nature, spanning from data collection to result analysis and interpretation, can lead to uncertainties, particularly among those new to this actively growing field. High analytical specificity for a typical QMP is achieved through sophisticated approaches that can leverage subtle cellular morphological changes. Here, we outline a systematic workflow to refine the QMP methodology. For a practical review, we describe the main steps of a typical QMP; in each step, we discuss the available methods, their applications, advantages, and disadvantages, along with the R functions and packages for easy implementation. This review does not cover theoretical backgrounds, but provides several references for interested researchers. It aims to broaden the horizons for future phenome studies and demonstrate how to exploit years of endeavors to achieve more with less.

Insight into the complexity of male infertility: a multi-omics review

Male infertility is a reproductive disorder, accounting for 40-50% of infertility. Currently, in about 70% of infertile men, the cause remains unknown. With the introduction of novel omics and advancement in high-throughput technology, potential biomarkers are emerging. The main purpose of our work was to overview different aspects of omics approaches in association with idiopathic male infertility and highlight potential genes, transcripts, non-coding RNA, proteins, and metabolites worth further exploring. Using the Gene Ontology (GO) analysis, we aimed to compare enriched GO terms from each omics approach and determine their overlapping. A PubMed database screening for the literature published between February 2014 and June 2022 was performed using the keywords: male infertility in association with different omics approaches: genomics, epigenomics, transcriptomics, ncRNAomics, proteomics, and metabolomics. A GO enrichment analysis was performed using the Enrichr tool. We retrieved 281 global studies: 171 genomics (DNA level), 21 epigenomics (19 of methylation and two histone residue modifications), 15 transcriptomics, 31 non-coding RNA, 29 proteomics, two protein posttranslational modification, and 19 metabolomics studies. Gene ontology comparison showed that different omics approaches lead to the identification of different molecular factors and that the corresponding GO terms, obtained from different omics approaches, do not overlap to a larger extent. With the integration of novel omics levels into the research of idiopathic causes of male infertility, using multi-omic systems biology approaches, we will be closer to finding the potential biomarkers and consequently becoming aware of the entire spectrum of male infertility, their cause, prognosis, and potential treatment.

Benchmarking feature selection and feature extraction methods to improve the performances of machine-learning algorithms for patient classification using metabolomics biomedical data

Objective

Classification tasks are an open challenge in the field of biomedicine. While several machine-learning techniques exist to accomplish this objective, several peculiarities associated with biomedical data, especially when it comes to omics measurements, prevent their use or good performance achievements. Omics approaches aim to understand a complex biological system through systematic analysis of its content at the molecular level. On the other hand, omics data are heterogeneous, sparse and affected by the classical "curse of dimensionality" problem, i.e. having much fewer observation, samples (n) than omics features (p). Furthermore, a major problem with multi-omics data is the imbalance either at the class or feature level. The objective of this work is to study whether feature extraction and/or feature selection techniques can improve the performances of classification machine-learning algorithms on omics measurements.

Methods

Among all omics, metabolomics has emerged as a powerful tool in cancer research, facilitating a deeper understanding of the complex metabolic landscape associated with tumorigenesis and tumor progression. Thus, we selected three publicly available metabolomics datasets, and we applied several feature extraction techniques both linear and non-linear, coupled or not with feature selection methods, and evaluated the performances regarding patient classification in the different configurations for the three datasets.

Results

We provide general workflow and guidelines on when to use those techniques depending on the characteristics of the data available. To further test the extension of our approach to other omics data, we have included a transcriptomics and a proteomics data. Overall, for all datasets, we showed that applying supervised feature selection improves the performances of feature extraction methods for classification purposes. Scripts used to perform all analyses are available at: https://github.com/Plant-Net/Metabolomic_project/.

Application of multi-omics techniques to androgenetic alopecia: Current status and perspectives

The rapid advancement of sequencing technologies has enabled the generation of vast datasets, allowing for the in-depth analysis of sequencing data. This analysis has facilitated the validation of novel pathogenesis hypotheses for understanding and treating diseases through ex vivo and in vivo experiments. Androgenetic alopecia (AGA), a common hair loss disorder, has been a key focus of investigators attempting to uncover its underlying mechanisms. Abnormal changes in mRNA, proteins, and metabolites have been identified in individuals with AGA, and future developments in sequencing technologies may reveal new biomarkers for AGA. By integrating multiple omics analysis datasets such as genomics, transcriptomics, proteomics, and metabolomics-along with clinical phenotype data-we can achieve a comprehensive understanding of the molecular underpinnings of AGA. This review summarizes the data-mining studies conducted on various omics analysis datasets as related to AGA that have been adopted to interpret the biological data obtained from different omics layers. We herein discuss the challenges of integrative omics analyses, and suggest that collaborative multi-omics studies can enhance the understanding of the complete pathomechanism(s) of AGA by focusing on the interaction networks comprising DNA, RNA, proteins, and metabolites.

Integrative analysis of transcriptome and proteome wide association studies prioritized functional genes for obesity

BACKGROUND

Genome-wide association studies have identified dozens of genomic loci for obesity. However, functional genes and their detailed genetic mechanisms underlying these loci are mainly unknown. In this study, we conducted an integrative study to prioritize plausibly functional genes by combining information from genome-, transcriptome- and proteome-wide association analyses.

METHODS

We first conducted proteome-wide association analyses and transcriptome-wide association analyses for the six obesity-related traits. We then performed colocalization analysis on the identified loci shared between the proteome- and transcriptome-association analyses. Finally, we validated the identified genes with other plasma/blood reference panels. The highlighted genes were assessed for expression of other tissues, single-cell and tissue specificity, and druggability.

RESULTS

We prioritized 4 high-confidence genes (FASN, ICAM1, PDCD6IP, and YWHAB) by proteome-wide association studies, transcriptome-wide association studies, and colocalization analyses, which consistently influenced the variation of obesity traits at both mRNA and protein levels. These 4 genes were successfully validated using other plasma/blood reference panels. These 4 genes shared regulatory structures in obesity-related tissues. Single-cell and tissue-specific analyses showed that FASN and ICAM1 were explicitly expressed in metabolism- and immunity-related tissues and cells. Furthermore, FASN and ICAM1 had been developed as drug targets.

CONCLUSION

Our study provided novel promising protein targets for further mechanistic and therapeutic studies of obesity.

Periodontal diseases in Africa

Periodontal diseases, a group of complex conditions marked by an excessive immune response and periodontal tissue destruction, are a global health concern. Since 1990, the incidence of these diseases has doubled, with Western sub-Saharan Africa experiencing the highest burden. Accurate diagnosis and case identification are crucial for understanding the etiology, features of disease, research, treatment and prevention. Modern perspectives on periodontal disease classification are based on commonality among those affected. However, current literature is often plagued by methodological inconsistencies and focused on disease mechanisms in European populations. Health inequalities in low- and middle-income countries (LMICs) are exacerbated by these challenges, with sub-Saharan Africa, and Nigeria specifically, facing unique difficulties such as clinical personnel shortages and limited research infrastructure. This review explored disparities in periodontal disease research, care and outcomes in African populations. We highlighted these disparities and identified the factors contributing to inequities in periodontal health outcomes. We further demonstrated the critical need for inclusive and equitable healthcare and research practices tailored to the unique challenges faced by diverse populations and regions with limited resources. Addressing these disparities is essential for ensuring that advancements in healthcare are accessible to all, thereby improving global oral health and general health.

Therapeutic targeting of senescent cells in the CNS

Senescent cells accumulate throughout the body with advanced age, diseases and chronic conditions. They negatively impact health and function of multiple systems, including the central nervous system (CNS). Therapies that target senescent cells, broadly referred to as senotherapeutics, recently emerged as potentially important treatment strategies for the CNS. Promising therapeutic approaches involve clearing senescent cells by disarming their pro-survival pathways with 'senolytics'; or dampening their toxic senescence-associated secretory phenotype (SASP) using 'senomorphics'. Following the pioneering discovery of first-generation senolytics dasatinib and quercetin, dozens of additional therapies have been identified, and several promising targets are under investigation. Although potentially transformative, senotherapies are still in early stages and require thorough testing to ensure reliable target engagement, specificity, safety and efficacy. The limited brain penetrance and potential toxic side effects of CNS-acting senotherapeutics pose challenges for drug development and translation to the clinic. This Review assesses the potential impact of senotherapeutics for neurological conditions by summarizing preclinical evidence, innovative methods for target and biomarker identification, academic and industry drug development pipelines and progress in clinical trials.

Rapid LA-REIMS-based metabolic fingerprinting of serum discriminates aflatoxin-exposed from non-exposed pregnant women: a prospective cohort from the Butajira Nutrition, Mental Health, and Pregnancy (BUNMAP) Study in rural Ethiopia

To date, the changes in maternal metabolic response associated with prenatal aflatoxin exposure remain largely unknown. This study investigated the effects of prenatal aflatoxin exposure on the maternal serum metabolome in rural Ethiopia. A total of 309 pregnant women were enrolled prospectively, and their serum aflatoxin concentrations were measured using targeted liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Serum metabolic fingerprints were obtained using laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS), followed by combination of univariate and multivariate statistical modelling to evaluate changes in circulating metabolic features between aflatoxin-exposed and unexposed mothers and to select discriminatory metabolic features. The analysis revealed that 81.8% of women were exposed to aflatoxins, with a median concentration of 12.9 pg/mg albumin. The orthogonal partial least square discriminant analysis (OPLS-DA) regression model demonstrated significant disparities in the serum metabolome when comparing Ethiopian pregnant women with low vs high aflatoxin exposure. Thirty-two differentially expressed metabolic features were identified, affecting aminoacyl-tRNA biosynthesis pathway. Several discriminatory metabolites have been identified, including glutamine, tryptophan, tyrosine, carnosine, and 1-methylnicotinamide. In conclusion, our findings indicate that aflatoxin exposure during pregnancy have shown disparities in the maternal serum metabolome, primarily affecting protein synthesis. Further research is needed to identify specific metabolite biomarkers and elucidate the underlying mechanisms.

Microbial-transcriptome integrative analysis of heat stress effects on amino acid metabolism and lipid peroxidation in poultry jejunum

Despite the significant threat of heat stress to livestock animals, only a few studies have considered the potential relationship between broiler chickens and their microbiota. Therefore, this study examined microbial modifications, transcriptional changes and host-microbiome interactions using a predicted metabolome data-based approach to understand the impact of heat stress on poultry. After the analysis, the host functional enrichment analysis revealed that pathways related to lipid and protein metabolism were elevated under heat stress conditions. In contrast, pathways related to the cell cycle were suppressed under normal environmental temperatures. In line with the transcriptome analysis, the microbial analysis results indicate that taxonomic changes affect lipid degradation. Heat stress engendered statistically significant difference in the abundance of 11 microorganisms, including Bacteroides and Peptostreptococcacea. Together, integrative approach analysis suggests that microbiota-induced metabolites affect host fatty acid peroxidation metabolism, which is correlated with the gene families of Acyl-CoA dehydrogenase long chain (ACADL), Acyl-CoA Oxidase (ACOX) and Acetyl-CoA Acyltransferase (ACAA). This integrated approach provides novel insights into heat stress problems and identifies potential biomarkers associated with heat stress.

Comprehensive multi-omics profiling identifies novel molecular subtypes of pancreatic ductal adenocarcinoma

Pancreatic cancer, a highly fatal malignancy, is predicted to rank as the second leading cause of cancer-related death in the next decade. This highlights the urgent need for new insights into personalized diagnosis and treatment. Although molecular subtypes of pancreatic cancer were well established in genomics and transcriptomics, few known molecular classifications are translated to guide clinical strategies and require a paradigm shift. Notably, chronically developing and continuously improving high-throughput technologies and systems serve as an important driving force to further portray the molecular landscape of pancreatic cancer in terms of epigenomics, proteomics, metabonomics, and metagenomics. Therefore, a more comprehensive understanding of molecular classifications at multiple levels using an integrated multi-omics approach holds great promise to exploit more potential therapeutic options. In this review, we recapitulated the molecular spectrum from different omics levels, discussed various subtypes on multi-omics means to move one step forward towards bench-to-beside translation of pancreatic cancer with clinical impact, and proposed some methodological and scientific challenges in store.

Salidroside attenuates sepsis-induced acute lung injury by inhibiting ferroptosis-dependent pathway

Sepsis triggers a systemic inflammatory response that can lead to acute lung injury (ALI). Salidroside (SAL) has many pharmacological activities such as antiinflammatory and anti-oxidation. The objective of the study was to explore the mechanism of SAL on ALI caused by sepsis. A model of ALI in septic mice was established by cecal ligation and puncture. Following SAL treatment, the effect of SAL on the ferroptosis pathway in mice was analyzed. The pathological damage of lung tissue, the levels of inflammatory factors and apoptosis in bronchoalveolar lavage fluid (BALF) of mice were evaluated, and the changes of gene expression level and metabolite content abundance were explored by combining transcriptomics and metabolomics analysis. The effect of SAL on ferroptosis in mice with lung injury was observed by intraperitoneal injection of ferroptosis activator Erastin or ferroptosis inhibitor Ferrostatin-1 to promote or inhibit ferroptosis in mice. SAL significantly alleviated the pathological damage of lung tissue, decreased the number of TUNEL positive cells and the levels of TNF-α, IL-1β, IL-6 in BALF, and increased the level of IL- 10 in lung injury mice. Moreover, the Fe2+ content and malondialdehyde decreased significantly, the reactive oxygen species and glutathione content increased significantly, and the arachidonic acid metabolites 20-hydroxyeicosatetraenoic acid (20- HETE), (5Z, 8Z, 10E, 14Z)-12-Oxoeicosa-5,8,10,14-tetraenoic acid (12-OxOETE), (5Z, 8Z, 10E, 14Z)-(12S)-12-Hydroxyeicosa-5,8,10,14-tetraenoic acid (12(S)-HETE), (5Z, 8Z, 14Z)-11,12-Dihydroxyeicosa-5,8,14-trienoic acid (11,12-DHET), (5Z, 11Z, 14Z)-8,9- Dihydroxyeicosa-5,11,14-trienoic acid, Leukotriene B4, Leukotriene D4 were significantly up-regulated after SAL treatment. Salidroside alleviates ALI caused by sepsis by inhibiting ferroptosis.

Advances in omics data for eosinophilic esophagitis: moving towards multi-omics analyses

Eosinophilic esophagitis (EoE) is a chronic, allergic inflammatory disease of the esophagus characterized by eosinophil accumulation and has a growing global prevalence. EoE significantly impairs quality of life and poses a substantial burden on healthcare resources. Currently, only two FDA-approved medications exist for EoE, highlighting the need for broader research into its management and prevention. Recent advancements in omics technologies, such as genomics, epigenetics, transcriptomics, proteomics, and others, offer new insights into the genetic and immunologic mechanisms underlying EoE. Genomic studies have identified genetic loci and mutations associated with EoE, revealing predispositions that vary by ancestry and indicating EoE's complex genetic basis. Epigenetic studies have uncovered changes in DNA methylation and chromatin structure that affect gene expression, influencing EoE pathology. Transcriptomic analyses have revealed a distinct gene expression profile in EoE, dominated by genes involved in activated type 2 immunity and epithelial barrier function. Proteomic approaches have furthered the understanding of EoE mechanisms, identifying potential new biomarkers and therapeutic targets. However, challenges in integrating diverse omics data persist, largely due to their complexity and the need for advanced computational methods. Machine learning is emerging as a valuable tool for analyzing extensive and intricate datasets, potentially revealing new aspects of EoE pathogenesis. The integration of multi-omics data through sophisticated computational approaches promises significant advancements in our understanding of EoE, improving diagnostics, and enhancing treatment effectiveness. This review synthesizes current omics research and explores future directions for comprehensively understanding the disease mechanisms in EoE.

A comprehensive study on the longissius dorsi muscle of Ashdan yaks under different feeding regimes based on transcriptomic and metabolomic analyses

Yak is an important dominant livestock species at high altitude, and the growth performance of yak has obvious differences under different feeding methods. This experiment was conducted to compare the effects of different feeding practices on growth performance and meat quality of yaks through combined transcriptomic and metabolomic analyses. In terms of yak growth performance, compared with traditional grazing, in-house feeding can significantly improve the average daily weight gain, carcass weight and net meat weight of yaks; in terms of yak meat quality, in-house feeding can effectively improve the quality of yak meat. A combined transcriptomic and metabolomic analysis revealed 31 co-enriched pathways, among which arginine metabolism, proline metabolism and glycerophospholipid metabolism may be involved in the development of the longissimus dorsi muscle of yak and the regulation of meat quality-related traits. The experimental results increased our understanding of yak meat quality and provided data materials for subsequent deep excavation of the mechanism of yak meat quality.

A duty to enhance? Genetic engineering for the human Mars settlement

Humans living off-world will face numerous physical, psychological and social challenges and are likely to suffer negative health effects due to their lack of evolutionary adaptation to space environments. While some of the necessary adaptations may develop naturally over many generations, genetic technologies could be used to speed this process along, potentially improving the wellbeing of early space settlers and their offspring. With broad support, such a program could lead to significant genetic modification of off-world communities, for example, to limit radiation damage on body systems or prevent bone and muscle loss in reduced gravity conditions. Given the extreme stressors of living off-world, and the need to have a healthy workforce to support a fledgling human settlement, those in favour of using genetic technologies to enhance settlers might even claim there is a moral imperative to protect their health in the face of the unique threats of space travel, especially for children born in settlements who did not take on these risks voluntarily. For some, this might simply be an extension of procreative beneficence. However, ethical concerns arise regarding the risks of embracing a eugenicist agenda and the potential impacts on the rights of future settlers to refuse such genetic enhancements for themselves or their children.

Advancement in Multi-omics approaches for Uterine Sarcoma

Uterine sarcoma (US) is a rare malignant tumor that has various pathological types and high heterogeneity in the female reproductive system. Its subtle early symptoms, frequent recurrence, and resistance to radiation and chemotherapy make the prognosis for US patients very poor. Therefore, understanding the molecular mechanisms underlying tumorigenesis and progression is essential for an accurate diagnosis and targeted therapy to improve patient outcomes. Recent advancements in high-throughput molecular sequencing have allowed for a deeper understanding of diseases through multi-omics technologies. In this review, the latest progress and future potential of multi-omics technologies in US research is examined, and their roles in biomarker discovery and their application in the precise diagnosis and treatment of US are highlighted.

Progress in research of proteomics related to digestive system tumor markers

The incidence and mortality of digestive system tumors are high. Even though the number of methods for tumor diagnosis and treatment is increasing, most of these tumors still cannot be diagnosed early, and their prognosis is poor. The lack of effective biomarkers and therapeutic targets is the reason why they cannot be diagnosed early and treated effectively. With the continuous development of proteomics technology, proteomics has become increasingly valuable in exploring the mechanisms of tumorigenesis and searching for biomarkers and drug targets. This article reviews the application progress of proteomics technology in screening of biomarkers for digestive system tumors, with an aim to provide new ideas for early diagnosis, prognosis, and treatment of digestive system tumors.

Construction of Protein Sequence Databases for Metaproteomics: A Review of the Current Tools and Databases

In metaproteomics studies, constructing a reference protein sequence database that is both comprehensive and not overly large is critical for the peptide identification step. Therefore, the availability of well-curated reference databases and tools for custom database construction is essential to enhance the performance of metaproteomics analyses. In this review, we first provide an overview of metaproteomics by presenting a concise historical background, outlining a typical experimental and bioinformatics workflow, emphasizing the crucial step of constructing a protein sequence database for metaproteomics. We then delve into the current tools available for building such databases, highlighting their individual approaches, utility, and advantages and limitations. Next, we examine existing protein sequence databases, detailing their scope and relevance in metaproteomics research. Then, we provide practical recommendations for constructing protein sequence databases for metaproteomics, along with an overview of the current challenges in this area. We conclude with a discussion of anticipated advancements, emerging trends, and future directions in the construction of protein sequence databases for metaproteomics.

V. S, Sekhar Mukhopadhyay C, Kashyap N, Sharma V, Singh N, Salajegheh Tazerji S, Kalantari R, Hajipour P, Singh Malik Y. Animal Wellness: The Power of Multiomics and Integrative Strategies: Multiomics in Improving Animal Health

The livestock industry faces significant challenges, with disease outbreaks being a particularly devastating issue. These diseases can disrupt the food supply chain and the livelihoods of those involved in the sector. To address this, there is a growing need to enhance the health and well-being of livestock animals, ultimately improving their performance while minimizing their environmental impact. To tackle the considerable challenge posed by disease epidemics, multiomics approaches offer an excellent opportunity for scientists, breeders, and policymakers to gain a comprehensive understanding of animal biology, pathogens, and their genetic makeup. This understanding is crucial for enhancing the health of livestock animals. Multiomic approaches, including phenomics, genomics, epigenomics, metabolomics, proteomics, transcriptomics, microbiomics, and metaproteomics, are widely employed to assess and enhance animal health. High-throughput phenotypic data collection allows for the measurement of various fitness traits, both discrete and continuous, which, when mathematically combined, define the overall health and resilience of animals, including their ability to withstand diseases. Omics methods are routinely used to identify genes involved in host-pathogen interactions, assess fitness traits, and pinpoint animals with disease resistance. Genome-wide association studies (GWAS) help identify the genetic factors associated with health status, heat stress tolerance, disease resistance, and other health-related characteristics, including the estimation of breeding value. Furthermore, the interaction between hosts and pathogens, as observed through the assessment of host gut microbiota, plays a crucial role in shaping animal health and, consequently, their performance. Integrating and analyzing various heterogeneous datasets to gain deeper insights into biological systems is a challenging task that necessitates the use of innovative tools. Initiatives like MiBiOmics, which facilitate the visualization, analysis, integration, and exploration of multiomics data, are expected to improve prediction accuracy and identify robust biomarkers linked to animal health. In this review, we discuss the details of multiomics concerning the health and well-being of livestock animals.

RettDb: the Rett syndrome omics database to navigate the Rett syndrome genomic landscape

Rett syndrome (RTT) is a neurodevelopmental disorder occurring almost exclusively in females and leading to a variety of impairments and disabilities from mild to severe. In >95% cases, RTT is due to mutations in the X-linked gene MECP2, but the molecular mechanisms determining RTT are unknown at present, and the complexity of the system is challenging. To facilitate and provide guidance to the unraveling of those mechanisms, we developed a database resource for the visualization and analysis of the genomic landscape in the context of wild-type or mutated Mecp2 gene in the mouse model. Our resource allows for the exploration of differential dynamics of gene expression and the prediction of new potential MECP2 target genes to decipher the RTT disorder molecular mechanisms. Database URL: https://biomedinfo.di.unipi.it/rett-database/.

AKT/mTOR-mediated autophagic signaling is associated with TCDD-induced cleft palate

In utero exposure to the environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) can contribute to high rates of cleft palate (CP) formation, but the mechanistic basis for these effects remains uncertain. Here, multi-omics-based metabolomic and transcriptomic analyses were employed to characterize the etiological basis for TCDD-induced CP on gestational day 14.5 (GD14.5). These analyses revealed that TCDD-induced CP formation is associated with calcium, MAPK, PI3K-Akt, and mTOR pathway signaling. PI3K-Akt and mTOR signaling activity is closely linked with the maintenance of cellular proliferation and survival. Moreover, mTOR-mediated regulation of autophagic activity is essential for ensuring an appropriate balance between metabolic activity and growth. Murine embryonic palatal mesenchymal (MEPM) cell proliferation was thus characterized, autophagic activity in these cells was evaluated through electron microscopy and western immunoblotting was used to compare the levels of autophagy- and AKT/mTOR-related protein between the control and TCDD groups on GD14.5. These analyses indicated that MEPM cell proliferative and autophagic activity was inhibited in response to TCDD exposure with the concomitant activation of AKT/mTOR signaling, in line with the multi-omics data. Together, these findings suggested that following TCDD exposure, the activation of AKT/mTOR-related autophagic signaling may play a role in the loss of appropriate palatal cell homeostasis, culminating in the incidence of CP.

Quantitative trait locus mapping in placenta: A comparative study of chorionic villus and birth placenta

The placenta, a pivotal player in the prenatal environment, holds crucial insights into early developmental pathways and future health outcomes. In this study, we explored genetic molecular regulation in chorionic villus samples (CVS) from the first trimester and placenta tissue at birth. We assessed quantitative trait locus (QTL) mapping on DNA methylation and gene expression data in a Finnish cohort of 574 individuals. We found more QTLs in birth placenta than in first-trimester placenta. Nevertheless, a substantial amount of associations overlapped in their effects and showed consistent direction in both tissues, with increasing molecular genetic effects from early pregnancy to birth placenta. The identified QTLs in birth placenta were most enriched in genes with placenta-specific expression. Conducting a phenome-wide-association study (PheWAS) on the associated SNPs, we observed numerous overlaps with genome-wide association study (GWAS) hits (spanning 57 distinct traits and 23 SNPs), with notable enrichments for immunological, skeletal, and respiratory traits. The QTL-SNP rs1737028 (chr6:29737993) presented with the highest number of GWAS hits. This SNP was related to HLA-G expression via DNA methylation and was associated with various immune, respiratory, and psychiatric traits. Our findings implicate increasing genetic molecular regulation during the course of pregnancy and support the involvement of placenta gene regulation, particularly in immunological traits. This study presents a framework for understanding placenta-specific gene regulation during pregnancy and its connection to health-related traits.

Personalized Treatment Strategies via Integration of Gene Expression Biomarkers in Molecular Profiling of Laryngeal Cancer

Laryngeal cancer poses a substantial challenge in head and neck oncology, and there is a growing focus on customized medicine techniques. The present state of gene expression indicators in laryngeal cancer and their potential to inform tailored therapy choices are thoroughly examined in this review. We examine significant molecular changes, such as TP53, CDKN2A, PIK3CA, and NOTCH1 mutations, which have been identified as important participants in the development of laryngeal cancer. The study investigates the predictive and prognostic significance of these genetic markers in addition to the function of epigenetic changes such as the methylation of the MGMT promoter. We also go over the importance of cancer stem cell-related gene expression patterns, specifically CD44 and ALDH1A1 expression, in therapy resistance and disease progression. The review focuses on indicators, including PD-L1, CTLA-4, and tumor mutational burden (TMB) in predicting immunotherapy responses, highlighting recent developments in our understanding of the intricate interactions between tumor genetics and the immune milieu. We also investigate the potential for improving prognosis accuracy and treatment selection by the integration of multi-gene expression panels with clinicopathological variables. The necessity for uniform testing and interpretation techniques is one of the difficulties, in implementing these molecular insights into clinical practice, that are discussed. This review seeks to provide a comprehensive framework for promoting personalized cancer therapy by combining the most recent data on gene expression profiling in laryngeal cancer. Molecularly guided treatment options may enhance patient outcomes.

Employing Multi-Omics Analyses to Understand Changes during Kidney Development in Perinatal Interleukin-6 Animal Model

Chronic kidney disease (CKD) is a leading cause of morbidity and mortality globally. Maternal obesity during pregnancy is linked to systemic inflammation and elevated levels of the pro-inflammatory cytokine interleukin-6 (IL-6). In our previous work, we demonstrated that increased maternal IL-6 during gestation impacts intrauterine development in mice. We hypothesized that IL-6-induced inflammation alters gene expression in the developing fetus. To test this, pregnant mice were administered IL-6 or saline during mid-gestation. Newborn mouse kidneys were analyzed using mRNA-seq, miRNA-seq and whole-genome bisulfite-seq (WGBS). A multi-omics approach was employed to quantify mRNA gene expression, miRNA expression and DNA methylation, using advanced bioinformatics and data integration techniques. Our analysis identified 19 key genes present in multiple omics datasets, regulated by epigenetics and miRNAs. We constructed a regulatory network for these genes, revealing disruptions in pathways such as Mannose type O-glycan biosynthesis, the cell cycle, apoptosis and FoxO signaling. Notably, the Atp7b gene was regulated by DNA methylation and miR-223 targeting, whereas the Man2a1 gene was controlled by DNA methylation affecting energy metabolism. These findings suggest that these genes may play a role in fetal programming, potentially leading to CKD later in life due to gestational inflammation.

TransCDR: a deep learning model for enhancing the generalizability of drug activity prediction through transfer learning and multimodal data fusion

BACKGROUND

Accurate and robust drug response prediction is of utmost importance in precision medicine. Although many models have been developed to utilize the representations of drugs and cancer cell lines for predicting cancer drug responses (CDR), their performances can be improved by addressing issues such as insufficient data modality, suboptimal fusion algorithms, and poor generalizability for novel drugs or cell lines.

RESULTS

We introduce TransCDR, which uses transfer learning to learn drug representations and fuses multi-modality features of drugs and cell lines by a self-attention mechanism, to predict the IC50 values or sensitive states of drugs on cell lines. We are the first to systematically evaluate the generalization of the CDR prediction model to novel (i.e., never-before-seen) compound scaffolds and cell line clusters. TransCDR shows better generalizability than 8 state-of-the-art models. TransCDR outperforms its 5 variants that train drug encoders (i.e., RNN and AttentiveFP) from scratch under various scenarios. The most critical contributors among multiple drug notations and omics profiles are Extended Connectivity Fingerprint and genetic mutation. Additionally, the attention-based fusion module further enhances the predictive performance of TransCDR. TransCDR, trained on the GDSC dataset, demonstrates strong predictive performance on the external testing set CCLE. It is also utilized to predict missing CDRs on GDSC. Moreover, we investigate the biological mechanisms underlying drug response by classifying 7675 patients from TCGA into drug-sensitive or drug-resistant groups, followed by a Gene Set Enrichment Analysis.

CONCLUSIONS

TransCDR emerges as a potent tool with significant potential in drug response prediction.

Saliva metabolomics: concepts and applications in oral disorders

OBJECTIVES

The purpose of this review was to present the basic concepts of metabolomics methodology and the use of saliva for diagnostic, prognostic, and predictive strategies.

MATERIAL AND METHODS

This review followed the focus in: "saliva metabolomics" and "oral diseases". The authors searched studies on PubMed database. The inclusion criteria were original studies and reviews that assessed metabolomics techniques. A descriptive analysis was performed considering the study design, approach system, clinical steps, and tools for the determination of profile or biomarkers metabolites, and the advantages and disadvantages.

RESULTS

Metabolomic analyses use a combination of analytical instrumentation and informatic tools to provide information on metabolite characteristics. In this review we described different technologies applied and the advantages and limitations of each technique. Furthermore, in the literature search, we retrieved 25 studies that investigated saliva metabolites in oral diseases: 8 studies used targeted analysis and 17 untargeted metabolomics approaches. Most studies included patients with periodontal diseases, oral squamous cell carcinoma, and Sjögren Syndrome. The most frequently reported metabolites were glycine, leucine, phenylalanine, dipeptides, linoleic acid, arachidonic acid, tyrosine, choline, taurine, lactate, valine, and proline.

CONCLUSIONS

Metabolomics analysis has emerged as a powerful tool for tumor diagnosis and to enhance tumor classification, including salivary gland tumors (SGTs). It also holds promise for developing personalized treatment plans and defining more precise prognostic categories.

CLINICAL RELEVANCE

Metabolomics is the most functional and comprehensive technique for monitoring and understanding gene functions and identifying the biochemical state of an organism in response to genetic and environmental changes.

Towards personalized vaccines

The emergence of vaccinomics and system vaccinology represents a transformative shift in immunization strategies, advocating for personalized vaccines tailored to individual genetic and immunological profiles. Integrating insights from genomics, transcriptomics, proteomics, and immunology, personalized vaccines offer the promise of enhanced efficacy and safety, revolutionizing the field of vaccinology. However, the development of personalized vaccines presents multifaceted challenges, including technical, ethical, economic, and regulatory considerations. Addressing these challenges is essential to ensure equitable access and safety of personalized vaccination strategies. Despite these hurdles, the potential of personalized vaccines to optimize responses and mitigate disease burden underscores the significance of ongoing research and collaboration in advancing precision medicine in immunization.

Liquid biopsy for early cancer detection: technological revolutions and clinical dilemma

INTRODUCTION

Liquid biopsy is an innovative advancement in oncology, offering a noninvasive method for early cancer detection and monitoring by analyzing circulating tumor cells, DNA, RNA, and other biomarkers in bodily fluids. This technique has the potential to revolutionize precision oncology by providing real-time analysis of tumor dynamics, enabling early detection, monitoring treatment responses, and tailoring personalized therapies based on the molecular profiles of individual patients.

AREAS COVERED

In this review, the authors discuss current methodologies, technological challenges, and clinical applications of liquid biopsy. This includes advancements in detecting minimal residual disease, tracking tumor evolution, and combining liquid biopsy with other diagnostic modalities for precision oncology. Key areas explored are the sensitivity, specificity, and integration of multi-omics, AI, ML, and LLM technologies.

EXPERT OPINION

Liquid biopsy holds great potential to revolutionize cancer care through early detection and personalized treatment strategies. However, its success depends on overcoming technological and clinical hurdles, such as ensuring high sensitivity and specificity, interpreting results amidst tumor heterogeneity, and making tests accessible and affordable. Continued innovation and collaboration are crucial to fully realize the potential of liquid biopsy in improving early cancer detection, treatment, and monitoring.

Assessing and mitigating batch effects in large-scale omics studies

Batch effects in omics data are notoriously common technical variations unrelated to study objectives, and may result in misleading outcomes if uncorrected, or hinder biomedical discovery if over-corrected. Assessing and mitigating batch effects is crucial for ensuring the reliability and reproducibility of omics data and minimizing the impact of technical variations on biological interpretation. In this review, we highlight the profound negative impact of batch effects and the urgent need to address this challenging problem in large-scale omics studies. We summarize potential sources of batch effects, current progress in evaluating and correcting them, and consortium efforts aiming to tackle them.

Application of spatial omics in gastric cancer

Gastric cancer (GC), a globally prevalent and lethal malignancy, continues to be a key research focus. However, due to its considerable heterogeneity and complex pathogenesis, the treatment and diagnosis of gastric cancer still face significant challenges. With the rapid development of spatial omics technology, which provides insights into the spatial information within tumor tissues, it has emerged as a significant tool in gastric cancer research. This technology affords new insights into the pathology and molecular biology of gastric cancer for scientists. This review discusses recent advances in spatial omics technology for gastric cancer research, highlighting its applications in the tumor microenvironment (TME), tumor heterogeneity, tumor genesis and development mechanisms, and the identification of potential biomarkers and therapeutic targets. Moreover, this article highlights spatial omics' potential in precision medicine and summarizes existing challenges and future directions. It anticipates spatial omics' continuing impact on gastric cancer research, aiming to improve diagnostic and therapeutic approaches for patients. With this review, we aim to offer a comprehensive overview to scientists and clinicians in gastric cancer research, motivating further exploration and utilization of spatial omics technology. Our goal is to improve patient outcomes, including survival rates and quality of life.

Utilizing omics technologies in the investigation of sepsis-induced cardiomyopathy

Sepsis-induced cardiomyopathy (SIC) is a common and high-mortality complication among critically ill patients. Uncertainties persist regarding the pathogenesis, pathophysiology, and diagnosis of SIC, underscoring the necessity to investigate potential biological mechanisms. With the rise of omics technologies, leveraging their high throughput and big data advantages, a systems biology perspective is employed to study the biological processes of SIC. This approach aids in gaining a better understanding of the disease's onset, progression, and outcomes, ultimately providing improved guidance for clinical practices. This review summarizes the currently applied omics technologies, omics studies related to SIC, and relevant omics databases.

Advancing personalized medicine: Integrating statistical algorithms with omics and nano-omics for enhanced diagnostic accuracy and treatment efficacy

Medical laboratory services enable precise measurement of thousands of biomolecules and have become an inseparable part of high-quality healthcare services, exerting a profound influence on global health outcomes. The integration of omics technologies into laboratory medicine has transformed healthcare, enabling personalized treatments and interventions based on individuals' distinct genetic and metabolic profiles. Interpreting laboratory data relies on reliable reference values. Presently, population-derived references are used for individuals, risking misinterpretation due to population heterogeneity, and leading to medical errors. Thus, personalized references are crucial for precise interpretation of individual laboratory results, and the interpretation of omics data should be based on individualized reference values. We reviewed recent advancements in personalized laboratory medicine, focusing on personalized omics, and discussed strategies for implementing personalized statistical approaches in omics technologies to improve global health and concluded that personalized statistical algorithms for interpretation of omics data have great potential to enhance global health. Finally, we demonstrated that the convergence of nanotechnology and omics sciences is transforming personalized laboratory medicine by providing unparalleled diagnostic precision and innovative therapeutic strategies.

Revolutionizing healthcare and medicine: The impact of modern technologies for a healthier future-A comprehensive review

The increasing integration of new technologies is driving a fundamental revolution in the healthcare sector. Developments in artificial intelligence (AI), machine learning, and big data analytics have completely transformed the diagnosis, treatment, and care of patients. AI-powered solutions are enhancing the efficiency and accuracy of healthcare delivery by demonstrating exceptional skills in personalized medicine, early disease detection, and predictive analytics. Furthermore, telemedicine and remote patient monitoring systems have overcome geographical constraints, offering easy and accessible healthcare services, particularly in underserved areas. Wearable technology, the Internet of Medical Things, and sensor technologies have empowered individuals to take an active role in tracking and managing their health. These devices facilitate real-time data collection, enabling preventive and personalized care. Additionally, the development of 3D printing technology has revolutionized the medical field by enabling the production of customized prosthetics, implants, and anatomical models, significantly impacting surgical planning and treatment strategies. Accepting these advancements holds the potential to create a more patient-centered, efficient healthcare system that emphasizes individualized care, preventive care, and better overall health outcomes. This review's novelty lies in exploring how these technologies are radically transforming the healthcare industry, paving the way for a more personalized and effective healthcare for all. It highlights the capacity of modern technology to revolutionize healthcare delivery by addressing long-standing challenges and improving health outcomes. Although the approval and use of digital technology and advanced data analysis face scientific and regulatory obstacles, they have the potential for transforming translational research. as these technologies continue to evolve, they are poised to significantly alter the healthcare environment, offering a more sustainable, efficient, and accessible healthcare ecosystem for future generations. Innovation across multiple fronts will shape the future of advanced healthcare technology, revolutionizing the provision of healthcare, enhancing patient outcomes, and equipping both patients and healthcare professionals with the tools to make better decisions and receive personalized treatment. As these technologies continue to develop and become integrated into standard healthcare practices, the future of healthcare will probably be more accessible, effective, and efficient than ever before.

Special Issue "Bioinformatics Study in Human Diseases: Integration of Omics Data for Personalized Medicine"

The field of bioinformatics has made remarkable strides in recent years, revolutionizing our approach to understanding and treating human diseases [...].

Enhancing Non-Small Cell Lung Cancer Survival Prediction through Multi-Omics Integration Using Graph Attention Network

Background: Cancer survival prediction is vital in improving patients' prospects and recommending therapies. Understanding the molecular behavior of cancer can be enhanced through the integration of multi-omics data, including mRNA, miRNA, and DNA methylation data. In light of these multi-omics data, we proposed a graph attention network (GAT) model in this study to predict the survival of non-small cell lung cancer (NSCLC). Methods: The different omics data were obtained from The Cancer Genome Atlas (TCGA) and preprocessed and combined into a single dataset using the sample ID. We used the chi-square test to select the most significant features to be used in our model. We used the synthetic minority oversampling technique (SMOTE) to balance the dataset and the concordance index (C-index) to measure the performance of our model on different combinations of omics data. Results: Our model demonstrated superior performance, with the highest value of the C-index obtained when we used both mRNA and miRNA data. This demonstrates that the multi-omics approach could be effective in predicting survival. Further pathway analysis conducted with KEGG showed that our GAT model provided high weights to the features that are associated with the viral entry pathways, such as the Epstein-Barr virus and Influenza A pathways, which are involved in lung cancer development. From our findings, it can be observed that the proposed GAT model leads to a significantly improved prediction of survival by exploiting the strengths of multiple omics datasets and the findings from the enriched pathways. Our GAT model outperforms other state-of-the-art methods that are used for NSCLC prediction. Conclusions: In this study, we developed a new model for the survival prediction of NSCLC using the GAT based on multi-omics data. Our model showed outstanding predictive values, and the KEGG analysis of the selected significant features showed that they were implicated in pivotal biological processes underlying pathways such as Influenza A and the Epstein-Barr virus infection, which are linked to lung cancer progression.

Home-ics: how experiences of the home impact biology and child neurodevelopmental outcomes

Studies on the -omics of child neurodevelopmental outcomes, e.g. genome, epigenome, microbiome, metabolome, and brain connectome aim to enable data-driven precision health to improve these outcomes, or deliver the right intervention, to the right child, at the right time. However, evidence suggests that neurodevelopmental outcomes are shaped by modifiable socioenvironmental factors. Everyday exposures including family and neighbourhood-level socioeconomic status, housing conditions, and interactions with those living in the home, are strongly associated with child health and have been suggested to alter -omics. Our aim was to review and understand the biological pathways by which home factors contribute to child neurodevelopment outcomes. We review studies suggestive of the home factors contributing to neurodevelopmental outcomes that encompass the hypothalamic-pituitary-adrenal axis, the brain, the gut-brain-axis, and the immune system. We thus conceptualize home-ics as the study of how the multi-faceted living environment can impact neurodevelopmental outcomes through biology and highlight the importance of targeting the modifiable aspects of a child's home to optimize outcomes. We encourage clinicians and health care providers to routinely assess home factors in patient encounters, and counsel families on modifiable aspects of the home. We conclude by discussing clinical and policy implications and future research directions of home-ics. IMPACT: Home-ics can be conceptualized as the study of how home factors may shape child neurodevelopmental outcomes through altering biology. Targeting modifiable aspects of a child's home environment (e.g. parenting style, early intervention, enriched environment) may lead to improved neurodevelopmental outcomes. Clinicians should routinely assess home factors and counsel families on modifiable aspects of the home.

Proteomic Signatures of Multisystem Inflammatory Syndrome in Children (MIS-C) Associated with COVID-19: A Narrative Review

BACKGROUND/OBJECTIVES

Multisystem Inflammatory Syndrome in Children (MIS-C) is a post-infectious complication of COVID-19. MIS-C has overlapping features with other pediatric inflammatory disorders including Kawasaki Disease (KD), Macrophage Activation Syndrome (MAS), Toxic Shock Syndrome and sepsis. The exact mechanisms responsible for the clinical overlap between MIS-C and these conditions remain unclear, and biomarkers that could distinguish MIS-C from its clinical mimics are lacking. This study aimed to provide an overview of how proteomic methods, like Mass Spectrometry (MS) and affinity-based proteomics, can offer a detailed understanding of pathophysiology and aid in the diagnosis and prognosis of MIS-C.

METHODS

A narrative review of relevant studies published up to July 2024 was conducted.

RESULTS

We identified 15 studies and summarized their key proteomic findings. These studies investigated the serum or plasma proteome of MIS-C patients using MS, Proximity Extension, or Aptamer-based assays. The studies associated the proteomic profile of MIS-C with laboratory and clinical parameters and/or compared it with that of other diseases including acute COVID-19, KD, MAS, pediatric rheumatic diseases, sepsis and myocarditis or pericarditis following COVID-19 mRNA immunization. Depending on the method and the control group, different proteins were increased or decreased in the MIS-C group. The limitations and challenges in MIS-C proteomic research are also discussed, and future research recommendations are provided.

CONCLUSIONS

Although proteomics appear to be a promising approach for understanding the pathogenesis and uncovering candidate biomarkers in MIS-C, proteomic studies are still needed to recognize and validate biomarkers that could accurately discriminate MIS-C from its clinical mimics.

Focus on Achalasia in the Omics Era

Achalasia is a rare and complex esophageal disease of unknown etiology characterized by difficulty in swallowing due to the lack of opening of the lower esophageal sphincter and the absence of esophageal peristalsis. Recent advancements in technology for analyzing DNA, RNA and biomolecules in high-throughput techniques are offering new opportunities to better understand the etiology and the pathogenetic mechanisms underlying achalasia. Through this narrative review of the scientific literature, we aim to provide a comprehensive assessment of the state-of-the-art knowledge on omics of achalasia, with particular attention to those considered relevant to the pathogenesis of the disease. The notion and importance of the multi-omics approach, its limitations and future directions are also introduced, and it is highlighted how the integration of single omics data will lead to new insights into the development of achalasia and offer clinical tools which will allow early diagnosis and better patient management.

Genome-scale models in human metabologenomics

Metabologenomics integrates metabolomics with other omics data types to comprehensively study the genetic and environmental factors that influence metabolism. These multi-omics data can be incorporated into genome-scale metabolic models (GEMs), which are highly curated knowledge bases that explicitly account for genes, transcripts, proteins and metabolites. By including all known biochemical reactions catalysed by enzymes and transporters encoded in the human genome, GEMs analyse and predict the behaviour of complex metabolic networks. Continued advancements to the scale and scope of GEMs - from cells and tissues to microbiomes and the whole body - have helped to design effective treatments and develop better diagnostic tools for metabolic diseases. Furthermore, increasing amounts of multi-omics data are incorporated into GEMs to better identify the underlying mechanisms, biomarkers and potential drug targets of metabolic diseases.

G2PDeep-v2: a web-based deep-learning framework for phenotype prediction and biomarker discovery using multi-omics data

The G2PDeep-v2 server is a web-based platform powered by deep learning, for phenotype prediction and markers discovery from multi-omics data in any organisms including humans, plants, animals, and viruses. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases and other biological processes. The G2PDeep-v2 server is publicly available at https://g2pdeep.org/.

Lifestyle differences between co-twins are associated with decreased similarity in their internal and external exposome profiles

Whether differences in lifestyle between co-twins are reflected in differences in their internal or external exposome profiles remains largely underexplored. We therefore investigated whether within-pair differences in lifestyle were associated with within-pair differences in exposome profiles across four domains: the external exposome, proteome, metabolome and epigenetic age acceleration (EAA). For each domain, we assessed the similarity of co-twin profiles using Gaussian similarities in up to 257 young adult same-sex twin pairs (54% monozygotic). We additionally tested whether similarity in one domain translated into greater similarity in another. Results suggest that a lower degree of similarity in co-twins' exposome profiles was associated with greater differences in their behavior and substance use. The strongest association was identified between excessive drinking behavior and the external exposome. Overall, our study demonstrates how social behavior and especially substance use are connected to the internal and external exposomes, while controlling for familial confounders.

Alkaloids of Aconiti Lateralis Radix Praeparata inhibit growth of non-small cell lung cancer by regulating PI3K/Akt-mTOR signaling and glycolysis

Aconiti Lateralis Radix Praeparata (Fuzi in Chinese) is widely used in the clinical treatment of tumors. This study aims to explore the active fractions and underlying mechanisms of Fuzi in the treatment of non-small cell lung cancer (NSCLC). Fuzi alkaloids (FZA) is prepared and found to inhibit the growth of NSCLC both in vitro and in vivo significantly. A total of 53 alkaloids are identified in FZA by UPLC-Q-TOF-MS. Proteomics experiment show that 238 differentially expressed proteins regulated by FZA are involved in amino acid anabolism, pyrimidine metabolism and PI3K/Akt-mTOR signaling pathway. Metabolomics analyses identify 32 significant differential metabolites which are mainly involved in amino acid metabolism, TCA cycle and other pathways. Multi-omics research combined with molecular biological assays suggest that FZA might regulate glycolysis through PI3K/Akt-mTOR pathway to treat NSCLC. The study lays a foundation for the anti-cancer investigation of Fuzi and provides a possible scientific basis for its clinical application.

Application of omics technologies in studies on antitumor effects of Traditional Chinese Medicine

Traditional Chinese medicine (TCM) is considered to be one of the most comprehensive and influential form of traditional medicine. It plays an important role in clinical treatment and adjuvant therapy for cancer. However, the complex composition of TCM presents challenges to the comprehensive and systematic understanding of its antitumor mechanisms, which hinders further development of TCM with antitumor effects. Omics technologies can immensely help in elucidating the mechanism of action of drugs. They utilize high-throughput sequencing and detection techniques to provide deeper insights into biological systems, revealing the intricate mechanisms through which TCM combats tumors. Multi-omics approaches can be used to elucidate the interrelationships among different omics layers by integrating data from various omics disciplines. By analyzing a large amount of data, these approaches further unravel the complex network of mechanisms underlying the antitumor effects of TCM and explain the mutual regulations across different molecular levels. In this study, we presented a comprehensive overview of the recent progress in single-omics and multi-omics research focused on elucidating the mechanisms underlying the antitumor effects of TCM. We discussed the significance of omics technologies in advancing research on the antitumor properties of TCM and also provided novel research perspectives and methodologies for further advancing this research field.

Stable biomarker discovery in multi-omics data via canonical correlation analysis

Multi-omics analysis offers a promising avenue to a better understanding of complex biological phenomena. In particular, untangling the pathophysiology of multifactorial health conditions such as the inflammatory bowel disease (IBD) could benefit from simultaneous consideration of several omics levels. However, taking full advantage of multi-omics data requires the adoption of suitable new tools. Multi-view learning, a machine learning technique that natively joins together heterogeneous data, is a natural source for such methods. Here we present a new approach to variable selection in unsupervised multi-view learning by applying stability selection to canonical correlation analysis (CCA). We apply our method, StabilityCCA, to simulated and real multi-omics data, and demonstrate its ability to find relevant variables and improve the stability of variable selection. In a case study on an IBD microbiome data set, we link together metagenomics and metabolomics, revealing a connection between their joint structure and the disease, and identifying potential biomarkers. Our results showcase the usefulness of multi-view learning in multi-omics analysis and demonstrate StabilityCCA as a powerful tool for biomarker discovery.

Application of multi-omics in the study of traditional Chinese medicine

Traditional Chinese medicine (TCM) is playing an increasingly important role in disease treatment due to the advantages of multi-target, multi-pathway mechanisms, low adverse reactions and cost-effectiveness. However, the complexity of TCM system poses challenges for research. In recent years, there has been a surge in the application of multi-omics integrated research to explore the active components and treatment mechanisms of TCM from various perspectives, which aids in advancing TCM's integration into clinical practice and holds immense importance in promoting modernization. In this review, we discuss the application of proteomics, metabolomics, and mass spectrometry imaging in the study of composition, quality evaluation, target identification, and mechanism of action of TCM based on existing literature. We focus on the workflows and applications of multi-omics based on mass spectrometry in the research of TCM. Additionally, potential research ideas for future exploration in TCM are outlined. Overall, we emphasize the advantages and prospects of multi-omics based on mass spectrometry in the study of the substance basis and mechanism of action of TCM. This synthesis of methodologies holds promise for enhancing our understanding of TCM and driving its further integration into contemporary medical practices.

AutoFocus: a hierarchical framework to explore multi-omic disease associations spanning multiple scales of biomolecular interaction

Recent advances in high-throughput measurement technologies have enabled the analysis of molecular perturbations associated with disease phenotypes at the multi-omic level. Such perturbations can range in scale from fluctuations of individual molecules to entire biological pathways. Data-driven clustering algorithms have long been used to group interactions into interpretable functional modules; however, these modules are typically constrained to a fixed size or statistical cutoff. Furthermore, modules are often analyzed independently of their broader biological context. Consequently, such clustering approaches limit the ability to explore functional module associations with disease phenotypes across multiple scales. Here, we introduce AutoFocus, a data-driven method that hierarchically organizes biomolecules and tests for phenotype enrichment at every level within the hierarchy. As a result, the method allows disease-associated modules to emerge at any scale. We evaluated this approach using two datasets: First, we explored associations of biomolecules from the multi-omic QMDiab dataset (n = 388) with the well-characterized type 2 diabetes phenotype. Secondly, we utilized the ROS/MAP Alzheimer's disease dataset (n = 500), consisting of high-throughput measurements of brain tissue to explore modules associated with multiple Alzheimer's Disease-related phenotypes. Our method identifies modules that are multi-omic, span multiple pathways, and vary in size. We provide an interactive tool to explore this hierarchy at different levels and probe enriched modules, empowering users to examine the full hierarchy, delve into biomolecular drivers of disease phenotype within a module, and incorporate functional annotations.

Environmental Temperature Variation Affects Brain Lipid Composition in Adult Zebrafish (Danio rerio)

This study delves deeper into the impact of environmental temperature variations on the nervous system in teleost fish. Previous research has demonstrated that exposing adult zebrafish (Danio rerio) to 18 °C and 34 °C for 4 or 21 days induces behavioural changes compared to fish kept at a control temperature of 26 °C, suggesting alterations in the nervous system. Subsequent studies revealed that these temperature conditions also modify brain protein expression, indicating potential neurotoxic effects. The primary aim of this work was to investigate the effects of prolonged exposure (21 days) to 18 °C or 34 °C on the brain lipidomes of adult zebrafish compared to a control temperature. Analysis of the brain lipidome highlighted significant alteration in the relative abundances of specific lipid molecules at 18 °C and 34 °C, confirming distinct effects induced by both tested temperatures. Exposure to 18 °C resulted in an increase in levels of phospholipids, such as phosphatidylethanolamine, alongside a general reduction in levels of sphingolipids, including sphingomyelin. Conversely, exposure to 34 °C produced more pronounced effects, with increases in levels of phosphatidylethanolamine and those of various sphingolipids such as ceramide, gangliosides, and sphingomyelin, alongside a reduction in levels of ether phospholipids, including lysophosphatidylethanolamine ether, phosphatidylethanolamine ether, and phosphatidylglycerol ether, as well as levels of glycolipids like monogalactosyldiacylglycerol. These results, when integrated with existing proteomic and behavioural data, offer new insights into the effects of thermal variations on the nervous system in teleost fish. Specifically, our proteomic and lipidomic findings suggest that elevated temperatures may disrupt mitochondrial function, increase neuronal susceptibility to oxidative stress and cytotoxicity, alter axonal myelination, impair nerve impulse transmission, hinder synapse function and neurotransmitter release, and potentially lead to increased neuronal death. These findings are particularly relevant in the fields of cell biology, neurobiology, and ecotoxicology, especially in the context of global warming.

Multi-omics integration reveals potential stage-specific druggable targets in T-cell acute lymphoblastic leukemia

T-cell acute lymphoblastic leukemia (T-ALL), a heterogeneous hematological malignancy, is caused by the developmental arrest of normal T-cell progenitors. The development of targeted therapeutic regimens is impeded by poor knowledge of the stage-specific aberrances in this disease. In this study, we performed multi-omics integration analysis, which included mRNA expression, chromatin accessibility, and gene-dependency database analyses, to identify potential stage-specific druggable targets and repositioned drugs for this disease. This multi-omics integration helped identify 29 potential pathological genes for T-ALL. These genes exhibited tissue-specific expression profiles and were enriched in the cell cycle, hematopoietic stem cell differentiation, and the AMPK signaling pathway. Of these, four known druggable targets (CDK6, TUBA1A, TUBB, and TYMS) showed dysregulated and stage-specific expression in malignant T cells and may serve as stage-specific targets in T-ALL. The TUBA1A expression level was higher in the early T cell precursor (ETP)-ALL cells, while TUBB and TYMS were mainly highly expressed in malignant T cells arrested at the CD4 and CD8 double-positive or single-positive stage. CDK6 exhibited a U-shaped expression pattern in malignant T cells along the naïve to maturation stages. Furthermore, mebendazole and gemcitabine, which target TUBA1A and TYMS, respectively, exerted stage-specific inhibitory effects on T-ALL cell lines, indicating their potential stage-specific antileukemic role in T-ALL. Collectively, our findings might aid in identifying potential stage-specific druggable targets and are promising for achieving more precise therapeutic strategies for T-ALL.

Body fluid multiomics in 3PM-guided ischemic stroke management: health risk assessment, targeted protection against health-to-disease transition, and cost-effective personalized approach are envisaged

Because of its rapid progression and frequently poor prognosis, stroke is the third major cause of death in Europe and the first one in China. Many independent studies demonstrated sufficient space for prevention interventions in the primary care of ischemic stroke defined as the most cost-effective protection of vulnerable subpopulations against health-to-disease transition. Although several studies identified molecular patterns specific for IS in body fluids, none of these approaches has yet been incorporated into IS treatment guidelines. The advantages and disadvantages of individual body fluids are thoroughly analyzed throughout the paper. For example, multiomics based on a minimally invasive approach utilizing blood and its components is recommended for real-time monitoring, due to the particularly high level of dynamics of the blood as a body system. On the other hand, tear fluid as a more stable system is recommended for a non-invasive and patient-friendly holistic approach appropriate for health risk assessment and innovative screening programs in cost-effective IS management. This article details aspects essential to promote the practical implementation of highlighted achievements in 3PM-guided IS management.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00376-2.