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Artificial intelligence (AI) has the potential to transform every facet of cardiovascular practice and research. The exponential rise in technology powered by AI is defining new frontiers in cardiovascular care, with innovations that span novel diagnostic modalities, new digital native biomarkers of disease, and high-performing tools evaluating care quality and prognosticating clinical outcomes. These digital innovations promise expanded access to cardiovascular screening and monitoring, especially among those without access to high-quality, specialized care historically. Moreover, AI is propelling biological and clinical discoveries that will make future cardiovascular care more personalized, precise, and effective. The review brings together these diverse AI innovations, highlighting developments in multimodal cardiovascular AI across clinical practice and biomedical discovery, and envisioning this new future backed by contemporary science and emerging discoveries. Finally, we define the critical path and the safeguards essential to realizing this AI-enabled future that helps achieve optimal cardiovascular health and outcomes for all. [Display omitted] • AI-enabled technologies are increasingly integrated into cardiovascular practice and investigation. • Over the next decade, we envision an AI-propelled future in which the cardiovascular diagnostic and therapeutic landscape will effectively leverage multimodal data at the point of care. • Innovations in biomedical discovery and cardiovascular research are also set to make the future of cardiovascular care more personalized, precise, and effective. • The path to this future requires equitable and regulated adoption that prioritizes fairness, equity, safety, and partnerships with innovators as well as our communities and society. [ABSTRACT FROM AUTHOR]

Tef research has little benefited from omics due to neglect of the crop by the global scientific community. However, the few utilized tools showed promising results. Chromosome-scale assembly of the genome reveals the evolution of tef. The whole-genome sequencing of drought-resurrecting Eragrostis nindensis and desiccation-sensitive Eragrostis curvula shows genes that play key roles in drought responses. A few studies using omics tools have revealed differentially expressed genes, proteins, and metabolites in tef plants exposed to drought. Molecular markers have deciphered diversity in tef germplasm collected from diverse agroecologies in Ethiopia. The application of targeting induced local lesions in genomes resulted in mutant tef lines with a semidwarf stature, which were later bred to locally adapted and high-yielding varieties. Genome editing using 'Green Revolution' genes resulted in a semidwarf and lodging-tolerant tef plant. Tef or teff [ Eragrostis tef (Zucc.) Trotter] is a cereal crop indigenous to the Horn of Africa, where it is a staple food for a large population. The popularity of tef arises from its resilience to environmental stresses and its nutritional value. For many years, tef has been considered an orphan crop, but recent research initiatives from across the globe are helping to unravel its undisclosed potential. Advanced omics tools and techniques have been directed toward the exploration of tef's diversity with the aim of increasing its productivity. In this review, we report on the most recent advances in tef omics that brought the crop into the spotlight of international research. [ABSTRACT FROM AUTHOR]

With the rapid development of molecular sequencing and imaging technology, the multi-omics of medicinal plants enters the single-cell era. We discuss spatial multi-omics applied in medicinal plants, evaluate the special products' biosynthesis pathways, and highlight the applications, perspectives, and challenges of biomanufacturing natural products (NPs). [ABSTRACT FROM AUTHOR]

[Display omitted] • Environmental concentrations of PS-MNPs increased body weight without affecting survivorship. • Exposures induced significant alterations in host metabolome, microbiome, and transcriptome. • Gut metabolites were correlated with the microbiome. • The observed biomarkers' responses were particle size-dependent i.e., PS-S > PS-M > PS-L. • Metabolic disorder and microbiome dysbiosis are the main mechanisms of the observed phenotype. Micro- and nanoplastics (MNPs) are emerging environmental pollutants that have raised serious concerns about their potential impact on ecosystem and organism health. Despite increasing efforts to investigate the impacts of micro- and nanoplastics (MNPs) on biota little is known about their potential impacts on terrestrial organisms, especially insects, at environmental concentrations. To address this gap, we used an insect model, silkworm Bombyx mori to examine the potential long-term impacts of different sizes of polystyrene (PS) MNPs at environmentally realistic concentrations (0.25 to 1.0 μg/mL). After exposure to PS-MNPs over most of the larval lifetime (from second to last instar), the endpoints were examined by an integrated physiological (growth and survival) and multiomics approach (metabolomics, 16S rRNA, and transcriptomics). Our results indicated that dietary exposures to PS-MNPs had no lethal effect on survivorship, but interestingly, increased host body weight. Multiomics analysis revealed that PS-MNPs exposure significantly altered multiple pathways, particularly lipid metabolism, leading to enriched energy reserves. Furthermore, the exposure changed the structure and composition of the gut microbiome and increased the abundance of gut bacteria Acinetobacter and Enterococcus. Notably, the predicted functional profiles and metabolite expressions were significantly correlated with bacterial abundance. Importantly, these observed effects were particle size-dependent and were ranked as PS-S (91.92 nm) > PS-M (5.69 µm) > PS-L (9.7 µm). Overall, PS-MNPs at environmentally realistic concentrations exerted stimulatory effects on energy metabolism that subsequently enhanced body weight in silkworms, suggesting that chronic PS-MNPs exposure might trigger weight gain in animals and humans by influencing host energy and microbiota homeostasis. [ABSTRACT FROM AUTHOR]

Forward genetic screens coupling CRISPR (clustered regularly interspaced short palindromic repeats) perturbations with single-cell sequencing have rapidly advanced in the past few years. Recent improvements include increased scale of perturbations – from screening hundreds to now millions of cells – and multimodal phenotypic readouts beyond transcriptomes, such as open chromatin and cell-surface proteins. Analysis of CRISPR perturbation screens with single-cell sequencing requires connecting cells with the delivered perturbations. For this purpose, initial screens used barcodes delivered in tandem with CRISPR guide RNAs (gRNAs) but were plagued by high rates of barcode swapping. It is now possible to directly capture the functional CRISPR gRNAs, thus improving the fidelity and utility of these screens. Multimodal single-cell CRISPR screens represent a major development in the toolbox of the molecular geneticist, and enable high-throughput studies of cell function in healthy and disease states. Programmable genome-engineering technologies, such as CRISPR (clustered regularly interspaced short palindromic repeats) nucleases and massively parallel CRISPR screens that capitalize on this programmability, have transformed biomedical science. These screens connect genes and noncoding genome elements to disease-relevant phenotypes, but until recently have been limited to individual phenotypes such as growth or fluorescent reporters of gene expression. By pairing massively parallel screens with high-dimensional profiling of single-cell types/states, we can now measure how individual genetic perturbations or combinations of perturbations impact the cellular transcriptome, proteome, and epigenome. We review technologies that pair CRISPR screens with single-cell multiomics and the unique opportunities afforded by extending pooled screens using deep multimodal phenotyping. Programmable genome-engineering technologies, such as CRISPR (clustered regularly interspaced short palindromic repeats) nucleases and massively parallel CRISPR screens that capitalize on this programmability, have transformed biomedical science. These screens connect genes and noncoding genome elements to disease-relevant phenotypes, but until recently have been limited to individual phenotypes such as growth or fluorescent reporters of gene expression. By pairing massively parallel screens with high-dimensional profiling of single-cell types/states, we can now measure how individual genetic perturbations or combinations of perturbations impact on the cellular transcriptome, proteome, and epigenome. We review technologies that pair CRISPR screens with single-cell multiomics and the unique opportunities afforded by extending pooled screens using deep multimodal phenotyping. [ABSTRACT FROM AUTHOR]

The ability of microorganisms to resuscitate from dormancy has major implications for ecosystem function. By conceptualizing the space of possible definitions of dormancy as a multidimensional trait space, we construct a pluralistic concept of dormancy that accommodates and allows intercomparison between distinct definitions of dormancy from a variety of disciplines. Our framework for describing and comparing definitions of dormancy facilitates cross-communication between subfields of microbiology. Our framework can be applied to extreme cases, such as permafrost, where it allows for discriminating between microorganisms that are adaptively dormant, and those that are simply stressed. Life can be stressful. One way to deal with stress is to simply wait it out. Microbes do this by entering a state of reduced activity and increased resistance commonly called 'dormancy'. But what is dormancy? Different scientific disciplines emphasize distinct traits and phenotypic ranges in defining dormancy for their microbial species and system-specific questions of interest. Here, we propose a unified definition of microbial dormancy, using a broad framework to place earlier discipline-specific definitions in a new context. We then discuss how this new definition and framework may improve our ability to investigate dormancy using multi-omics tools. Finally, we leverage our framework to discuss the diversity of genomic mechanisms for dormancy in an extreme environment that challenges easy definitions – the permafrost. [ABSTRACT FROM AUTHOR]

Depression is a chronic, relapsing mental illness, often accompanied by loss of appetite, increased fatigue, insomnia and poor concentration. Here, we performed serum and urine metabolomics and fecal 16S rDNA sequencing studies on 57 unmedicated patients with major depressive disorder (MDD) and 57 healthy controls to characterize the metabolic and flora profile of MDD patients. We observed significant differences in serum and urinary metabolome between MDD patients and healthy individuals. Specifically, glycerophospholipid metabolism, primary bile acid biosynthesis and linoleic acid metabolism were significantly disordered in serum, and aminoacyl-tRNA biosynthesis, arginine biosynthesis, purine metabolism, phenylalanine metabolism, alanine, aspartate and glutamate metabolism, and pyrimidine metabolism were significantly impaired in urine. On this basis, we identified four potential diagnostic biomarkers for carnitine and four fatty acid classes in serum and urine, respectively. In addition, we observed significant disturbances of the gut microbiota in MDD patients. Spearman correlation analysis showed that imbalances in the gut microbiota were associated with metabolic disturbances, suggesting an important role of the gut microbiota in the pathogenesis of MDD. Our study provides a theoretical basis for further understanding of the pathogenesis of depression and for future clinical diagnosis and screening, as well as a basis for targeting the gut flora to optimize its structure for the prevention and treatment of depression. • Metabolic and gut microbiota profiles were analyzed in MDD patients and healthy individuals. • MDD patients have disturbed blood and urine metabolism and imbalanced gut microbiota. • Gut microbiota is involved in the pathophysiological mechanisms of overall metabolism in MDD patients. • Gut microbiota is an important target for the treatment of MDD. [ABSTRACT FROM AUTHOR]

The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice. • ML and DL (AI techniques) have been used to analyse big genomic and proteomic data in immuno-oncology. • This systematic review highlights the potential of AI in identifying new biomarkers that can personalise immunotherapy. • AI algorithms are promising in optimising treatment selection, and improving overall patient outcomes. • The article emphasises the need for further research and validation of the AI algorithm to switch from research to clinic. • Trustworthy explainable AI can be of great utility by providing explanations of the algorithms and discovery of biomarkers. [ABSTRACT FROM AUTHOR]

The ability to predict the disease course of individuals with major depressive disorder (MDD) is essential for optimal treatment planning. Here, we used a data-driven machine learning approach to assess the predictive value of different sets of biological data (whole-blood proteomics, lipid metabolomics, transcriptomics, genetics), both separately and added to clinical baseline variables, for the longitudinal prediction of 2-year remission status in MDD at the individual-subject level. Prediction models were trained and cross-validated in a sample of 643 patients with current MDD (2-year remission n = 325) and subsequently tested for performance in 161 individuals with MDD (2-year remission n = 82). Proteomics data showed the best unimodal data predictions (area under the receiver operating characteristic curve = 0.68). Adding proteomic to clinical data at baseline significantly improved 2-year MDD remission predictions (area under the receiver operating characteristic curve = 0.63 vs. 0.78, p =.013), while the addition of other omics data to clinical data did not yield significantly improved model performance. Feature importance and enrichment analysis revealed that proteomic analytes were involved in inflammatory response and lipid metabolism, with fibrinogen levels showing the highest variable importance, followed by symptom severity. Machine learning models outperformed psychiatrists' ability to predict 2-year remission status (balanced accuracy = 71% vs. 55%). This study showed the added predictive value of combining proteomic data, but not other omics data, with clinical data for the prediction of 2-year remission status in MDD. Our results reveal a novel multimodal signature of 2-year MDD remission status that shows clinical potential for individual MDD disease course predictions from baseline measurements. [ABSTRACT FROM AUTHOR]

Multiomics approaches are pivotal in understanding atherosclerotic cardiovascular disease (ASCVD) and offer promising preventive and therapeutic strategies beyond traditional risk factors. Integrating genomics, epigenomics, transcriptomics, proteomics, and metabolomics data enhances risk stratification quality. Artificial intelligence (AI) and machine learning (ML) models provide advanced tools for accurate ASCVD risk prediction by integrating multiomics and clinical data. Large-scale collaborative efforts are essential for gathering comprehensive data sets to train AI/ML models effectively. Standardized data, interdisciplinary collaboration, and regulatory approval are crucial for successful implementation. Multiomics studies offer accurate preventive and therapeutic strategies for atherosclerotic cardiovascular disease (ASCVD) beyond traditional risk factors. By using artificial intelligence (AI) and machine learning (ML) approaches, it is possible to integrate multiple 'omics and clinical data sets into tools that can be utilized for the development of personalized diagnostic and therapeutic approaches. However, currently multiple challenges in data quality, integration, and privacy still need to be addressed. In this opinion, we emphasize that joined efforts, exemplified by the AtheroNET COST Action, have a pivotal role in overcoming the challenges to advance multiomics approaches in ASCVD research, with the aim to foster more precise and effective patient care. [ABSTRACT FROM AUTHOR]

Advanced 3D breast cancer models offer a promising, clinically relevant in vitro tool for researchers. By closely mimicking the interaction between tumor cells and the extracellular matrix compartment, these models could be used to help screen drug efficacy and tailor personalised treatments. Clinical translation remains challenging because these models are often biologically underevaluated and do not consider the models' diversity at the genomic, transcriptomic, proteomic, or epigenomic level in relation to matched patients. Multiomics technologies applied to bioengineered 3D breast cancer models could be game changing to unravel the detailed mechanisms of interaction between cancer cells and stroma for each specific breast cancer subtype and to foster their validation and translation into clinics. Great effort is being made in using spatial multiomics. However, the highly demanding costs of sequencing and informatic analysis are still bottlenecks that need to be overcome. The fields of tissue bioengineering, -omics, and spatial biology are advancing rapidly, each offering the opportunity for a paradigm shift in breast cancer research. However, to date, collaboration between these fields has not reached its full potential. In this review, we describe the most recently generated 3D breast cancer models regarding the biomaterials and technological platforms employed. Additionally, their biological evaluation is reported, highlighting their advantages and limitations. Specifically, we focus on the most up-to-date -omics and spatial biology techniques, which can generate a deeper understanding of the biological relevance of bioengineered 3D breast cancer in vitro models, thus paving the way towards truly clinically relevant microphysiological systems, improved drug development success rates, and personalised medicine approaches. [ABSTRACT FROM AUTHOR]

The prefrontal cortex (PFC) has been implicated as a key brain region responsible for age-related cognitive decline. Little is known about aging-related molecular changes in PFC that may mediate these effects. To date, no studies have used untargeted discovery methods with integrated analyses to determine PFC molecular changes in healthy female primates. We quantified PFC changes associated with healthy aging in female baboons by integrating multiple omics data types (transcriptomics, proteomics, metabolomics) from samples across the adult age span. Our integrated omics approach using unbiased weighted gene co-expression network analysis to integrate data and treat age as a continuous variable, revealed highly interconnected known and novel pathways associated with PFC aging. We found Gamma-aminobutyric acid (GABA) tissue content associated with these signaling pathways, providing 1 potential biomarker to assess PFC changes with age. These highly coordinated pathway changes during aging may represent early steps for aging-related decline in PFC functions, such as learning and memory, and provide potential biomarkers to assess cognitive status in humans. [ABSTRACT FROM AUTHOR]

[Display omitted] • Herbaceous plants were the primary native vegetation in the landfill cover. • Rumex Acetosa increased non-rhizosphere soil CH 4 oxidation capacity nearly 20-fold. • Root exudates induced microbial migration and specific metabolic pathways. • Limonexic acid enhanced methanotrophic bacteria activity and CH 4 degradation. Vegetation root exudates have the ability to shape soil microbial community structures, thereby enhancing CH 4 bio-oxidation capacity in landfill cover systems. In this study, the CH 4 oxidation capacity of indigenous vegetation rhizosphere microorganisms within operational landfill covers in Chongqing, China, was investigated for the first time, with the objective of identifying suitable plant candidates for CH 4 mitigation initiatives within landfill cover systems. Furthermore, a multi-omics methodology was employed to explore microbial community structures and metabolic variances within the rhizospheric environment of diverse vegetation types. The primary aim was to elucidate the fundamental factors contributing to divergent CH 4 oxidation capacities observed in rhizosphere soils. The findings demonstrated that herbaceous vegetation predominated in landfill covers. Notably, Rumex acetosa exhibited the highest CH 4 oxidation capacity in the rhizosphere soil, approximately 20 times greater than that in non-rhizosphere soil. Root exudates played a crucial role in inducing the colonization of CH 4 -oxidizing functional microorganisms in the rhizosphere, subsequently prompting the development of specific metabolic pathways. This process, in turn, enhanced the functional activity of the microorganisms while concurrently bolstering their tolerance to microbial pollutants. Consequently, the addition of substances like Limonexic acid strengthened the CH 4 bio-oxidation process, thereby underscoring the suitability of Rumex acetosa and similar vegetation species as preferred choices for landfill cover vegetation restoration. [ABSTRACT FROM AUTHOR]

Biofilms formed by pathogenic or spoilage microorganisms have become serious issues in the dairy industry, as this mode of life renders such microorganisms highly resistant to cleaning-in-place (CIP) procedures, disinfectants, desiccation, and other control strategies. The advent of omics techniques, especially the integration of different omics tools, has greatly improved our understanding of the features of microbial biofilms, and provided in-depth knowledge on developing effective methods that are directly against deleterious biofilms. This review provides novel insights into the single use of each omics tool and the application of multiomics tools to unravel the mechanisms of biofilm formation, specific molecular phenotypes exhibited by biofilms, and biofilm control strategies. Challenges and future perspective on the integration of omics tools for biofilm studies are also addressed. [ABSTRACT FROM AUTHOR]

[Display omitted] Epstein-Barr virus (EBV) is the oncogenic driver of multiple cancers. However, the underlying mechanism of virus-cancer immunological interaction during disease pathogenesis remains largely elusive. Here we reported the first comprehensive proteogenomic characterization of natural killer/T-cell lymphoma (NKTCL), a representative disease model to study EBV-induced lymphomagenesis, incorporating genomic, transcriptomic, and in-depth proteomic data. Our multi-omics analysis of NKTCL revealed that EBV gene pattern correlated with immune-related oncogenic signaling. Single-cell transcriptome further delineated the tumor microenvironment as immune-inflamed, -deficient, and -desert phenotypes, in association with different setpoints of cancer-immunity cycle. EBV interacted with transcriptional factors to provoke GPCR interactome (GPCRome) reprogramming. Enhanced expression of chemokine receptor-1 (CCR1) on malignant and immunosuppressive cells modulated virus-cancer interaction on microenvironment. Therapeutic targeting CCR1 showed promising efficacy with EBV eradication, T-cell activation, and lymphoma cell killing in NKTCL organoid. Collectively, our study identified a previously unknown GPCR-mediated malignant progression and translated sensors of viral molecules into EBV-specific anti-cancer therapeutics. [ABSTRACT FROM AUTHOR]

Despite major advances in pharmacological treatment for patients with heart failure, residual mortality remains high. This suggests that important pathways are not yet targeted by current heart failure therapies. We sought integration of genetic, transcriptomic, and proteomic data in a large cohort of patients with heart failure to detect major pathways related to progression of heart failure leading to death. We used machine learning methodology based on stacked generalization framework and gradient boosting algorithms, using 54 clinical phenotypes, 403 circulating plasma proteins, 36,046 transcript expression levels in whole blood, and 6 million genomic markers to model all-cause mortality in 2,516 patients with heart failure from the BIOSTAT-CHF (Systems BIOlogy Study to TAilored Treatment in Chronic Heart Failure) study. Results were validated in an independent cohort of 1,738 patients. The mean age of the patients was 70 years (Q1-Q3: 61-78 years), 27% were female, median N-terminal pro–B-type natriuretic peptide was 4,275 ng/L (Q1-Q3: 2,360-8,486 ng/L), and 7% had heart failure with preserved ejection fraction. During a median follow-up of 21 months, 657 (26%) of patients died. The 4 major pathways with a significant association to all-cause mortality were: 1) the PI3K/Akt pathway; 2) the MAPK pathway; 3) the Ras signaling pathway; and 4) epidermal growth factor receptor tyrosine kinase inhibitor resistance. Results were validated in an independent cohort of 1,738 patients. A systems biology approach integrating genomic, transcriptomic, and proteomic data identified 4 major pathways related to mortality. These pathways are related to decreased activation of the cardioprotective ERBB2 receptor, which can be modified by neuregulin. [Display omitted] [ABSTRACT FROM AUTHOR]

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Genome-wide association studies have identified dozens of genetic risk loci for Alzheimer's disease (AD), yet the underlying causal variants and biological mechanisms remain elusive, especially for loci with complex linkage disequilibrium and regulation. To fully untangle the causal signal at a single locus, we performed a functional genomic study of 11p11.2 (the CELF 1 / SPI1 locus). Genome-wide association study signals at 11p11.2 were integrated with datasets of histone modification, open chromatin, and transcription factor binding to distill potentially functional variants (fVars). Their allelic regulatory activities were confirmed by allele imbalance, reporter assays, and base editing. Expressional quantitative trait loci and chromatin interaction data were incorporated to assign target genes to fVars. The relevance of these genes to AD was assessed by convergent functional genomics using bulk brain and single-cell transcriptomic, epigenomic, and proteomic datasets of patients with AD and control individuals, followed by cellular assays. We found that 24 potential fVars, rather than a single variant, were responsible for the risk of 11p11.2. These fVars modulated transcription factor binding and regulated multiple genes by long-range chromatin interactions. Besides SPI1 , convergent evidence indicated that 6 target genes (MTCH2 , ACP2 , NDUFS3 , PSMC3 , C1QTNF4 , and MADD) of fVars were likely to be involved in AD development. Disruption of each gene led to cellular amyloid-β and phosphorylated tau changes, supporting the existence of multiple likely causal genes at 11p11.2. Multiple variants and genes at 11p11.2 may contribute to AD risk. This finding provides new insights into the mechanistic and therapeutic challenges of AD. [ABSTRACT FROM AUTHOR]

Metabolic differences have been reported between individuals with and without major depressive disorder (MDD), but their consistency and causal relevance have been unclear. We conducted a metabolome-wide association study of MDD with 249 metabolomic measures available in the UK Biobank (n = 29,757). We then applied two-sample bidirectional Mendelian randomization and colocalization analysis to identify potentially causal relationships between each metabolite and MDD. A total of 191 metabolites tested were significantly associated with MDD (false discovery rate–corrected p <.05), which decreased to 129 after adjustment for likely confounders. Lower abundance of omega-3 fatty acid measures and a higher omega-6 to omega-3 ratio showed potentially causal effects on liability to MDD. There was no evidence of a causal effect of MDD on metabolite levels. Furthermore, genetic signals associated with docosahexaenoic acid colocalized with loci associated with MDD within the fatty acid desaturase gene cluster. Post hoc Mendelian randomization of gene-transcript abundance within the fatty acid desaturase cluster demonstrated a potentially causal association with MDD. In contrast, colocalization analysis did not suggest a single causal variant for both transcript abundance and MDD liability, but rather the likely existence of two variants in linkage disequilibrium with one another. Our findings suggest that decreased docosahexaenoic acid and increased omega-6 to omega-3 fatty acids ratio may be causally related to MDD. These findings provide further support for the causal involvement of fatty acids in MDD. [ABSTRACT FROM AUTHOR]

Microorganisms present biogeographical distribution patterns and defined environmental niches. Community complexity and methodological limitations have hindered investigations of the environmental niches of microorganisms and the associated ecological applications. As a step forward, multi-omics can now help define the metabolic niches of microorganisms. The proposed metabolic niche framework opens new ways to investigate metabolic plasticity, assess niche shifts, differentiate specialist from generalist phylotypes, evaluate the impacts of microbial invasions, and contribute to culturing efforts. The environmental niche concept describes the distribution of a taxon in the environment and can be used to understand community dynamics, biological invasions, and the impact of environmental changes. The uses and applications are still restricted in microbial ecology, largely due to the complexity of microbial systems and associated methodological limitations. The development of shotgun metagenomics and metatranscriptomics opens new ways to investigate the microbial niche by focusing on the metabolic niche within the environmental space. Here, we propose the metabolic niche framework, which, by defining the fundamental and realised metabolic niche of microorganisms, has the potential to not only provide novel insights into habitat preferences and the metabolism associated, but also to inform on metabolic plasticity, niche shifts, and microbial invasions. [ABSTRACT FROM AUTHOR]

[Display omitted] • Dynamic factors altering distal intestine exposure and MDM rate co-contribute to MDM impact on drug PK. • Evolving therapeutic landscape demands GM metabolism consideration for risk management and translational enhancement. • Cultural, analytical, and computational advances have reshaped experimental models for mechanistic inquiry and MDM prediction. • An adequate GM framework involves high-throughput screening, functional/mechanistic exploration, and translational validation. • Refining models, expanding accessibility and public database overhaul are critical for reliable and translational GM research. The gut microbiome is a diverse system within the gastrointestinal tract composed of trillions of microorganisms (gut microbiota), along with their genomes. Accumulated evidence has revealed the significance of the gut microbiome in human health and disease. Due to its ability to alter drug/xenobiotic pharmacokinetics and therapeutic outcomes, this once-forgotten "metabolic organ" is receiving increasing attention. In parallel with the growing microbiome-driven studies, traditional analytical techniques and technologies have also evolved, allowing researchers to gain a deeper understanding of the functional and mechanistic effects of gut microbiome. From a drug development perspective, microbial drug metabolism is becoming increasingly critical as new modalities (e.g., degradation peptides) with potential microbial metabolism implications emerge. The pharmaceutical industry thus has a pressing need to stay up-to-date with, and continue pursuing, research efforts investigating clinical impact of the gut microbiome on drug actions whilst integrating advances in analytical technology and gut microbiome models. Our review aims to practically address this need by comprehensively introducing the latest innovations in microbial drug metabolism research— including strengths and limitations, to aid in mechanistically dissecting the impact of the gut microbiome on drug metabolism and therapeutic impact, and to develop informed strategies to address microbiome-related drug liability and minimize clinical risk. We present comprehensive mechanisms and co-contributing factors by which the gut microbiome influences drug therapeutic outcomes. We highlight in vitro, in vivo, and in silico models for elucidating the mechanistic role and clinical impact of the gut microbiome on drugs in combination with high-throughput, functionally oriented, and physiologically relevant techniques. Integrating pharmaceutical knowledge and insight, we provide practical suggestions to pharmaceutical scientists for when, why, how, and what is next in microbial studies for improved drug efficacy and safety, and ultimately, support precision medicine formulation for personalized and efficacious therapies. [ABSTRACT FROM AUTHOR]

[Display omitted] • Molecular toxicity of microplastic to the avian digestive system was characterized. • Unlike other species, microplastic caused intestinal necrosis and pyroptosis in avian. • The avian liver toxicity caused by microplastic is lipid metabolism disorder and apoptosis. • The gut microbiota participates in microplastic induced liver injury by the gut-liver axis. • Caffeine and melanin may be potential natural resistances to microplastic toxicity. Microplastic pollution seriously threatens the health and safety of humans and wildlife. Avian is one of the main species endangered by microplastics. However, the damage mechanism of microplastics to the digestive system of avian is not clear. The gut-liver axis is a bidirectional channel that regulates the exchange of information between the gut and the liver and is also a key target for tissue damage caused by pollutants. This study aimed to elucidate the digestive toxicity of microplastics in avian and the key role of the gut-liver axis in it. We constructed an exposure model for microplastics in environmental concentrations and toxicological concentrations in chickens and reveal the digestive toxicity of polystyrene microplastics (PS-MPs) in avian by 16S rRNA, transcriptomics and metabolomics. PS-MPs changed the death mode from apoptosis to necrosis and pyroptosis by upregulating Caspase 8, disrupting the intestinal vascular barrier, disturbing the intestinal flora and promoting the accumulation of lipopolysaccharide. Harmful flora and metabolites were translocated to the liver through the liver-gut axis, eliciting hepatic immune responses and promoting hepatic lipid metabolism disorders and apoptosis. Liver injury involves multiple molecular effects of mitochondrial dynamics disturbance, oxidative stress, endoplasmic reticulum stress, and cell cycle disturbance. Furthermore, metabolomics suggested that caffeine and melanin metabolites may be potential natural resistance substances for microplastics. Taken together, our data demonstrate the digestive damage of PS-MPs in avian, revealing a critical role of the liver-gut axis in it. This will provide a reference for protecting the safety of avian populations. [ABSTRACT FROM AUTHOR]

Precision medicine is devoted to discovering personalized therapy for complex and difficult diseases like cancer. Many machine learning approaches have been developed for drug response prediction towards precision medicine. Notwithstanding, genetic profiles based multi-view graph learning schemes have not yet been explored for drug response prediction in previous works. Furthermore, multi-scale latent feature fusion is not considered sufficiently in the existing frameworks of graph neural networks (GNNs). Previous works on drug response prediction mainly depend on sequence data or single-view graph data. In this paper, we propose to construct multi-view graph by means of multi-omics data and STRING protein–protein association data, and develop a new architecture of GNNs for drug response prediction in cancer. Specifically, we propose hybrid multi-view and multi-scale graph duplex-attention networks (HMM-GDAN), in which both multi-view self-attention mechanism and view-level attention mechanism are devised to capture the complementary information of views and emphasize on the importance of each view collaboratively, and rich multi-scale features are constructed and integrated to further form high-level representations for better prediction. Experiments on GDSC2 dataset verify the superiority of the proposed HMM-GDAN when compared with state-of-the-art baselines. The effectiveness of multi-view and multi-scale strategies is demonstrated by the ablation study. [ABSTRACT FROM AUTHOR]

Heat-shock proteins (Hsps) are a family of proteins essential in preserving the vitality and functionality of proteins under stress conditions. Cucumber (Cucumis sativus) is a widely grown plant with high nutritional value and is used as a model organism in many studies. This study employed a genomics, transcriptomics, and metabolomics approach to investigate cucumbers' Hsps against abiotic stress conditions. Bioinformatics methods were used to identify six Hsp families in the cucumber genome and to characterize family members. Transcriptomics data from the Sequence Read Archive (SRA) database was also conducted to select CsHsp genes for further study. Real-time PCR was used to evaluate gene expression levels under different stress conditions, revealing that CssHsp-08 was a vital gene for resistance to stress conditions; including drought, salinity, cold, heat stresses, and ABA application. Gas Chromatography-Mass Spectrometry (GC-MS) analysis of plant extracts revealed that amino acids accumulate in leaves under high temperatures and roots under drought, while sucrose accumulates in both tissues under applied most stress factors. The study provides valuable insights into the structure, organization, evolution, and expression profiles of the Hsp family and contributes to a better understanding of plant stress mechanisms. These findings have important implications for developing crops that can withstand environmental stress conditions better. • Heat Shock Protein Family members in Cucumis sativus genome were identified and characterized with bioinformatics methods. • Real-time PCR indicates increased CssHsp-08 , CsHsp40-70 , CsHsp70-06 gene expression under various stress scenarios. • Stress conditions trigger notable accumulations of amino acids in leaves and sucrose in both cucumber tissues, metabolite analysis shows. • Study provides valuable insights into cucumber heat shock protein family's organization, metabolic, and stress-induced expression profiles. [ABSTRACT FROM AUTHOR]

Disease modeling and target discovery are crucial initial steps in the drug discovery process and significantly impact on the success of drug development. Given the advantages of analyzing large datasets and complex biological networks, artificial intelligence (AI) is playing a growing role in modern drug target identification. We discuss the use of deep learning models for target discovery, AI-identified targets validated through experiments, and the use of synthetic data produced using generative AI for target identification. Novelty, in addition to druggability and toxicity, is a crucial factor in target selection. There is a trade-off between choosing high-confidence and novel targets. Over the past few years several AI-derived drugs have entered clinical trials, signaling the dawn of a new era in AI-driven drug discovery. Disease modeling and target identification are the most crucial initial steps in drug discovery, and influence the probability of success at every step of drug development. Traditional target identification is a time-consuming process that takes years to decades and usually starts in an academic setting. Given its advantages of analyzing large datasets and intricate biological networks, artificial intelligence (AI) is playing a growing role in modern drug target identification. We review recent advances in target discovery, focusing on breakthroughs in AI-driven therapeutic target exploration. We also discuss the importance of striking a balance between novelty and confidence in target selection. An increasing number of AI-identified targets are being validated through experiments and several AI-derived drugs are entering clinical trials; we highlight current limitations and potential pathways for moving forward. [ABSTRACT FROM AUTHOR]

Psoriasis is an immune-mediated inflammatory skin disease. Psoriasis severity evaluation is important for clinicians in the assessment of disease severity and subsequent clinical decision making. However, no objective biomarker is available for accurately evaluating disease severity in psoriasis. To define and compare biomarkers of disease severity and progression in psoriatic skin. We performed proteome profiling to study the proteins circulating in the serum from patients with psoriasis, psoriatic arthritis and ankylosing spondylitis, and transcriptome sequencing to investigate the gene expression in skin from the same cohort. We then used machine learning approaches to evaluate different biomarker candidates across several independent cohorts. In order to reveal the cell-type specificity of different biomarkers, we also analyzed a single-cell dataset of skin samples. In-situ staining was applied for the validation of biomarker expression. We identified that the peptidase inhibitor 3 (PI3) was significantly correlated with the corresponding local skin gene expression, and was associated with disease severity. We applied machine learning methods to confirm that PI3 was an effective psoriasis classifier, Finally, we validated PI3 as psoriasis biomarker using in-situ staining and public datasets. Single-cell data and in-situ staining indicated that PI3 was specifically highly expressed in keratinocytes from psoriatic lesions. Our results suggest that PI3 may be a psoriasis-specific biomarker for disease severity and hyper-keratinization. • PI3 is highly expressed in psoriatic skin, compared to non-involved skin or healthy skin. • Our data reveals the pathogenic relationship between skin transcriptome and serum proteome from same donors. • Single-cell data and in-situ staining indicated PI3 was specifically expressed by keratinocytes found in psoriatic lesions. • PI3 can be used as a liquid biopsy-based biomarker of disease severity and hyper-keratinization progress in psoriasis. [ABSTRACT FROM AUTHOR]

• Deletion of TMEM38B causes downregulation and disruption of cell-cell adhesion processes in type XIV OI osteoblasts. • Gap and tight junctions are decreased in human and murine TMEM38B -null osteoblasts, emphasizing a role of TRIC-B across the different species. • Cell proliferation and cell cycle are slower in the absence of TMEM38B. • TMEM38B -null osteoblasts exhibit changes in mitochondrial morphology, together with altered fusion/fission markers and increased mt-ROS production, which is the first evidence of disrupted mitochondrial function in human recessive OI. • TRIC-B deletion may reveal possible causal link between disrupted mitochondrial metabolism and cell adhesion. Osteogenesis Imperfecta (OI) is a heritable collagen-related bone dysplasia characterized by bone fractures, growth deficiency and skeletal deformity. Type XIV OI is a recessive OI form caused by null mutations in TMEM38B, which encodes the ER membrane intracellular cation channel TRIC-B. Previously, we showed that absence of TMEM38B alters calcium flux in the ER of OI patient osteoblasts and fibroblasts, which further disrupts collagen synthesis and secretion. How the absence of TMEM38B affects osteoblast function is still poorly understood. Here we further investigated the role of TMEM38B in human osteoblast differentiation and mineralization. TMEM38B -null osteoblasts showed altered expression of osteoblast marker genes and decreased mineralization. RNA-Seq analysis revealed that cell-cell adhesion was one of the most downregulated pathways in TMEM38B -null osteoblasts, with further validation by real-time PCR and Western blot. Gap and tight junction proteins were also decreased by TRIC-B absence, both in patient osteoblasts and in calvarial osteoblasts of Tmem38b -null mice. Disrupted cell adhesion decreased mutant cell proliferation and cell cycle progression. An important novel finding was that TMEM38B -null osteoblasts had elongated mitochondria with altered fusion and fission markers, MFN2 and DRP1. In addition, TMEM38B -null osteoblasts exhibited a significant increase in superoxide production in mitochondria, further supporting mitochondrial dysfunction. Together these results emphasize the novel role of TMEM38B /TRIC-B in osteoblast differentiation, affecting cell-cell adhesion processes, gap and tight junction, proliferation, cell cycle, and mitochondrial function. [ABSTRACT FROM AUTHOR]

Elucidating biosynthetic pathways of plant specialized metabolites is a tricky but essential task for the biotechnological production of plant drugs. In a new report, Li et al. used a single-cell multi-omics approach to provide an integrative view of the architecture and regulation of anticancer alkaloid routes in Madagascar periwinkle. [ABSTRACT FROM AUTHOR]

Are there any differences between conjoined twin fetuses at the molecular level? Skin tissues were collected from thoracopagus conjoined twins at 15+4 weeks of gestation. The skin tissues were collected from the thigh side of conjoined twins after the abortion procedure. All specimens were obtained after written informed patient consent and were fully anonymized. All relevant ethical regulations were followed. Every specimen underwent multiomics sequencing analysis to determine associations among the DNA methylome, transcriptome and mutations in the exon regions in the conjoined twins. The global methylation pattern was similar in the two fetuses of conjoined twins, while significant differences were seen in local regions such as CpG islands (P = 0.026), enhancers (P < 0.001) and various repetitive elements (P < 0.05), which showed significant differences. The conjoined twins also differed in genes related to growth and development, cellular component morphogenesis and cellular stress, both in terms of DNA methylation levels and gene expression levels. Exon data analysis revealed that the common mutations in conjoined twins mainly occurred in neural development, lipid metabolism and microtubule morphogenesis. Specific mutations were associated with cellular component biosynthesis, behaviour and germ cell development. Conjoined twins were similar to each other globally, but there were significant differences related to growth and development, cellular component morphogenesis and cellular stress. The current study reveals the molecular features of conjoined twins for the first time, laying the foundation for future exploration of the mechanism of conjoined twins. [ABSTRACT FROM AUTHOR]

To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry. [ABSTRACT FROM AUTHOR]

Phytohormones are ubiquitous signaling molecules that regulate crucial aspects of plant growth, development, and environmental stress responses. Unraveling their functions and molecular mechanisms will help uncover how phytohormones balance plant growth and stress responses based on environmental cues. Comprehensively characterizing phytohormone levels and signaling network activities remains a major challenge. Technological improvements in mass spectrometry (MS) in terms of its resolution, mass accuracy and sensitivity have led to considerable biological advances. MS techniques are powerful, robust tools for uncovering phytohormone structures and signaling networks and mapping phytohormone-dependent protein interactions. Further developments in computational algorithms and multiomics approaches should provide new insights into the cooperation of these dynamic regulatory networks. Phytohormones have crucial roles in plant growth, development, and acclimation to environmental stress; however, measuring phytohormone levels and unraveling their complex signaling networks and interactions remains challenging. Mass spectrometry (MS) has revolutionized the study of complex biological systems, enabling the comprehensive identification and quantification of phytohormones and their related targets. Here, we review recent advances in MS technologies and highlight studies that have used MS to discover and analyze phytohormone-mediated molecular events. In particular, we focus on the application of MS for profiling phytohormones, elucidating phosphorylation signaling, and mapping protein interactions in plants. [ABSTRACT FROM AUTHOR]

A comprehensive overview of various omics techniques to understand the biological responsive to engineered biomaterials is provided. Specifically, the role of omics-based approaches in designing next-generation of bioresponsive materials for various regenerative medicine approaches, as well as future outlook on integrated multi-omics platform are discussed. [Display omitted] Recent developments in biomedical engineering have focused on designing smart and bio-responsive materials. However, a critical step in designing this next generation of biomaterials is to evaluate their holistic cellular and molecular interactions. Recent technological advances in biology, specifically in omics techniques, can provide high-throughput functional readouts of the cell genome, epigenome, transcriptome, proteome and metabolome. The ability of omics-based approaches to predict the biological response to engineered biomaterials have the potential to revolutionize biomedical research and replace the traditional "trial and error" approach as the first step in designing the next-generation of biomaterials. In addition, these omics-based approaches can be used to gain insight into in vivo responses of biomaterials, which currently relies heavily on semi-quantitative imaging-based methodologies rather than rigorous computational approaches. In this review, we have outlined various omics techniques that have been utilized to quantify and understand the biological performance of engineered biomaterials. Additionally, we have critically discussed the role of omics-based approaches in designing new biomaterials for regenerative medicine, immune engineering, and drug delivery. Finally, the potential of integrated multi-omics approaches to build a holistic understanding of the biological responses to newly developed biomaterials have been discussed. [ABSTRACT FROM AUTHOR]

Cholangiocytes transit from quiescence to hyperproliferation during cystogenesis in polycystic liver disease (PLD), the severity of which displays prominent sex differences. Epigenetic regulation plays important roles in cell state transition. We aimed to investigate the sex-specific epigenetic basis of hepatic cystogenesis and to develop therapeutic strategies targeting epigenetic modifications for PLD treatment. Normal and cystic primary cholangiocytes were isolated from wild-type and PLD mice of both sexes. Chromatin states were characterized by analyzing chromatin accessibility (ATAC sequencing) and multiple histone modifications (chromatin immunoprecipitation sequencing). Differential gene expression was determined by transcriptomic analysis (RNA sequencing). Pharmacologic inhibition of epigenetic modifying enzymes was undertaken in PLD model mice. Through genome-wide profiling of chromatin dynamics, we revealed a profound increase of global chromatin accessibility during cystogenesis in both male and female PLD cholangiocytes. We identified a switch from H3K9me3 to H3K9ac on cis -regulatory DNA elements of cyst-associated genes and showed that inhibition of H3K9ac acetyltransferase or H3K9me3 demethylase slowed cyst growth in male, but not female, PLD mice. In contrast, we found that H3K27ac was specifically increased in female PLD mice and that genes associated with H3K27ac-gained regions were enriched for cyst-related pathways. In an integrated epigenomic and transcriptomic analysis, we identified an estrogen receptor alpha-centered transcription factor network associated with the H3K27ac-regulated cystogenic gene expression program in female PLD mice. Our findings highlight the multi-layered sex-specific epigenetic dynamics underlying cholangiocyte state transition and reveal a potential epigenetic therapeutic strategy for male PLD patients. In the present study, we elucidate a sex-specific epigenetic mechanism underlying the cholangiocyte state transition during hepatic cystogenesis and identify epigenetic drugs that effectively slow cyst growth in male PLD mice. These findings underscore the importance of sex difference in the pathogenesis of PLD and may guide researchers and physicians to develop sex-specific personalized approaches for PLD treatment. [Display omitted] • Chromatin accessibility is increased in both male and female PLD cholangiocytes. • A switch from H3K9me3 to H3K9ac activates cyst-associated genes in male PLD cholangiocytes. • Reducing H3K9ac or increasing H3K9me3 slows cyst growth in male, but not female, PLD mice. • Gain of H3K27ac specifically correlates with cyst-associated gene expression in female PLD cholangiocytes. • An ERα-centered TF network is enriched in H3K27ac-gained regions in female PLD cholangiocytes. [ABSTRACT FROM AUTHOR]

Despite the increased access to high-quality plant genome sequences, the set of genes with a known function remains far from complete. With the advent of novel bulk and single-cell omics profiling methods, we are entering a new era where advanced and highly integrative functional annotation strategies are being developed to elucidate the functions of all plant genes. Here, we review different multi-omics approaches to improve functional and regulatory gene characterization and highlight the power of machine learning and network biology to fully exploit the complementary information embedded in different omics layers. Finally, we discuss the potential of emerging single-cell methods and algorithms to further increase the resolution, allowing generation of functional insights about plant biology. Different omics profiling methods are becoming available for a variety of plant species, offering new opportunities to explore new gene functions at various molecular levels. The combination of multiple omics data types enables the characterization of different levels of gene regulation for a biological process of interest. Studies targeting a specific biological process, as well as untargeted multi-omics approaches, shed light on novel gene functions in plant biology. Different integration strategies offer a practical solution to exploit the information captured by complementary omics layers and allow the generation of new testable hypotheses for various biological processes and pathways. New computational methods, modeling coexpression and regulatory networks, are being developed to accommodate the technological advances in omics profiling and fully exploit the information available in functional genomics databases. [ABSTRACT FROM AUTHOR]

The color and fragrance of rose flowers affect their commercial value. However, several rose varieties with new floral colors developed by the bud mutation method lost their fragrance during the breeding process, raising the question: Is there a relationship between floral color and aroma traits? Rose cultivar 'Yellow Island' (YI) with intensely aroma and yellow petals, while its bud mutant 'Past Feeling' (PF) with light aroma and pink petals mixing some yellow, two cultivars were used to explore this question using multiomics approaches. We investigated the genomic polymorphisms between PF and YI by whole−genome resequencing. 71 differentially abundant metabolites and 155 related differentially expressed genes identified in petals between PF and YI. From this, we constructed a model of metabolic changes affecting floral color and fragrance integrating shikimate, terpenoid, carotenoid, and green leaf volatile metabolites and predicted the associated key genes and transcription factors. This study provides a reference for understanding the molecular mechanism of variation in rose floral color and aroma traits. [Display omitted] • Integrative analyses of molecular mechanism of variation in rose floral color and aroma traits were performed. • A total of 71 differentially metabolites, 155 biosynthetic genes and 316 differentially expressed TFs were identified. • Network of biosynthetic pathways of shikimate, carotenoids, terpenoids, and GLV derivatives in R. hybrida were constructed. [ABSTRACT FROM AUTHOR]

Purpose: The availability of validated biomarkers to assess radiation exposure and to assist in developing medical countermeasures remains an unmet need.Methods and Materials: We used a cobalt-60 γ-irradiated nonhuman primate (NHP) model to delineate a multiomics-based serum probability index of radiation exposure. Both male and female NHPs were irradiated with different doses ranging from 6.0 to 8.5 Gy, with 0.5 Gy increments between doses. We leveraged high-resolution mass spectrometry for analysis of metabolites, lipids, and proteins at 1, 2, and 6 days postirradiation in NHP serum.Results: A logistic regression model was implemented to develop a 4-analyte panel to stratify irradiated NHPs from unirradiated with high accuracy that was agnostic for all doses of γ-rays tested in the study, up to 6 days after exposure. This panel was comprised of Serpin family A9, acetylcarnitine, glycerophosphocholine (16:0/22:6), and suberylglycine, which showed 2- to 4-fold elevation in serum abundance upon irradiation in NHPs and can potentially be translated as a molecular diagnostic for human use after larger validation studies.Conclusions: Taken together, this study, for the first time, demonstrates the utility of a combinatorial molecular characterization approach using an NHP model for developing minimally invasive assays from small volumes of blood that can be effectively used for radiation exposure assessments. [ABSTRACT FROM AUTHOR]

Single-cell transcriptomic approaches have revolutionised the study of complex biological systems, with the routine measurement of gene expression in thousands of cells enabling construction of whole-organism cell atlases. However, the transcriptome is just one layer amongst many that coordinate to define cell type and state and, ultimately, function. In parallel with the widespread uptake of single-cell RNA-seq (scRNA-seq), there has been a rapid emergence of methods that enable multiomic profiling of individual cells, enabling parallel measurement of intercellular heterogeneity in the genome, epigenome, transcriptome, and proteomes. Linking measurements from each of these layers has the potential to reveal regulatory and functional mechanisms underlying cell behaviour in healthy development and disease. To understand intercellular heterogeneity within an organism, it is essential to make coordinated measurements linking the genome and its epigenetic regulation to gene and protein expression at the single-cell level. Rapid advances in single-cell multiomics approaches have enabled analysis of multiple molecular modalities from the same single cell. Methods incorporating several modalities now exist, although several challenges remain with regard to resolution, data integration, and scale. Further developments in multiomics approaches will provide unique insights into the regulatory processes governing how individual cells function collectively to produce whole-organism phenotypes in development, health, and disease. [ABSTRACT FROM AUTHOR]

An increasing number of studies have highlighted the biological significance of neutrophil activation and polarization in tumor progression. However, the characterization of tumor-associated neutrophils (TANs) is inadequately investigated. Patients' expression profiles were obtained from TCGA, GEO, and IMvigor210 databases. Six algorithms were used to assess immune cell infiltration. RNA sequencing was conducted to evaluate the differentially expressed genes between induced N1- and N2-like neutrophils. A TAN-associated risk score (TRS) model was established using a combination of weighted gene co-expression network analysis (WGCNA) and RNA-seq data and further assessed in pan-cancer. A clinical cohort of 117 GC patients was enrolled to assess the role of TANs in GC via immunohistochemistry (IHC). A TRS signature was built with 10 TAN-related genes (TRGs) and most TRGs were highly abundant in the TANs of the GC microenvironment. The TRS model could accurately predict patients' prognosis, as well as their responses to chemotherapy and immunotherapy. The TRS was positively correlated with pro-tumor immune cells and exhibited negative relationship with anti-tumor immune cells. Additional functional analyses revealed that the signature was positively related to pro-tumor and immunosuppression pathways, such as the hypoxia pathway, across pan-cancer. Furthermore, our clinical cohort demonstrated TANs as an independent prognostic factor for GC patients. This study constructed and confirmed the value of a novel TRS model for prognostic prediction of GC and pan-cancer. Further evaluation of TRS and TANs will help strengthen the understanding of the tumor microenvironment and guide more effective therapeutic strategies. [ABSTRACT FROM AUTHOR]

Psoraleae Fructus (Bu Gu Zhi) is the fruit of Psoralea corylifolia L. (PCL) and has been used for centuries in traditional Chinese medicine formulas to treat osteoporosis (OP). A new drug called "BX" has been developed from PCL, but its mechanism for treating OP is not yet fully understood. To explore the mechanism of action of BX in the treatment of ovariectomy-induced OP based function-oriented multi-omics analysis of gut microbiota (GM) and metabolites. C57BL/6 mice were bilaterally ovariectomized to replicate the OP model. The therapeutic efficacy of BX was evaluated by bone parameters (BMD, BV/TV, Tb.N, Tb.Sp), hematoxylin and eosin (H&E) staining results, and determination of bone formation markers procollagen type Ⅰ amino-terminal peptide (PⅠNP) and bone-specific alkaline phosphatase (BALP). Serum and fecal metabolomics and high-throughput 16S rDNA sequencing were performed to evaluate effects on endogenous metabolites and GM. In addition, an enzyme-based functional correlation algorithm (EBFC) algorithm was used to investigate functional correlations between GM and metabolites. BX improved OP in OVX mice by increasing BMD, BV/TV, serum PⅠNP, BALP, and improving Tb.N and Tb.Sp. A total of 59 differential metabolites were identified, and 9 metabolic pathways, including arachidonic acid metabolism, glycerophospholipid metabolism, purine metabolism, and tryptophan metabolism, were found to be involved in the progression of OP. EBFC analysis results revealed that the enzymes related to purine and tryptophan metabolism, which are from Lachnospiraceae_NK4A136_group , Blautia , Rs-E47_termite_group , UCG-009, and Clostridia_UCG-014 , were identified as the intrinsic link between GM and metabolites. The regulation of GM and restoration of metabolic disorders may be the mechanisms of action of BX in alleviating OP. This research provides insights into the function-oriented mechanism discovery of traditional Chinese medicine in the treatment of OP. [Display omitted] • BX exhibited therapeutic effects in the treatment of osteoporosis (OP). • BX modulated the gut microbiota (GM) composition and metabolism. • Functional correlations between GM and differential metabolites were discovered. • BX regulated purine and tryptophan metabolism-related GM and its metabolites. [ABSTRACT FROM AUTHOR]

Anlotinib, an orally administered small molecule inhibitor of receptor tyrosine kinases (RTKs), exerts significant anti-angiogenic and vascular normalization effects. However, the mechanisms underlying its involvement in tumor metabolic reprogramming are still unclear. This study aims to investigate the distribution and expression levels of metabolites within tumors after anlotinib treatment using spatial metabolomics analysis. Subsequently, by integrating the transcriptomics and proteomics analyses, we identified that anlotinib treatment primarily modulated four metabolic pathways, including taurine and hypotaurine metabolism, steroid synthesis, pentose phosphate pathway, and lipid biosynthesis. This regulation significantly influenced the metabolic levels of compounds such as sulfonic acids, cholesterol, inositol phosphate pyrophosphate, and palmitoyl-CoA in the tumor, thereby impacting tumor initiation and progression. This study provides potential metabolic biomarkers for anlotinib treatment in tumors. [ABSTRACT FROM AUTHOR]

• Integration of metabolomics and proteomics has been popular for MS based foodomics. • Modified MS data acquisition improves the identification on metabolites and proteins. • Advancing MS data processing provides more accurate and comprehensive information for food analysis. • Multiomics analysis explores food components and functions at systematic levels. Foodomics has become a popular methodology in food science studies. Mass spectrometry (MS) based metabolomics and proteomics analysis played indispensable roles in foodomics research. So far, several methodologies have been developed to detect the metabolites and proteins in diets and consumers, including sample preparation, MS data acquisition, annotation and interpretation. Moreover, multiomics analysis integrated metabolomics and proteomics have received considerable attentions in the field of food safety and nutrition, because of more comprehensive and deeply. In this context, we intended to review the emerging strategies and their applications in MS-based foodomics, as well as future challenges and trends. The principle and application of multiomics were also discussed, such as the optimization of data acquisition, development of analysis algorithm and exploration of systems biology. [ABSTRACT FROM AUTHOR]

Fritillaria cirrhosa , an endangered plant endemic to plateau regions, faces escalating cadmium (Cd) stress due to pollution in the Qinghai-Tibet Plateau. This study employed physiological, cytological, and multi-omics techniques to investigate the toxic effects of Cd stress and detoxification mechanisms of F. cirrhosa. The results demonstrated that Cd caused severe damage to cell membranes and organelles, leading to significant oxidative damage and reducing photosynthesis, alkaloid and nucleoside contents, and biomass. Cd application increased cell wall thickness by 167.89% in leaves and 445.78% in bulbs, leading to weight percentage of Cd increases of 76.00% and 257.14%, respectively. PER , CESA , PME , and SUS, genes responsible for cell wall thickening, were significantly upregulated. Additionally, the levels of metabolites participating in the scavenging of reactive oxygen species, including oxidized glutathione, D-proline, L-citrulline, and putrescine, were significantly increased under Cd stress. Combined multi-omics analyses revealed that glutathione metabolism and cell wall biosynthesis pathways jointly constituted the detoxification mechanism of F. cirrhosa in response to Cd stress. This study provides a theoretical basis for further screening of new cultivars for Cd tolerance and developing appropriate cultivation strategies to alleviate Cd toxicity. [Display omitted] ● Cd significantly impacts the photosynthesis capacity of F. cirrhosa. ● F. cirrhosa cell walls were significantly thickened with dense deposits by Cd. ● Cd significantly impacts medicinal active ingredients of F. cirrhosa. ● F. cirrhosa offsets Cd toxicity by glutathione and cell wall biosynthesis pathways. ● The content of D-proline, L-citrulline and putrescine were increased by Cd. [ABSTRACT FROM AUTHOR]

Domoic acid (DA)-producing algal blooms are a global marine environmental issue. However, there has been no previous research addressing the question regarding the fate of DA in marine benthic environments. In this work, we investigated the DA fate in the water-sediment microcosm via the integrative analysis of a top-down metabolic model, metagenome, and metabolome. Results demonstrated that biodegradation is the leading mechanism for the nonconservative attenuation of DA. Specifically, DA degradation was prominently completed by the sediment aerobic community, with a degradation rate of 0.0681 ± 0.00954 d−1. The DA degradation pathway included hydration, dehydrogenation, hydrolysis, decarboxylation, automatic ring opening of hydration, and β oxidation reactions. Moreover, the reverse ecological analysis demonstrated that the microbial community transitioned from nutrient competition to metabolic cross-feeding during DA degradation, further enhancing the cooperation between DA degraders and other taxa. Finally, we reconstructed the metabolic process of microbial communities during DA degradation and confirmed that the metabolism of amino acid and organic acid drove the degradation of DA. Overall, our work not only elucidated the fate of DA in marine environments but also provided crucial insights for applying metabolic models and multi-omics to investigate the biotransformation of other contaminants. [Display omitted] • Applying metabolic modeling and multi-omics to explore DA degradation mechanism. • The microbial attenuation of DA was driven by aerobic communities. • Microbial community preferred metabolic cross-feeding during DA degradation. • Reconstructed the metabolism of microbial community during DA degradation. • DA degradation was driven by the metabolism of amino acids and organic acid. [ABSTRACT FROM AUTHOR]