Omics Profiling Research Articles

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

BackgroundAccurate 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.ResultsWe 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.ConclusionsTransCDR emerges as a potent tool with significant potential in drug response prediction.

High-dimensional Biomarker Identification for Scalable and Interpretable Disease Prediction via Machine Learning Models.

Omics data generated from high-throughput technologies and clinical features jointly impact many complex human diseases. Identifying key biomarkers and clinical risk factors is essential for understanding disease mechanisms and advancing early disease diagnosis and precision medicine. However, the high-dimensionality and intricate associations between disease outcomes and omics profiles present significant analytical challenges. To address these, we propose an ensemble data-driven biomarker identification tool, Hybrid Feature Screening (HFS), to construct a candidate feature set for downstream advanced machine learning models. The pre-screened candidate features from HFS are further refined using a computationally efficient permutation-based feature importance test, forming the comprehensive High-dimensional Feature Importance Test (HiFIT) framework. Through extensive numerical simulations and real-world applications, we demonstrate HiFIT's superior performance in both outcome prediction and feature importance identification. An R package implementing HiFIT is available on GitHub ( https://github.com/BZou-lab/HiFIT ).

Identification of bio-markers for insulin resistance and sensitivity through multi-omics analysis

Aims: This study aims to identify multi-omics bio-markers for insulin resistance and sensitivity using machine learning approaches on a dataset integrated from several omics. Methods: The study included 362 patients with Insulin Resistance and Insulin Sensitivity from the Integrative Personal Omics Profiling (iPOP) database. Combining the multi-omics data from the Integrative Human Microbiome Project, this study used machine learning to reveal the relationship between insulin resistance and insulin sensitivity. Results: Of 362 patients 186 were insulin resistance and 176 were insulin sensitivity. 11,585 features were used, including clinical features, RNA transcripts, gut microbiota, cytokines, proteins, and metabolomics. We found 21 features capable of distinguishing insulin resistance from insulin sensitivity using a well-known artificial neural network (ANN) method. The model had an area under the receiver operating characteristic (AUC) of 0.97 in the validation dataset and 0.89 in the test dataset. The ANN model’s performance was compared with Random Forest model. Of the 21 new findings, two metabolites (methyl-uric acid and methylxanthine) are xenobiotics, and three RNA transcripts (SERPINF1, SLC2A2, and CHL1). Conclusion: A small number of multi-omics features identified from 11,585 potential candidates for a machine learning model can accurately predict insulin resistance and sensitivity.

Cancer Patient-Derived Cell-Based Models: Applications and Challenges in Functional Precision Medicine.

The field of oncology has witnessed remarkable progress in personalized cancer therapy. Functional precision medicine has emerged as a promising avenue for achieving superior treatment outcomes by integrating omics profiling and sensitivity testing of patient-derived cancer cells. This review paper provides an in-depth analysis of the evolution of cancer-directed drugs, resistance mechanisms, and the role of functional precision medicine platforms in revolutionizing individualized treatment strategies. Using two-dimensional (2D) and three-dimensional (3D) cell cultures, patient-derived xenograft (PDX) models, and advanced functional assays has significantly improved our understanding of tumor behavior and drug response. This progress will lead to identifying more effective treatments for more patients. Considering the limited eligibility of patients based on a genome-targeted approach for receiving targeted therapy, functional precision medicine provides unprecedented opportunities for customizing medical interventions according to individual patient traits and individual drug responses. This review delineates the current landscape, explores limitations, and presents future perspectives to inspire ongoing advancements in functional precision medicine for personalized cancer therapy.

Soil volatilomics uncovers tight linkage between soybean presence and soil omics profiles in agricultural fields

Securing a stable food supply and achieving sustainable agricultural production are essential for mitigating future food insecurity. Soil metabolomics is a promising tool for capturing soil status, which is a critical issue for future sustainable food security. This study aims to provide deeper insights into the status of soybean-grown fields under varying soil conditions over three years by employing comprehensive soil volatile organic compound (VOC) profiling, also known as soil volatilomics. Profiling identified approximately 200 peaks in agricultural fields. The soil of soybean-presented plots exhibited markedly higher VOC levels than those of non-soybean plots during the flowering season. Pentanoic acid, 2,2,4-trimethyl-3-carboxyisopropyl, isobutyl ester, a discriminative soil VOC, was identified through multivariate data analysis as a distinctively present VOC in fields with or without soybean plants during the flowering period. Soil VOC profiles exhibited strong correlations with soil-related omics datasets (soil ionome, microbiome, metabolome, and physics) and no significant correlations with root microbiome and rhizosphere chemicals. These findings indicate that soil VOC profiles could serve as a valuable indicator for assessing soil status, thereby supporting efforts to ensure future global food security.

Multiomic Profiling and Neuroprotective Bioactivity of Salvia Hairy Root-Derived Extracellular Vesicles in a Cellular Model of Parkinson's Disease.

Extracellular vesicles (EVs) are promising tools for nanomedicine and nanobiotechnology. The purification of mammalian-derived EVs involves intensive processes, and their therapeutic application raises multiple safety and regulatory issues. Plants have the potential to serve as nonconventional sources of therapeutically relevant EVs. In this context, we recently identified hairy roots (HRs) of medicinal plants as a novel biotechnological platform to produce EVs for human health. Herein, we report the purification, omics profiling, and bioactivity of EVs isolated from HRs of the medicinal plants S. sclarea and S. dominica. EVs were isolated from conditioned media of HR cultures using differential ultracentrifugation (dUC) and size exclusion chromatography (SEC). The isolated EVs were characterized by nanoparticle tracking analysis (NTA) and electron microscopy. The proteomic and metabolomic profiles of the EVs were determined using mass spectrometry. Uptake studies and bioactivity assays, including confocal microscopy, MTT, flow cytometry, ROS quantification, and untargeted metabolomics analyses, were conducted in SH-SY5Y cells treated with the neurotoxin 6-hydroxydopamine (6-OHDA) to evaluate the therapeutic potential of EVs in an in vitro model of Parkinson's disease. S. sclarea HRs released nanosized round-shaped EVs with a distinctive molecular signature. HR EVs from S. sclarea and S. dominica revealed conserved cargo of secondary metabolites, predominantly triterpenoids, which are known for their antioxidant properties. We showed that HR EVs are safe, enter the cells, and strongly inhibit apoptosis in a cellular model of Parkinson's disease. Cellular metabolomics revealed that EVs preserved metabolic homeostasis and mitigated cellular oxidative stress when co-administered with 6-OHDA. Mechanistically, HR EVs inhibited 6-OHDA autoxidation and substantially reduced the accumulation of its oxidative products, which are responsible for 6-OHDA-induced toxicity. Collectively, our findings provide compelling evidence that EVs isolated from the hairy roots of Salvia species are promising, non-mammalian alternative for the design of novel therapies targeting neurological disorders.

Multi-task deep latent spaces for cancer survival and drug sensitivity prediction.

Cancer is a very heterogeneous disease that can be difficult to treat without addressing the specific mechanisms driving tumour progression in a given patient. High-throughput screening and sequencing data from cancer cell-lines has driven many developments in drug development, however, there are important aspects crucial to precision medicine that are often overlooked, namely the inherent differences between tumours in patients and the cell-lines used to model them in vitro. Recent developments in transfer learning methods for patient and cell-line data have shown progress in translating results from cell-lines to individual patients in silico. However, transfer learning can be forceful and there is a risk that clinically relevant patterns in the omics profiles of patients are lost in the process. We present MODAE, a novel deep learning algorithm to integrate omics profiles from cell-lines and patients for the purposes of exploring precision medicine opportunities. MODAE implements patient survival prediction as an additional task in a drug-sensitivity transfer learning schema and aims to balance autoencoding, domain adaptation, drug-sensitivity prediction, and survival prediction objectives in order to better preserve the heterogeneity between patients that is relevant to survival. While burdened with these additional tasks, MODAE performed on par with baseline survival models, but struggled in the drug-sensitivity prediction task. Nevertheless, these preliminary results were promising and show that MODAE provides a novel AI-based method for prioritizing drug treatments for high-risk patients. https://github.com/UEFBiomedicalInformaticsLab/MODAE.

Graph Fourier transform for spatial omics representation and analyses of complex organs

Spatial omics technologies decipher functional components of complex organs at cellular and subcellular resolutions. We introduce Spatial Graph Fourier Transform (SpaGFT) and apply graph signal processing to a wide range of spatial omics profiling platforms to generate their interpretable representations. This representation supports spatially variable gene identification and improves gene expression imputation, outperforming existing tools in analyzing human and mouse spatial transcriptomics data. SpaGFT can identify immunological regions for B cell maturation in human lymph nodes Visium data and characterize variations in secondary follicles using in-house human tonsil CODEX data. Furthermore, it can be integrated seamlessly into other machine learning frameworks, enhancing accuracy in spatial domain identification, cell type annotation, and subcellular feature inference by up to 40%. Notably, SpaGFT detects rare subcellular organelles, such as Cajal bodies and Set1/COMPASS complexes, in high-resolution spatial proteomics data. This approach provides an explainable graph representation method for exploring tissue biology and function.

Noncanonical formation of SNX5 gene-derived circular RNA regulates cancer growth

Oral squamous cell carcinoma (OSCC) is a prevalent cancer worldwide, exhibiting unique regional prevalence. Despite advancements in diagnostics and therapy, the 5-year survival rate for patients has seen limited improvement. A deeper understanding of OSCC pathogenesis, especially its molecular underpinnings, is essential for improving detection, prevention, and treatment. In this context, noncoding RNAs, such as circular RNAs (circRNAs), have gained recognition as crucial regulators and potential biomarkers in OSCC progression. Our study highlights the discovery of previously uncharacterized circRNAs, including a SNX5 gene-derived circRNA, circSNX5, through deep sequencing of OSCC patient tissue transcriptomes. We established circSNX5’s tumor-specific expression and its strong correlation with patient survival using structure-specific and quantitative PCR analyses. In vitro and in vivo experiments underscored circSNX5 RNA’s regulatory role in cancer growth and metastasis. Further, our omics profiling and functional assays revealed that ADAM10 is a critical effector in circSNX5-mediated cancer progression, with circSNX5 maintaining ADAM10 expression by sponging miR-323. This novel circRNA-miRNA-mRNA regulatory axis significantly contributes to oral cancer progression and malignancy. Moreover, we discovered that circSNX5 RNA is produced via noncanonical sequential back-splicing of pre-mRNA, a process negatively regulated by the RNA-binding protein STAU1. This finding adds a new dimension to our understanding of exonic circRNA biogenesis in the eukaryotic transcriptome. Collectively, our findings offer a detailed mechanistic dissection and functional interpretation of a novel circRNA, shedding light on the role of the noncoding transcriptome in cancer biology and potentially paving the way for innovative therapeutic strategies.

MOSBY enables multi-omic inference and spatial biomarker discovery from whole slide images

The utility of deep neural nets has been demonstrated for mapping hematoxylin-and-eosin (H&E) stained image features to expression of individual genes. However, these models have not been employed to discover clinically relevant spatial biomarkers. Here we develop MOSBY (Multi-Omic translation of whole slide images for Spatial Biomarker discoverY) that leverages contrastive self-supervised pretraining to extract improved H&E whole slide images features, learns a mapping between image and bulk omic profiles (RNA, DNA, and protein), and utilizes tile-level information to discover spatial biomarkers. We validate MOSBY gene and gene set predictions with spatial transcriptomic and serially-sectioned CD8 IHC image data. We demonstrate that MOSBY-inferred colocalization features have survival-predictive power orthogonal to gene expression, and enable concordance indices highly competitive with survival-trained multimodal networks. We identify and validate (1) an ER stress-associated colocalization feature as a chemotherapy-specific risk factor in lung adenocarcinoma, and (2) the colocalization of T effector cell vs cysteine signatures as a negative prognostic factor in multiple cancer indications. The discovery of clinically relevant biologically interpretable spatial biomarkers showcases the utility of the model in unraveling novel insights in cancer biology as well as informing clinical decision-making.

Bootstrap Evaluation of Association Matrices (BEAM) for Integrating Multiple Omics Profiles with Multiple Outcomes.

Large datasets containing multiple clinical and omics measurements for each subject motivate the development of new statistical methods to integrate these data to advance scientific discovery. We propose bootstrap evaluation of association matrices (BEAM), which integrates multiple omics profiles with multiple clinical endpoints. BEAM associates a set omic features with clinical endpoints via regression models and then uses bootstrap resampling to determine statistical significance of the set. Unlike existing methods, BEAM uniquely accommodates an arbitrary number of omic profiles and endpoints. In simulations, BEAM performed similarly to the theoretically best simple test and outperformed other integrated analysis methods. In an example pediatric leukemia application, BEAM identified several genes with biological relevance established by a CRISPR assay that had been missed by univariate screens and other integrated analysis methods. Thus, BEAM is a powerful, flexible, and robust tool to identify genes for further laboratory and/or clinical research evaluation. Source code, documentation, and a vignette for BEAM are available on GitHub at: https://github.com/annaSeffernick/BEAMR . The R package is available from CRAN at: https://cran.r-project.org/package=BEAMR . Stanley.Pounds@stjude.org. Supplementary data are available at the journal's website.

Tissue characterization at an enhanced resolution across spatial omics platforms with deep generative model

Recent advances in spatial omics have expanded the spectrum of profiled molecular categories beyond transcriptomics. However, many of these technologies are constrained by limited spatial resolution, hindering our ability to deeply characterize intricate tissue architectures. Existing computational methods primarily focus on the resolution enhancement of transcriptomics data, lacking the adaptability to address the emerging spatial omics technologies that profile various omics types. Here, we introduce soScope, a unified generative framework designed to enhance data quality and spatial resolution for molecular profiles obtained from diverse spatial technologies. soScope aggregates multimodal tissue information from omics, spatial relations and images, and jointly infers omics profiles at enhanced resolutions with omics-specific modeling through distribution priors. With comprehensive evaluations on diverse spatial omics platforms, including Visium, Xenium, spatial-CUT&Tag, and slide-DNA/RNA-seq, soScope improves performances in identifying biologically meaningful intestine and kidney architectures, revealing embryonic heart structure that cannot be resolved at the original resolution and correcting sample and technical biases arising from sequencing and sample processing. Furthermore, soScope extends to spatial multiomics technology spatial-CITE-seq and spatial ATAC-RNA-seq, leveraging cross-omics reference for simultaneous multiomics enhancement. soScope provides a versatile tool to improve the utilization of continually expanding spatial omics technologies and resources.

Integrating Multi-Omics with environmental data for precision health: A novel analytic framework and case study on prenatal mercury induced childhood fatty liver disease

BackgroundPrecision Health aims to revolutionize disease prevention by leveraging information across multiple omic datasets (multi-omics). However, existing methods generally do not consider personalized environmental risk factors (e.g., environmental pollutants). ObjectiveTo develop and apply a precision health framework which combines multiomic integration (including early, intermediate, and late integration, representing sequential stages at which omics layers are combined for modeling) with mediation approaches (including high-dimensional mediation to identify biomarkers, mediation with latent factors to identify pathways, and integrated/quasi-mediation to identify high-risk subpopulations) to identify novel biomarkers of prenatal mercury induced metabolic dysfunction-associated fatty liver disease (MAFLD), elucidate molecular pathways linking prenatal mercury with MAFLD in children, and identify high-risk children based on integrated exposure and multiomics data. MethodsThis prospective cohort study used data from 420 mother–child pairs from the Human Early Life Exposome (HELIX) project. Mercury concentrations were determined in maternal or cord blood from pregnancy. Cytokeratin 18 (CK-18; a MAFLD biomarker) and five omics layers (DNA Methylation, gene transcription, microRNA, proteins, and metabolites) were measured in blood in childhood (age 6–10 years). ResultsEach standard deviation increase in prenatal mercury was associated with a 0.11 [95% confidence interval: 0.02–0.21] standard deviation increase in CK-18. High dimensional mediation analysis identified 10 biomarkers linking prenatal mercury and CK-18, including six CpG sites and four transcripts. Mediation with latent factors identified molecular pathways linking mercury and MAFLD, including altered cytokine signaling and hepatic stellate cell activation. Integrated/quasi-mediation identified high risk subgroups of children based on unique combinations of exposure levels, omics profiles (driven by epigenetic markers), and MAFLD. ConclusionsPrenatal mercury exposure is associated with elevated liver enzymes in childhood, likely through alterations in DNA methylation and gene expression. Our analytic framework can be applied across many different fields and serve as a resource to help guide future precision health investigations.

Data-driven drug repositioning using olfactory omics profiles: challenges and perspectives in neurodegeneration.

Data-driven drug repositioning using olfactory omics profiles: challenges and perspectives in neurodegeneration.

Network-based integrative multi-omics approach reveals biosignatures specific to COVID-19 disease phases.

COVID-19 disease is characterized by a spectrum of disease phases (mild, moderate, and severe). Each disease phase is marked by changes in omics profiles with corresponding changes in the expression of features (biosignatures). However, integrative analysis of multiple omics data from different experiments across studies to investigate biosignatures at various disease phases is limited. Exploring an integrative multi-omics profile analysis through a network approach could be used to determine biosignatures associated with specific disease phases and enable the examination of the relationships between the biosignatures. To identify and characterize biosignatures underlying various COVID-19 disease phases in an integrative multi-omics data analysis. We leveraged a multi-omics network-based approach to integrate transcriptomics, metabolomics, proteomics, and lipidomics data. The World Health Organization Ordinal Scale WHO Ordinal Scale was used as a disease severity reference to harmonize COVID-19 patient metadata across two studies with independent data. A unified COVID-19 knowledge graph was constructed by assembling a disease-specific interactome from the literature and databases. Disease-state specific omics-graphs were constructed by integrating multi-omics data with the unified COVID-19 knowledge graph. We expanded on the network layers of multiXrank, a random walk with restart on multilayer network algorithm, to explore disease state omics-specific graphs and perform enrichment analysis. Network analysis revealed the biosignatures involved in inducing chemokines and inflammatory responses as hubs in the severe and moderate disease phases. We observed distinct biosignatures between severe and moderate disease phases as compared to mild-moderate and mild-severe disease phases. Mild COVID-19 cases were characterized by a unique biosignature comprising C-C Motif Chemokine Ligand 4 (CCL4), and Interferon Regulatory Factor 1 (IRF1). Hepatocyte Growth Factor (HGF), Matrix Metallopeptidase 12 (MMP12), Interleukin 10 (IL10), Nuclear Factor Kappa B Subunit 1 (NFKB1), and suberoylcarnitine form hubs in the omics network that characterizes the moderate disease state. The severe cases were marked by biosignatures such as Signal Transducer and Activator of Transcription 1 (STAT1), Superoxide Dismutase 2 (SOD2), HGF, taurine, lysophosphatidylcholine, diacylglycerol, triglycerides, and sphingomyelin that characterize the disease state. This study identified both biosignatures of different omics types enriched in disease-related pathways and their associated interactions (such as protein-protein, protein-transcript, protein-metabolite, transcript-metabolite, and lipid-lipid interactions) that are unique to mild, moderate, and severe COVID-19 disease states. These biosignatures include molecular features that underlie the observed clinical heterogeneity of COVID-19 and emphasize the need for disease-phase-specific treatment strategies. The approach implemented here can be used to find associations between transcripts, proteins, lipids, and metabolites in other diseases.

Impact of Different Red Blood Cell Storage Solutions and Conditions on Cell Function and Viability: A Systematic Review.

Red blood cell (RBC) storage solutions have evolved significantly over the past decades to optimize the preservation of cell viability and functionality during hypothermic storage. This comprehensive review provides an in-depth analysis of the effects of various storage solutions and conditions on critical RBC parameters during refrigerated preservation. A wide range of solutions, from basic formulations such as phosphate-buffered saline (PBS), to advanced additive solutions (ASs), like AS-7 and phosphate, adenine, glucose, guanosine, saline, and mannitol (PAGGSM), are systematically compared in terms of their ability to maintain key indicators of RBC integrity, including adenosine triphosphate (ATP) levels, morphology, and hemolysis. Optimal RBC storage requires a delicate balance of pH buffering, metabolic support, oxidative damage prevention, and osmotic regulation. While the latest alkaline solutions enable up to 8 weeks of storage, some degree of metabolic and morphological deterioration remains inevitable. The impacts of critical storage conditions, such as the holding temperature, oxygenation, anticoagulants, irradiation, and processing methods, on the accumulation of storage lesions are also thoroughly investigated. Personalized RBC storage solutions, tailored to individual donor characteristics, represent a promising avenue for minimizing storage lesions and enhancing transfusion outcomes. Further research integrating omics profiling with customized preservation media is necessary to maximize post-transfusion RBC survival and functions. The continued optimization of RBC storage practices will not only enhance transfusion efficacy but also enable blood banking to better meet evolving clinical needs.

A unified model-based framework for doublet or multiplet detection in single-cell multiomics data

Droplet-based single-cell sequencing techniques rely on the fundamental assumption that each droplet encapsulates a single cell, enabling individual cell omics profiling. However, the inevitable issue of multiplets, where two or more cells are encapsulated within a single droplet, can lead to spurious cell type annotations and obscure true biological findings. The issue of multiplets is exacerbated in single-cell multiomics settings, where integrating cross-modality information for clustering can inadvertently promote the aggregation of multiplet clusters and increase the risk of erroneous cell type annotations. Here, we propose a compound Poisson model-based framework for multiplet detection in single-cell multiomics data. Leveraging experimental cell hashing results as the ground truth for multiplet status, we conducted trimodal DOGMA-seq experiments and generated 17 benchmarking datasets from two tissues, involving a total of 280,123 droplets. We demonstrated that the proposed method is an essential tool for integrating cross-modality multiplet signals, effectively eliminating multiplet clusters in single-cell multiomics data—a task at which the benchmarked single-omics methods proved inadequate.

Quantification and Profiling of Early and Late Differentiation Stage T Cells in Mantle Cell Lymphoma Reveals Immunotherapeutic Targets in Subsets of Patients.

With the aim to advance the understanding of immune regulation in MCL and to identify targetable T-cell subsets, we set out to combine image analysis and spatial omic technology focused on both early and late differentiation stages of T cells. MCL patient tissue (n = 102) was explored using image analysis and GeoMx spatial omics profiling of 69 proteins and 1812 mRNAs. Tumor cells, T helper (TH) cells and cytotoxic (TC) cells of early (CD57-) and late (CD57+) differentiation stage were analyzed. An image analysis workflow was developed based on fine-tuned Cellpose models for cell segmentation and classification. TC and CD57+ subsets of T cells were enriched in tumor-rich compared to tumor-sparse regions. Tumor-sparse regions had a higher expression of several key immune suppressive proteins, tentatively controlling T-cell expansion in regions close to the tumor. We revealed that T cells in late differentiation stages (CD57+) are enriched among MCL infiltrating T cells and are predictive of an increased expression of immune suppressive markers. CD47, IDO1 and CTLA-4 were identified as potential targets for patients with T-cell-rich MCL TIME, while GITR might be a feasible target for MCL patients with sparse T-cell infiltration. In subgroups of patients with a high degree of CD57+ TC-cell infiltration, several immune checkpoint inhibitors, including TIGIT, PD-L1 and LAG3 were increased, emphasizing the immune-suppressive features of this highly differentiated T-cell subset not previously described in MCL.

A long-term growth stable Halomonas sp. deleted with multiple transposases guided by its metabolic network model Halo-ecGEM

Microbial instability is a common problem during bio-production based on microbial hosts. Halomonas bluephagenesis has been developed as a chassis for next generation industrial biotechnology (NGIB) under open and unsterile conditions. However, the hidden genomic information and peculiar metabolism have significantly hampered its deep exploitation for cell-factory engineering. Based on the freshly completed genome sequence of H. bluephagenesis TD01, which reveals 1889 biological process-associated genes grouped into 84 GO-slim terms. An enzyme constrained genome-scale metabolic model Halo-ecGEM was constructed, which showed strong ability to simulate fed-batch fermentations. A visible salt-stress responsive landscape was achieved by combining GO-slim term enrichment and CVT-based omics profiling, demonstrating that cells deploy most of the protein resources by force to support the essential activity of translation and protein metabolism when exposed to salt stress. Under the guidance of Halo-ecGEM, eight transposases were deleted, leading to a significantly enhanced stability for its growth and bioproduction of various polyhydroxyalkanoates (PHA) including 3-hydroxybutyrate (3HB) homopolymer PHB, 3HB and 3-hydroxyvalerate (3HV) copolymer PHBV, as well as 3HB and 4-hydroxyvalerate (4HB) copolymer P34HB. This study sheds new light on the metabolic characteristics and stress-response landscape of H. bluephagenesis, achieving for the first time to construct a long-term growth stable chassis for industrial applications. For the first time, it was demonstrated that genome encoded transposons are the reason for microbial instability during growth in flasks and fermentors.

The chemical profiles and cytotoxicity of gaharu bouya oil from Borneo's Gonystylus bancanus wood.

Gaharu bouya oil obtained from distillation of the woods from Gonystylus genus has attracted essential oil industry interest. However, the information about gaharu bouya essential oil profile is limited. The presence of Gonystylus species is also critically endangered on the IUCN Red List. Therefore, exploring the -omics profiles of Gonystylus bancanus, a native plant from Borneo Island, is important for Indonesia to conserve the population. This research investigated the metabolite profiling of G. bancanus oil, especially the volatile components of its essential oils. Distillations were performed in two technical ways: hydrodistillation on a laboratory scale and steam distillation on an industrial scale. According to LC-MS and GC-MS profiles, both essential oils displayed similar chemical compositions. This article also discusses the similarity of the chemical contents of gaharu bouya oil and agarwood oil from the gaharu superior type (Aquilaria) to support the value of the oil. This research also investigated the cytotoxicity of gaharu bouya oil against three cell lines: HeLa, MCF-7, and HT-29.