The mitochondrial multi-omic response to exercise training across rat tissues
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Cell Biology↗
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Results for “multi 'omics”
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Associations between SARS-CoV-2 Infection or COVID-19 Vaccination and Human Milk Composition: A Multi-Omics Approach
Background: The risk of contracting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) via human milk-feeding is virtually nonexistent. Adverse effects of coronavirus disease 2019 (COVID-19) vaccination for lactating individuals are not different from the general population, and no evidence has been found that their infants exhibit adverse effects. Yet, there remains substantial hesitation among this population globally regarding the safety of these vaccines. Objectives: Herein, we aimed to determine if compositional changes in milk occur following SARS-CoV-2 infection or COVID-19 vaccination, including any evidence of vaccine components. Methods: An extensive multiomics approach was taken using a subset of milk samples obtained as part of our broad studies examining the effects on milk of SARS-CoV-2 infection and COVID-19 vaccination. Results: We found that compared with unvaccinated individuals, SARS-CoV-2 infection was associated with significant compositional differences in 67 proteins, 385 lipids, and 13 metabolites. In contrast, COVID-19 vaccination was not associated with any changes in lipids or metabolites, although it was associated with changes in 13 or fewer proteins. Compositional changes in milk differed by vaccine. Changes following vaccination were greatest after 1–6 h for the mRNA-based Moderna vaccine (8 changed proteins), 3 d for the mRNA-based Pfizer (4 changed proteins), and adenovirus-based Johnson and Johnson (13 changed proteins) vaccines. Proteins that changed after both natural infection and Johnson and Johnson vaccine were associated mainly with systemic inflammatory responses. In addition, no vaccine components were detected in any milk sample. Conclusions: Together, our data provide evidence of only minimal changes in milk composition because of COVID-19 vaccination, with much greater changes after natural SARS-CoV-2 infection.
60 APPLIED LIFE SCIENCES↗
The Addition of Transcriptomics to the Bead-Enabled Accelerated Monophasic Multi-Omics Method: A Step toward Universal Sample Preparation
Not Available
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
High-resolution multi-omics enhances prediction and detection of smORF-encoded proteins in the human gut microbiome
Explore the source record for details and available documents.
Davin, Megan [ORNL]↗
Multi-omics reveals nitrogen dynamics associated with soil microbial blooms during snowmelt
Snowmelt triggers a soil microbial bloom and crash that affects nitrogen (N) export in high-elevation watersheds. The mechanisms underlying these microbial dynamics are uncertain, making soil nitrogen processes difficult to predict as snowpack declines globally. Here, integration of genome-resolved metagenomics, metatranscriptomics and metabolomics in a high-elevation watershed revealed ecologically distinct soil microorganisms linked across the snowmelt time-period by their unique nitrogen cycling capacities. The molecular properties and transformations of dissolved organic N suggested that degradation or recycling of microbial biomass provided N for biosynthesis during the microbial bloom. Winter-adapted Bradyrhizobia spp. oxidized amino acids anaerobically and had the highest gene expression for denitrification during the microbial bloom. A pulse of nitrate was driven by spring-adapted Nitrososphaerales after snowmelt, but dissimilatory nitrate reduction to ammonia (DNRA) gene expression indicated significant nitrate retention potential. These findings inform our understanding of nitrogen cycling in environments sensitive to snowpack decline due to global change.
Sorensen, Patrick O↗
A multi-omic characterization of temperature stress in a halotolerant Scenedesmus strain for algal biotechnology
Microalgae efficiently convert sunlight into lipids and carbohydrates, offering bio-based alternatives for energy and chemical production. Improving algal productivity and robustness against abiotic stress requires a systems level characterization enabled by functional genomics. Here, we characterize a halotolerant microalga Scenedesmus sp. NREL 46B-D3 demonstrating peak growth near 25 °C that reaches 30 g/m 2 /day and the highest biomass accumulation capacity post cell division reported to date for a halotolerant strain. Functional genomics analysis revealed that genes involved in lipid production, ion channels and antiporters are expanded and expressed. Exposure to temperature stress shifts fatty acid metabolism and increases amino acids synthesis. Co-expression analysis shows that many fatty acid biosynthesis genes are overexpressed with specific transcription factors under cold stress. These and other genes involved in the metabolic and regulatory response to temperature stress can be further explored for strain improvement.
09 BIOMASS FUELS↗
Multi-omics analysis reveals the dynamic interplay between Vero host chromatin structure and function during vaccinia virus infection
The genome folds into complex configurations and structures thought to profoundly impact its function. The intricacies of this dynamic structure-function relationship are not well understood particularly in the context of viral infection. To unravel this interplay, here we provide a comprehensive investigation of simultaneous host chromatin structural (via Hi-C and ATAC-seq) and functional changes (via RNA-seq) in response to vaccinia virus infection. Over time, infection significantly impacts global and local chromatin structure by increasing long-range intra-chromosomal interactions and B compartmentalization and by decreasing chromatin accessibility and inter-chromosomal interactions. Local accessibility changes are independent of broad-scale chromatin compartment exchange (~12% of the genome), underscoring potential independent mechanisms for global and local chromatin reorganization. While infection structurally condenses the host genome, there is nearly equal bidirectional differential gene expression. Despite global weakening of intra-TAD interactions, functional changes including downregulated immunity genes are associated with alterations in local accessibility and loop domain restructuring. Therefore, chromatin accessibility and local structure profiling provide impactful predictions for host responses and may improve development of efficacious anti-viral counter measures including the optimization of vaccine design.
59 BASIC BIOLOGICAL SCIENCES↗
A multi-omics approach to lignocellulolytic enzyme discovery reveals a new ligninase activity from Parascedosporium putredinis NO1
Lignocellulose, the structural component of plant cells, is a major agricultural byproduct and the most abundant terrestrial source of biopolymers on Earth. The complex and insoluble nature of lignocellulose limits its conversion into value-added commodities, and currently, efficient transformation requires expensive pretreatments and high loadings of enzymes. In this work, we report on a fungus from the Parascedosporium genus, isolated from a wheat-straw composting community, that secretes a large and diverse array of carbohydrate-active enzymes (CAZymes) when grown on lignocellulosic substrates. We describe an oxidase activity that cleaves the major β-ether units in lignin, thereby releasing the flavonoid tricin from monocot lignin and enhancing the digestion of lignocellulose by polysaccharidase mixtures. We show that the enzyme, which holds potential for the biorefining industry, is widely distributed among lignocellulose-degrading fungi from the Sordariomycetes phylum.
59 BASIC BIOLOGICAL SCIENCES↗
RWRtoolkit: multi-omic network analysis using random walks on multiplex networks in any species
Abstract We introduce RWRtoolkit, a multiplex generation, exploration, and statistical package built for R and command-line users. RWRtoolkit enables the efficient exploration of large and highly complex biological networks generated from custom experimental data and/or from publicly available datasets, and is species agnostic. A range of functions can be used to find topological distances between biological entities, determine relationships within sets of interest, search for topological context around sets of interest, and statistically evaluate the strength of relationships within and between sets. The command-line interface is designed for parallelization on high-performance cluster systems, which enables high-throughput analysis such as permutation testing. Several tools in the package have also been made available for use in reproducible workflows via the KBase web application.
Kainer, David (ORCID:0000000172714676)↗
Unveiling the microbial realm with VEBA 2.0: a modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic and viral multi-omics from either short- or long-read sequencing
Abstract The microbiome is a complex community of microorganisms, encompassing prokaryotic (bacterial and archaeal), eukaryotic, and viral entities. This microbial ensemble plays a pivotal role in influencing the health and productivity of diverse ecosystems while shaping the web of life. However, many software suites developed to study microbiomes analyze only the prokaryotic community and provide limited to no support for viruses and microeukaryotes. Previously, we introduced the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite to address this critical gap in microbiome research by extending genome-resolved analysis beyond prokaryotes to encompass the understudied realms of eukaryotes and viruses. Here we present VEBA 2.0 with key updates including a comprehensive clustered microeukaryotic protein database, rapid genome/protein-level clustering, bioprospecting, non-coding/organelle gene modeling, genome-resolved taxonomic/pathway profiling, long-read support, and containerization. We demonstrate VEBA’s versatile application through the analysis of diverse case studies including marine water, Siberian permafrost, and white-tailed deer lung tissues with the latter showcasing how to identify integrated viruses. VEBA represents a crucial advancement in microbiome research, offering a powerful and accessible software suite that bridges the gap between genomics and biotechnological solutions.
59 BASIC BIOLOGICAL SCIENCES↗
Propelling sustainable energy: Multi-omics analysis of pennycress FATTY ACID ELONGATION1 knockout for biofuel production
Abstract The aviation industry’s growing interest in renewable jet fuel has encouraged the exploration of alternative oilseed crops. Replacing traditional fossil fuels with a sustainable, domestically sourced crop can substantially reduce carbon emissions, thus mitigating global climate instability. Pennycress (Thlaspi arvense L.) is an emerging oilseed intermediate crop that can be grown during the offseason between maize (Zea mays) and soybean (Glycine max) to produce renewable biofuel. Pennycress is being domesticated through breeding and mutagenesis, providing opportunities for trait enhancement. Here, we employed metabolic engineering strategies to improve seed oil composition and bolster the plant's economic competitiveness. FATTY ACID ELONGATION1 (FAE1) was targeted using CRISPR-Cas 9 gene editing to eliminate very long chain fatty acids (VLCFAs) from pennycress seed oil, thereby enhancing its cold flow properties. Through an integrated multiomics approach, we investigated the impact of eliminating VLCFAs in developing and mature plant embryos. Our findings revealed improved cold-germination efficiency in fae1, with seedling emergence occurring up to 3 d earlier at 10 °C. However, these alterations led to a tradeoff between storage oil content and composition. Additionally, these shifts in lipid biosynthesis were accompanied by broad metabolic changes, such as the accumulation of glucose and ADP-glucose quantities consistent with increased starch production. Furthermore, shifts to shorter FA chains triggered the upregulation of heat shock proteins, underscoring the importance of VLCFAs in stress signaling pathways. Overall, this research provides crucial insights for optimizing pennycress seed oil while preserving essential traits for biofuel applications.
Rasoul, Amira (ORCID:0009000714290175)↗
Multi-Omics Reveals Temporal Scales of Carbon Metabolism in Synechococcus Elongatus PCC 7942 Under Light Disturbance
Central carbon metabolism in model cyanobacteria involves multiple pathways to adapt to energy-light limitations across diel cycles. However, the success in mechanistic modeling for phenotypic prediction of the protein regulators in the metabolic state depends on capturing the vast possibilities emerging from multiple regulatory pathways in complex biological processes. Here, we developed a physics-informed machine learning approach based on energy-landscape concepts to predict regulatory proteins responding to cyclic circadian and unforeseen light perturbations in cyanobacterial metabolic networks. Our approach provides interpretable de novo models for inferring gene expression dynamics from Synechococcus elongatus over diel cycles and using redox proteome analysis to distinguish immediate light-responsive elements from circadian-regulated processes in carbon metabolism pathways. We identified distinct temporal signatures with the analysis of the redox proteome: there was an immediate shift in cysteine redox states accompanied by a limited change in protein abundance under constant illumination and after 2 hours of darkness. This discovery indicates that the generation of reductants coordinates photoinduced electron transport with redox metabolic pathways in two discernable molecular mechanisms: fast redox-based protein modifications occur immediately after the light disturbance, followed by slow transcriptional regulations across networks. This temporal regulation reveals how metabolic networks integrate rapid light responses with programmed circadian rhythms to maintain cellular homeostasis under the light-energy limitations over the diel cycle.
Biomolecular & subcellular processes↗
The need for an integrated multi-OMICs approach in microbiome science in the food system
Microbiome science as an interdisciplinary research field has evolved rapidly over the past two decades, becoming a popular topic not only in the scientific community and among the general public, but also in the food industry due to the growing demand for microbiome-based technologies that provide added-value solutions. Microbiome research has expanded in the context of food systems, strongly driven by methodological advances in different -omics fields that leverage our understanding of microbial diversity and function. However, managing and integrating different complex -omics layers are still challenging. Within the Coordinated Support Action MicrobiomeSupport (https://www.microbiomesupport.eu/), a project supported by the European Commission, the workshop “Metagenomics, Metaproteomics and Metabolomics: the need for data integration in microbiome research” gathered 70 participants from different microbiome research fields relevant to food systems, to discuss challenges in microbiome research and to promote a switch from microbiome-based descriptive studies to functional studies, elucidating the biology and interactive roles of microbiomes in food systems. A combination of technologies is proposed. This will reduce the biases resulting from each individual technology and result in a more comprehensive view of the biological system as a whole. Although combinations of different datasets are still rare, advanced bioinformatics tools and artificial intelligence approaches can contribute to understanding, prediction, and management of the microbiome, thereby providing the basis for the improvement of food quality and safety.
60 APPLIED LIFE SCIENCES↗
Cryptococcus neoformans - Infected Macrophages Release Proinflammatory Extracellular Vesicles: Insight into Their Components by Multi-omics
Cryptococcus neoformans causes cryptococcal meningitis, which is frequent in patients with HIV/AIDS, especially in less-developed countries. The incidence of cryptococcal meningitis is close to 1 million each year globally.
59 BASIC BIOLOGICAL SCIENCES↗
Machine learning approaches for integrating multi-omics data to expand microbiome annotation
Preliminary: This final report corresponds to a grant (DE-SC0021216) that was awarded to the University of Montana. Mid-way through the grant period, I relocated from the University of Montana to the University of Arizona. The grant was ended at University of Montana in late 2022, with all efforts concluding on 08/26/22; the remaining funds supporting the project were relinquished by University of Montana, and were later awarded to University of Arizona under a new grant, with start date 04/01/23. This report focuses on results of research efforts at UMontana through 08/26/22. Results: We made progress in each of the three aims of the proposal. We released software that identifies and fills gaps in the annotation of metabolic proteins within bacterial genomes. We made substantial progress in developing software for alignment-based annotation of protein coding DNA, allowing for coding frameshifts caused by sequencing error. Finally, we made notable progress in developing AI methods (specifically: a neural embedding model) for identifying similarities between protein sequences based on amino-wise latent vectors. These efforts were supplemented by development of methods for protein modeling in support of predicting protein-drug binding activity, and by my leadership of a team in the NIH/DOE 2021 Petabyte-Scale Sequence Search hack-a-thon.
59 BASIC BIOLOGICAL SCIENCES↗
Accelerating engineered microbe optimization through machine learning and multi-omics datasets
Summary presentation for 2021 peer review session
60 APPLIED LIFE SCIENCES↗
Accelerating engineered microbe optimization through machine learning and multi-omics datasets
Summary presentation for 2023 BETO peer review session.
Lennen, Rebecca↗
Machine learning approaches for integrating multi-omics data to expand microbiome annotation (Final Technical Report)
We fulfilled all original three aims of the proposal. Following the earlier release (during the first phase of the project at Montana) of software that identifies and fills gaps in the annotation of metabolic proteins within bacterial genomes, we have nearly completed a second gap-filling tool that improves accuracy and explainability. We completed software for alignment-based annotation of protein coding DNA, allowing for coding frameshifts caused by sequencing error. Finally, we completed a neural embedding model for identifying similarities between protein sequences based on amino-wise latent vectors.
59 BASIC BIOLOGICAL SCIENCES↗