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PubMed2026

Spatially resolved multi-omics analysis of indigenous Bacillus-fortified high-temperature Daqu.

Layer-dependent patterns associated with indigenous Bacillus fortification on high-temperature Daqu remain unclear. Here, six indigenous functional Bacillus strains were combined to fortify Daqu at three inoculation levels (QH4, QH5, QH6), with non-fortified as the control (CK). Upper, middle, and lower shelf-layer samples were profiled by physicochemical measurements, volatilomics, organic acid analysis, untargeted metabolomics, 16S/ITS amplicon sequencing, and metagenomics. PERMANOVA showed significant effects of treatment, spatial layer, and their interaction on physicochemical, volatile, bacterial, and fungal profiles (P = 0.001). Among the three inoculation levels, QH5 showed the most balanced performance: QH5_M exhibited the highest observed mean peak temperature (63.3 °C; +4.5 °C relative to CK_M), and its group-mean temperature remained ≥ 60 °C for seven consecutive days. Multi-omics analyses indicated coordinated, non-linear, and layer-dependent differences associated with indigenous Bacillus fortification, with QH5_M showing the most pronounced combined thermal, pyrazine, substrate, microbial, and predicted functional profile. These findings indicate that moderate indigenous Bacillus fortification was associated with distinct layer-dependent thermal and flavor profiles and coordinated microbial, metabolic, and predicted functional differences.

Bacillus↗
PubMed2025

Unveiling the mechanism of micro-and-nano plastic phytotoxicity on terrestrial plants: A comprehensive review of omics approaches.

Micro-and-nano plastics (MNPs) are pervasive in terrestrial ecosystems and represent an increasing threat to plant health; however, the mechanisms underlying their phytotoxicity remain inadequately understood. MNPs can infiltrate plants through roots or leaves, causing a range of toxic effects, including inhibiting water and nutrient uptake, reducing seed germination rates, and impeding photosynthesis, resulting in oxidative damage within the plant system. The effects of MNPs are complex and influenced by various factors including size, shape, functional groups, and concentration. Recent advancements in omics technologies such as proteomics, metabolomics, transcriptomics, and microbiomics, coupled with emerging technologies like 4D omics, phenomics, spatial transcriptomics, and single-cell omics, offer unprecedented insight into the physiological, molecular, and cellular responses of terrestrial plants to MNPs exposure. This literature review synthesizes current findings regarding MNPs-induced phytotoxicity, emphasizing alterations in gene expression, protein synthesis, metabolic pathways, and physiological disruptions as revealed through omics analyses. We summarize how MNPs interact with plant cellular structures, disrupt metabolic processes, and induce oxidative stress, ultimately affecting plant growth and productivity. Furthermore, we have identified critical knowledge gaps and proposed future research directions, highlighting the necessity for integrative omics studies to elucidate the complex pathways of MNPs toxicity in terrestrial plants. In conclusion, this review underscores the potential of omics approaches to elucidate the mechanisms of MNPs-phytotoxicity and to develop strategies for mitigating the environmental impact of MNPs on plant health.

Plants↗
PubMed2026

Convergent methodologies in prosthetic joint infection research: integrating transdisciplinary approaches to understand and prevent biofilm-driven failure of orthopaedic prostheses.

Prosthetic joint infections (PJIs) remain among the most devastating complications of arthroplasty, imposing substantial clinical, economic and patient burdens. Although culture-based diagnostics underpin current clinical practice, PJIs are biofilm-driven infections shaped by taxonomic diversity, spatial organization, host responses and surface interactions, meaning conventional approaches provide only a partial and often decontextualized view of the infection process. We examine how convergent methodologies can transform PJI research by integrating approaches that have traditionally been studied in isolation, including sequencing, transcriptomics, metabolomics, advanced imaging and culture-based characterization. We discuss how whole-genome sequencing, shotgun metagenomics, transcriptomic and metabolomic approaches resolve pathogen identity, functional activity and adaptive persistence and how cross-scale imaging and spatial biology techniques reveal where microbes colonize, interact and survive across implant surfaces. We highlight emerging opportunities to unify these datasets into coherent frameworks that capture both the molecular and physical dimensions of PJIs. Integrating these complementary approaches will enable a multi-layered understanding of PJIs that link composition, function and spatial organization. Ultimately, this provides a foundation for predictive diagnostics, precision antimicrobial strategies and improved implant design and supports a shift towards more effective, mechanism-informed management of implant-associated infection.

Prosthesis-Related Infections↗
PubMed2026

A spatially resolved genomic-molecular atlas of human white-matter microstructure.

Human white matter has been linked to inherited variation, circulating molecular state and brain disease, but these layers have rarely been mapped onto the same tract anatomy. Here we measured genetic effects along 6,090 atlas-aligned fiber pathways sampled at 609,000 locations in 72,185 UK Biobank participants, and integrated proteomic and metabolomic profiles within the same anatomical frame. Genetic effects were not whole-tract properties: each locus formed a spatial footprint along fiber trajectories, ranging from single locations to broad multi-tract patterns and reflecting regional polygenicity rather than tract heritability. This map identified 258, 186 and 298 previously unreported loci for fractional anisotropy, mean diffusivity and axial diffusivity; spatial patterns replicated in adults and 157 of 315 FA loci replicated in adolescence in ABCD. Mendelian randomization linked localized genetic effects to neurodegenerative and psychiatric traits, with Alzheimer's disease showing directional effects across 12 of 17 tracts. Multi-omic analyses identified 97 proteomic and 161 metabolomic associations, with the broadest signals from lipid metabolites including linoleic acid and phosphatidylcholines. The strongest lipid-metabolite and genetic signals converged in the corpus callosum, placing inherited variation, disease risk and systemic lipid metabolism on the same localized tract segments.

Journal Article↗
PubMed2003

Metabolic networks of Cucurbita maxima phloem.

Metabolomic analysis aims at a comprehensive characterization of biological samples. Yet, biologically meaningful interpretations are often limited by the poor spatial and temporal resolution of the acquired data sets. One way to remedy this is to limit the complexity of the cell types being studied. Cucurbita maxima Duch. vascular exudates provide an excellent material for metabolomics in this regard. Using automated mass spectral deconvolution, over 400 components have been detected in these exudates, but only 90 of them were tentatively identified. Many amino compounds were found in vascular exudates from leaf petioles at concentrations several orders of magnitude higher than in tissue disks from the same leaves, whereas hexoses and sucrose were found in far lower amounts. In order to find the expected impact of assimilation rates on sugar levels, total phloem composition of eight leaves from four plants was followed over 4.5 days. Surprisingly, no diurnal rhythm was found for any of the phloem metabolites that was statistically valid for all eight leaves. Instead, each leaf had its own distinct vascular exudate profile similar to leaves from the same plant, but clearly different from leaves harvested from plants at the same developmental stage. Thirty to forty per cent of all metabolite levels of individual leaves were different from the average of all metabolite profiles. Using metabolic co-regulation analysis, similarities and differences between the exudate profiles were more accurately characterized through network computation, specifically with respect to nitrogen metabolism.

Circadian Rhythm↗
PubMed2026

Subchronic benzo[a]pyrene exposure disrupts APOE4-regulated lipid metabolism to induce Tau hyperphosphorylation and cognitive deficits.

BACKGROUND: Benzo[a]pyrene (B[a]P) is both a carcinogen and a potent neurotoxic pollutant. Despite growing evidence linking B[a]P to neurological dysfunction, the responsible mechanisms have not been elucidated. METHODS: Here, we employed human apolipoprotein E4 (hAPOE4) transgenic mice and APOE knockout (APOE-KO) mice to evaluate the influence of APOE on B[a]P-mediated neurotoxicity. hAPOE4 mice overexpress the human APOE4 isoform, whereas APOE-KO mice lack APOE expression; wild-type C57BL/6 J mice served as controls. Animals received intraperitoneal injections of B[a]P at 0, 2.5, or 6.25 mg/kg on alternate days for 3 months. Spatial memory and learning were examined via Morris Water Maze (MWM). Neuronal morphology, including dendritic branching and spine density in the CA1 region of the hippocampus and dentate gyrus (DG), was assessed using Golgi-Cox staining. Neurofibrillary tangles were detected by silver glycine staining. Tau, phosphorylated Tau (Ser199 and Ser396), and LRP1 were evaluated using Western blot and immunohistochemical analyses. Chromatin immunoprecipitation PCR (ChIP-PCR) was undertaken to examine the regulation of APOE4 expression by the aryl hydrocarbon receptor (AHR). In addition, both untargeted metabolomics and lipidomics analyses were conducted following B[a]P exposure. RESULTS: B[a]P led to pronounced impairments in mouse spatial memory and learning, shown by greater escape latency, less time in the target quadrant, and a decreased number of platform crossings in MWM tests. Structural analyses revealed a significant reduction in dendritic branching within the hippocampal CA1 and DG regions. These neurobehavioral and morphological deficits were most severe in hAPOE4 mice, which displayed greater cognitive impairment and more extensive dendritic loss than B[a]P-treated wild-type mice, indicating that APOE4 amplifies B[a]P-induced neurotoxicity. ChIP assays demonstrated that B[a]P modulates APOE4 transcription through AHR-dependent mechanisms. Additionally, metabolomics and lipidomics analyses revealed widespread B[a]P-induced metabolic remodeling, suggesting that disrupted lipid metabolism and altered neuronal membrane integrity may contribute to the observed neurotoxicity and cognitive dysfunction. CONCLUSION: Collectively, the results indicate that B[a]P-mediated neurotoxicity may be facilitated, at least in part, by APOE4-dependent dysregulation of lipid metabolic pathways.

Animals↗
PubMed2026

Multi-omics and spatial transcriptomics reveal that S100A10 drives CD8+ T-cell exhaustion and immune evasion in hepatocellular carcinoma through cPLA2-5-LOX-mediated arachidonic acid metabolism and ferroptosis.

Immune evasion in hepatocellular carcinoma (HCC) represents a major biological barrier limiting the efficacy of immunotherapy, yet its molecular basis remains incompletely understood. Increasing evidence indicates that tumor metabolic reprogramming and ferroptosis-related signaling play critical roles in shaping an immunosuppressive tumor microenvironment (TME); however, the specific regulatory factors involved remain unclear. This study aims to systematically elucidate the functional role of S100 calcium-binding protein A10 (S100A10) in immune evasion in HCC, with a particular focus on the molecular mechanisms by which S100A10 regulates CD8+ T-cell exhaustion through arachidonic acid (AA) metabolism and ferroptosis, as well as its potential therapeutic implications. To this end, data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort are integrated to analyze the expression patterns of S100A10, its prognostic value, and its association with the immune microenvironment. S100A10 overexpression and knockout models are established in HCCLM3 and MHCC97L cell lines, and S100A10-mediated metabolic pathway reprogramming is characterized using transcriptomic profiling, untargeted metabolomics, and ferroptosis-related functional assays. In parallel, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics are employed to delineate the cell-type specificity and spatial distribution of S100A10. Furthermore, human CD8+ T-cell co-culture systems and orthotopic mouse HCC models are used to evaluate the impact of S100A10 on immune function and responsiveness to anti-programmed cell death protein 1 (anti-PD-1) therapy. The results demonstrate that S100A10 is significantly upregulated in HCC and is closely associated with poor prognosis and an immunosuppressive state. Mechanistically, S100A10 activates cytosolic phospholipase A2-arachidonate 5-lipoxygenase (cPLA2-5-LOX)-mediated AA oxidative metabolism, leading to the accumulation of lipid peroxidation products and ferroptosis-associated signals, thereby driving CD8+ T-cell exhaustion and promoting immune evasion. Significantly, inhibition of S100A10 reshapes the tumor immune microenvironment (TIME) and enhances the therapeutic efficacy of anti-PD-1 treatment. Collectively, these findings identify S100A10 as a critical regulator of metabolic-immune coupling in HCC and provide a theoretical basis for combinatorial strategies targeting metabolism and immunotherapy.

Arachidonic acid metabolism↗
PubMed2026

Pathogenesis of psoriasis and psoriatic arthritis: Insights from animal models and single-cell and spatial transcriptomic analyses of skin, synovium and entheses.

Psoriasis (PsO) and psoriatic arthritis (PsA) are immune-mediated diseases characterized by chronic systemic inflammation, including inflammation of the skin and joints. Recent advances in animal models, single-cell transcriptomics, spatial transcriptomics, and proteomics have greatly enhanced our understanding of disease pathogenesis. Mouse models exhibit key features of skin and joint inflammation, facilitating analysis of molecular pathways, and identification of therapeutic targets. Single-cell and spatial transcriptomic analyses have revealed cell-type-specific contributions to inflammation, highlighting interactions between keratinocytes, T cells, fibroblasts, and dendritic cells that drive psoriatic pathology. In psoriatic synovium, type 17 tissue-resident memory T cells, monocytes, and fibroblasts contribute to local inflammation and joint damage, whereas the roles of B cells and plasma cells are less clear. Proteomic and metabolomic profiling in patients with PsA has identified circulating protein signatures and metabolites associated with disease progression, sex-specific differences, and response to therapy. The integration of these multiomic approaches provides a detailed map of immune-stromal-epithelial crosstalk across skin, synovium, and entheses, uncovering mechanisms that were previously inaccessible. These insights have implications for predicting disease progression, identifying novel therapeutic targets, and optimizing treatment strategies. Collectively, advances in animal models and multiomic profiling are reshaping our understanding of PsO and PsA, providing a framework for future research, disease monitoring, and therapeutic development.

Animals↗
PubMed2026

Hyperprogression Upon Cemiplimab Alone or With Short Course Chemotherapy in PD-L1 ≥ 50% Non-small Cell Lung Cancer: A Biomarker Guided Multicenter International Phase 2 Trial-HYPERBOLIC Study.

BACKGROUND: Immune checkpoint inhibitor (ICI) monotherapy is the standard first-line treatment for advanced non-small cell lung cancer (NSCLC) with PD-L1 ≥ 50%; however, up to 30% of patients experience early progression or death, including cases of hyperprogressive disease (HPD). High baseline levels (≥ 30.5%) of circulating CD10- low-density neutrophils (LDNs) have been associated with increased HPD occurrence. Emerging evidence suggests that combining ICI with platinum-based chemotherapy (PCT) may mitigate the risk of HPD. Currently, no prospective studies have addressed HPD prevention in this context. PATIENTS AND METHODS: HYPERBOLIC (NCT07274384) is a phase 2, randomized, open-label, multicenter, international trial evaluating whether adding 3 cycles of PCT to first-line cemiplimab reduces HPD rate in stage IV NSCLC with PD-L1 ≥ 50% and CD10- LDNs (identified by flow cytometry as CD15⁺CD11b⁺ within the PBMC fraction, with immature cells defined by loss of CD10) ≥ 30.5%. Seventy-four patients will be randomized (1:1 ratio) to receive cemiplimab alone or cemiplimab plus 3 PCT cycles, followed by cemiplimab maintenance. Randomization will be stratified by Lung Immune Prognostic Index. The first computed tomography scan at week 7 after treatment start will assess HPD occurrence, defined as RECIST v 1.1. disease progression with a delta tumor growth rate (ΔTGR) ≥ 50% and/or TGR ratio ≥ 2. The primary endpoint will be the combined rate of HPD and early death (death within 12 weeks with no radiological evaluation). Secondary endpoints will be HPD rate according to alternative definitions, overall survival, progression free survival, objective response rate, and safety. An extensive translational research platform will include spatial transcriptomics of tumor tissue, single-cell RNA sequencing of PBMCs, circulating-free DNA and plasma factors profiling, and saliva/stool microbiome genomics and metabolomics, to longitudinally explore tumor-host dynamic interactions during treatment. CONCLUSION: to our knowledge, HYPERBOLIC is the first prospective, biomarker-driven trial investigating early treatment escalation based on HPD risk in PD-L1-high NSCLC.

CD10↗
PubMed2026

Next-Generation Disease Profiling by Integrating Histopathology with Spatial Multi-Omics Data.

The field of pathology has experienced several transformative changes in recent years with the advent of digital pathology and spatial multi-omics. These technologies have enhanced every aspect of pathology practice, from streamlining daily workflows to generating high-fidelity multi-omics data that provide pathologists with novel tools to refine disease profiling and clinical diagnosis. Each layer of multimodal data (genomic, metabolomic, proteomic, or transcriptomic) has uncovered a distinct facet of disease pathologies, and combined with machine learning/artificial intelligence-based data analysis and pattern recognition models, has provided holistic understanding of regulatory mechanisms underpinning them. However, high-dimensional data have far exceeded the volume, scale, and complexity of immunostaining methods implemented by pathologists and, thus, have generated significant challenges related to deconvolution, interpretation, and clinical translation. Furthermore, these multimodal studies have predominantly relied on computational methods to process data and extract disease-relevant insights, thus raising questions around relevance or role of a pathologist in this new era of multi-omics. This review will provide a perspective on the evolving fields of molecular histopathology and spatial -omics, leveraging them to approach disease profiling, and redefining the role of a pathologist during this process.

Humans↗
PubMed2002

The propagator (retarded Green function) formalism as a new calculation method to predict the time evolution of bands in capillary electrophoresis and microchannels.

Capillary electrophoresis (CE) and microchannel (MC) techniques are important tools in chemical and biological sciences, mainly in the study of genomes, transcriptomes, proteomes, metabolomes, and organic as well as inorganic ions. The speed of DNA sequencing increased significantly during the last decade with the use of capillary electrophoresis (CE). The time evolution of the bands' spatial profile inside capillaries and channels is of paramount importance, since the main goal of these techniques is to maximize resolution (the ratio between the spacing of the peaks and their mean standard deviations). In the present work, the propagator (retarded Green function) formalism is applied to solve a few problems which are typical for CE and MC. We also apply this mathematical method to the problem of velocity gradients along the capillary, i.e., the time evolution of the bands is analyzed when they enter regions where they migrate with different velocities.

Diffusion↗
PubMed2003

From measurements of metabolites to metabolomics: an 'on the fly' perspective illustrated by recent studies of carbon-nitrogen interactions.

Techniques for surveying metabolite levels provide a powerful tool for basic plant research and biotechnology. They offer the possibility of specialised measurements of specific classes of metabolites, profiling of a very broad range of low molecular weight compounds, and resolving the spatial and/or temporal localisation of selected key metabolites. Recent applications in the field of carbon-nitrogen interactions provide a framework to discuss how metabolite datasets are being utilised in the post-genomic era to characterise system responses, link transcript data to phenotypic responses, analyse underlying regulation mechanisms, and implement an in-context analysis of gene function. A major future challenge concerns the integration of the information gained from metabolite profiling into an accessible body of knowledge.

Carbon↗
PubMed2026

Lipid metabolism is a key central, systemic and gut microbial feature of the decline in rat hippocampal function during middle age.

Middle age is emerging as a turning point in brain ageing, prognostic of future cognitive health and amenable to intervention. Metabolic and proteomic differences during this period are not yet fully understood and may potentially influence functions of the hippocampus, a brain area that regulates memory and anxiety. While the gut microbiota is implicated in brain ageing, the relationship between the gut microbiota, the metabolic state, and hippocampal proteome in middle age has not been investigated. We hypothesise that peripheral metabolic or protein features are associated with hippocampal vulnerability in middle age. Therefore, young adult and middle-aged rats were assessed for behavioural, proteomic, metabolic, and gut microbiota differences. Proteomic profiling of the hippocampus revealed differential expression of proteins indicative of altered synaptic signalling. Concurrently, adult hippocampal neurogenesis was decreased in middle age. Hippocampal microglia exhibited a lipid rich, inflammatory phenotype in middle age which correlated with poorer memory performance. CSF and serum proteomic and metabolomic analyses identified dysregulated lipid-related pathways potentially contributing to hippocampal vulnerability in middle age. Furthermore, 16S rRNA sequencing revealed reduced abundance of bacteria involved in lipid metabolism regulation. However, faecal microbiota transfer from young to middle aged rats was not sufficient to robustly improve hippocampus-dependent spatial memory. Together, these findings highlight dysfunctional lipid metabolism as a key feature of middle age that may contribute to decline in hippocampal function. Given that the scope for intervention is limited during older age, targeting biomarkers involved in metabolic and lipid homeostasis may be pivotal for the development of pharmacological or lifestyle-based interventions during middle age which could ultimately delay future cognitive ageing.

Animals↗
PubMed2026

Proteomics in environmental pollution research: Advances, challenges, and future directions.

Environmental proteomics has emerged as a powerful approach for elucidating the molecular mechanisms underlying pollutant-induced biological effects. Although this field has developed rapidly, the systematic review of recent proteomics applications in environmental pollution research remains limited. This review explored the emerging roles of toxicoproteomics in biomarker discovery and mechanistic elucidation, as well as ecotoxicoproteomics in ecological risk assessment and bioremediation strategies. Here, we review the field, highlighting recent trends such as the integration of proteomics with genomics, transcriptomics, and metabolomics to provide a comprehensive view of biological responses to environmental stressors. We further discuss the growing application of artificial intelligence in improving proteomics data interpretation and accelerating biomarker discovery. In addition, recent technological advances in environmental proteomics are highlighted, including next-generation tissue microarray proteomics, nanoscale proteomics, single-cell proteomics, and spatial proteomics. Despite its potential, proteomics faces challenges, such as high operational costs, computational complexity in analysis, and technical limitations in low-abundance protein detection. We propose that the convergence of proteomics with artificial intelligence and multi-omics approaches offers promising solutions to these challenges, enhancing the practical application of proteomics in environmental monitoring and risk assessment.

Proteomics↗
PubMed2025

Multi-omics technologies: Novel tools and methods for assessing nerve injury and regeneration.

Recently, with the rapid advancement of multi-omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, new tools and approaches have been introduced for studying nerve injury and regeneration. This review highlights the application and progress of multi-omics in uncovering the mechanisms of nerve injury, guiding the development of regenerative strategies, and promoting clinical translation. By integrating multi-omics datasets, researchers can comprehensively track dynamic molecular changes following nerve injury, including abnormal gene expression, disrupted protein signaling, altered metabolic programs, and shifts in the immune microenvironment. Single-cell multi-omics technologies resolve cellular heterogeneity, revealing the distinct functions of neurons, glial cells, and immune cell subpopulations during the injury response. Spatially resolved transcriptomics maintain the spatial context of lesion and regeneration sites, enabling precise localization for targeted interventions. Multi-omics technologies not only identify key molecular players involved in nerve regeneration but also create opportunities for personalized medicine. Nonetheless, integrating multi-omics data poses technical challenges, including high dimensionality, batch effects, and algorithmic constraints, while ethical concerns related to stem cell therapy and gene editing require stringent oversight. To transition from structural reconstruction to functional remodeling, future research should emphasize artificial intelligence-driven data integration, organ-on-a-chip modeling, and cross-disciplinary collaboration to overcome existing technical barriers and accelerate the clinical application of neuroregenerative therapies.

artificial intelligence↗
PubMed2026

Addressing lignin composition and content via Arabidopsis arogenate dehydratase knockout and over-expression genotypes.

Following the down-selection of 14 Arabidopsis thaliana arogenate dehydratase (ADT) knockout and over-expression (OE) genotypes, the most highly contrasting quadruple knockout adt3/4/5/6 and ADT OE genotypes were subjected to proteomics, metabolomics, and scanning electron microscopy (SEM) analyses as needed, with results compared to Columbia wild-type (WT). The basal adt3/4/5/6 stem cross-sections, ∼70% lignin content reduced, exhibited buckled vessel cell walls and partially detached xylary fibers, in contrast to WT and ADT4m/5 m OE genotypes that did not. Anatomical defects primarily resulted from guaiacyl lignin level reductions in vessels with concomitant increased stem syringyl:guaiacyl (S/G) ratios. Phenylpropanoid and various upstream shikimate-chorismate pathway enzyme abundances, as well as specific monolignol oxidases (laccases/peroxidases), generally increased in adt3/4/5/6 at different stem and rosette leaf growth/development stages, relative to WT. Opposite effects were largely observed with the ADT5m OE genotype. By contrast, flavonoid and glucosinolate pathway enzyme amounts varied. Such enzyme abundance increases were overall unproductive as adt3/4/5/6 was unable to restore WT, ADT4 OE, ADT5 OE, ADT5m OE, and ADT4m/5 m OE secondary metabolite (lignin, phenylpropanoid, lignan, flavonoid, phenolic acid, and glucosinolate) levels. Conversely, ADT OE genotypes did not significantly increase programmed lignin levels or alter S/G compositions. In sum, proteomics analyses of adt3/4/5/6 and adt5 'perceived' that lignin and low molecular weight secondary metabolite amounts were not at 'programmed' levels as for WT and ADT OE genotypes but observed increases in relevant pathway protein abundances were futile. Notably though, proteomics analyses did not lead to predicting that lignin and associated biochemical pathways would have reduced metabolite levels, relative to WT and ADT OE genotypes. Genotype adt3/4/5/6, possibly the highest lignin level reduced genotype reported, did not utilize other phenolics to compensate. By contrast, the differential temporal and spatial deposition of cell wall oxidases again indicate the exquisite control over lignin deposition, and our lack of knowledge of precise lignin structure and assembly in subcellular regions of the lignified cell walls.

Lignin↗
PubMed2005

The Orbitrap: a new mass spectrometer.

Research areas such as proteomics and metabolomics are driving the demand for mass spectrometers that have high performance but modest power requirements, size, and cost. This paper describes such an instrument, the Orbitrap, based on a new type of mass analyzer invented by Makarov. The Orbitrap operates by radially trapping ions about a central spindle electrode. An outer barrel-like electrode is coaxial with the inner spindlelike electrode and mass/charge values are measured from the frequency of harmonic ion oscillations, along the axis of the electric field, undergone by the orbitally trapped ions. This axial frequency is independent of the energy and spatial spread of the ions. Ion frequencies are measured non-destructively by acquisition of time-domain image current transients, with subsequent fast Fourier transforms (FFTs) being used to obtain the mass spectra. In addition to describing the Orbitrap mass analyzer, this paper also describes a complete Orbitrap-based mass spectrometer, equipped with an electrospray ionization source (ESI). Ions are transferred from the ESI source through three stages of differential pumping using RF guide quadrupoles. The third quadrupole, pressurized to less than 10(-3) Torr with collision gas, acts as an ion accumulator; ion/neutral collisions slow the ions and cause them to pool in an axial potential well at the end of the quadrupole. Ion bunches are injected from this pool into the Orbitrap analyzer for mass analysis. The ion injection process is described in a simplified way, including a description of electrodynamic squeezing, field compensation for the effects of the ion injection slit, and criteria for orbital stability. Features of the Orbitrap at its present stage of development include high mass resolution (up to 150,000), large space charge capacity, high mass accuracy (2-5 ppm), a mass/charge range of at least 6000, and dynamic range greater than 10(3). Applications based on electrospray ionization are described, including characterization of transition-metal complexes, oligosaccharides, peptides, and proteins. Use is also made of the high-resolution capabilities of the Orbitrap to confirm the presence of metaclusters of serine octamers in ESI mass spectra and to perform H/D exchange experiments on these ions in the storage quadrupole.

Alcohol Dehydrogenase↗
PubMed2005

Genetically encoded sensors for metabolites.

BACKGROUND: Metabolomics, i.e., the multiparallel analysis of metabolite changes occurring in a cell or an organism, has become feasible with the development of highly efficient mass spectroscopic technologies. Functional genomics as a standard tool helped to identify the function of many of the genes that encode important transporters and metabolic enzymes over the past few years. Advanced expression systems and analysis technologies made it possible to study the biochemical properties of the corresponding proteins in great detail. We begin to understand the biological functions of the gene products by systematic analysis of mutants using systematic PTGS/RNAi, knockout and TILLING approaches. However, one crucial set of data especially relevant in the case of multicellular organisms is lacking: the knowledge of the spatial and temporal profiles of metabolite levels at cellular and subcellular levels. METHODS: We therefore developed genetically encoded nanosensors for several metabolites to provide a basic set of tools for the determination of cytosolic and subcellular metabolite levels in real time by using fluorescence microscopy. RESULTS: Prototypes of these sensors were successfully used in vitro and also in vivo, i.e., to measure sugar levels in fungal and animal cells. CONCLUSIONS: One of the future goals will be to expand the set of sensors to a wider spectrum of substrates by using the natural spectrum of periplasmic binding proteins from bacteria and by computational design of proteins with altered binding pockets in conjunction with mutagenesis. This toolbox can then be applied for four-dimensional imaging of cells and tissues to elucidate the spatial and temporal distribution of metabolites as a discovery tool in functional genomics, as a tool for high-throughput, high-content screening for drugs, to test metabolic models, and to analyze the interplay of cells in a tissue or organ.

Animals↗