Real-time metabolome profiling of the metabolic switch between starvation and growth

Accelerated dissemination of antibiotic resistant genes via conjugative transfer driven by deficient denitrification in biochar-based biofiltration systems

Biofiltration systems harbored and disseminated antibiotic resistance genes (ARGs), when confronting antibiotic-contained wastewater. Biochar, a widely used environmental remediation material, can mitigate antibiotic stress on adjoining microbes by lowering the availability of sorbed antibiotics, and enhance the attachment of denitrifiers. Herein, bench-scale biofiltration systems, packed with commercial biochars, were established to explore the pivotal drivers affecting ARG emergence. Results showed that biofiltration columns, achieving higher TN removal and denitrification capacity, showed a significant decrease in ARG accumulation (p < 0.05). The relative abundance of ARGs (0.014 ± 0.0008) in the attached biofilms decreased to 1/5-folds of that in the control group (0.065 ± 0.004). Functional analysis indicated ARGs' accumulation was less attributed to ARG activation or horizontal gene transfer (HGT) driven by sorbed antibiotics. Most denitrifiers, like Bradyrhizobium, Geothrix, etc., were found to be enriched and host ARGs. Nitrosative stress from deficient denitrification was demonstrated to be the dominant driver for affecting ARG accumulation and dissemination. Metagenomic and metaproteomic analysis revealed that nitrosative stress promoted the conjugative HGT of ARGs mainly via increasing the transmembrane permeability and enhancing the amino acid transport and metabolism, such as cysteine, methionine, and valine metabolism. Overall, this study highlighted the risks of deficient denitrification in promoting ARG transfer and transmission in biofiltration systems and natural ecosystems.

The metabolic domestication syndrome of budding yeast

Cellular metabolism evolves through changes in the structure and quantitative states of metabolic networks. Here, we explore the evolutionary dynamics of metabolic states by focusing on the collection of metabolite levels, the metabolome, which captures key aspects of cellular physiology. Using a phylogenetic framework, we profiled metabolites in 27 populations of nine budding yeast species, providing a graduated view of metabolic variation across multiple evolutionary time scales. Metabolite levels evolve more rapidly and independently of changes in the metabolic network's structure, providing complementary information to enzyme repertoire. Although metabolome variation accumulates mainly gradually over time, it is profoundly affected by domestication. We found pervasive signatures of convergent evolution in the metabolomes of independently domesticated clades of Saccharomyces cerevisiae. Such recurring metabolite differences between wild and domesticated populations affect a substantial part of the metabolome, including rewiring of the TCA cycle and several amino acids that influence aroma production, likely reflecting adaptation to human niches. Overall, our work reveals previously unrecognized diversity in central metabolism and the pervasive influence of human-driven selection on metabolite levels in yeasts.

AutonoMS: Automated Ion Mobility Metabolomic Fingerprinting

Automation is dramatically changing the nature of laboratory life science. Robotic lab hardware that can perform manual operations with greater speed, endurance, and reproducibility opens an avenue for faster scientific discovery with less time spent on laborious repetitive tasks. A major bottleneck remains in integrating cutting-edge laboratory equipment into automated workflows, notably specialized analytical equipment, which is designed for human usage. Here we present AutonoMS, a platform for automatically running, processing, and analyzing high-throughput mass spectrometry experiments. AutonoMS is currently written around an ion mobility mass spectrometry (IM-MS) platform and can be adapted to additional analytical instruments and data processing flows. AutonoMS enables automated software agent-controlled end-to-end measurement and analysis runs from experimental specification files that can be produced by human users or upstream software processes. We demonstrate the use and abilities of AutonoMS in a high-throughput flow-injection ion mobility configuration with 5 s sample analysis time, processing robotically prepared chemical standards and cultured yeast samples in targeted and untargeted metabolomics applications. The platform exhibited consistency, reliability, and ease of use while eliminating the need for human intervention in the process of sample injection, data processing, and analysis. The platform paves the way toward a more fully automated mass spectrometry analysis and ultimately closed-loop laboratory workflows involving automated experimentation and analysis coupled to AI-driven experimentation utilizing cutting-edge analytical instrumentation. AutonoMS documentation is available at https://autonoms.readthedocs.io.

Microfluidic approaches in microbial ecology

Microbial life is at the heart of many diverse environments and regulates most natural processes, from the functioning of animal organs to the cycling of global carbon. Yet, the study of microbial ecology is often limited by challenges in visualizing microbial processes and replicating the environmental conditions under which they unfold. Microfluidics operates at the characteristic scale at which microorganisms live and perform their functions, thus allowing for the observation and quantification of behaviors such as growth, motility, and responses to external cues, often with greater detail than classical techniques. By enabling a high degree of control in space and time of environmental conditions such as nutrient gradients, pH levels, and fluid flow patterns, microfluidics further provides the opportunity to study microbial processes in conditions that mimic the natural settings harboring microbial life. In this review, we describe how recent applications of microfluidic systems to microbial ecology have enriched our understanding of microbial life and microbial communities. We highlight discoveries enabled by microfluidic approaches ranging from single-cell behaviors to the functioning of multi-cellular communities, and we indicate potential future opportunities to use microfluidics to further advance our understanding of microbial processes and their implications.

SAND: Automated Time-Domain Modeling of NMR Spectra Applied to Metabolite Quantification

Developments in untargeted nuclear magnetic resonance (NMR) metabolomics enable the profiling of thousands of biological samples. The exploitation of this rich source of information requires a detailed quantification of spectral features. However, the development of a consistent and automatic workflow has been challenging because of extensive signal overlap. To address this challenge, we introduce the software Spectral Automated NMR Decomposition (SAND). SAND follows on from the previous success of time-domain modeling and automatically quantifies entire spectra without manual interaction. The SAND approach uses hybrid optimization with Markov chain Monte Carlo methods, employing subsampling in both time and frequency domains. In particular, SAND randomly divides the time-domain data into training and validation sets to help avoid overfitting. We demonstrate the accuracy of SAND, which provides a correlation of ∼0.9 with ground truth on cases including highly overlapped simulated data sets, a two-compound mixture, and a urine sample spiked with different amounts of a four-compound mixture. We further demonstrate an automated annotation using correlation networks derived from SAND decomposed peaks, and on average, 74% of peaks for each compound can be recovered in single clusters. SAND is available in NMRbox, the cloud computing environment for NMR software hosted by the Network for Advanced NMR (NAN). Since the SAND method uses time-domain subsampling (i.e., random subset of time-domain points), it has the potential to be extended to a higher dimensionality and nonuniformly sampled data.

Arginine reprograms metabolism in liver cancer via RBM39

Metabolic reprogramming is a hallmark of cancer. However, mechanisms underlying metabolic reprogramming and how altered metabolism in turn enhances tumorigenicity are poorly understood. Here, we report that arginine levels are elevated in murine and patient hepatocellular carcinoma (HCC), despite reduced expression of arginine synthesis genes. Tumor cells accumulate high levels of arginine due to increased uptake and reduced arginine-to-polyamine conversion. Importantly, the high levels of arginine promote tumor formation via further metabolic reprogramming, including changes in glucose, amino acid, nucleotide, and fatty acid metabolism. Mechanistically, arginine binds RNA-binding motif protein 39 (RBM39) to control expression of metabolic genes. RBM39-mediated upregulation of asparagine synthesis leads to enhanced arginine uptake, creating a positive feedback loop to sustain high arginine levels and oncogenic metabolism. Thus, arginine is a second messenger-like molecule that reprograms metabolism to promote tumor growth.

Icescape-scale metabolomics reveals cyanobacterial and topographic control of the core metabolism of the cryoconite ecosystem of an Arctic ice cap

Glaciers host ecosystems comprised of biodiverse and active microbiota. Among glacial ecosystems, less is known about the ecology of ice caps since most studies focus on valley glaciers or ice sheet margins. Previously we detailed the microbiota of one such high Arctic ice cap, focusing on cryoconite as a microbe-mineral aggregate formed by cyanobacteria. Here, we employ metabolomics at the scale of an entire ice cap to reveal the major metabolic pathways prevailing in the cryoconite of Foxfonna, central Svalbard. We reveal how geophysical and biotic processes influence the metabolomes of its resident cryoconite microbiota. We observed differences in amino acid, fatty acid, and nucleotide synthesis across the cap reflecting the influence of ice topography and the cyanobacteria within cryoconite. Ice topography influences central carbohydrate metabolism and nitrogen assimilation, whereas bacterial community structure governs lipid, nucleotide, and carotenoid biosynthesis processes. The prominence of polyamine metabolism and nitrogen assimilation highlights the importance of recycling nitrogenous nutrients. To our knowledge, this study represents the first application of metabolomics across an entire ice mass, demonstrating its utility as a tool for revealing the fundamental metabolic processes essential for sustaining life in supraglacial ecosystems experiencing profound change due to Arctic climate change-driven mass loss.

Genomic footprinting uncovers global transcription factor responses to amino acids in Escherichia coli

Our knowledge of transcriptional responses to changes in nutrient availability comes primarily from few well-studied transcription factors (TFs), often lacking an unbiased genome-wide perspective. Leveraging recent advances allowing bacterial genomic footprinting, we comprehensively mapped the genome-wide regulatory responses of Escherichia coli to exogenous leucine, methionine, alanine, and lysine. The global TF Lrp was found to individually sense three amino acids and mount three different target gene responses. Overall, 531 genes had altered RNA polymerase occupancy, and 32 TFs responded directly or indirectly to the presence of amino acids, including regulators of membrane and osmotic pressure homeostasis. About 70% of the detected TF-DNA interactions had not been reported before. We thus identified 682 previously unknown TF-binding locations, for a subset of which the involved TFs were identified by affinity purification. This comprehensive map of amino acid regulation illustrates the incompleteness of the known transcriptional regulation network, even in E. coli.

Coenzyme biosynthesis in response to precursor availability reveals incorporation of β-alanine from pantothenate in prototrophic bacteria

Coenzymes are important for all classes of enzymatic reactions and essential for cellular metabolism. Most coenzymes are synthesized from dedicated precursors, also referred to as vitamins, which prototrophic bacteria can either produce themselves from simpler substrates or take up from the environment. The extent to which prototrophs use supplied vitamins and whether externally available vitamins affect the size of intracellular coenzyme pools and control endogenous vitamin synthesis is currently largely unknown. Here, we studied coenzyme pool sizes and vitamin incorporation into coenzymes during growth on different carbon sources and vitamin supplementation regimes using metabolomics approaches. We found that the model bacterium Escherichia coli incorporated pyridoxal, niacin, and pantothenate into pyridoxal 5'-phosphate, NAD, and coenzyme A (CoA), respectively. In contrast, riboflavin was not taken up and was produced exclusively endogenously. Coenzyme pools were mostly homeostatic and not affected by externally supplied precursors. Remarkably, we found that pantothenate is not incorporated into CoA as such but is first degraded to pantoate and β-alanine and then rebuilt. This pattern was conserved in various bacterial isolates, suggesting a preference for β-alanine over pantothenate utilization in CoA synthesis. Finally, we found that the endogenous synthesis of coenzyme precursors remains active when vitamins are supplied, which is consistent with described expression data of genes for enzymes involved in coenzyme biosynthesis under these conditions. Continued production of endogenous coenzymes may ensure rapid synthesis of the mature coenzyme under changing environmental conditions, protect against coenzyme limitation, and explain vitamin availability in naturally oligotrophic environments.

Ribose 5-phosphate: the key metabolite bridging the metabolisms of nucleotides and amino acids during stringent response in Escherichia coli?

The bacterial stringent response and its effector alarmone guanosine penta- or tetra - phosphates (p)ppGpp are vital for bacterial tolerance and survival of various stresses in environments (including antibiotics) and host cells (virulence). (p)ppGpp does so by binding to its numerous target proteins and reprograming bacterial transcriptome to tune down the synthesis of nucleotides and rRNA/tRNA, and up-regulate amino acid biosynthesis genes. Recent identification of more novel (p)ppGpp direct binding proteins in Escherichia coli and their deep studies have unveiled unprecedented details of how (p)ppGpp coordinates the nucleotide and amino acid metabolic pathways upon stringent response; however, the mechanistic link between nucleotide and amino acid metabolisms remains still incompletely understood. Here we propose the metabolite ribose 5'-phosphate as the key link between nucleotide and amino acid metabolisms and a working model integrating both the transcriptional and metabolic effects of (p)ppGpp on E. coli physiological adaptation during the stringent response.

Moving beyond DNA: towards functional analysis of the vaginal microbiome by non-sequencing-based methods

Over the last two decades, sequencing-based methods have revolutionised our understanding of niche-specific microbial complexity. In the lower female reproductive tract, these approaches have enabled identification of bacterial compositional structures associated with health and disease. Application of metagenomics and metatranscriptomics strategies have provided insight into the putative function of these communities but it is increasingly clear that direct measures of microbial and host cell function are required to understand the contribution of microbe-host interactions to pathophysiology. Here we explore and discuss current methods and approaches, many of which rely upon mass-spectrometry, being used to capture functional insight into the vaginal mucosal interface. In addition to improving mechanistic understanding, these methods offer innovative solutions for the development of diagnostic and therapeutic strategies designed to improve women's health.

An automated workflow for multi-omics screening of microbial model organisms

Multi-omics datasets are becoming of key importance to drive discovery in fundamental research as much as generating knowledge for applied biotechnology. However, the construction of such large datasets is usually time-consuming and expensive. Automation might enable to overcome these issues by streamlining workflows from sample generation to data analysis. Here, we describe the construction of a complex workflow for the generation of high-throughput microbial multi-omics datasets. The workflow comprises a custom-built platform for automated cultivation and sampling of microbes, sample preparation protocols, analytical methods for sample analysis and automated scripts for raw data processing. We demonstrate possibilities and limitations of such workflow in generating data for three biotechnologically relevant model organisms, namely Escherichia coli, Saccharomyces cerevisiae, and Pseudomonas putida.

UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis

Metabolomics experiments generate highly complex datasets, which are time and work-intensive, sometimes even error-prone if inspected manually. Therefore, new methods for automated, fast, reproducible, and accurate data processing and dereplication are required. Here, we present UmetaFlow, a computational workflow for untargeted metabolomics that combines algorithms for data pre-processing, spectral matching, molecular formula and structural predictions, and an integration to the GNPS workflows Feature-Based Molecular Networking and Ion Identity Molecular Networking for downstream analysis. UmetaFlow is implemented as a Snakemake workflow, making it easy to use, scalable, and reproducible. For more interactive computing, visualization, as well as development, the workflow is also implemented in Jupyter notebooks using the Python programming language and a set of Python bindings to the OpenMS algorithms (pyOpenMS). Finally, UmetaFlow is also offered as a web-based Graphical User Interface for parameter optimization and processing of smaller-sized datasets. UmetaFlow was validated with in-house LC-MS/MS datasets of actinomycetes producing known secondary metabolites, as well as commercial standards, and it detected all expected features and accurately annotated 76% of the molecular formulas and 65% of the structures. As a more generic validation, the publicly available MTBLS733 and MTBLS736 datasets were used for benchmarking, and UmetaFlow detected more than 90% of all ground truth features and performed exceptionally well in quantification and discriminating marker selection. We anticipate that UmetaFlow will provide a useful platform for the interpretation of large metabolomics datasets.

Molecular Signatures of the Eagle Effect Induced by the Artificial Siderophore Conjugate LP-600 in E. coli

Achieving cellular uptake is a central challenge for novel antibiotics targeting Gram-negative bacterial pathogens. One strategy is to hijack the bacterial iron transport system by siderophore-antibiotic conjugates that are actively imported into the cell. This was realized with the MECAM-ampicillin conjugate LP-600 we recently reported that was highly active against E. coli. In the present study, we investigate a paradoxical regrowth of E. coli upon treatment of LP-600 at concentrations 16-32 times above the minimum inhibitory concentration (MIC). The phenomenon, coined "Eagle-effect" in other systems, was not due to resistance formation, and it occurred for the siderophore conjugate but not for free ampicillin. To investigate the molecular imprint of the Eagle effect, a combined transcriptome and untargeted metabolome analysis was conducted. LP-600 induced the expression of genes involved in iron acquisition, SOS response, and the e14 prophage upon regrowth conditions. The Eagle effect was diminished in the presence of sulbactam, which we ascribe to a putative synergistic antibiotic action but not to β-lactamase inhibition. The study highlights the relevance of the Eagle effect for siderophore conjugates. Through the first systematic -omics investigations, it also demonstrates that the Eagle effect manifests not only in a paradoxical growth but also in unique gene expression and metabolite profiles.

Systematic analysis of in-source modifications of primary metabolites during flow-injection time-of-flight mass spectrometry

Flow-injection mass spectrometry (FI-MS) enables metabolomics studies with a very high sample-throughput. However, FI-MS is prone to in-source modifications of analytes because samples are directly injected into the electrospray ionization source of a mass spectrometer without prior chromatographic separation. Here, we spiked authentic standards of 160 primary metabolites individually into an Escherichia coli metabolite extract and measured the thus derived 160 spike-in samples by FI-MS. Our results demonstrate that FI-MS can capture a wide range of chemically diverse analytes within 30 s measurement time. However, the data also revealed extensive in-source modifications. Across all 160 spike-in samples, we identified significant increases of 11,013 ion peaks in positive and negative mode combined. To explain these unknown m/z features, we connected them to the m/z feature of the (de-)protonated metabolite using information about mass differences and MS2 spectra. This resulted in networks that explained on average 49 % of all significant features. The networks showed that a single metabolite undergoes compound specific and often sequential in-source modifications like adductions, chemical reactions, and fragmentations. Our results show that FI-MS generates complex MS1 spectra, which leads to an overestimation of significant features, but neutral losses and MS2 spectra explain many of these features.

Stringent response ensures the timely adaptation of bacterial growth to nutrient downshift

Timely adaptation to nutrient downshift is crucial for bacteria to maintain fitness during feast and famine cycle in the natural niche. However, the molecular mechanism that ensures the timely adaption of bacterial growth to nutrient downshift remains poorly understood. Here, we quantitatively investigated the adaptation of Escherichia coli to various kinds of nutrient downshift. We found that relA deficient strain, which is devoid of stringent response, exhibits a significantly longer growth lag than wild type strain during adapting to both amino acid downshift and carbon downshift. Quantitative proteomics show that increased (p)ppGpp level promotes the growth adaption of bacteria to amino acid downshift via triggering the proteome resource re-allocation from ribosome synthesis to amino acid biosynthesis. Such type of proteome re-allocation is significantly delayed in the relA-deficient strain, which underlies its longer lag than wild type strain during amino acid downshift. During carbon downshift, a lack of stringent response in relA deficient strain leads to disruption of the transcription-translation coordination, thus compromising the transcription processivity and further the timely expression of related catabolic operons for utilizing secondary carbon sources. Our studies shed light on the fundamental strategy of bacteria to maintain fitness under nutrient-fluctuating environments.

Hyperspectral Visualization-Based Mass Spectrometry Imaging by LMJ-SSP: A Novel Strategy for Rapid Natural Product Profiling in Bacteria

Mass spectrometry imaging (MSI) has been widely used to discover natural products (NPs) from underexplored microbiological sources. However, the technique is limited by incompatibility with complicated/uneven surface topography and labor-intensive sample preparation, as well as lengthy compound profiling procedures. Here, liquid micro-junction surface sampling probe (LMJ-SSP)-based MSI is used for rapid profiling of natural products from Gram-negative marine bacteria Pseudoalteromonas on nutrient agar media without any sample preparation. A conductance-based autosampling platform with 1 mm spatial resolution and an innovative multivariant analysis-driven method was used to create one hyperspectral image for the sampling area. NP discovery requires general spatial correlation between m/z and colony location but not highly precise spatial resolution. The hyperspectral image was used to annotate different m/z by straightforward color differences without the need to directly interrogate the spectra. To demonstrate the utility of our approach, the rapid analysis of Pseudoalteromonas rubra DSM6842, Pseudoalteromonas tunicata DSM14096, Pseudoalteromonas piscicida JCM20779, and Pseudoalteromonas elyakovii ATCC700519 cultures was directly performed on Agar. Various natural products, including prodiginine and tambjamine analogues, were quickly identified from the hyperspectral image, and the dynamic extracellular environment was shown with compound heatmaps. Hyperspectral visualization-based MSI is an efficient and sensitive strategy for direct and rapid natural product profiling from different Pseudoalteromonas strains.

Significant Changes in Cytoplasmic Amino Acid Composition Occur in the Transition between Mid-Exponential and Stationary Phases of Growth of Staphylococcus aureus: An Example of Adaptive Homeostasis in Response to Nutrient Limitations

The bacterial pathogen Staphylococcus aureus causes a wide range of infections that result in high morbidity and mortality rates worldwide. S. aureus is known for its capacity to survive harsh environments between hosts and certain strains are very efficient as opportunistic pathogens. It is important to understand their capacities for metabolic adaptation in response to changing environmental conditions. This investigation aimed to explore the alterations in the amino acid compositions of the cytoplasm as nutrients became limiting during the growth of S. aureus. Cells were grown under optimal growth conditions and harvested at the mid-exponential and stationary phases of growth and then extracted for the analyses of amino acids in the cytoplasm. The analyses revealed that the stationary phase cells had a significantly higher concentration of total cytoplasmic amino acids compared with cells at the mid-exponential phase and displayed substantial alterations in amino acid composition. Aspartic acid was the major amino acid in the stationary phase cells, whereas glutamic acid was the most abundant in the mid-exponential cells. The glutamic acid was reduced by 47% of its original value when the growth was extended to the stationary phase. Interestingly, certain amino acids were either absent or present depending on the phase of growth. These outcomes are in line with the premise that bacterial cells of S. aureus transition into a different form of metabolic homeostasis in the shift between the exponential and stationary phases of growth, as nutrients become depleted and waste products accumulate in the external medium. The ability of S. aureus to continually and promptly adapt to differences within growth phases may represent an essential strategy assisting its virulence as a successful opportunistic pathogen to establish infections. An understanding of the switch mechanisms controlling these obvious alterations in amino acids through the growth/life cycle of this virulent pathogen may provide novel clinical strategies to battle infection.

Application of Metabolomics in Fungal Research

Metabolomics is an essential method to study the dynamic changes of metabolic networks and products using modern analytical techniques, as well as reveal the life phenomena and their inherent laws. Currently, more and more attention has been paid to the development of metabolic histochemistry in the fungus field. This paper reviews the application of metabolomics in fungal research from five aspects: identification, response to stress, metabolite discovery, metabolism engineering, and fungal interactions with plants.

Metabolomics and modelling approaches for systems metabolic engineering

Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.

Sorting-free metabolic profiling uncovers the vulnerability of fatty acid β-oxidation in in vitro quiescence models

Quiescent cancer cells are rare nondiving cells with the unique ability to evade chemotherapies and resume cell division after treatment. Despite the associated risk of cancer recurrence, how cells can reversibly switch between rapid proliferation and quiescence remains a long‐standing open question. By developing a unique methodology for the cell sorting‐free separation of metabolic profiles in cell subpopulations in vitro, we unraveled metabolic characteristics of quiescent cells that are largely invariant to basal differences in cell types and quiescence‐inducing stimuli. Consistent with our metabolome‐based analysis, we show that impairing mitochondrial fatty acid β‐oxidation (FAO) can induce apoptosis in quiescence‐induced cells and hamper their return to proliferation. Our findings suggest that in addition to mediating energy and redox balance, FAO can play a role in preventing the buildup of toxic intermediates during transitioning to quiescence. Uncovering metabolic strategies to enter, maintain, and exit quiescence can reveal fundamental principles in cell plasticity and new potential therapeutic targets beyond cancer.

Changes in Amino Acid Metabolism of Staphylococcus aureus following Growth to the Stationary Phase under Adjusted Growth Conditions

The sharp increase in infections due to Staphylococcus aureus is associated with its ability to adapt to changes in its habitat. This study aimed to investigate the differences in the cytoplasmic amino acid profiles of a clinical strain of S. aureus under five combinations of stress-induced conditions representative of a wound site by varying temperature 35-37 °C, adding 0-5% NaCl and adjusting pH 6-8. The results indicated that aspartic acid, lysine, glutamic acid and histidine were the most abundant cytoplasmic amino acids in the control samples grown under optimal growth conditions. However, the magnitudes and levels of these amino acids were altered under the various wound site conditions, which led to differential cytoplasmic amino acid profiles as characterized by multivariate analyses (PLS-DA). The total cytoplasmic amino acid content was significantly reduced in the cells grown with 2.5% NaCl added at pH 7 and 37 °C relative to the control samples and other growth regimes. However, all combinations of enhanced stress conditions showed unique and characteristic changes in the concentration profiles of the cytoplasmic amino acids. These outcomes supported the hypothesis that bacterial cells of S. aureus maintain different metabolic homeostasis under various stress-induced conditions. The potent capability of S. aureus to constantly and rapidly acclimatize to variations within the environment may reflect the crucial feature supporting its virulence as an opportunistic pathogenic bacterium to invade the wound site. Understanding the control systems governing these marked changes in amino acids during the adaptation to the potential wound site conditions of this dangerous bacterium may offer new clinical controls to combat infection.

Using off-gas for insights through online monitoring of ethanol and baker's yeast volatilome using SESI-Orbitrap MS

Volatile organic compounds play an essential role in every domain of life, with diverse functions. In this study, we use novel secondary electrospray ionisation high-resolution Orbitrap mass spectrometry (SESI-Orbitrap MS) to monitor the complete yeast volatilome every 2.3 s. Over 200 metabolites were identified during growth in shake flasks and bioreactor cultivations, all with their unique intensity profile. Special attention was paid to ethanol as biotech largest product and to acetaldehyde as an example of a low-abundance but highly-volatile metabolite. While HPLC and Orbitrap measurements show a high agreement for ethanol, acetaldehyde could be measured five hours earlier in the SESI-Orbitrap MS. Volatilome shifts are visible, e.g. after glucose depletion, fatty acids are converted to ethyl esters in a detoxification mechanism after stopped fatty acid biosynthesis. This work showcases the SESI-Orbitrap MS system for tracking microbial physiology without the need for sampling and for time-resolved discoveries during metabolic transitions.

On-line untargeted metabolomics monitoring of an E. coli succinate fermentation process

The real‐time monitoring of metabolites (RTMet) is instrumental for the industrial production of biobased fermentation products. This study shows the first application of untargeted on‐line metabolomics for the monitoring of undiluted fermentation broth samples taken automatically from a 5 L bioreactor every 5 min via flow injection mass spectrometry. The travel time from the bioreactor to the mass spectrometer was 30 s. Using mass spectrometry allows, on the one hand, the direct monitoring of targeted key process compounds of interest and, on the other hand, provides information on hundreds of additional untargeted compounds without requiring previous calibration data. In this study, this technology was applied in an Escherichia coli succinate fermentation process and 886 different m/z signals were monitored, including key process compounds (glucose, succinate, and pyruvate), potential biomarkers of biomass formation such as (R)‐2,3‐dihydroxy‐isovalerate and (R)‐2,3‐dihydroxy‐3‐methylpentanoate and compounds from the pentose phosphate pathway and nucleotide metabolism, among others. The main advantage of the RTMet technology is that it allows the monitoring of hundreds of signals without the requirement of developing partial least squares regression models, making it a perfect tool for bioprocess monitoring and for testing many different strains and process conditions for bioprocess development.

Effects of Storage Temperature on Indica-Japonica Hybrid Rice Metabolites, Analyzed Using Liquid Chromatography and Mass Spectrometry

The Yongyou series of indica-japonica hybrid rice has excellent production potential and storage performance. However, little is known about the underlying mechanism of its storage resistance. In this study, Yongyou 1540 rice (Oryza sativa cv. yongyou 1540) was stored at different temperatures, and the storability was validated though measuring nutritional components and apparent change. In addition, a broad-targeted metabolomic approach coupled with liquid chromatography-mass spectrometry was applied to analyze the metabolite changes. The study found that under high temperature storage conditions (35 °C), Yongyou 1540 was not significantly worse in terms of fatty acid value, whiteness value, and changes in electron microscope profile. A total of 19 key differential metabolites were screened, and lipid metabolites related to palmitoleic acid were found to affect the aging of rice. At the same time, two substances, guanosine 3′,5′-cyclophosphate and pipecolic acid, were beneficial to enhance the resistance of rice under harsh storage conditions, thereby delaying the deterioration of its quality and maintaining its quality. Significant regulation of galactose metabolism, alanine, aspartate and glutamate metabolism, butyrate metabolism, and arginine and proline metabolism pathways were probably responsible for the good storage capacity of Yongyou 1540.

Nitric Oxide: A Neglected Driver for the Conjugative Transfer of Antibiotic Resistance Genes among Wastewater Microbiota

The dissemination of plasmid-borne antibiotic resistance genes (ARGs) in wastewater is becoming an urgent concern. Previous studies mainly focused on the effects of coexisting contaminants on plasmid conjugation, but ignored the potential contribution of some byproducts inevitably released from wastewater treatment processes. Herein, we demonstrate for the first time that nitric oxide (NO), an intermediate of the wastewater nitrogen cycle, can significantly boost the conjugative transfer of plasmid RP4 from Escherichia coli K12 to different recipients (E. coli HB101, Salmonella typhimurium, and wastewater microbiota). Phenotypic and genotypic tests confirmed that NO-induced promotion was not attributed to the SOS response, a well-recognized driver for horizontal gene transfer. Instead, NO exposure increased the outer membrane permeability of both the donor and recipient by inhibiting the expression of key genes involved in lipopolysaccharide biosynthesis (such as waaJ), thereby lowering the membrane barrier for conjugation. On the other hand, NO exposure not only resulted in the accumulation of intracellular tryptophan but also triggered the deficiency of intracellular methionine, both of which were validated to play key roles in regulating the global regulatory genes (korA, korB, and trbA) of plasmid RP4, activating its encoding transfer apparatus (represented by trfAp and trbBp). Overall, our findings highlighted the risks of NO in spreading ARGs among wastewater microbiota and updated the regulation mechanism of plasmid conjugation.

Cellular perception of growth rate and the mechanistic origin of bacterial growth law

Many cellular activities in bacteria are organized according to their growth rate. The notion that ppGpp measures the cell’s growth rate is well accepted in the field of bacterial physiology. However, despite decades of interrogation and the identification of multiple molecular interactions that connects ppGpp to some aspects of cell growth, we lack a system-level, quantitative picture of how this alleged “measurement” is performed. Through quantitative experiments, we show that the ppGpp pool responds inversely to the rate of translational elongation in Escherichia coli. Together with its roles in inhibiting ribosome biogenesis and activity, ppGpp closes a key regulatory circuit that enables the cell to perceive and control the rate of its growth across conditions. The celebrated linear growth law relating the ribosome content and growth rate emerges as a consequence of keeping a supply of ribosome reserves while maintaining elongation rate in slow growth conditions. Further analysis suggests the elongation rate itself is detected by sensing the ratio of dwelling and translocating ribosomes, a strategy employed to collapse the complex, high-dimensional dynamics of the molecular processes underlying cell growth to perceive the physiological state of the whole.

Uncovering in vivo biochemical patterns from time-series metabolic dynamics

System biology relies on holistic biomolecule measurements, and untangling biochemical networks requires time-series metabolomics profiling. With current metabolomic approaches, time-series measurements can be taken for hundreds of metabolic features, which decode underlying metabolic regulation. Such a metabolomic dataset is untargeted with most features unannotated and inaccessible to statistical analysis and computational modeling. The high dimensionality of the metabolic space also causes mechanistic modeling to be rather cumbersome computationally. We implemented a faster exploratory workflow to visualize and extract chemical and biochemical dependencies. Time-series metabolic features (about 300 for each dataset) were extracted by Ridge Tracking-based Extract (RTExtract) on measurements from continuous in vivo monitoring of metabolism by NMR (CIVM-NMR) in Neurospora crassa under different conditions. The metabolic profiles were then smoothed and projected into lower dimensions, enabling a comparison of metabolic trends in the cultures. Next, we expanded incomplete metabolite annotation using a correlation network. Lastly, we uncovered meaningful metabolic clusters by estimating dependencies between smoothed metabolic profiles. We thus sidestepped the processes of time-consuming mechanistic modeling, difficult global optimization, and labor-intensive annotation. Multiple clusters guided insights into central energy metabolism and membrane synthesis. Dense connections with glucose 1-phosphate indicated its central position in metabolism in N. crassa. Our approach was benchmarked on simulated random network dynamics and provides a novel exploratory approach to analyzing high-dimensional metabolic dynamics.

Distinct N and C Cross-Feeding Networks in a Synthetic Mouse Gut Consortium

The complex interactions between the gut microbiome and host or pathogen colonization resistance cannot be understood solely from community composition. Missing are causal relationships, such as metabolic interactions among species, to better understand what shapes the microbiome. Here, we focused on metabolic niches generated and occupied by the Oligo-Mouse-Microbiota (OMM) consortium, a synthetic community composed of 12 members that is increasingly used as a model for the mouse gut microbiome. Combining monocultures and spent medium experiments with untargeted metabolomics revealed broad metabolic diversity in the consortium, constituting a dense cross-feeding network with more than 100 pairwise interactions. Quantitative analysis of the cross-feeding network revealed distinct C and N food webs, highlighting the two Bacteroidetes members Bacteroides caecimuris and Muribaculum intestinale as primary suppliers of carbon and a more diverse group as nitrogen providers. Cross-fed metabolites were mainly carboxylic acids, amino acids, and the so far not reported nucleobases. In particular, the dicarboxylic acids malate and fumarate provided a strong physiological benefit to consumers, presumably used in anaerobic respiration. Isotopic tracer experiments validated the fate of a subset of cross-fed metabolites, such as the conversion of the most abundant cross-fed compound succinate to butyrate. Thus, we show that this consortium is tailored to produce the anti-inflammatory metabolite butyrate. Overall, we provide evidence for metabolic niches generated and occupied by OMM members that lays a metabolic foundation to facilitate an understanding of the more complex in vivo behavior of this consortium in the mouse gut.

IMPORTANCE This article maps out the cross-feeding network among 10 members of a synthetic consortium that is increasingly used as the model mouse gut microbiota. Combining metabolomics with in vitro cultivations, two dense networks of carbon and nitrogen exchange are described. The vast majority of the ∼100 interactions are synergistic in nature, in several cases providing distinct physiological benefits to the recipient species. These networks lay the groundwork toward understanding gut community dynamics and host-gut microbe interactions.

Unraveling antimicrobial resistance using metabolomics

The emergence of antimicrobial resistance (AMR) in bacterial pathogens represents a global health threat. The metabolic state of bacteria is associated with a range of genetic and phenotypic resistance mechanisms. This review provides an overview of the roles of metabolic processes that are associated with AMR mechanisms, including energy production, cell wall synthesis, cell-cell communication, and bacterial growth. These metabolic processes can be targeted with the aim of re-sensitizing resistant pathogens to antibiotic treatments. We discuss how state-of-the-art metabolomics approaches can be used for comprehensive analysis of microbial AMR-related metabolism, which may facilitate the discovery of novel drug targets and treatment strategies. TEASER: Novel treatment strategies are needed to address the emerging threat of antimicrobial resistance (AMR) in bacterial pathogens. Metabolomics approaches may help to unravel the biochemical underpinnings of AMR, thereby facilitating the discovery of metabolism-associated drug targets and treatment strategies.

Spectroscopic investigation of faeces with surface-enhanced Raman scattering: a case study with coeliac patients on gluten-free diet

Surface-enhanced Raman scattering (SERS) spectra of faecal samples can be obtained by adding AuNP to their methanol extracts according to the reported protocol, and display bands that are due to bilirubin-like species but also to xanthine and hypoxanthine, two metabolic products secreted by gut bacteria. A total of 27 faecal samples from three different groups, i.e. coeliac patients (n = 9), coeliac patients on gluten-free diet (n = 10) and a control group (n = 8), were characterized with both SERS spectroscopy and 16S rRNA sequencing analysis. Significant differences are present between SERS spectra of coeliac patients and those on gluten-free diet, with a marked increase in the relative intensity of both xanthine and hypoxanthine for the latter. Interestingly, these differences do not correlate with bacterial composition as derived from 16S rRNA sequencing.

Microbial storage and its implications for soil ecology

Organisms throughout the tree of life accumulate chemical resources, in particular forms or compartments, to secure their availability for future use. Here we review microbial storage and its ecological significance by assembling several rich but disconnected lines of research in microbiology, biogeochemistry, and the ecology of macroscopic organisms. Evidence is drawn from various systems, but we pay particular attention to soils, where microorganisms play crucial roles in global element cycles. An assembly of genus-level data demonstrates the likely prevalence of storage traits in soil. We provide a theoretical basis for microbial storage ecology by distinguishing a spectrum of storage strategies ranging from surplus storage (storage of abundant resources that are not immediately required) to reserve storage (storage of limited resources at the cost of other metabolic functions). This distinction highlights that microorganisms can invest in storage at times of surplus and under conditions of scarcity. We then align storage with trait-based microbial life-history strategies, leading to the hypothesis that ruderal species, which are adapted to disturbance, rely less on storage than microorganisms adapted to stress or high competition. We explore the implications of storage for soil biogeochemistry, microbial biomass, and element transformations and present a process-based model of intracellular carbon storage. Our model indicates that storage can mitigate against stoichiometric imbalances, thereby enhancing biomass growth and resource-use efficiency in the face of unbalanced resources. Given the central roles of microbes in biogeochemical cycles, we propose that microbial storage may be influential on macroscopic scales, from carbon cycling to ecosystem stability.

Application of Metabolomics in the Study of Starvation-Induced Autophagy in Saccharomyces cerevisiae: A Scoping Review

This scoping review is aimed at the application of the metabolomics platform to dissect key metabolites and their intermediates to observe the regulatory mechanisms of starvation-induced autophagy in Saccharomyces cerevisiae. Four research papers were shortlisted in this review following the inclusion and exclusion criteria. We observed a commonly shared pathway undertaken by S. cerevisiae under nutritional stress. Targeted and untargeted metabolomics was applied in either of these studies using varying platforms resulting in the annotation of several different observable metabolites. We saw a commonly shared pathway undertaken by S. cerevisiae under nutritional stress. Following nitrogen starvation, the concentration of cellular nucleosides was altered as a result of autophagic RNA degradation. Additionally, it is also found that autophagy replenishes amino acid pools to sustain macromolecule synthesis. Furthermore, in glucose starvation, nucleosides were broken down into carbonaceous metabolites that are being funneled into the non-oxidative pentose phosphate pathway. The ribose salvage allows for the survival of starved yeast. Moreover, acute glucose starvation showed autophagy to be involved in maintaining ATP/energy levels. We highlighted the practicality of metabolomics as a tool to better understand the underlying mechanisms involved to maintain homeostasis by recycling degradative products to ensure the survival of S. cerevisiae under starvation. The application of metabolomics has extended the scope of autophagy and provided newer intervention targets against cancer as well as neurodegenerative diseases in which autophagy is implicated.

Observation of universal ageing dynamics in antibiotic persistence

Stress responses allow cells to adapt to changes in external conditions by activating specific pathways¹. Here we investigate the dynamics of single cells that were subjected to acute stress that is too strong for a regulated response but not lethal. We show that when the growth of bacteria is arrested by acute transient exposure to strong inhibitors, the statistics of their regrowth dynamics can be predicted by a model for the cellular network that ignores most of the details of the underlying molecular interactions. We observed that the same stress, applied either abruptly or gradually, can lead to totally different recovery dynamics. By measuring the regrowth dynamics after stress exposure on thousands of cells, we show that the model can predict the outcome of antibiotic persistence measurements. Our results may account for the ubiquitous antibiotic persistence phenotype², as well as for the difficulty in attempts to link it to specific genes³. More generally, our approach suggests that two different cellular states can be observed under stress: a regulated state, which prepares cells for fast recovery, and a disrupted cellular state due to acute stress, with slow and heterogeneous recovery dynamics. The disrupted state may be described by general properties of large random networks rather than by specific pathway activation. Better understanding of the disrupted state could shed new light on the survival and evolution of cells under stress.

Lactate and pyruvate levels correlation with lactate dehydrogenase gene expression and glucose consumption in Tamoxifen-resistant MCF-7 cells using capillary electrophoresis with contactless conductivity detection (CE-C4 D)

Breast cancer is the second leading cause of cancer death in women after lung cancer. The first-line treatment of metastatic breast cancer in premenopausal women relies on tamoxifen. The development of tamoxifen resistance is not fully understood. In this study, capillary electrophoresis with capacitively coupled contactless conductivity detector was developed to monitor the changes in lactate and pyruvate levels in supernatant media of three models of developed MCF-7 tamoxifen-resistant cells and correlate these metabolites changes with lactate dehydrogenase genes expression and glucose consumption. The electrophoretic separation was achieved under reversed electroosmotic flow conditions. The linear ranges were 0.15-5 and 0.01-1 mM with a correlation coefficient of 0.9966 and 0.9971 and the limits of detection were 0.01 and 0.02 μM for lactate and pyruvate, respectively. Inter- and intrarun accuracy were in the range of 96.88-105.94% with precision (CV, %) of ≤7.35%. The method was completely validated and the results were in agreement with those obtained using the lactate and glucose assay kits. The results revealed a significant increase in both lactate and pyruvate production in the three tamoxifen-resistant MCF-7 cells models compared to control cells. This increase was correlated with the increase of lactate dehydrogenase genes expression and the increase of glucose consumption.

Differentiation of Closely Related Oak-Associated Gram-Negative Bacteria by Label-Free Surface Enhanced Raman Spectroscopy (SERS)

Due to the harmful effects of chemical fertilizers and pesticides, the need for an eco-friendly solution to improve soil fertility has become a necessity, thus microbial biofertilizer research is on the rise. Plant endophytic bacteria inhabiting internal tissues represent a novel niche for research into new biofertilizer strains. However, the number of species and strains that need to be differentiated and identified to facilitate faster screening in future plant-bacteria interaction studies, is enormous. Surface enhanced Raman spectroscopy (SERS) may provide a platform for bacterial discrimination and identification, which, compared with the traditional methods, is relatively rapid, uncomplicated and ensures high specificity. In this study, we attempted to differentiate 18 bacterial isolates from two oaks via morphological, physiological, biochemical tests and SERS spectra analysis. Previous 16S rRNA gene fragment sequencing showed that three isolates belong to Paenibacillus, 3-to Pantoea and 12-to Pseudomonas genera. Additional tests were not able to further sort these bacteria into strain-specific groups. However, the obtained label-free SERS bacterial spectra along with the high-accuracy principal component (PCA) and discriminant function analyses (DFA) demonstrated the possibility to differentiate these bacteria into variant strains. Furthermore, we collected information about the biochemical characteristics of selected isolates. The results of this study suggest a promising application of SERS in combination with PCA/DFA as a rapid, non-expensive and sensitive method for the detection and identification of plant-associated bacteria.

Data-driven network models for genetic circuits from time-series data with incomplete measurements

Synthetic gene networks are frequently conceptualized and visualized as static graphs. This view of biological programming stands in stark contrast to the transient nature of biomolecular interaction, which is frequently enacted by labile molecules that are often unmeasured. Thus, the network topology and dynamics of synthetic gene networks can be difficult to verify in vivo or in vitro, due to the presence of unmeasured biological states. Here we introduce the dynamical structure function as a new mesoscopic, data-driven class of models to describe gene networks with incomplete measurements of state dynamics. We develop a network reconstruction algorithm and a code base for reconstructing the dynamical structure function from data, to enable discovery and visualization of graphical relationships in a genetic circuit diagram as time-dependent functions rather than static, unknown weights. We prove a theorem, showing that dynamical structure functions can provide a data-driven estimate of the size of crosstalk fluctuations from an idealized model. We illustrate this idea with numerical examples. Finally, we show how data-driven estimation of dynamical structure functions can explain failure modes in two experimentally implemented genetic circuits, a previously reported in vitro genetic circuit and a new E. coli-based transcriptional event detector.

A brief review on recent development of multidisciplinary engineering in fermentation of Saccharomyces cerevisiae

Fermentation technology has unprecedented potential to upgrade state-of-art biotechnology and refine the processes used in existing ones, taking into account of complex technical, economic and environmental factors. Given the economic importance and ongoing challenges of biotech sector, multidisciplinary engineering technologies is poised to become an increasingly important tool along with the emergence of modern technology and innovation. This article reviews recent technology advancement in the field of fermentation using Saccharomyces cerevisiae. Interesting research progress has been made by leveraging multiple engineering fields such as electrical engineering, information engineering, electrochemical engineering and new material development, leading to recent development of novel real-time probes (electronic nose technology, analysis of yeast morphology and metabolites, timely control of glucose feed), improved understanding of electro-fermentation (enhanced electronic transfer provision), as well as application of cost-effective and sustainable materials (bioreactor vessel manufactured from textile, and yeast immobilisation support matrix made from abundant natural biomass). To the best of our knowledge, the subject is reviewed for the first time in recent years. Furthermore, this review also constitutes a futuristic S. cerevisiae fermentation process based on the recent advancement discussed.

Removal of peptidoglycan and inhibition of active cellular processes leads to daptomycin tolerance in Enterococcus faecalis

Daptomycin is a cyclic lipopeptide antibiotic used in the clinic for treatment of severe enterococcal infections. Recent reports indicate that daptomycin targets active cellular processes, specifically, peptidoglycan biosynthesis. Within, we examined the efficacy of daptomycin against Enterococcus faecalis under a range of environmental growth conditions including inhibitors that target active cellular processes. Daptomycin was far less effective against cells in late stationary phase compared to cells in exponential phase, and this was independent of cellular ATP levels. Further, the addition of either the de novo protein synthesis inhibitor chloramphenicol or the fatty acid biosynthesis inhibitor cerulenin induced survival against daptomycin far better than controls. Alterations in metabolites associated with peptidoglycan synthesis correlated with protection against daptomycin. This was further supported as removal of peptidoglycan induced physiological daptomycin tolerance, a synergistic relation between daptomycin and fosfomycin, an inhibitor of the fist committed step peptidoglycan synthesis, was observed, as well as an additive effect when daptomycin was combined with ampicillin, which targets crosslinking of peptidoglycan strands. Removal of the peptidoglycan of Enterococcus faecium, Staphylococcus aureus, and Bacillus subtilis also resulted in significant protection against daptomycin in comparison to whole cells with intact cell walls. Based on these observations, we conclude that bacterial growth phase and metabolic activity, as well as the presence/absence of peptidoglycan are major contributors to the efficacy of daptomycin.

A microfluidic microwell device operated by the automated microfluidic control system for surface-enhanced Raman scattering-based antimicrobial susceptibility testing

Bloodstream infection (BSI) is a serious public health issue worldwide. Timely and effective antibiotics for controlling infection are crucial towards patient outcomes. However, the current culture-based methods of identifying bacteria and antimicrobial susceptibility testing (AST) remain labor-intensive and time-consuming, and are unable to provide early support to physicians in critical hours. To improve the effectiveness of early antibiotic therapy, Surface-enhanced Raman scattering (SERS) technology, has been used in bacterial detection and AST based on its high specificity and label-free features. To simplify sample preparation steps in SERS-AST, we proposed an automated microfluidic control system to integrate all required procedures into a single device. Our preliminary results demonstrated the system can achieve on-chip reagent replacement, bacteria trapping, and buffer exchange. Finally, in-situ SERS-AST was performed within 3.5 h by loading isolates of ampicilin susceptible and resistant E. coli and clear discrimination of two strains under antibiotic treatment was demonstrated. Overall, our system can standardize and simplify the SERS-AST protocol and implicate parallel bacterial detection. This prototypical integration demonstrates timely microbiological support to optimize early antibiotic therapy for fighting bacteremia.

A method to analyze time expression profiles demonstrated in a database of chili pepper fruit development

RNA-Seq experiments allow genome-wide estimation of relative gene expression. Estimation of gene expression at different time points generates time expression profiles of phenomena of interest, as for example fruit development. However, such profiles can be complex to analyze and interpret. We developed a methodology that transforms original RNA-Seq data from time course experiments into standardized expression profiles, which can be easily interpreted and analyzed. To exemplify this methodology we used RNA-Seq data obtained from 12 accessions of chili pepper (Capsicum annuum L.) during fruit development. All relevant data, as well as functions to perform analyses and interpretations from this experiment, were gathered into a publicly available R package: “Salsa”. Here we explain the rational of the methodology and exemplify the use of the package to obtain valuable insights into the multidimensional time expression changes that occur during chili pepper fruit development. We hope that this tool will be of interest for researchers studying fruit development in chili pepper as well as in other angiosperms.

Next-Generation Mass Spectrometry Metabolomics Revives the Functional Analysis of Plant Metabolic Diversity

The remarkable diversity of specialized metabolites produced by plants has inspired several decades of research and nucleated a long list of theories to guide empirical ecological studies. However, analytical constraints and the lack of untargeted processing workflows have long precluded comprehensive metabolite profiling and, consequently, the collection of the critical currencies to test theory predictions for the ecological functions of plant metabolic diversity. Developments in mass spectrometry (MS) metabolomics have revolutionized the large-scale inventory and annotation of chemicals from biospecimens. Hence, the next generation of MS metabolomics propelled by new bioinformatics developments provides a long-awaited framework to revisit metabolism-centered ecological questions, much like the advances in next-generation sequencing of the last two decades impacted all research horizons in genomics. Here, we review advances in plant (computational) metabolomics to foster hypothesis formulation from complex metabolome data. Additionally, we reflect on how next-generation metabolomics could reinvigorate the testing of long-standing theories on plant metabolic diversity.

Morphological Features and Cold-Response Gene Expression in Mesophilic Bacillus cereus Group and Psychrotolerant Bacillus cereus Group under Low Temperature

At low temperatures, psychrotolerant B. cereus group strains exhibit a higher growth rate than mesophilic strains do. However, the different survival responses of the psychrotolerant strain (BCG³⁴) and the mesophilic strain (BCG^T) at low temperatures are unclear. We investigated the morphological and genomic features of BCG^T and BCG³⁴ to characterize their growth strategies at low temperatures. At low temperatures, morphological changes were observed only in BCG^T. These morphological changes included the elongation of rod-shaped cells, whereas the cell shape in BCG³⁴ was unchanged at the low temperature. A transcriptomic analysis revealed that both species exhibited different growth-related traits during low-temperature growth. The BCG^T strain induces fatty acid biosynthesis, sulfur assimilation, and methionine and cysteine biosynthesis as a survival mechanism in cold systems. Increases in energy metabolism and fatty acid biosynthesis in the mesophilic B. cereus group strain might explain its ability to grow at low temperatures. Several pathways involved in carbohydrate mechanisms were downregulated to conserve the energy required for growth. Peptidoglycan biosynthesis was upregulated, implying that a change of gene expression in both RNA-Seq and RT-qPCR contributed to sustaining its growth and rod shape at low temperatures. These results improve our understanding of the growth response of the B. cereus group, including psychrotolerant B. cereus group strains, at low temperatures and provide information for improving bacterial inhibition strategies in the food industry.

Pareto optimality between growth-rate and lag-time couples metabolic noise to phenotypic heterogeneity in Escherichia coli

Despite mounting evidence that in clonal bacterial populations, phenotypic variability originates from stochasticity in gene expression, little is known about noise-shaping evolutionary forces and how expression noise translates to phenotypic differences. Here we developed a high-throughput assay that uses a redox-sensitive dye to couple growth of thousands of bacterial colonies to their respiratory activity and show that in Escherichia coli, noisy regulation of lower glycolysis and citric acid cycle is responsible for large variations in respiratory metabolism. We found that these variations are Pareto optimal to maximization of growth rate and minimization of lag time, two objectives competing between fermentative and respiratory metabolism. Metabolome-based analysis revealed the role of respiratory metabolism in preventing the accumulation of toxic intermediates of branched chain amino acid biosynthesis, thereby supporting early onset of cell growth after carbon starvation. We propose that optimal metabolic tradeoffs play a key role in shaping and preserving phenotypic heterogeneity and adaptation to fluctuating environments.

Emerging and divergent roles of pyrophosphorylated nucleotides in bacterial physiology and pathogenesis

Bacteria inhabit diverse environmental niches and consequently must modulate their metabolism to adapt to stress. The nucleotide second messengers guanosine tetraphosphate (ppGpp) and guanosine pentaphosphate (pppGpp) (collectively referred to as (p)ppGpp) are essential for survival during nutrient starvation. (p)ppGpp is synthesized by the RelA-SpoT homologue (RSH) protein family and coordinates the control of cellular metabolism through its combined effect on over 50 proteins. While the role of (p)ppGpp has largely been associated with nutrient limitation, recent studies have shown that (p)ppGpp and related nucleotides have a previously underappreciated effect on different aspects of bacterial physiology, such as maintaining cellular homeostasis and regulating bacterial interactions with a host, other bacteria, or phages. (p)ppGpp produced by pathogenic bacteria facilitates the evasion of host defenses such as reactive nitrogen intermediates, acidic pH, and the complement system. Additionally, (p)ppGpp and pyrophosphorylated derivatives of canonical adenosine nucleotides called (p)ppApp are emerging as effectors of bacterial toxin proteins. Here, we review the RSH protein family with a focus on its unconventional roles during host infection and bacterial competition.

Energy Starvation Induces a Cell Cycle Arrest in Escherichia coli by Triggering Degradation of the DnaA Initiator Protein

During steady-state Escherichia coli growth, the amount and activity of the initiator protein, DnaA, controls chromosome replication tightly so that initiation only takes place once per origin in each cell cycle, regardless of growth conditions. However, little is known about the mechanisms involved during transitions from one environmental condition to another or during starvation stress. ATP depletion is one of the consequences of long-term carbon starvation. Here we show that DnaA is degraded in ATP-depleted cells. A chromosome replication initiation block is apparent in such cells as no new rounds of DNA replication are initiated while replication events that have already started proceed to completion.

Need for speed: evaluation of dilute and shoot-mass spectrometry for accelerated metabolic phenotyping in bioprocess development

With the utilization of small-scale and highly parallelized cultivation platforms embedded in laboratory robotics, microbial phenotyping and bioprocess development have been substantially accelerated, thus generating a bottleneck in bioanalytical bioprocess sample analytics. While microscale cultivation platforms allow the monitoring of typical process parameters, only limited information about product and by-product formation is provided without comprehensive analytics. The use of liquid chromatography mass spectrometry can provide such a comprehensive and quantitative insight, but is often limited by analysis runtime and throughput. In this study, we developed and evaluated six methods for amino acid quantification based on two strong cation exchanger columns and a dilute and shoot approach in hyphenation with either a triple-quadrupole or a quadrupole time-of-flight mass spectrometer. Isotope dilution mass spectrometry with 13C15N labeled amino acids was used to correct for matrix effects. The versatility of the methods for metabolite profiling studies of microbial cultivation supernatants is confirmed by a detailed method validation study. The methods using chromatography columns showed a linear range of approx. 4 orders of magnitude, sufficient response factors, and low quantification limits (7-443 nM) for single analytes. Overall, relative standard deviation was comparable for all analytes, with < 8% and < 11% for unbuffered and buffered media, respectively. The dilute and shoot methods with an analysis time of 1 min provided similar performance but showed a factor of up to 35 times higher throughput. The performance and applicability of the dilute and shoot method are demonstrated using a library of Corynebacterium glutamicum strains producing L-histidine, obtained from random mutagenesis, which were cultivated in a microscale cultivation platform.

Current updates on precision therapy for breast cancer associated brain metastasis: Emphasis on combination therapy

Cancer therapies have undergone a tremendous progress over the past decade. Precision medicine provides a more tailored approach, making the combination of existing therapies more precise. Different types of cancers are characterized by unique biomarkers that are targeted using various genomic approaches by clinicians and companies worldwide to achieve efficient treatment with minimal side effects. Precision medicine has two broad approaches namely stratified and personalized medicine. The driver mutations could vary within a subtype while the same driver mutations could be found across different subtypes. Precision medicine has recently gained a lot of importance for breast cancer therapy. Various kinds of mutations like hotspot mutations, gene alterations, gene amplification mutations are targeted to design a more specific therapy. Apart from these known gene mutations there are various unknown mutations. Thus, tumor heterogeneity can pose a challenge to precision medicine. For breast cancer, one of the most successful models developed in case of precision medicine is the anti-HER2 therapies as HER2 was considered to have the worst prognosis being highly malignant. But now due to the advent of HER2 receptor targeted therapies, it has a good prognosis. Moreover, precision medicine helps in identifying if the drug molecules being used for the treatment of one kind of cancer can be beneficial in the treatment of another kind of cancer as well, considering the signaling pathways and machinery is similar in most of the cancers. This reduces the time for new drug development and is economically more feasible. Precision medicine will prove to be very advantageous in case of brain metastasis.

RTExtract: time-series NMR spectra quantification based on 3D surface ridge tracking

MOTIVATION

Time-series nuclear magnetic resonance (NMR) has advanced our knowledge about metabolic dynamics. Before analyzing compounds through modeling or statistical methods, chemical features need to be tracked and quantified. However, because of peak overlap and peak shifting, the available protocols are time consuming at best or even impossible for some regions in NMR spectra.

RESULTS

We introduce Ridge Tracking-based Extract (RTExtract), a computer vision-based algorithm, to quantify time-series NMR spectra. The NMR spectra of multiple time points were formulated as a 3D surface. Candidate points were first filtered using local curvature and optima, then connected into ridges by a greedy algorithm. Interactive steps were implemented to refine results. Among 173 simulated ridges, 115 can be tracked (RMSD < 0.001). For reproducing previous results, RTExtract took less than 2 h instead of ∼48 h, and two instead of seven parameters need tuning. Multiple regions with overlapping and changing chemical shifts are accurately tracked.

AVAILABILITY AND IMPLEMENTATION

Source code is freely available within Metabolomics toolbox GitHub repository (https://github.com/artedison/Edison_Lab_Shared_Metabolomics_UGA/tree/master/metabolomics_toolbox/code/ridge_tracking) and is implemented in MATLAB and R.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.