ATG7(2) Interacts With Metabolic Proteins and Regulates Central Energy Metabolism.

Macroautophagy/autophagy is an essential catabolic process that targets a wide variety of cellular components including proteins, organelles, and pathogens. ATG7, a protein involved in the autophagy process, plays a crucial role in maintaining cellular homeostasis and can contribute to the development of diseases such as cancer. ATG7 initiates autophagy by facilitating the lipidation of the ATG8 proteins in the growing autophagosome membrane. The noncanonical isoform ATG7(2) is unable to perform ATG8 lipidation; however, its cellular regulation and function are unknown. Here, we uncovered a distinct regulation and function of ATG7(2) in contrast with ATG7(1), the canonical isoform. First, affinity-purification mass spectrometry analysis revealed that ATG7(2) establishes direct protein-protein interactions (PPIs) with metabolic proteins, whereas ATG7(1) primarily interacts with autophagy machinery proteins. Furthermore, we identified that ATG7(2) mediates a decrease in metabolic activity, highlighting a novel splice-dependent function of this important autophagy protein. Then, we found a divergent expression pattern of ATG7(1) and ATG7(2) across human tissues. Conclusively, our work uncovers the divergent patterns of expression, protein interactions, and function of ATG7(2) in contrast to ATG7(1). These findings suggest a molecular switch between main catabolic processes through isoform-dependent expression of a key autophagy gene.

Gene regulatory network topology governs resistance and treatment escape in glioma stem-like cells.

Poor prognosis and drug resistance in glioblastoma (GBM) can result from cellular heterogeneity and treatment-induced shifts in phenotypic states of tumor cells, including dedifferentiation into glioma stem-like cells (GSCs). This rare tumorigenic cell subpopulation resists temozolomide, undergoes proneural-to-mesenchymal transition (PMT) to evade therapy, and drives recurrence. Through inference of transcriptional regulatory networks (TRNs) of patient-derived GSCs (PD-GSCs) at single-cell resolution, we demonstrate how the topology of transcription factor interaction networks drives distinct trajectories of cell state transitions in PD-GSCs resistant or susceptible to cytotoxic drug treatment. By experimentally testing predictions based on TRN simulations, we show that drug treatment drives surviving PD-GSCs along a trajectory of intermediate states, exposing vulnerability to potentiated killing by siRNA or a second drug targeting treatment-induced transcriptional programs governing non-genetic cell plasticity. Our findings demonstrate an approach to uncover TRN topology and use it to rationally predict combinatorial treatments that disrupts acquired resistance in GBM.

A comprehensive spectral assay library to quantify the Halobacterium salinarum NRC-1 proteome by DIA/SWATH-MS.

Data-Independent Acquisition (DIA) is a mass spectrometry-based method to reliably identify and reproducibly quantify large fractions of a target proteome. The peptide-centric data analysis strategy employed in DIA requires a priori generated spectral assay libraries. Such assay libraries allow to extract quantitative data in a targeted approach and have been generated for human, mouse, zebrafish, E. coli and few other organisms. However, a spectral assay library for the extreme halophilic archaeon Halobacterium salinarum NRC-1, a model organism that contributed to several notable discoveries, is not publicly available yet. Here, we report a comprehensive spectral assay library to measure 2,563 of 2,646 annotated H. salinarum NRC-1 proteins. We demonstrate the utility of this library by measuring global protein abundances over time under standard growth conditions. The H. salinarum NRC-1 library includes 21,074 distinct peptides representing 97% of the predicted proteome and provides a new, valuable resource to confidently measure and quantify any protein of this archaeon. Data and spectral assay libraries are available via ProteomeXchange (PXD042770, PXD042774) and SWATHAtlas (SAL00312-SAL00319).

Metabolic Response in Endothelial Cells to Catecholamine Stimulation Associated with Increased Vascular Permeability.

Disruption to endothelial cell homeostasis results in an extensive variety of human pathologies that are particularly relevant to major trauma. Circulating catecholamines, such as adrenaline and noradrenaline, activate endothelial adrenergic receptors triggering a potent response in endothelial function. The regulation of the endothelial cell metabolism is distinct and profoundly important to endothelium homeostasis. However, a precise catalogue of the metabolic alterations caused by sustained high catecholamine levels that results in endothelial dysfunction is still underexplored. Here, we uncover a set of up to 46 metabolites that exhibit a dose-response relationship to adrenaline-noradrenaline equimolar treatment. The identified metabolites align with the glutathione-ascorbate cycle and the nitric oxide biosynthesis pathway. Certain key metabolites, such as arginine and reduced glutathione, displayed a differential response to treatment in early (4 h) compared to late (24 h) stages of sustained stimulation, indicative of homeostatic metabolic feedback loops. Furthermore, we quantified an increase in the glucose consumption and aerobic respiration in endothelial cells upon catecholamine stimulation. Our results indicate that oxidative stress and nitric oxide metabolic pathways are downstream consequences of endothelial cell stimulation with sustained high levels of catecholamines. A precise understanding of the metabolic response in endothelial cells to pathological levels of catecholamines will facilitate the identification of more efficient clinical interventions in trauma patients.

Quantitative prediction of conditional vulnerabilities in regulatory and metabolic networks using PRIME.

The ability of Mycobacterium tuberculosis (Mtb) to adopt heterogeneous physiological states underlies its success in evading the immune system and tolerating antibiotic killing. Drug tolerant phenotypes are a major reason why the tuberculosis (TB) mortality rate is so high, with over 1.8 million deaths annually. To develop new TB therapeutics that better treat the infection (faster and more completely), a systems-level approach is needed to reveal the complexity of network-based adaptations of Mtb. Here, we report a new predictive model called PRIME (Phenotype of Regulatory influences Integrated with Metabolism and Environment) to uncover environment-specific vulnerabilities within the regulatory and metabolic networks of Mtb. Through extensive performance evaluations using genome-wide fitness screens, we demonstrate that PRIME makes mechanistically accurate predictions of context-specific vulnerabilities within the integrated regulatory and metabolic networks of Mtb, accurately rank-ordering targets for potentiating treatment with frontline drugs.

Selective Translation of Low Abundance and Upregulated Transcripts in Halobacterium salinarum.

When organisms encounter an unfavorable environment, they transition to a physiologically distinct, quiescent state wherein abundant transcripts from the previous active growth state continue to persist, albeit their active transcription is downregulated. In order to generate proteins for the new quiescent physiological state, we hypothesized that the translation machinery must selectively translate upregulated transcripts in an intracellular milieu crowded with considerably higher abundance transcripts from the previous active growth state. Here, we have analyzed genome-wide changes in the transcriptome (RNA sequencing [RNA-seq]), changes in translational regulation and efficiency by ribosome profiling across all transcripts (ribosome profiling [Ribo-seq]), and protein level changes in assembled ribosomal proteins (sequential window acquisition of all theoretical mass spectra [SWATH-MS]) to investigate the interplay of transcriptional and translational regulation in Halobacterium salinarum as it transitions from active growth to quiescence. We have discovered that interplay of regulatory processes at different levels of information processing generates condition-specific ribosomal complexes to translate preferentially pools of low abundance and upregulated transcripts. Through analysis of the gene regulatory network architecture of H. salinarum, Escherichia coli, and Saccharomyces cerevisiae, we demonstrate that this conditional, modular organization of regulatory programs governing translational systems is a generalized feature across all domains of life.IMPORTANCE Our findings demonstrate conclusively that low abundance and upregulated transcripts are preferentially translated, potentially by environment-specific translation systems with distinct ribosomal protein composition. We show that a complex interplay of transcriptional and posttranscriptional regulation underlies the conditional and modular regulatory programs that generate ribosomes of distinct protein composition. The modular regulation of ribosomal proteins with other transcription, translation, and metabolic genes is generalizable to bacterial and eukaryotic microbes. These findings are relevant to how microorganisms adapt to unfavorable environments when they transition from active growth to quiescence by generating proteins from upregulated transcripts that are in considerably lower abundance relative to transcripts associated with the previous physiological state. Selective translation of transcripts by distinct ribosomes could form the basis for adaptive evolution to new environments through a modular regulation of the translational systems.

Systems biology approaches towards predictive microbial ecology.

Through complex interspecies interactions, microbial processes drive nutrient cycling and biogeochemistry. However, we still struggle to predict specifically which organisms, communities and biotic and abiotic processes are determining ecosystem function and how environmental changes will alter their roles and stability. While the tools to create such a predictive microbial ecology capability exist, cross-disciplinary integration of high-resolution field measurements, detailed laboratory studies and computation is essential. In this perspective, we emphasize the importance of pursuing a multiscale, systems approach to iteratively link ecological processes measured in the field to testable hypotheses that drive high-throughput laboratory experimentation. Mechanistic understanding of microbial processes gained in controlled lab systems will lead to the development of theory that can be tested back in the field. Using N2 O production as an example, we review the current status of field and laboratory research and layout a plausible path to the kind of integration that is needed to enable prediction of how N-cycling microbial communities will respond to environmental changes. We advocate for the development of realistic and predictive gene regulatory network models for environmental responses that extend from single-cell resolution to ecosystems, which is essential to understand how microbial communities involved in N2 O production and consumption will respond to future environmental conditions.

Ocean acidification conditions increase resilience of marine diatoms.

The fate of diatoms in future acidified oceans could have dramatic implications on marine ecosystems, because they account for ~40% of marine primary production. Here, we quantify resilience of Thalassiosira pseudonana in mid-20th century (300 ppm CO2) and future (1000 ppm CO2) conditions that cause ocean acidification, using a stress test that probes its ability to recover from incrementally higher amount of low-dose ultraviolet A (UVA) and B (UVB) radiation and re-initiate growth in day-night cycles, limited by nitrogen. While all cultures eventually collapse, those growing at 300 ppm CO2 succumb sooner. The underlying mechanism for collapse appears to be a system failure resulting from "loss of relational resilience," that is, inability to adopt physiological states matched to N-availability and phase of the diurnal cycle. Importantly, under elevated CO2 conditions diatoms sustain relational resilience over a longer timeframe, demonstrating increased resilience to future acidified ocean conditions. This stress test framework can be extended to evaluate and predict how various climate change associated stressors may impact microbial community resilience.

Adaptive Prediction Emerges Over Short Evolutionary Time Scales.

Adaptive prediction is a capability of diverse organisms, including microbes, to sense a cue and prepare in advance to deal with a future environmental challenge. Here, we investigated the timeframe over which adaptive prediction emerges when an organism encounters an environment with novel structure. We subjected yeast to laboratory evolution in a novel environment with repetitive, coupled exposures to a neutral chemical cue (caffeine), followed by a sublethal dose of a toxin (5-FOA), with an interspersed requirement for uracil prototrophy to counter-select mutants that gained constitutive 5-FOA resistance. We demonstrate the remarkable ability of yeast to internalize a novel environmental pattern within 50-150 generations by adaptively predicting 5-FOA stress upon sensing caffeine. We also demonstrate how novel environmental structure can be internalized by coupling two unrelated response networks, such as the response to caffeine and signaling-mediated conditional peroxisomal localization of proteins.

Robustness of a model microbial community emerges from population structure among single cells of a clonal population.

Microbial populations can withstand, overcome and persist in the face of environmental fluctuation. Previously, we demonstrated how conditional gene regulation in a fluctuating environment drives dilution of condition-specific transcripts, causing a population of Desulfovibrio vulgaris Hildenborough (DvH) to collapse after repeatedly transitioning from sulfate respiration to syntrophic conditions with the methanogen Methanococcus maripaludis. Failure of the DvH to successfully transition contributed to the collapse of this model community. We investigated the mechanistic basis for loss of robustness by examining whether conditional gene regulation altered heterogeneity in gene expression across individual DvH cells. We discovered that robustness of a microbial population across environmental transitions was attributable to the retention of cells in two states that exhibited different condition-specific gene expression patterns. In our experiments, a population with disrupted conditional regulation successfully alternated between cell states. Meanwhile, a population with intact conditional regulation successfully switched between cell states initially, but collapsed after repeated transitions, possibly due to the high energy requirements of regulation. These results demonstrate that the survival of this entire model microbial community is dependent on the regulatory system's influence on the distribution of distinct cell states among individual cells within a clonal population.

Mechanism for microbial population collapse in a fluctuating resource environment.

Managing trade-offs through gene regulation is believed to confer resilience to a microbial community in a fluctuating resource environment. To investigate this hypothesis, we imposed a fluctuating environment that required the sulfate-reducer Desulfovibrio vulgaris to undergo repeated ecologically relevant shifts between retaining metabolic independence (active capacity for sulfate respiration) and becoming metabolically specialized to a mutualistic association with the hydrogen-consuming Methanococcus maripaludis Strikingly, the microbial community became progressively less proficient at restoring the environmentally relevant physiological state after each perturbation and most cultures collapsed within 3-7 shifts. Counterintuitively, the collapse phenomenon was prevented by a single regulatory mutation. We have characterized the mechanism for collapse by conducting RNA-seq analysis, proteomics, microcalorimetry, and single-cell transcriptome analysis. We demonstrate that the collapse was caused by conditional gene regulation, which drove precipitous decline in intracellular abundance of essential transcripts and proteins, imposing greater energetic burden of regulation to restore function in a fluctuating environment.

Transcriptional program for nitrogen starvation-induced lipid accumulation in Chlamydomonas reinhardtii.

BACKGROUND: Algae accumulate lipids to endure different kinds of environmental stresses including macronutrient starvation. Although this response has been extensively studied, an in depth understanding of the transcriptional regulatory network (TRN) that controls the transition into lipid accumulation remains elusive. In this study, we used a systems biology approach to elucidate the transcriptional program that coordinates the nitrogen starvation-induced metabolic readjustments that drive lipid accumulation in Chlamydomonas reinhardtii. RESULTS: We demonstrate that nitrogen starvation triggered differential regulation of 2147 transcripts, which were co-regulated in 215 distinct modules and temporally ordered as 31 transcriptional waves. An early-stage response was triggered within 12 min that initiated growth arrest through activation of key signaling pathways, while simultaneously preparing the intracellular environment for later stages by modulating transport processes and ubiquitin-mediated protein degradation. Subsequently, central metabolism and carbon fixation were remodeled to trigger the accumulation of triacylglycerols. Further analysis revealed that these waves of genome-wide transcriptional events were coordinated by a regulatory program orchestrated by at least 17 transcriptional regulators, many of which had not been previously implicated in this process. We demonstrate that the TRN coordinates transcriptional downregulation of 57 metabolic enzymes across a period of nearly 4 h to drive an increase in lipid content per unit biomass. Notably, this TRN appears to also drive lipid accumulation during sulfur starvation, while phosphorus starvation induces a different regulatory program. The TRN model described here is available as a community-wide web-resource at http://networks.systemsbiology.net/chlamy-portal. CONCLUSIONS: In this work, we have uncovered a comprehensive mechanistic model of the TRN controlling the transition from N starvation to lipid accumulation. The program coordinates sequentially ordered transcriptional waves that simultaneously arrest growth and lead to lipid accumulation. This study has generated predictive tools that will aid in devising strategies for the rational manipulation of regulatory and metabolic networks for better biofuel and biomass production.

A refined genome-scale reconstruction of Chlamydomonas metabolism provides a platform for systems-level analyses.

Microalgae have reemerged as organisms of prime biotechnological interest due to their ability to synthesize a suite of valuable chemicals. To harness the capabilities of these organisms, we need a comprehensive systems-level understanding of their metabolism, which can be fundamentally achieved through large-scale mechanistic models of metabolism. In this study, we present a revised and significantly improved genome-scale metabolic model for the widely-studied microalga, Chlamydomonas reinhardtii. The model, iCre1355, represents a major advance over previous models, both in content and predictive power. iCre1355 encompasses a broad range of metabolic functions encoded across the nuclear, chloroplast and mitochondrial genomes accounting for 1355 genes (1460 transcripts), 2394 and 1133 metabolites. We found improved performance over the previous metabolic model based on comparisons of predictive accuracy across 306 phenotypes (from 81 mutants), lipid yield analysis and growth rates derived from chemostat-grown cells (under three conditions). Measurement of macronutrient uptake revealed carbon and phosphate to be good predictors of growth rate, while nitrogen consumption appeared to be in excess. We analyzed high-resolution time series transcriptomics data using iCre1355 to uncover dynamic pathway-level changes that occur in response to nitrogen starvation and changes in light intensity. This approach enabled accurate prediction of growth rates, the cessation of growth and accumulation of triacylglycerols during nitrogen starvation, and the temporal response of different growth-associated pathways to increased light intensity. Thus, iCre1355 represents an experimentally validated genome-scale reconstruction of C. reinhardtii metabolism that should serve as a useful resource for studying the metabolic processes of this and related microalgae.

Topological augmentation to infer hidden processes in biological systems.

MOTIVATION: A common problem in understanding a biochemical system is to infer its correct structure or topology. This topology consists of all relevant state variables-usually molecules and their interactions. Here we present a method called topological augmentation to infer this structure in a statistically rigorous and systematic way from prior knowledge and experimental data. RESULTS: Topological augmentation starts from a simple model that is unable to explain the experimental data and augments its topology by adding new terms that capture the experimental behavior. This process is guided by representing the uncertainty in the model topology through stochastic differential equations whose trajectories contain information about missing model parts. We first apply this semiautomatic procedure to a pharmacokinetic model. This example illustrates that a global sampling of the parameter space is critical for inferring a correct model structure. We also use our method to improve our understanding of glutamine transport in yeast. This analysis shows that transport dynamics is determined by glutamine permeases with two different kinds of kinetics. Topological augmentation can not only be applied to biochemical systems, but also to any system that can be described by ordinary differential equations. AVAILABILITY AND IMPLEMENTATION: Matlab code and examples are available at: http://www.csb.ethz.ch/tools/index

Statistical analysis of global connectivity and activity distributions in cellular networks.

Various molecular interaction networks have been claimed to follow power-law decay for their global connectivity distribution. It has been proposed that there may be underlying generative models that explain this heavy-tailed behavior by self-reinforcement processes such as classical or hierarchical scale-free network models. Here, we analyze a comprehensive data set of protein-protein and transcriptional regulatory interaction networks in yeast, an Escherichia coli metabolic network, and gene activity profiles for different metabolic states in both organisms. We show that in all cases the networks have a heavy-tailed distribution, but most of them present significant differences from a power-law model according to a stringent statistical test. Those few data sets that have a statistically significant fit with a power-law model follow other distributions equally well. Thus, while our analysis supports that both global connectivity interaction networks and activity distributions are heavy-tailed, they are not generally described by any specific distribution model, leaving space for further inferences on generative models. Supplementary Material is available online at www.liebertonline.com.