SEARCH · bioRxiv Science

Results for “biochemistry”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 433 records · Page 24Linked to original sources

bioRxiv2021

Membrane surfaces regulate assembly of a ribonucleoprotein condensate

Biomolecular condensates organize biochemistry in time and space, yet little is known about how cells control either the position or scale of these assemblies. In cells, condensates often appear as dispersed, relatively small assemblies that do not grow (coarsen) into a single droplet despite their propensity to coalesce. Here we report that ribonucleoprotein condensates of the Q-rich protein Whi3 interact with the endoplasmic reticulum, prompting us to hypothesize that membrane association controls the position and size of condensates. Reconstitution of Whi3 condensates on supported lipid bilayers reveals that association with a diffusive lipid surface promotes condensation at both physiological ionic strength and protein concentration. Notably, these assemblies rapidly arrest, matching size distributions seen in cells. The timing of the arrest is influenced by the ordering of protein-protein and protein-RNA interactions and controlled by the slow diffusion of complexes induced by the membrane. This slowed diffusion limits both transfer of small protein-RNA complexes between condensates and their coalescence, thus driving coarsening to arrest. Our experiments reveal a tradeoff between locally-enhanced protein concentration at membranes, which favors condensation, and an accompanying reduction in diffusion, which restricts coarsening. Thus, membranes can maintain a population of small condensates in the absence of active mechanisms. Given that many condensates are bound to endomembranes, we predict that the biophysical properties of lipid bilayers are key for controlling condensate sizes throughout the cell. One sentence summaryAssembly on a membrane surface positions and scales biomolecular condensates by controlling relative diffusion rates of proteins and nucleic acids.

biophysics↗

bioRxiv2021

Expression of a CO2-permeable aquaporin enhances mesophyll conductance in the C4 species Setaria viridis

A fundamental limitation of photosynthetic carbon fixation is the availability of CO4. In C4 plants, primary carboxylation occurs in mesophyll cytosol, and little is known about the role of CO2 diffusion in facilitating C4 photosynthesis. We have examined the expression, localization, and functional role of selected plasma membrane intrinsic aquaporins (PIPs) from Setaria italica (foxtail millet) and discovered that SiPIP2;7 is CO2-permeable. When ectopically expressed in mesophyll cells of S. viridis (green foxtail), SiPIP2;7 was localized to the plasma membrane and caused no marked changes in leaf biochemistry. Gas-exchange and C18O16O discrimination measurements revealed that targeted expression of SiPIP2;7 enhanced the conductance to CO2 diffusion from the intercellular airspace to the mesophyll cytosol. Our results demonstrate that mesophyll conductance limits C4 photosynthesis at low pCO2 and that SiPIP2;7 is a functional CO2 permeable aquaporin that can improve CO2 diffusion at the airspace/mesophyll interface and enhance C4 photosynthesis.

plant biology↗

bioRxiv2021

Rules for designing protein fold switches and their implications for the folding code

We have engineered switches between the three most common small folds, 3, 4{beta}+, and /{beta}-plait, referred to here as A, B, and S, respectively. Mutations were introduced into the natural S protein until sequences were created that have a stable S-fold in their longer ([~]90 amino acid) form and have an alternative fold (either A or B) in their shorter (56 amino acid) form. Five sequence pairs were designed and key structures were determined using NMR spectroscopy. Each protein pair is 100% identical in the 56 amino acid region of overlap. Several rules for engineering switches emerged. First, designing one sequence with good native state interactions in two folds requires care but is feasible. Once this condition is met, fold populations are determined by the stability of the embedded A- or B-fold relative to the S-fold and the conformational propensities of the ends that are generated in the switch to the embedded fold. If the stabilities of the embedded fold and the longer fold are similar, conformation is highly sensitive to mutation so that even a single amino acid substitution can radically shift the population to the alternative fold. The results provide insight into why dimorphic sequences can be engineered and sometimes exist in nature, while most natural protein sequences populate single folds. Proteins may evolve toward unique folds because dimorphic sequences generate interactions that destabilize and can produce aberrant functions. Thus, two-state behavior may result from natures negative design rather than being an inherent property of the folding code. Significance StatementWe establish general rules for designing protein fold switches by engineering dimorphic sequences that link the three most common small folds. The fact that switches can be engineered in arbitrary and common protein folds, sheds light on several important questions: 1) What is the generality of fold switching? 2) What types of folds are amenable to switching? 3) What properties are shared by sequences that can fold into two completely different structures? This work has implications for understanding how amino acid sequence encodes structure, how proteins evolve, how mutation is related to disease, and how function is annotated to sequences of unknown structure. ClassificationBiological Sciences: Biochemistry; Physical Sciences: Biophysics and Computational Biology

biophysics↗

bioRxiv2021

Pathway analysis in metabolomics: pitfalls and best practice for the use of over-representation analysis

Over-representation analysis (ORA) is one of the commonest pathway analysis approaches used for the functional interpretation of metabolomics datasets. Despite the widespread use of ORA in metabolomics, the community lacks guidelines detailing its best-practice use. Many factors have a pronounced impact on the results, but to date their effects have received little systematic attention in the field. We developed in-silico simulations using five publicly available datasets and illustrated that changes in parameters, such as the background set, differential metabolite selection methods, and pathway database choice, could all lead to profoundly different ORA results. The use of a non-assay-specific background set, for example, resulted in large numbers of false-positive pathways. Pathway database choice, evaluated using three of the most popular metabolic pathway databases: KEGG, Reactome, and BioCyc, led to vastly different results in both the number and function of significantly enriched pathways. Metabolomics data specific factors, such as reliability of compound identification and assay chemical bias also impacted ORA results. Simulated metabolite misidentification rates as low as 4% resulted in both gain of false-positive pathways and loss of truly significant pathways across all datasets. Our results have several practical implications for ORA users, as well as those using alternative pathway analysis methods. We offer a set of recommendations for the use of ORA in metabolomics, alongside a set of minimal reporting guidelines, as a first step towards the standardisation of pathway analysis in metabolomics. Author summaryMetabolomics is a rapidly growing field of study involving the profiling of small molecules within an organism. It allows researchers to understand the effects of biological status (such as health or disease) on cellular biochemistry, and has wide-ranging applications, from biomarker discovery and personalised medicine in healthcare to crop protection and food security in agriculture. Pathway analysis helps to understand which biological pathways, representing collections of molecules performing a particular function, are involved in response to a disease phenotype, or drug treatment, for example. Over-representation analysis (ORA) is perhaps the most common pathway analysis method used in the metabolomics community. However, ORA can give drastically different results depending on the input data and parameters used. In this work, we have established the effects of these factors on ORA results using computational simulations applied to five real-world datasets. Based on our results, we offer the research community a set of best-practice recommendations applicable not only to ORA but also to other pathway analysis methods to help ensure the reliability and reproducibility of results.

bioinformatics↗

bioRxiv2021

A metabolomics pipeline enables mechanistic interrogation of the gut microbiome

Gut microbes modulate host phenotypes and are associated with numerous health effects in humans, ranging from cancer immunotherapy response to metabolic disease and obesity. However, difficulty in accurate and high-throughput functional analysis of human gut microbes has hindered defining mechanistic connections between individual microbial strains and host phenotypes. One key way the gut microbiome influences host physiology is through the production of small molecules1-3, yet progress in elucidating this chemical interplay has been hindered by limited tools calibrated to detect products of anaerobic biochemistry in the gut. Here we construct a microbiome-focused, integrated mass-spectrometry pipeline to accelerate the identification of microbiota-dependent metabolites (MDMs) in diverse sample types. We report the metabolic profiles of 178 gut microbe strains using our library of 833 metabolites. Leveraging this metabolomics resource we establish deviations in the relationships between phylogeny and metabolism, use machine learning to discover novel metabolism in Bacteroides, and employ comparative genomics-based discovery of candidate biochemical pathways. MDMs can be detected in diverse biofluids in gnotobiotic and conventional mice and traced back to corresponding metabolomic profiles of cultured bacteria. Collectively, our microbiome-focused metabolomics pipeline and interactive metabolomics profile explorer are a powerful tool for characterizing microbe and microbe-host interactions.

microbiology↗

bioRxiv2021

Chronic PPARγ Stimulation Shifts Amyloidosis to Higher Fibrillarity but Improves Cognition

BackgroundWe undertook longitudinal {beta}-amyloid positron emission tomography (A{beta}-PET) imaging as a translational tool for monitoring of chronic treatment with the peroxisome proliferator-activated receptor gamma (PPAR{gamma}) agonist pioglitazone in A{beta} model mice. We thus tested the hypothesis this treatment would rescue from increases of the A{beta}-PET signal while promoting spatial learning and preservation of synaptic density. MethodsPS2APP mice (N=23; baseline age: 8 months) and AppNL-G-F mice (N=37; baseline age: 5 months) were investigated longitudinally for five months using A{beta}-PET. Groups of mice were treated with pioglitazone or vehicle during the follow-up interval. We tested spatial memory performance and confirmed terminal PET findings by immunohistochemical and biochemistry analyses. ResultsSurprisingly, A{beta}-PET and immunohistochemistry revealed a shift towards higher fibrillary composition of A{beta}-plaques during upon chronic pioglitazone treatment. Nonetheless, synaptic density and spatial learning were improved in transgenic mice with pioglitazone treatment, in association with the increased plaque fibrillarity. ConclusionThese translational data suggest that a shift towards higher plaque fibrillarity protects cognitive function and brain integrity. Increases in the A{beta}-PET signal upon immunomodulatory treatments targeting A{beta} aggregation can thus be protective.

neuroscience↗

bioRxiv2021

It happened again: convergent evolution of acylglucose specialized metabolism in black nightshade and wild tomato

Plants synthesize myriad phylogenetically-restricted specialized (aka secondary) metabolites with diverse structures. Metabolism of acylated sugar esters in epidermal glandular secreting trichomes across the Solanaceae (nightshade) family are ideal for investigating the mechanisms of evolutionary metabolic diversification. We developed methods to structurally analyze acylhexose mixtures by 2D NMR, which led to the insight that the Old World species black nightshade (Solanum nigrum) accumulates acylglucoses and acylinositols in the same tissue. Detailed in vitro biochemistry - cross validated by in vivo virus induced gene silencing - revealed two unique features of the four-step acylglucose biosynthetic pathway: a trichome-expressed, neofunctionalized invertase-like enzyme, SnASFF1, converts BAHD-produced acylsucroses to acylglucoses, which in turn are substrates for the first-reported acylglucose acyltransferase, SnAGAT1. This biosynthetic pathway evolved independently from that recently described in the wild tomato S. pennellii, reinforcing that acylsugar biosynthesis is evolutionarily dynamic with independent examples of primary metabolic enzyme cooption and additional variation in BAHD acyltransferases. TeaserAnalysis of plant protective surface hair chemistry revealed evolutionary mechanisms leading to metabolic innovation.

plant biology↗

bioRxiv2021

Metabolic model of nitrogen-fixing obligate aerobe Azotobacter vinelandii demonstrates adaptation to oxygen concentration and metal availability

There is considerable interest in promoting biological nitrogen fixation as a mechanism to reduce the inputs of nitrogenous fertilizers in agriculture, a problem of agronomic, economic, and environmental importance. For the potential impact of biological nitrogen fixation in agriculture to be realized, there are considerable fundamental knowledge gaps that need to be addressed. Biological nitrogen fixation or the reduction of N2 to NH3 is catalyzed by nitrogenase which requires a large amount of energy in the form of ATP and low potential electrons. Nitrogen-fixing organisms that respire aerobically have an advantage in meeting the energy demands of biological nitrogen fixation but face challenges of protecting nitrogenase from inactivation in the presence of oxygen. Here, we have constructed a genome-scale metabolic model of the aerobic metabolism of nitrogen-fixing bacteria Azotobacter vinelandii, which uses a complex electron transport system, termed respiratory protection, to consume oxygen at a high rate keeping intracellular conditions microaerobic. Our model accurately determines growth rate under high oxygen and high substrate concentration conditions, demonstrating the large flux of energy directed to respiratory protection. While respiratory protection mechanisms compensate the energy balance in high oxygen conditions, it does not account for all substrate intake, leading to increased maintenance rates. We have also shown how A. vinelandii can adapt under different oxygen concentrations and metal availability by rearranging flux through the electron transport system. Accurately determining the energy balance in a genome-scale metabolic model is required for future engineering approaches. ImportanceThe worlds dependence on industrially produced nitrogenous fertilizers has created a dichotomy of issues. Some parts of the globe lack access to fertilizers and associated poor crop yields, significantly limiting nutrition, contributing to disease and starvation. In contrast, in other parts of the world, abundant nitrogenous fertilizers and associated overuse result in compromised soil quality and downstream environmental issues. There is considerable interest in expanding the impacts of biological nitrogen fixation to promote improved crop yields in places struggling with access to industrial fertilizers and reducing fertilizers inputs in areas where overuse is resulting in the degradation of soil health and other environmental problems. A more robust and fundamental understanding of biological nitrogen fixations biochemistry and microbial physiology will enable strategies to promote new and more robust associations between nitrogen-fixing microorganisms and crop plants.

microbiology↗

bioRxiv2021

Damaged goods? Evaluating the impact of X-ray damage on conformational heterogeneity in room temperature and cryo-cooled protein crystals

X-ray crystallography is a cornerstone of biochemistry. Traditional freezing of protein crystals to cryo-temperatures mitigates X-ray damage and facilitates crystal handling but provides an incomplete window into the ensemble of conformations at the heart of protein function and energetics. Room temperature (RT) X-ray crystallography provides more extensive ensemble information, and recent developments allow conformational heterogeneity, the experimental manifestation of ensembles, to be extracted from single crystal data. However, high sensitivity to X-ray damage at RT raises concerns about data reliability. To systematically address this critical question, we obtained increasingly X-ray-damaged high-resolution datasets (1.02-1.52 [A]) from single thaumatin, proteinase K, and lysozyme crystals. Heterogeneity analyses indicated a modest increase in conformational disorder with X-ray damage. Nevertheless, these effects do not alter overall conclusions and can be minimized by limiting the extent of X-ray damage or eliminated by extrapolation to obtain heterogeneity information free from X-ray damage effects. To compare these effects to damage at cryo temperature and to learn more about damage and heterogeneity in cryo-cooled crystals, we carried out an analogous analysis of increasingly damaged proteinase K cryo datasets (0.9-1.16 [A]). We found X-ray damage-associated heterogeneity changes that were not observed at RT. This observation and the scarcity of reported X-ray doses and damage extent render it difficult to distinguish real from artifactual conformations, including those occurring as a function of temperature. The ability to aquire reliable heterogeneity information from single crystals at RT provides strong motivation for further development and routine implementation of RT X-ray crystallography to obtain conformational ensemble information. SignificanceX-ray crystallography has allowed biologists to visualize the proteins that carry out complex biological processes and has provided powerful insights into how these molecules function. Our next level of understanding requires information about the ensemble of conformations that is at the heart of protein function and energetics. Prior results have shown that room temperature (RT) X-ray crystallography provides extensive ensemble information, but are subject to extenstive X-ray damage. We found that ensemble information with little or no effects from X-ray damage can be collected at RT. We also found that damage effects may be more prevalent than recognized in structures obtained under current standard cryogenic conditions. RT X-ray crystallography can be routinely implemented to obtain needed information about conformational ensembles.

biophysics↗

bioRxiv2021

Mechanistic model-driven exometabolomic characterisation of human dopaminergic neuronal metabolism

Starting with a comprehensive generic reconstruction of human metabolism, we generated high-quality, constraint-based, genome-scale, cell-type and condition specific models of metabolism in human dopaminergic neurons, the cell type most vulnerable to degeneration in Parkinsons disease. They are a synthesis of extensive manual curation of the biochemical literature on neuronal metabolism, together with novel, quantitative, transcriptomic and targeted exometabolomic data from human stem cell-derived, midbrainspecific, dopaminergic neurons in vitro. Thermodynamic constraint-based modelling enabled qualitatively accurate and moderately quantitatively accurate prediction of dopaminergic neuronal metabolite exchange fluxes, including predicting the consequences of metabolic perturbations in a manner also consistent with literature on monogenic mitochondrial Parkinsons disease. These dopaminergic neurons models provide a foundation for a quantitative systems biochemistry approach to metabolic dysfunction in Parkinsons disease. Moreover, the plethora of novel mathematical and computational approaches required to develop them are generalisable to study any other disease associated with metabolic dysfunction.

systems biology↗

bioRxiv2021

Nonclinical Safety and Immunogenicity of an rVSV-ΔG-SARS-CoV-2-S vaccine in mice, hamsters, rabbits and pigs

rVSV-{Delta}G-SARS-CoV-2-S is a clinical stage (Phase 2) replication competent recombinant vaccine against SARS-CoV-2. Nonclinical safety, immunogenicity and efficacy studies were conducted in 4 animal species, using multiple dose levels (up to 108 PFU/animal) and various dosing regimens. There were no treatment related mortalities in any study, or any noticeable clinical signs. Compared to unvaccinated controls, hematology and biochemistry parameters were unremarkable and no adverse histopathological findings gave cause for safety concern in any of the studies. There was no viral shedding in urine, nor viral RNA detected in whole blood or serum samples 7 days post vaccination. The rVSV-{Delta}G-SARS-CoV-2-S vaccine immune response gave rise to neutralizing antibodies, cellular immune response, and increased lymphocytic cellularity in the spleen germinal centers and regional lymph node. No evidence for neurovirulence was found in C57BL/6 immune competent mice or in highly sensitive IFNAR KO mice. Vaccine virus replication and distribution in K18 hACE2 transgenic mice showed a gradual clearance from the vaccination site with no vaccine virus recovered from the lungs. The rVSV-{Delta}G-SARS-CoV-2-S vaccine was well tolerated locally and systemically and elicited an effective immunogenic response up to the highest dose tested, supporting further clinical development.

pharmacology and toxicology↗

bioRxiv2021

Susceptibilities of human ACE2 genetic variants in coronavirus infection

The COVID-19 pandemic, caused by SARS-CoV-2, has resulted in more than 1603 million cases worldwide and 3.4 million deaths (as of May 2021), with varying incidences and death rates among regions/ethnicities. Human genetic variation can affect disease progression and outcome, but little is known about genetic risk factors for SARS-CoV-2 infection. The coronaviruses SARS-CoV, SARS-CoV-2 and HCoV-NL63 all utilize the human protein angiotensin-converting enzyme 2 (ACE2) as the receptor to enter cells. We hypothesized that the genetic variability in ACE2 may contribute to the variable clinical outcomes of COVID-19. To test this hypothesis, we first conducted an in silico investigation of single-nucleotide polymorphisms (SNPs) in the coding region of ACE2 gene. We then applied an integrated approach of genetics, biochemistry and virology to explore the capacity of select ACE2 variants to bind coronavirus spike protein and mediate viral entry. We identified the ACE2 D355N variant that restricts the spike protein-ACE2 interaction and consequently limits infection both in vitro and in vivo. In conclusion, ACE2 polymorphisms could modulate susceptibility to SARS-CoV-2, which may lead to variable disease severity.

microbiology↗

bioRxiv2021

An untargeted metabolomic approach to identify antiviral defense mechanisms in memory leukocytes secreting in vitro IgG anti-SARS-Cov-2

Available knowledge shows that individuals infected by SARS-CoV-2 undergo an altered metabolic state in multiple organs. Metabolic activities are directly involved in modulating the immune responses against infectious diseases, yet our understanding remains limited on how host metabolism relates with inflammatory responses. To better elucidate the underlying biochemistry of leukocytes response, we focused our analysis on the possible relationships between SARS-CoV-2 post-infection stages and distinct metabolic pathways. Indeed, in cultures of peripheral blood mononuclear cells (PBMC, n=48) obtained 60-90 days after infection and showing in vitro IgG antibody memory for spike-S1 antigen (n=19), we observed a significant altered metabolism of tryptophan and urea cycle pathways. This work for the first time identifies metabolic routes in cell metabolism possibly related to later stages of immune defense against SARS-Cov-2 infection, namely when circulating antibodies may be absent, but an antibody memory is present. The results suggest a reprogramming of leukocyte metabolism after viral pathogenesis through activation of specific amino acid pathways possibly related to protective immunity against SARS-CoV-2.

cell biology↗

bioRxiv2021

Microbial association networks in cheese: a meta-analysis

Interactions among starter and non-starter microorganisms (starter bacteria, naturally occurring or intentionally added non-starter bacteria, yeasts and filamentous fungi, spoilage and pathogenic microorganisms and, finally bacteriophages and even arthropods) deeply affect the dynamics of cheese microbial communities and, as a consequence, multiple aspects of cheese quality, from metabolites affecting the taste, aroma and flavour, to body, texture and colour. Understanding and exploiting microbial interactions is therefore key to managing cheese quality. This is true for the simplest systems (fresh cheeses produced from pasteurized milk using defined starters composed solely of Lactic Acid Bacteria) and the more so for complex, dynamic systems, like surface ripened cheese produced from raw milk, in which a dynamic succession of diverse microorganisms is essential for obtained the desired combination of sensory properties while guaranteeing safety. Positive (commensalism, protocooperation) and negative (competition, amensalism, predation and parasitism) among members of the cheese biota have been reviewed multiple times. Although the complex, multidimensional datasets generated by multi-omic approaches to cheese microbiology and biochemistry are ideally suited for the representation of biotic and metabolic interactions as networks, network science concepts and approaches are rarely applied to cheese microbiology. In this review we first illustrate concepts relevant to the description of microbial interaction networks using network science concepts. Then, we briefly review methods used for the inference and analysis of microbial association networks and their potential use in the interpretation of the cheese interactome. Since these methods can only be used for mining microbial associations, a review of the experimental methods used to confirm the nature of microbial interactions among cheese microbes. Finally, we demonstrate the potential of microbial association network inference by mining metataxonomic data stored in the public database DairyFMBN, a specialized version of FoodMicrobionet which collates data on 74 metataxonomic studies on dairy products. Microbial association networks were inferred from 34 studies on cheese with up to 4 different methods and the results discussed to evaluate several aspects (choice of method, level of taxonomic resolution for the analysis, network, node and edge properties) which provide insight on the usefulness of this approach as explorative tool in the detection of microbial interactions in cheese. HighlightsO_LIApproaches for inference of association networks from metataxonomic data were reviewed C_LIO_LIA metastudy on association networks in cheese was carried out using 34 recent studies C_LIO_LIInference method and taxonomic resolution should be chosen carefully C_LIO_LISPIEC-EASI may be used as a conservative method for microbial association inference C_LIO_LIEdge and node properties support the formulation of testable hypotheses for microbial interactions C_LI

microbiology↗

bioRxiv2021

PEGR: a flexible management platform for reproducible epigenomic and genomic research

Reproducibility is a significant challenge in (epi)genomic research due to the complexity of experiments composed of traditional biochemistry and informatics. Recent advances have exacerbated this challenge as high-throughput sequencing data is generated at an unprecedented pace. Here we report on our development of a Platform for Epi-Genomic Research (PEGR), a web-based project management platform that tracks and quality controls experiments from conception to publication-ready figures, compatible with multiple assays and bioinformatic pipelines. It supports rigor and reproducibility for biochemists working at the wet bench, while continuing to fully support reproducibility and reliability for bioinformaticians through integration with the Galaxy platform.

bioinformatics↗

bioRxiv2021

Modular assembly of dynamic models in systems biology

It is widely acknowledged that the construction of large-scale dynamic models in systems biology requires complex modelling problems to be broken up into more manageable pieces. To this end, both modelling and software frameworks are required to enable modular modelling. While there has been consistent progress in the development of software tools to enhance model reusability, there has been a relative lack of consideration for how underlying biophysical principles can be applied to this space. Bond graphs combine the aspects of both modularity and physics-based modelling. In this paper, we argue that bond graphs are compatible with recent developments in modularity and abstraction in systems biology, and are thus a desirable framework for constructing large-scale models. We use two examples to illustrate the utility of bond graphs in this context: a model of a mitogen-activated protein kinase (MAPK) cascade to illustrate the reusability of modules and a model of glycolysis to illustrate the ability to modify the model granularity. Author summaryThe biochemistry within a cell is complex, being composed of numerous biomolecules and reactions. In order to develop fully detailed mathematical models of cells, smaller submodels need to be constructed and connected together. Software and standards can assist in this endeavour, but challenges remain in ensuring submodels are both consistent with each other and consistent with the fundamental conservation laws of physics. In this paper, we propose a new approach using bond graphs from engineering. In this approach, connections between models are defined using physical conservation laws. We show that this approach is compatible with current software approaches in the field, and can therefore be readily used to incorporate physical consistency into existing model integration methodologies. We illustrate the utility of this approach in streamlining the development of models for a signalling network (the MAPK cascade) and a metabolic network (the glycolysis pathway). The advantage of this approach is that models can be developed in a scalable manner while also ensuring consistency with the laws of physics, enhancing the range of data available to train models. This approach can be used to quickly construct detailed and accurate models of cells, facilitating future advances in biotechnology and personalised medicine.

systems biology↗

bioRxiv2021

1,25(OH)2D3 attenuates hepatic insulin resistance in rats with streptozotocin -induced diabetes via antioxidation

AimThis study was conducted to explore the mechanism by which 1,25(OH)2D3 ameliorates hepatic insulin resistance in rats with streptozotocin (STZ)-induced type 2 diabetes. MethodsSprague Dawley (SD) rats were randomly divided into five groups: normal, control, and 1,25(OH)2D3 administered at 0.075, 0.15, and 0.3 g/kg/d. Tissue and blood samples were obtained from all five groups after 4 and 12 weeks of treatment. Morphological changes in the livers were observed with HE staining. The levels of fasting blood glucose (FBG), insulin, and lipids were determined using an automatic biochemistry analyzer. The levels of hepatic glycogen, superoxide dismutase (SOD), malondialdehyde (MDA), aspartate aminotransferase (AST), and alanine aminotransferase (ALT) were analyzed by spectrophotometry. Protein expression of AKT and GLUT2 was examined by western blotting. Results1,25(OH)2D3 treatment reduced the FBG content as well as hepatic MDA, AST, and ALT levels, improved the activity of SOD, and enhanced the expression of GLUT2 and AKT in diabetic rats. These findings suggest that 1,25(OH)2D3, exerts antioxidant effects and ameliorates insulin resistance in the liver. Conclusion1,25(OH)2D3 is a potential agent that could be used in the treatment of insulin resistance in the liver.

pharmacology and toxicology↗

bioRxiv2021

Integrative analysis of the salt stress response in cyanobacteria

Microorganisms evolved specific acclimation strategies to thrive in environments of high or fluctuating salinities. Here, salt acclimation in the model cyanobacterium Synechocystis sp. PCC 6803 was analyzed by integrating transcriptomic, proteomic and metabolomic data. A dynamic reorganization of the transcriptome and proteome occurred during the first hours after salt shock, e.g. involving the upregulation of genes to activate compatible solute biochemistry balancing osmotic pressure. The massive accumulation of glucosylglycerol then had a measurable impact on the overall carbon and nitrogen metabolism. In addition, we observed the coordinated induction of putative regulatory RNAs and of several proteins known for their involvement in other stress responses. Overall, salt-induced changes in the proteome and transcriptome showed good correlations, especially among the stably up-regulated proteins and their transcripts. We define an extended salt stimulon comprising proteins directly or indirectly related to compatible solute metabolism, ion and water movements, and a distinct set of regulatory RNAs involved in post-transcriptional regulation. Our comprehensive data set provides the basis for engineering cyanobacterial salt tolerance and to further understand its regulation.

systems biology↗