SwePub - sökning: WFRF:(Zelezniak Aleksej 1984)

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1.	Alam, M. T., et al. (författare) The self-inhibitory nature of metabolic networks and its alleviation through compartmentalization 2017 Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 8, s. Article no 16018- Tidskriftsartikel (refereegranskat)abstract Metabolites can inhibit the enzymes that generate them. To explore the general nature of metabolic self-inhibition, we surveyed enzymological data accrued from a century of experimentation and generated a genome-scale enzyme-inhibition network. Enzyme inhibition is often driven by essential metabolites, affects the majority of biochemical processes, and is executed by a structured network whose topological organization is reflecting chemical similarities that exist between metabolites. Most inhibitory interactions are competitive, emerge in the close neighbourhood of the inhibited enzymes, and result from structural similarities between substrate and inhibitors. Structural constraints also explain one-third of allosteric inhibitors, a finding rationalized by crystallographic analysis of allosterically inhibited L-lactate dehydrogenase. Our findings suggest that the primary cause of metabolic enzyme inhibition is not the evolution of regulatory metabolite-enzyme interactions, but a finite structural diversity prevalent within the metabolome. In eukaryotes, compartmentalization minimizes inevitable enzyme inhibition and alleviates constraints that self-inhibition places on metabolism.
2.	Buric, Filip, 1988, et al. (författare) Parallel Factor Analysis Enables Quantification and Identification of Highly Convolved Data-Independent-Acquired Protein Spectra 2020 Ingår i: Patterns. - : Elsevier BV. - 2666-3899. ; 1:9 Tidskriftsartikel (refereegranskat)abstract The latest high-throughput mass spectrometry-based technologies can record virtually all molecules from complex biological samples, providing a holistic picture of proteomes in cells and tissues and enabling an evaluation of the overall status of a person's health. However, current best practices are still only scratching the surface of the wealth of available information obtained from the massive proteome datasets, and efficient novel data-driven strategies are needed. Powered by advances in GPU hardware and open-source machine-learning frameworks, we developed a data-driven approach, CANDIA, which disassembles highly complex proteomics data into the elementary molecular signatures of the proteins in biological samples. Our work provides a performant and adaptable solution that complements existing mass spectrometry techniques. As the central mathematical methods are generic, other scientific fields that are dealing with highly convolved datasets will benefit from this work.
3.	Cámara, Elena, 1985, et al. (författare) Data mining of Saccharomyces cerevisiae mutants engineered for increased tolerance towards inhibitors in lignocellulosic hydrolysates 2022 Ingår i: Biotechnology Advances. - : Elsevier BV. - 0734-9750. ; 57 Forskningsöversikt (refereegranskat)abstract The use of renewable plant biomass, lignocellulose, to produce biofuels and biochemicals using microbial cell factories plays a fundamental role in the future bioeconomy. The development of cell factories capable of efficiently fermenting complex biomass streams will improve the cost-effectiveness of microbial conversion processes. At present, inhibitory compounds found in hydrolysates of lignocellulosic biomass substantially influence the performance of a cell factory and the economic feasibility of lignocellulosic biofuels and chemicals. Here, we present and statistically analyze data on Saccharomyces cerevisiae mutants engineered for altered tolerance towards the most common inhibitors found in lignocellulosic hydrolysates: acetic acid, formic acid, furans, and phenolic compounds. We collected data from 7971 experiments including single overexpression or deletion of 3955 unique genes. The mutants included in the analysis had been shown to display increased or decreased tolerance to individual inhibitors or combinations of inhibitors found in lignocellulosic hydrolysates. Moreover, the data included mutants grown on synthetic hydrolysates, in which inhibitors were added at concentrations that mimicked those of lignocellulosic hydrolysates. Genetic engineering aimed at improving inhibitor or hydrolysate tolerance was shown to alter the specific growth rate or length of the lag phase, cell viability, and vitality, block fermentation, and decrease product yield. Different aspects of strain engineering aimed at improving hydrolysate tolerance, such as choice of strain and experimental set-up are discussed and put in relation to their biological relevance. While successful genetic engineering is often strain and condition dependent, we highlight the conserved role of regulators, transporters, and detoxifying enzymes in inhibitor tolerance. The compiled meta-analysis can guide future engineering attempts and aid the development of more efficient cell factories for the conversion of lignocellulosic biomass.
4.	Campbell, Kate, 1987, et al. (författare) Biochemical principles enabling metabolic cooperativity and phenotypic heterogeneity at the single cell level 2018 Ingår i: Current Opinion in Systems Biology. - : Elsevier BV. - 2452-3100. ; 8, s. 97-108 Forskningsöversikt (refereegranskat)abstract All biosynthetically active cells release metabolites, in part due to membrane leakage and cell lysis, but also in part due to overflow metabolism and ATP-dependent membrane export. At the same time, cells are adapted to sense and take up extracellular nutrients when available, to minimize the number of biochemical reactions that have to operate within a cell in parallel, and ultimately, to gain metabolic efficiency and biomass. Within colonies, biofilms or tissues, the co-occurrence of metabolite export and import enables the sharing of metabolites as well as metabolic specialization of single cells. In this review we discuss emerging biochemical concepts that give reasoning for why cells overproduce and release metabolites, and how these form the foundations for cooperative metabolite exchange activity between cells. We place particular emphasis on discussing the role of overflow metabolism in cells that exhibit either the Warburg or Crabtree effect. Furthermore, we discuss the profound physiological changes that cells undergo when their metabolism switches from metabolite synthesis to uptake, providing an explanation why metabolic specialization results in non-genotypic heterogeneity at the single cell level.
5.	Cao, Zhejian, 1991, et al. (författare) Synthesis of Metal-Organic Frameworks through Enzymatically Recycled Polyethylene Terephthalate 2023 Ingår i: ACS Sustainable Chemistry & Engineering. - 2168-0485. ; 11:43, s. 15506-15512 Tidskriftsartikel (refereegranskat)abstract Polyethylene terephthalate (PET) as one of the most produced plastics contributes to global waste pollution. Upcycling PET into value-added products therefore is of environmental and economic interest. Terephthalic acid (TPA), the monomer of PET, is a common linker for metal-organic framework (MOF) synthesis; thus, PET-to-MOF upcycling raises much research attention. However, conventional PET-to-MOF upcycling often requires PET depolymerization with strong acids or bases and high temperatures, which can lead to environmental and energy penalties. As an alternative, PETase offers a sustainable approach to depolymerizing PET under mesophilic and mild pH conditions. Here we report UiO-66, MOF-5, and MIL-101 syntheses using enzymatically recycled TPA as linkers. The enzymatically recycled TPA demonstrated low impurity, and the obtained MOFs possessed comparable crystallinity, thermal stability, and surface area. These results reveal the feasibility of MOF synthesis by using enzymatically recycled PET.
6.	Correia-Melo, Clara, et al. (författare) Cell-cell metabolite exchange creates a pro-survival metabolic environment that extends lifespan 2023 Ingår i: Cell. - : Elsevier BV. - 0092-8674 .- 1097-4172. ; 186:1, s. 63-79.e21 Tidskriftsartikel (refereegranskat)abstract Metabolism is deeply intertwined with aging. Effects of metabolic interventions on aging have been explained with intracellular metabolism, growth control, and signaling. Studying chronological aging in yeast, we reveal a so far overlooked metabolic property that influences aging via the exchange of metabolites. We observed that metabolites exported by young cells are re-imported by chronologically aging cells, resulting in cross-generational metabolic interactions. Then, we used self-establishing metabolically cooperating communities (SeMeCo) as a tool to increase metabolite exchange and observed significant lifespan extensions. The longevity of the SeMeCo was attributable to metabolic reconfigurations in methionine consumer cells. These obtained a more glycolytic metabolism and increased the export of protective metabolites that in turn extended the lifespan of cells that supplied them with methionine. Our results establish metabolite exchange interactions as a determinant of cellular aging and show that metabolically cooperating cells can shape the metabolic environment to extend their lifespan.
7.	Demichev, Vadim, et al. (författare) A time-resolved proteomic and prognostic map of COVID-19 2021 Ingår i: Cell Systems. - : Elsevier BV. - 2405-4712 .- 2405-4720. ; 12:8, s. 780-794.e7 Tidskriftsartikel (refereegranskat)abstract COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease.
8.	Gossmann, Toni I., et al. (författare) Ice-Age Climate Adaptations Trap the Alpine Marmot in a State of Low Genetic Diversity 2019 Ingår i: Current Biology. - : Elsevier BV. - 0960-9822 .- 1879-0445. ; 29:10, s. 1712-1720.e7 Tidskriftsartikel (refereegranskat)abstract © 2019 The Author(s) Some species responded successfully to prehistoric changes in climate [1, 2], while others failed to adapt and became extinct [3]. The factors that determine successful climate adaptation remain poorly understood. We constructed a reference genome and studied physiological adaptations in the Alpine marmot (Marmota marmota), a large ground-dwelling squirrel exquisitely adapted to the “ice-age” climate of the Pleistocene steppe [4, 5]. Since the disappearance of this habitat, the rodent persists in large numbers in the high-altitude Alpine meadow [6, 7]. Genome and metabolome showed evidence of adaptation consistent with cold climate, affecting white adipose tissue. Conversely, however, we found that the Alpine marmot has levels of genetic variation that are among the lowest for mammals, such that deleterious mutations are less effectively purged. Our data rule out typical explanations for low diversity, such as high levels of consanguineous mating, or a very recent bottleneck. Instead, ancient demographic reconstruction revealed that genetic diversity was lost during the climate shifts of the Pleistocene and has not recovered, despite the current high population size. We attribute this slow recovery to the marmot's adaptive life history. The case of the Alpine marmot reveals a complicated relationship between climatic changes, genetic diversity, and conservation status. It shows that species of extremely low genetic diversity can be very successful and persist over thousands of years, but also that climate-adapted life history can trap a species in a persistent state of low genetic diversity. Despite being highly abundant and well adapted, Gossmann et al. report that the Alpine marmot is among the least genetically diverse animal species. The low diversity is found to be the consequence of consecutive, climate-related events, including long-term extreme niche adaptation, that also greatly retarded the recovery of its genetic diversity.
9.	Haas, Robert, et al. (författare) Designing and interpreting 'multi-omic' experiments that may change our understanding of biology 2017 Ingår i: Current Opinion in Systems Biology. - : Elsevier BV. - 2452-3100. ; 6, s. 37-45 Forskningsöversikt (refereegranskat)abstract Most biological mechanisms involve more than one type of biomolecule, and hence operate not solely at the level of either genome, transcriptome, proteome, metabolome or ionome. Datasets resulting from single-omic analysis are rapidly increasing in throughput and quality, rendering multi-omic studies feasible. These should offer a comprehensive, structured and interactive overview of a biological mechanism. However, combining single-omic datasets in a meaningful manner has so far proved challenging, and the discovery of new biological information lags behind expectation. One reason is that experiments conducted in different laboratories can typically not to be combined without restriction. Second, the interpretation of multi-omic datasets represents a significant challenge by nature, as the biological datasets are heterogeneous not only for technical, but also for biological, chemical, and physical reasons. Here, multi-layer network theory and methods of artificial intelligence might contribute to solve these problems. For the efficient application of machine learning however, biological datasets need to become more systematic, more precise - and much larger. We conclude our review with basic guidelines for the successful set-up of a multi-omic experiment.
10.	Hakansson, Samuel, et al. (författare) Optimizing epilepsy treatment: big data from population-wide registers suggest room for improvement in drug selection 2021 Ingår i: Epilepsia. - 1528-1167 .- 0013-9580. ; 62, s. 168-168 Konferensbidrag (övrigt vetenskapligt/konstnärligt)
11.	Håkansson, Samuel, 1996, et al. (författare) Potential for improved retention rate by personalized antiseizure medication selection: A register-based analysis 2021 Ingår i: Epilepsia. - : Wiley. - 0013-9580 .- 1528-1167. ; 62:9, s. 2123-2132 Tidskriftsartikel (refereegranskat)abstract Objective The first antiseizure medication (ASM) is ineffective or intolerable in 50% of epilepsy cases. Selection between more than 25 available ASMs is guided by epilepsy factors, but also age and comorbidities. Randomized evidence for particular patient subgroups is seldom available. We asked whether register data could be used for retention rate calculations based on demographics, comorbidities, and ASM history, and quantified the potential improvement in retention rates of the first ASM in several large epilepsy cohorts. We also describe retention rates in patients with epilepsy after traumatic brain injury and dementia, patient groups with little available evidence. Methods We used medical, demographic, and drug prescription data from epilepsy cohorts from comprehensive Swedish registers, containing 6380 observations. By analyzing 381 840 prescriptions, we studied retention rates of first- and second-line ASMs for patients with epilepsy in multiple sclerosis (MS), brain infection, dementia, traumatic brain injury, or stroke. The rank of retention rates of ASMs was validated by comparison to published randomized control trials. We identified the optimal stratification for each brain disease, and quantified the potential improvement if all patients had received the optimal ASM. Results Using optimal stratification for each brain disease, the potential improvement in retention rate (percentage points) was MS, 20%; brain infection, 21%; dementia, 14%; trauma, 21%; and stroke, 14%. In epilepsy after trauma, levetiracetam had the highest retention rate at 80% (95% confidence interval [CI] = 65-89), exceeding that of the most commonly prescribed ASM, carbamazepine (p = .04). In epilepsy after dementia, lamotrigine (77%, 95% CI = 68-84) and levetiracetam (74%, 95% CI = 68-79) had higher retention rates than carbamazepine (p = .006 and p = .01, respectively). Significance We conclude that personalized ASM selection could improve retention rates and that national registers have potential as big data sources for personalized medicine in epilepsy.
12.	Johnson, Sean R., et al. (författare) Computational scoring and experimental evaluation of enzymes generated by neural networks 2024 Ingår i: Nature Biotechnology. - 1087-0156 .- 1546-1696. ; In Press Tidskriftsartikel (refereegranskat)abstract In recent years, generative protein sequence models have been developed to sample novel sequences. However, predicting whether generated proteins will fold and function remains challenging. We evaluate a set of 20 diverse computational metrics to assess the quality of enzyme sequences produced by three contrasting generative models: ancestral sequence reconstruction, a generative adversarial network and a protein language model. Focusing on two enzyme families, we expressed and purified over 500 natural and generated sequences with 70–90% identity to the most similar natural sequences to benchmark computational metrics for predicting in vitro enzyme activity. Over three rounds of experiments, we developed a computational filter that improved the rate of experimental success by 50–150%. The proposed metrics and models will drive protein engineering research by serving as a benchmark for generative protein sequence models and helping to select active variants for experimental testing.
13.	Li, Gang, 1991, et al. (författare) Bayesian genome scale modelling identifies thermal determinants of yeast metabolism 2021 Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 12:1 Tidskriftsartikel (refereegranskat)abstract The molecular basis of how temperature affects cell metabolism has been a long-standing question in biology, where the main obstacles are the lack of high-quality data and methods to associate temperature effects on the function of individual proteins as well as to combine them at a systems level. Here we develop and apply a Bayesian modeling approach to resolve the temperature effects in genome scale metabolic models (GEM). The approach minimizes uncertainties in enzymatic thermal parameters and greatly improves the predictive strength of the GEMs. The resulting temperature constrained yeast GEM uncovers enzymes that limit growth at superoptimal temperatures, and squalene epoxidase (ERG1) is predicted to be the most rate limiting. By replacing this single key enzyme with an ortholog from a thermotolerant yeast strain, we obtain a thermotolerant strain that outgrows the wild type, demonstrating the critical role of sterol metabolism in yeast thermosensitivity. Therefore, apart from identifying thermal determinants of cell metabolism and enabling the design of thermotolerant strains, our Bayesian GEM approach facilitates modelling of complex biological systems in the absence of high-quality data and therefore shows promise for becoming a standard tool for genome scale modeling.
14.	Li, Gang, 1991, et al. (författare) Learning deep representations of enzyme thermal adaptation 2022 Ingår i: Protein Science. - : Wiley. - 1469-896X .- 0961-8368. ; 31:12 Tidskriftsartikel (refereegranskat)abstract Temperature is a fundamental environmental factor that shapes the evolution of organisms. Learning thermal determinants of protein sequences in evolution thus has profound significance for basic biology, drug discovery, and protein engineering. Here, we use a data set of over 3 million BRENDA enzymes labeled with optimal growth temperatures (OGTs) of their source organisms to train a deep neural network model (DeepET). The protein-temperature representations learned by DeepET provide a temperature-related statistical summary of protein sequences and capture structural properties that affect thermal stability. For prediction of enzyme optimal catalytic temperatures and protein melting temperatures via a transfer learning approach, our DeepET model outperforms classical regression models trained on rationally designed features and other deep-learning-based representations. DeepET thus holds promise for understanding enzyme thermal adaptation and guiding the engineering of thermostable enzymes.
15.	Li, Gang, 1991, et al. (författare) Performance of Regression Models as a Function of Experiment Noise 2021 Ingår i: Bioinformatics and Biology Insights. - : SAGE Publications. - 1177-9322. ; 15 Tidskriftsartikel (refereegranskat)abstract Background: A challenge in developing machine learning regression models is that it is difficult to know whether maximal performance has been reached on the test dataset, or whether further model improvement is possible. In biology, this problem is particularly pronounced as sample labels (response variables) are typically obtained through experiments and therefore have experiment noise associated with them. Such label noise puts a fundamental limit to the metrics of performance attainable by regression models on the test dataset. Results: We address this challenge by deriving an expected upper bound for the coefficient of determination (R2) for regression models when tested on the holdout dataset. This upper bound depends only on the noise associated with the response variable in a dataset as well as its variance. The upper bound estimate was validated via Monte Carlo simulations and then used as a tool to bootstrap performance of regression models trained on biological datasets, including protein sequence data, transcriptomic data, and genomic data. Conclusions: The new method for estimating upper bounds for model performance on test data should aid researchers in developing ML regression models that reach their maximum potential. Although we study biological datasets in this work, the new upper bound estimates will hold true for regression models from any research field or application area where response variables have associated noise.
16.	Messner, Christoph B., et al. (författare) Ultra-fast proteomics with Scanning SWATH 2021 Ingår i: Nature Biotechnology. - : Springer Science and Business Media LLC. - 1087-0156 .- 1546-1696. ; 39:7, s. 846-854 Tidskriftsartikel (refereegranskat)abstract Accurate quantification of the proteome remains challenging for large sample series and longitudinal experiments. We report a data-independent acquisition method, Scanning SWATH, that accelerates mass spectrometric (MS) duty cycles, yielding quantitative proteomes in combination with short gradients and high-flow (800 µl min ) chromatography. Exploiting a continuous movement of the precursor isolation window to assign precursor masses to tandem mass spectrometry (MS/MS) fragment traces, Scanning SWATH increases precursor identifications by ~70% compared to conventional data-independent acquisition (DIA) methods on 0.5–5-min chromatographic gradients. We demonstrate the application of ultra-fast proteomics in drug mode-of-action screening and plasma proteomics. Scanning SWATH proteomes capture the mode of action of fungistatic azoles and statins. Moreover, we confirm 43 and identify 11 new plasma proteome biomarkers of COVID-19 severity, advancing patient classification and biomarker discovery. Thus, our results demonstrate a substantial acceleration and increased depth in fast proteomic experiments that facilitate proteomic drug screens and clinical studies. –1
17.	Messner, Christoph B., et al. (författare) Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection 2020 Ingår i: Cell Systems. - : Elsevier BV. - 2405-4712 .- 2405-4720. ; 11:1, s. 11-24.E4 Tidskriftsartikel (refereegranskat)abstract The COVID-19 pandemic is an unprecedented global challenge, and point-of-care diagnostic classifiers are urgently required. Here, we present a platform for ultra-high-throughput serum and plasma proteomics that builds on ISO13485 standardization to facilitate simple implementation in regulated clinical laboratories. Our low-cost workflow handles up to 180 samples per day, enables high precision quantification, and reduces batch effects for large-scale and longitudinal studies. We use our platform on samples collected from a cohort of early hospitalized cases of the SARS-CoV-2 pandemic and identify 27 potential biomarkers that are differentially expressed depending on the WHO severity grade of COVID-19. They include complement factors, the coagulation system, inflammation modulators, and pro-inflammatory factors upstream and downstream of interleukin 6. All protocols and software for implementing our approach are freely available. In total, this work supports the development of routine proteomic assays to aid clinical decision making and generate hypotheses about potential COVID-19 therapeutic targets.
18.	Repecka, Donatas, et al. (författare) Expanding functional protein sequence spaces using generative adversarial networks 2021 Ingår i: Nature Machine Intelligence. - : Springer Science and Business Media LLC. - 2522-5839. ; 3:4, s. 324-333 Tidskriftsartikel (refereegranskat)abstract De novo protein design for catalysis of any desired chemical reaction is a long-standing goal in protein engineering because of the broad spectrum of technological, scientific and medical applications. However, mapping protein sequence to protein function is currently neither computationally nor experimentally tangible. Here, we develop ProteinGAN, a self-attention-based variant of the generative adversarial network that is able to ‘learn’ natural protein sequence diversity and enables the generation of functional protein sequences. ProteinGAN learns the evolutionary relationships of protein sequences directly from the complex multidimensional amino-acid sequence space and creates new, highly diverse sequence variants with natural-like physical properties. Using malate dehydrogenase (MDH) as a template enzyme, we show that 24% (13 out of 55 tested) of the ProteinGAN-generated and experimentally tested sequences are soluble and display MDH catalytic activity in the tested conditions in vitro, including a highly mutated variant of 106 amino-acid substitutions. ProteinGAN therefore demonstrates the potential of artificial intelligence to rapidly generate highly diverse functional proteins within the allowed biological constraints of the sequence space.
19.	Saghaleyni, Rasool, 1987, et al. (författare) Enhanced metabolism and negative regulation of ER stress support higher erythropoietin production in HEK293 cells 2022 Ingår i: Cell Reports. - : Elsevier BV. - 2211-1247. ; 39:11 Tidskriftsartikel (refereegranskat)abstract Recombinant protein production can cause severe stress on cellular metabolism, resulting in limited titer and product quality. To investigate cellular and metabolic characteristics associated with these limitations, we compare HEK293 clones producing either erythropoietin (EPO) (secretory) or GFP (non-secretory) protein at different rates. Transcriptomic and functional analyses indicate significantly higher metabolism and oxidative phosphorylation in EPO producers compared with parental and GFP cells. In addition, ribosomal genes exhibit specific expression patterns depending on the recombinant protein and the production rate. In a clone displaying a dramatically increased EPO secretion, we detect higher gene expression related to negative regulation of endoplasmic reticulum (ER) stress, including upregulation of ATF6B, which aids EPO production in a subset of clones by overexpression or small interfering RNA (siRNA) knockdown. Our results offer potential target pathways and genes for further development of the secretory power in mammalian cell factories.
20.	Sánchez, Benjamín José, 1988, et al. (författare) Benchmarking accuracy and precision of intensity-based absolute quantification of protein abundances in Saccharomyces cerevisiae 2021 Ingår i: Proteomics. - : Wiley. - 1615-9853 .- 1615-9861. ; 21:6 Tidskriftsartikel (refereegranskat)abstract Protein quantification via label-free mass spectrometry (MS) has become an increasingly popular method for predicting genome-wide absolute protein abundances. A known caveat of this approach, however, is the poor technical reproducibility, that is, how consistent predictions are when the same sample is measured repeatedly. Here, we measured proteomics data for Saccharomyces cerevisiae with both biological and inter-batch technical triplicates, to analyze both accuracy and precision of protein quantification via MS. Moreover, we analyzed how these metrics vary when applying different methods for converting MS intensities to absolute protein abundances. We demonstrate that our simple normalization and rescaling approach can perform as accurately, yet more precisely, than methods which rely on external standards. Additionally, we show that inter-batch reproducibility is worse than biological reproducibility for all evaluated methods. These results offer a new benchmark for assessing MS data quality for protein quantification, while also underscoring current limitations in this approach.
21.	Tramontano, Melanie, et al. (författare) Nutritional preferences of human gut bacteria reveal their metabolic idiosyncrasies 2018 Ingår i: Nature Microbiology. - : Springer Science and Business Media LLC. - 2058-5276. ; 3:4, s. 514-522 Tidskriftsartikel (refereegranskat)abstract Bacterial metabolism plays a fundamental role in gut microbiota ecology and host-microbiome interactions. Yet the metabolic capabilities of most gut bacteria have remained unknown. Here we report growth characteristics of 96 phylogenetically diverse gut bacterial strains across 4 rich and 15 defined media. The vast majority of strains (76) grow in at least one defined medium, enabling accurate assessment of their biosynthetic capabilities. These do not necessarily match phylogenetic similarity, thus indicating a complex evolution of nutritional preferences. We identify mucin utilizers and species inhibited by amino acids and short-chain fatty acids. Our analysis also uncovers media for in vitro studies wherein growth capacity correlates well with in vivo abundance. Further value of the underlying resource is demonstrated by correcting pathway gaps in available genome-scale metabolic models of gut microorganisms. Together, the media resource and the extracted knowledge on growth abilities widen experimental and computational access to the gut microbiota.
22.	Vernardis,, et al. (författare) The Impact of Acute Nutritional Interventions on the Plasma Proteome 2023 Ingår i: Journal of Clinical Endocrinology and Metabolism. - : The Endocrine Society. - 1945-7197 .- 0021-972X. ; 108:8, s. 2087-2098 Tidskriftsartikel (refereegranskat)abstract Context: Humans respond profoundly to changes in diet, while nutrition and environment have a great impact on population health. It is therefore important to deeply characterize the human nutritional responses. Objective: Endocrine parameters and the metabolome of human plasma are rapidly responding to acute nutritional interventions such as caloric restriction or a glucose challenge. It is less well understood whether the plasma proteome would be equally dynamic, and whether it could be a source of corresponding biomarkers. Methods: We used high-throughput mass spectrometry to determine changes in the plasma proteome of i) 10 healthy, young, male individuals in response to 2 days of acute caloric restriction followed by refeeding; ii) 200 individuals of the Ely epidemiological study before and after a glucose tolerance test at 4 time points (0, 30, 60, 120 minutes); and iii) 200 random individuals from the Generation Scotland study. We compared the proteomic changes detected with metabolome data and endocrine parameters. Results: Both caloric restriction and the glucose challenge substantially impacted the plasma proteome. Proteins responded across individuals or in an individual-specific manner. We identified nutrient-responsive plasma proteins that correlate with changes in the metabolome, as well as with endocrine parameters. In particular, our study highlights the role of apolipoprotein C1 (APOC1), a small, understudied apolipoprotein that was affected by caloric restriction and dominated the response to glucose consumption and differed in abundance between individuals with and without type 2 diabetes. Conclusion: Our study identifies APOC1 as a dominant nutritional responder in humans and highlights the interdependency of acute nutritional response proteins and the endocrine system.
23.	Vowinckel, Jakob, et al. (författare) Cost-effective generation of precise label-free quantitative proteomes in high-throughput by microLC and data-independent acquisition 2018 Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322 .- 2045-2322. ; 8:1 Tidskriftsartikel (refereegranskat)abstract Quantitative proteomics is key for basic research, but needs improvements to satisfy an increasing demand for large sample series in diagnostics, academia and industry. A switch from nanoflowrate to microflowrate chromatography can improve throughput and reduce costs. However, concerns about undersampling and coverage have so far hampered its broad application. We used a QTOF mass spectrometer of the penultimate generation (TripleTOF5600), converted a nanoLC system into a microflow platform, and adapted a SWATH regime for large sample series by implementing retention time-A nd batch correction strategies. From 3 μg to 5 μg of unfractionated tryptic digests that are obtained from proteomics-typical amounts of starting material, microLC-SWATH-MS quantifies up to 4000 human or 1750 yeast proteins in an hour or less. In the acquisition of 750 yeast proteomes, retention times varied between 2% and 5%, and quantified the typical peptide with 5-8% signal variation in replicates, and below 20% in samples acquired over a five-months period. Providing precise quantities without being dependent on the latest hardware, our study demonstrates that the combination of microflow chromatography and data-independent acquisition strategies has the potential to overcome current bottlenecks in academia and industry, enabling the cost-effective generation of precise quantitative proteomes in large scale.
24.	Zelezniak, Aleksej, 1984 (författare) From AI-driven synthetic biology to prediction of molecular phenotypes 2022 Ingår i: Molecular and Cellular Proteomics. - : Elsevier BV. - 1535-9476 .- 1535-9484. ; 21:8, s. S22-S22 Konferensbidrag (övrigt vetenskapligt/konstnärligt)
25.	Zelezniak, Aleksej, 1984, et al. (författare) Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts 2018 Ingår i: Cell Systems. - : Elsevier BV. - 2405-4712 .- 2405-4720. ; 7:3, s. 269-283 Tidskriftsartikel (refereegranskat)abstract A challenge in solving the genotype-to-phenotype relationship is to predict a cell's metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype. Predicting metabolomes from gene expression data is a key challenge in understanding the genotype-phenotype relationship. Studying the enzyme expression proteome in kinase knockouts, we reveal the importance of a so far overlooked metabolism-regulatory mechanism. Enzyme expression changes are impacting on metabolite levels through many changes acting in concert. We show that one can map regulatory enzyme expression patterns using machine learning and use them to predict the metabolome of kinase-deficient cells on the basis of their enzyme expression proteome. Our study quantifies the role of enzyme abundance in the regulation of metabolism and by doing so reveals the potential of machine learning in gaining understanding about complex metabolism regulation.
26.	Zelezniak, Aleksej, 1984 (författare) Plastic Feast For Bugs 2023 Ingår i: Chemistry and Industry. - : Wiley. - 0009-3068 .- 2047-6329. ; 87:2, s. 26-29 Tidskriftsartikel (refereegranskat)abstract Plastic waste generation has doubled over the past three decades, presenting huge environmental problems. But could plastic-eating organisms such as microbes and insect larvae hold the answer by converting our mountains of waste to useful biomass?
27.	Zorrilla, Francisco, 1994, et al. (författare) metaGEM: reconstruction of genome scale metabolic models directly from metagenomes 2021 Ingår i: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 49:21 Tidskriftsartikel (refereegranskat)abstract Metagenomic analyses of microbial communities have revealed a large degree of interspecies and intraspecies genetic diversity through the reconstruction of metagenome assembled genomes (MAGs). Yet, metabolic modeling efforts mainly rely on reference genomes as the starting point for reconstruction and simulation of genome scale metabolic models (GEMs), neglecting the immense intra- and inter-species diversity present in microbial communities. Here, we present metaGEM (https://github.com/franciscozo rrilla/metaGEM), an end-to-end pipeline enabling metabolic modeling of multi-species communities directly from metagenomes. The pipeline automates all steps from the extraction of context-specific prokaryotic GEMs from MAGs to community level flux balance analysis (FBA) simulations. To demonstrate the capabilities of metaGEM, we analyzed 483 samples spanning lab culture, human gut, plant-associated, soil, and ocean metagenomes, reconstructing over 14,000 GEMs. We show that GEMs reconstructed from metagenomes have fully represented metabolism comparable to isolated genomes. We demonstrate that metagenomic GEMs capture intraspecies metabolic diversity and identify potential differences in the progression of type 2 diabetes at the level of gut bacterial metabolic exchanges. Overall, metaGEM enables FBA-ready metabolic model reconstruction directly from metagenomes, provides a resource of metabolic models, and showcases community-level modeling of microbiomes associated with disease conditions allowing generation of mechanistic hypotheses.
28.	Zrimec, Jan, 1981, et al. (författare) Controlling gene expression with deep generative design of regulatory DNA 2022 Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 13:1, s. 5099- Tidskriftsartikel (refereegranskat)abstract Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Using mutagenesis typically requires screening sizable random DNA libraries, which limits the designs to span merely a short section of the promoter and restricts their control of gene expression. Here, we prototype a deep learning strategy based on generative adversarial networks (GAN) by learning directly from genomic and transcriptomic data. Our ExpressionGAN can traverse the entire regulatory sequence-expression landscape in a gene-specific manner, generating regulatory DNA with prespecified target mRNA levels spanning the whole gene regulatory structure including coding and adjacent non-coding regions. Despite high sequence divergence from natural DNA, in vivo measurements show that 57% of the highly-expressed synthetic sequences surpass the expression levels of highly-expressed natural controls. This demonstrates the applicability and relevance of deep generative design to expand our knowledge and control of gene expression regulation in any desired organism, condition or tissue.
29.	Zrimec, Jan, 1981, et al. (författare) Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure 2020 Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11:1 Tidskriftsartikel (refereegranskat)abstract Understanding the genetic regulatory code governing gene expression is an important challenge in molecular biology. However, how individual coding and non-coding regions of the gene regulatory structure interact and contribute to mRNA expression levels remains unclear. Here we apply deep learning on over 20,000 mRNA datasets to examine the genetic regulatory code controlling mRNA abundance in 7 model organisms ranging from bacteria to Human. In all organisms, we can predict mRNA abundance directly from DNA sequence, with up to 82% of the variation of transcript levels encoded in the gene regulatory structure. By searching for DNA regulatory motifs across the gene regulatory structure, we discover that motif interactions could explain the whole dynamic range of mRNA levels. Co-evolution across coding and non-coding regions suggests that it is not single motifs or regions, but the entire gene regulatory structure and specific combination of regulatory elements that define gene expression levels. Regulatory and coding regions of genes are shaped by evolution to control expression levels. Here, the authors use deep learning to identify rules controlling gene expression levels and suggest that all parts of the gene regulatory structure interact in this.
30.	Zrimec, Jan, 1981, et al. (författare) Learning the Regulatory Code of Gene Expression 2021 Ingår i: Frontiers in Molecular Biosciences. - : Frontiers Media SA. - 2296-889X. ; 8 Forskningsöversikt (refereegranskat)abstract Data-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well as mRNA and protein levels. Deep neural networks automatically learn informative sequence representations and interpreting them enables us to improve our understanding of the regulatory code governing gene expression. Here, we review the latest developments that apply shallow or deep learning to quantify molecular phenotypes and decode the cis-regulatory grammar from prokaryotic and eukaryotic sequencing data. Our approach is to build from the ground up, first focusing on the initiating protein-DNA interactions, then specific coding and non-coding regions, and finally on advances that combine multiple parts of the gene and mRNA regulatory structures, achieving unprecedented performance. We thus provide a quantitative view of gene expression regulation from nucleotide sequence, concluding with an information-centric overview of the central dogma of molecular biology.
31.	Zrimec, Jan, 1981, et al. (författare) Plastic-Degrading Potential across the Global Microbiome Correlates with Recent Pollution Trends 2021 Ingår i: mBio. - 2150-7511 .- 2161-2129. ; 12:5 Tidskriftsartikel (refereegranskat)abstract Biodegradation is a plausible route toward sustainable management of the millions of tons of plastic waste that have accumulated in terrestrial and marine environments. However, the global diversity of plastic-degrading enzymes remains poorly understood. Taking advantage of global environmental DNA sampling projects, here we constructed hidden Markov models from experimentally verified enzymes and mined ocean and soil metagenomes to assess the global potential of microorganisms to degrade plastics. By controlling for false positives using gut microbiome data, we compiled a catalogue of over 30,000 nonredundant enzyme homologues with the potential to degrade 10 different plastic types. While differences between the ocean and soil microbiomes likely reflect the base compositions of these environments, we find that ocean enzyme abundance increases with depth as a response to plastic pollution and not merely taxonomic composition. By obtaining further pollution measurements, we observed that the abundance of the uncovered enzymes in both ocean and soil habitats significantly correlates with marine and country-specific plastic pollution trends. Our study thus uncovers the earth microbiome's potential to degrade plastics, providing evidence of a measurable effect of plastic pollution on the global microbial ecology as well as a useful resource for further applied research. IMPORTANCE Utilization of synthetic biology approaches to enhance current plastic degradation processes is of crucial importance, as natural plastic degradation processes are very slow. For instance, the predicted lifetime of a polyethylene terephthalate (PET) bottle under ambient conditions ranges from 16 to 48 years. Moreover, although there is still unexplored diversity in microbial communities, synergistic degradation of plastics by microorganisms holds great potential to revolutionize the management of global plastic waste. To this end, the methods and data on novel plastic-degrading enzymes presented here can help researchers by (i) providing further information about the taxonomic diversity of such enzymes as well as understanding of the mechanisms and steps involved in the biological breakdown of plastics, (ii) pointing toward the areas with increased availability of novel enzymes, and (iii) giving a basis for further application in industrial plastic waste biodegradation. Importantly, our findings provide evidence of a measurable effect of plastic pollution on the global microbial ecology.
32.	Zrimec, Jan, et al. (författare) Toward learning the principles of plant gene regulation 2022 Ingår i: Trends in Plant Science. - : Elsevier BV. - 1360-1385. ; 27:12, s. 1206-1208 Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract Advanced machine learning (ML) algorithms produce highly accurate models of gene expression, uncovering novel regulatory features in nucleotide sequences involving multiple cis-regulatory regions across whole genes and structural properties. These broaden our understanding of gene regulation and point to new principles to test and adopt in the field of plant science.

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