| 1. |
- Campos, Ana Carolina da Cruz, et al.
(författare)
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Determining the virulence properties of Escherichia coli ST131 containing bacteriocin-encoding plasmids using short-and long-read sequencing and comparing them with those of other E. Coli lineages
- 2019
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Ingår i: Microorganisms. - : MDPI AG. - 2076-2607. ; 7:11
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Tidskriftsartikel (refereegranskat)abstract
- T. Escherichia coli ST131 is a clinical challenge due to its multidrug resistant profile and successful global spread. They are often associated with complicated infections, particularly urinary tract infections (UTIs). Bacteriocins play an important role to outcompete other microorganisms present in the human gut. Here, we characterized bacteriocin-encoding plasmids found in ST131 isolates of patients suffering from a UTI using both short-and long-read sequencing. Colicins Ia, Ib and E1, and microcin V, were identified among plasmids that also contained resistance and virulence genes. To investigate if the potential transmission range of the colicin E1 plasmid is influenced by the presence of a resistance gene, we constructed a strain containing a plasmid which had both the colicin E1 and blaCMY-2 genes. No difference in transmission range was found between transformant and wild-type strains. However, a statistically significantly difference was found in adhesion and invasion ability. Bacteriocin-producing isolates from both ST131 and non-ST131 lineages were able to inhibit the growth of other E. coli isolates, including other ST131. In summary, plasmids harboring bacteriocins give additional advantages for highly virulent and resistant ST131 isolates, improving the ability of these isolates to compete with other microbiota for a niche and thereby increasing the risk of infection.
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| 2. |
- Zrimec, Jan, 1981, et al.
(författare)
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Controlling gene expression with deep generative design of regulatory DNA
- 2022
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 13:1, s. 5099-
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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.
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| 3. |
- Buric, Filip, 1988, et al.
(författare)
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Parallel Factor Analysis Enables Quantification and Identification of Highly Convolved Data-Independent-Acquired Protein Spectra
- 2020
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Ingår i: Patterns. - : Elsevier BV. - 2666-3899. ; 1:9
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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.
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| 4. |
- Cámara, Elena, 1985, et al.
(författare)
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Data mining of Saccharomyces cerevisiae mutants engineered for increased tolerance towards inhibitors in lignocellulosic hydrolysates
- 2022
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Ingår i: Biotechnology Advances. - : Elsevier BV. - 0734-9750. ; 57
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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.
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| 5. |
- Li, Gang, 1991, et al.
(författare)
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Bayesian genome scale modelling identifies thermal determinants of yeast metabolism
- 2021
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 12:1
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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.
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| 6. |
- Li, Gang, 1991, et al.
(författare)
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Learning deep representations of enzyme thermal adaptation
- 2022
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Ingår i: Protein Science. - : Wiley. - 1469-896X .- 0961-8368. ; 31:12
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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.
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| 7. |
- Li, Gang, 1991, et al.
(författare)
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Performance of Regression Models as a Function of Experiment Noise
- 2021
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Ingår i: Bioinformatics and Biology Insights. - : SAGE Publications. - 1177-9322. ; 15
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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.
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| 8. |
- Repecka, Donatas, et al.
(författare)
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Expanding functional protein sequence spaces using generative adversarial networks
- 2021
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Ingår i: Nature Machine Intelligence. - : Springer Science and Business Media LLC. - 2522-5839. ; 3:4, s. 324-333
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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.
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| 9. |
- Rijavec, Tomaž, et al.
(författare)
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Natural Microbial Communities Can Be Manipulated by Artificially Constructed Biofilms
- 2019
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Ingår i: Advanced Science. - : Wiley. - 2198-3844 .- 2198-3844. ; 6:22
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Tidskriftsartikel (refereegranskat)abstract
- Biofouling proceeds in successive steps where the primary colonizers affect the phylogenetic and functional structure of a future microbial consortium. Using microbiologically influenced corrosion (MIC) as a study case, a novel approach for material surface protection is described, which does not prevent biofouling, but rather shapes the process of natural biofilm development to exclude MIC-related microorganisms. This approach interferes with the early steps of natural biofilm formation affecting how the community is finally developed. It is based on a multilayer artificial biofilm, composed of electrostatically modified bacterial cells, producing antimicrobial compounds, extracellular antimicrobial polyelectrolyte matrix, and a water-proof rubber elastomer barrier. The artificial biofilm is constructed layer-by-layer (LBL) by manipulating the electrostatic interactions between microbial cells and material surfaces. Field testing on standard steel coupons exposed in the sea for more than 30 days followed by laboratory analyses using molecular-biology tools demonstrate that the preapplied artificial biofilm affects the phylogenetic structure of the developing natural biofilm, reducing phylogenetic diversity and excluding MIC-related bacteria. This sustainable solution for material protection showcases the usefulness of artificially guiding microbial evolutionary processes via the electrostatic modification and controlled delivery of bacterial cells and extracellular matrix to the exposed material surfaces.
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| 10. |
- Saghaleyni, Rasool, 1987, et al.
(författare)
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Enhanced metabolism and negative regulation of ER stress support higher erythropoietin production in HEK293 cells
- 2022
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Ingår i: Cell Reports. - : Elsevier BV. - 2211-1247. ; 39:11
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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.
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| 11. |
- Zrimec, Jan, 1981, et al.
(författare)
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Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure
- 2020
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11:1
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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.
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| 12. |
- Zrimec, Jan, 1981, et al.
(författare)
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DNA structure at the plasmid origin-of-Transfer indicates its potential transfer range
- 2018
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322 .- 2045-2322. ; 8:1
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Tidskriftsartikel (refereegranskat)abstract
- Horizontal gene transfer via plasmid conjugation enables antimicrobial resistance (AMR) to spread among bacteria and is a major health concern. The range of potential transfer hosts of a particular conjugative plasmid is characterised by its mobility (MOB) group, which is currently determined based on the amino acid sequence of the plasmid-encoded relaxase. To facilitate prediction of plasmid MOB groups, we have developed a bioinformatic procedure based on analysis of the origin-of-Transfer (oriT), a merely 230 bp long non-coding plasmid DNA region that is the enzymatic substrate for the relaxase. By computationally interpreting conformational and physicochemical properties of the oriT region, which facilitate relaxase-oriT recognition and initiation of nicking, MOB groups can be resolved with over 99% accuracy. We have shown that oriT structural properties are highly conserved and can be used to discriminate among MOB groups more efficiently than the oriT nucleotide sequence. The procedure for prediction of MOB groups and potential transfer range of plasmids was implemented using published data and is available at http://dnatools.eu/MOB/plasmid.HTML.
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| 13. |
- Zrimec, Jan, 1981, et al.
(författare)
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Learning the Regulatory Code of Gene Expression
- 2021
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Ingår i: Frontiers in Molecular Biosciences. - : Frontiers Media SA. - 2296-889X. ; 8
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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.
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| 14. |
- Zrimec, Jan, 1981
(författare)
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Multiple plasmid origin-of-transfer regions might aid the spread of antimicrobial resistance to human pathogens
- 2020
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Ingår i: MicrobiologyOpen. - : Wiley. - 2045-8827. ; 9:12
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Tidskriftsartikel (refereegranskat)abstract
- Antimicrobial resistance poses a great danger to humanity, in part due to the widespread horizontal gene transfer of plasmids via conjugation. Modeling of plasmid transfer is essential to uncovering the fundamentals of resistance transfer and for the development of predictive measures to limit the spread of resistance. However, a major limitation in the current understanding of plasmids is the incomplete characterization of the conjugative DNA transfer mechanisms, which conceals the actual potential for plasmid transfer in nature. Here, we consider that the plasmid-borne origin-of-transfer substrates encode specific DNA structural properties that can facilitate finding these regions in large datasets and develop a DNA structure-based alignment procedure for typing the transfer substrates that outperforms sequence-based approaches. Thousands of putative DNA transfer substrates are identified, showing that plasmid mobility can be twofold higher and span almost twofold more host species than is currently known. Over half of all putative mobile plasmids contain the means for mobilization by conjugation systems belonging to different mobility groups, which can hypothetically link previously confined host ranges across ecological habitats into a robust plasmid transfer network. This hypothetical network is found to facilitate the transfer of antimicrobial resistance from environmental genetic reservoirs to human pathogens, which might be an important driver of the observed rapid resistance development in humans and thus an important point of focus for future prevention measures.
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| 15. |
- Zrimec, Jan, 1981, et al.
(författare)
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Plastic-Degrading Potential across the Global Microbiome Correlates with Recent Pollution Trends
- 2021
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Ingår i: mBio. - 2150-7511 .- 2161-2129. ; 12:5
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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.
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| 16. |
- Zrimec, Jan, 1981
(författare)
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Structural representations of DNA regulatory substrates can enhance sequence-based algorithms by associating functional sequence variants
- 2020
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Ingår i: Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020. - New York, NY, USA : ACM. ; 21 September 2020
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Konferensbidrag (refereegranskat)abstract
- The nucleotide sequence representation of DNA can be inadequate for resolving protein-DNA binding sites and regulatory substrates, such as those involved in gene expression and horizontal gene transfer. Considering that sequence-like representations are algorithmically very useful, here we fused over 60 currently available DNA physicochemical and conformational variables into compact structural representations that can encode single DNA binding sites to whole regulatory regions. We find that the main structural components reflect key properties of protein-DNA interactions and can be condensed to the amount of information found in a single nucleotide position. The most accurate structural representations compress functional DNA sequence variants by 30% to 50%, as each instance encodes from tens to thousands of sequences. We show that a structural distance function discriminates among groups of DNA substrates more accurately than nucleotide sequence-based metrics. As this opens up a variety of implementation possibilities, we develop and test a distance-based alignment algorithm, demonstrating the potential of using the structural representations to enhance sequence-based algorithms. Due to the bias of most current bioinformatic methods to nucleotide sequence representations, it is possible that considerable performance increases might still be achievable with such solutions.
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