| 1. |
- Baptista, Ines S. C., et al.
(author)
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Sequence-dependent model of genes with dual s factor preference
- 2022
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In: Biochimica et Biophysica Acta. Gene Regulatory Mechanisms. - : Elsevier. - 1874-9399 .- 1876-4320. ; 1865:3
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Journal article (peer-reviewed)abstract
- Escherichia coli uses sigma factors to quickly control large gene cohorts during stress conditions. While most of its genes respond to a single sigma factor, approximately 5% of them have dual sigma factor preference. The most common are those responsive to both sigma(70), which controls housekeeping genes, and sigma(38), which activates genes during stationary growth and stresses. Using RNA-seq and flow-cytometry measurements, we show that 'sigma(70+38) genes' are nearly as upregulated in stationary growth as 'sigma(38) genes'. Moreover, we find a clear quantitative relationship between their promoter sequence and their response strength to changes in sigma(38) levels. We then propose and validate a sequence dependent model of sigma(70+38) genes, with dual sensitivity to sigma(38) and sigma(70), that is applicable in the exponential and stationary growth phases, as well in the transient period in between. We further propose a general model, applicable to other stresses and sigma factor combinations. Given this, promoters controlling sigma 70+38 genes (and variants) could become important building blocks of synthetic circuits with predictable, sequence-dependent sensitivity to transitions between the exponential and stationary growth phases.
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| 2. |
- Hallström, Erik, et al.
(author)
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Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy
- 2023
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In: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 19:11
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Journal article (peer-reviewed)abstract
- Reliable detection and classification of bacteria and other pathogens in the human body, animals, food, and water is crucial for improving and safeguarding public health. For instance, identifying the species and its antibiotic susceptibility is vital for effective bacterial infection treatment. Here we show that phase contrast time-lapse microscopy combined with deep learning is sufficient to classify four species of bacteria relevant to human health. The classification is performed on living bacteria and does not require fixation or staining, meaning that the bacterial species can be determined as the bacteria reproduce in a microfluidic device, enabling parallel determination of susceptibility to antibiotics. We assess the performance of convolutional neural networks and vision transformers, where the best model attained a class-average accuracy exceeding 98%. Our successful proof-of-principle results suggest that the methods should be challenged with data covering more species and clinically relevant isolates for future clinical use. Bacterial infections are a leading cause of premature death worldwide, and growing antibiotic resistance is making treatment increasingly challenging. To effectively treat a patient with a bacterial infection, it is essential to quickly detect and identify the bacterial species and determine its susceptibility to different antibiotics. Prompt and effective treatment is crucial for the patient's survival. A microfluidic device functions as a miniature "lab-on-chip" for manipulating and analyzing tiny amounts of fluids, such as blood or urine samples from patients. Microfluidic chips with chambers and channels have been designed for quickly testing bacterial susceptibility to different antibiotics by analyzing bacterial growth. Identifying bacterial species has previously relied on killing the bacteria and applying species-specific fluorescent probes. The purpose of the herein proposed species identification is to speed up decisions on treatment options by already in the first few imaging frames getting an idea of the bacterial species, without interfering with the ongoing antibiotics susceptibility testing. We introduce deep learning models as a fast and cost-effective method for identifying bacteria species. We envision this method being employed concurrently with antibiotic susceptibility tests in future applications, significantly enhancing bacterial infection treatments.
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| 3. |
- Kandavalli, Vinodh, et al.
(author)
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Rapid antibiotic susceptibility testing and species identification for mixed samples
- 2022
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In: Nature Communications. - : Springer Nature. - 2041-1723. ; 13:1
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Journal article (peer-reviewed)abstract
- Antimicrobial resistance is an increasing problem on a global scale. Rapid antibiotic susceptibility testing (AST) is urgently needed in the clinic to enable personalized prescriptions in high-resistance environments and to limit the use of broad-spectrum drugs. Current rapid phenotypic AST methods do not include species identification (ID), leaving time-consuming plating or culturing as the only available option when ID is needed to make the sensitivity call. Here we describe a method to perform phenotypic AST at the single-cell level in a microfluidic chip that allows subsequent genotyping by in situ FISH. By stratifying the phenotypic AST response on the species of individual cells, it is possible to determine the susceptibility profile for each species in a mixed sample in 2 h. In this proof-of-principle study, we demonstrate the operation with four antibiotics and mixed samples with combinations of seven species.
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| 4. |
- L. B. Almeida, Bilena, et al.
(author)
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The transcription factor network of E. coli steers global responses to shifts in RNAP concentration
- 2022
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In: Nucleic Acids Research. - : Oxford University Press. - 0305-1048 .- 1362-4962. ; 50:12, s. 6801-6819
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Journal article (peer-reviewed)abstract
- The robustness and sensitivity of gene networks to environmental changes is critical for cell survival. How gene networks produce specific, chronologically ordered responses to genome-wide perturbations, while robustly maintaining homeostasis, remains an open question. We analysed if short- and mid-term genome-wide responses to shifts in RNA polymerase (RNAP) concentration are influenced by the known topology and logic of the transcription factor network (TFN) of Escherichia coli. We found that, at the gene cohort level, the magnitude of the single-gene, mid-term transcriptional responses to changes in RNAP concentration can be explained by the absolute difference between the gene's numbers of activating and repressing input transcription factors (TFs). Interestingly, this difference is strongly positively correlated with the number of input TFs of the gene. Meanwhile, short-term responses showed only weak influence from the TFN. Our results suggest that the global topological traits of the TFN of E. coli shape which gene cohorts respond to genome-wide stresses.
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