A mini-TGA protein modulates gene expression through heterogeneous association with transcription factors.

TGA (TGACG-binding) transcription factors, which bind their target DNA through a conserved basic region leucine zipper (bZIP) domain, are vital regulators of gene expression in salicylic acid (SA)-mediated plant immunity. Here, we investigated the role of StTGA2.1, a potato (Solanum tuberosum) TGA lacking the full bZIP, which we named a mini-TGA. Such truncated proteins have been widely assigned as loss-of-function mutants. We, however, confirmed that StTGA2.1 overexpression compensates for SA-deficiency, indicating a distinct mechanism of action compared with model plant species. To understand the underlying mechanisms, we showed that StTGA2.1 can physically interact with StTGA2.2 and StTGA2.3, while its interaction with DNA was not detected. We investigated the changes in transcriptional regulation due to StTGA2.1 overexpression, identifying direct and indirect target genes. Using in planta transactivation assays, we confirmed that StTGA2.1 interacts with StTGA2.3 to activate StPRX07, a member of class III peroxidases (StPRX), which are known to play role in immune response. Finally, via structural modeling and molecular dynamics simulations, we hypothesized that the compact molecular architecture of StTGA2.1 distorts DNA conformation upon heterodimer binding to enable transcriptional activation. This study demonstrates how protein truncation can lead to distinct functions and that such events should be studied carefully in other protein families.

Learning deep representations of enzyme thermal adaptation.

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.

Toward learning the principles of plant gene regulation.

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.

Controlling gene expression with deep generative design of regulatory DNA.

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.

Enhanced metabolism and negative regulation of ER stress support higher erythropoietin production in HEK293 cells.

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.

Data mining of Saccharomyces cerevisiae mutants engineered for increased tolerance towards inhibitors in lignocellulosic hydrolysates.

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.

Plastic-Degrading Potential across the Global Microbiome Correlates with Recent Pollution Trends.

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.

Performance of Regression Models as a Function of Experiment Noise.

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 (R 2) 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.

Learning the Regulatory Code of Gene Expression.

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.

Parallel Factor Analysis Enables Quantification and Identification of Highly Convolved Data-Independent-Acquired Protein Spectra.

High-throughput data-independent acquisition (DIA) is the method of choice for quantitative proteomics, combining the best practices of targeted and shotgun approaches. The resultant DIA spectra are, however, highly convolved and with no direct precursor-fragment correspondence, complicating biological sample analysis. Here, we present CANDIA (canonical decomposition of data-independent-acquired spectra), a GPU-powered unsupervised multiway factor analysis framework that deconvolves multispectral scans to individual analyte spectra, chromatographic profiles, and sample abundances, using parallel factor analysis. The deconvolved spectra can be annotated with traditional database search engines or used as high-quality input for de novo sequencing methods. We demonstrate that spectral libraries generated with CANDIA substantially reduce the false discovery rate underlying the validation of spectral quantification. CANDIA covers up to 33 times more total ion current than library-based approaches, which typically use less than 5% of total recorded ions, thus allowing quantification and identification of signals from unexplored DIA spectra.

Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure.

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.

Multiple plasmid origin-of-transfer regions might aid the spread of antimicrobial resistance to human pathogens.

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.

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.

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.

Natural Microbial Communities Can Be Manipulated by Artificially Constructed Biofilms.

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.

DNA structure at the plasmid origin-of-transfer indicates its potential transfer range.

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 .

Bridge-Induced Translocation between NUP145 and TOP2 Yeast Genes Models the Genetic Fusion between the Human Orthologs Associated With Acute Myeloid Leukemia.

In mammalian organisms liquid tumors such as acute myeloid leukemia (AML) are related to spontaneous chromosomal translocations ensuing in gene fusions. We previously developed a system named bridge-induced translocation (BIT) that allows linking together two different chromosomes exploiting the strong endogenous homologous recombination system of the yeast Saccharomyces cerevisiae. The BIT system generates a heterogeneous population of cells with different aneuploidies and severe aberrant phenotypes reminiscent of a cancerogenic transformation. In this work, thanks to a complex pop-out methodology of the marker used for the selection of translocants, we succeeded by BIT technology to precisely reproduce in yeast the peculiar chromosome translocation that has been associated with AML, characterized by the fusion between the human genes NUP98 and TOP2B. To shed light on the origin of the DNA fragility within NUP98, an extensive analysis of the curvature, bending, thermostability, and B-Z transition aptitude of the breakpoint region of NUP98 and of its yeast ortholog NUP145 has been performed. On this basis, a DNA cassette carrying homologous tails to the two genes was amplified by PCR and allowed the targeted fusion between NUP145 and TOP2, leading to reproduce the chimeric transcript in a diploid strain of S. cerevisiae. The resulting translocated yeast obtained through BIT appears characterized by abnormal spherical bodies of nearly 500 nm of diameter, absence of external membrane and defined cytoplasmic localization. Since Nup98 is a well-known regulator of the post-transcriptional modification of P53 target genes, and P53 mutations are occasionally reported in AML, this translocant yeast strain can be used as a model to test the constitutive expression of human P53. Although the abnormal phenotype of the translocant yeast was never rescued by its expression, an exogenous P53 was recognized to confer increased vitality to the translocants, in spite of its usual and well-documented toxicity to wild-type yeast strains. These results obtained in yeast could provide new grounds for the interpretation of past observations made in leukemic patients indicating a possible involvement of P53 in cell transformation toward AML.

Fast Prediction of DNA Melting Bubbles Using DNA Thermodynamic Stability.

DNA melting bubbles are the basis of many DNA-protein interactions, such as those in regulatory DNA regions driving gene expression, DNA replication and bacterial horizontal gene transfer. Bubble formation is affected by DNA duplex stability and thermally induced duplex destabilization (TIDD). Although prediction of duplex stability with the nearest neighbor (NN) method is much faster than prediction of TIDD with the Peyrard-Bishop-Dauxois (PBD) model, PBD predicted TIDD defines regulatory DNA regions with higher accuracy and detail. Here, we considered that PBD predicted TIDD is inherently related to the intrinsic duplex stabilities of destabilization regions. We show by regression modeling that NN duplex stabilities can be used to predict TIDD almost as accurately as is predicted with PBD. Predicted TIDD is in fact ascribed to non-linear transformation of NN duplex stabilities in destabilization regions as well as effects of neighboring regions relative to destabilization size. Since the prediction time of our models is over six orders of magnitude shorter than that of PBD, the models present an accessible tool for researchers. TIDD can be predicted on our webserver at http://tidd.immt.eu.

Band smearing of PCR amplified bacterial 16S rRNA genes: dependence on initial PCR target diversity.

Band smearing in agarose gels of PCR amplified bacterial 16S rRNA genes is understood to comprise amplicons of varying sizes arising from PCR errors, and requires elimination. We consider that with amplified heterogeneous DNA, delayed electro-migration is caused not by PCR errors but by dsDNA structures that arise from imperfect strand pairing. The extent of band smearing was found to be proportional to the sequence heterogeneity in 16S rRNA variable regions. Denaturing alkaline gels showed that all amplified DNA was of the correct size. A novel bioinformatic approach was used to reveal that band smearing occurred due to imperfectly paired strands of the amplified DNA. Since the smear is a structural fraction of the correct size PCR product, it carries important information on richness and diversity of the target DNA. For accurate analysis, the origin of the smear must first be identified before it is eliminated by examining the amplified DNA in denaturing alkaline gels.