RNA:protein ratio of the unicellular organism as a characteristic of phosphorous and nitrogen stoichiometry and of the cellular requirement of ribosomes for protein synthesis

A coarse-grained bacterial cell model for resource-aware analysis and design of synthetic gene circuits

Within a cell, synthetic and native genes compete for expression machinery, influencing cellular process dynamics through resource couplings. Models that simplify competitive resource binding kinetics can guide the design of strategies for countering these couplings. However, in bacteria resource availability and cell growth rate are interlinked, which complicates resource-aware biocircuit design. Capturing this interdependence requires coarse-grained bacterial cell models that balance accurate representation of metabolic regulation against simplicity and interpretability. We propose a coarse-grained E. coli cell model that combines the ease of simplified resource coupling analysis with appreciation of bacterial growth regulation mechanisms and the processes relevant for biocircuit design. Reliably capturing known growth phenomena, it provides a unifying explanation to disparate empirical relations between growth and synthetic gene expression. Considering a biomolecular controller that makes cell-wide ribosome availability robust to perturbations, we showcase our model's usefulness in numerically prototyping biocircuits and deriving analytical relations for design guidance.

Quantification of elongation stalls and impact on gene expression in yeast

Ribosomal pauses are a critical part of cotranslational events including protein folding and localization. However, extended ribosome pauses can lead to ribosome collisions, resulting in the activation of ribosome rescue pathways and turnover of protein and mRNA. While this relationship has been known, there has been little exploration of how ribosomal stalls impact translation duration at a quantitative level. We have taken a method used to measure elongation time and adapted it for use in Saccharomyces cerevisiae to quantify the impact of elongation stalls. We find, in transcripts containing Arg CGA codon repeat-induced stalls, a Hel2-mediated dose-dependent decrease in protein expression and mRNA level and an elongation delay on the order of minutes. In transcripts that contain synonymous substitutions to nonoptimal Leu codons, there is a decrease in protein and mRNA levels, as well as similar elongation delay, but this occurs through a non-Hel2-mediated mechanism. Finally, we find that Dhh1 selectively increases protein expression, mRNA level, and elongation rate. This indicates that distinct poorly translated mRNAs will activate different rescue pathways despite similar elongation stall durations. Taken together, these results provide new quantitative mechanistic insight into the surveillance of translation and the roles of Hel2 and Dhh1 in mediating ribosome pausing events.

Physical modeling of ribosomes along messenger RNA: Estimating kinetic parameters from ribosome profiling experiments using a ballistic model

Gene expression is the synthesis of proteins from the information encoded on DNA. One of the two main steps of gene expression is the translation of messenger RNA (mRNA) into polypeptide sequences of amino acids. Here, by taking into account mRNA degradation, we model the motion of ribosomes along mRNA with a ballistic model where particles advance along a filament without excluded volume interactions. Unidirectional models of transport have previously been used to fit the average density of ribosomes obtained by the experimental ribo-sequencing (Ribo-seq) technique in order to obtain the kinetic rates. The degradation rate is not, however, accounted for and experimental data from different experiments are needed to have enough parameters for the fit. Here, we propose an entirely novel experimental setup and theoretical framework consisting in splitting the mRNAs into categories depending on the number of ribosomes from one to four. We solve analytically the ballistic model for a fixed number of ribosomes per mRNA, study the different regimes of degradation, and propose a criterion for the quality of the inverse fit. The proposed method provides a high sensitivity to the mRNA degradation rate. The additional equations coming from using the monosome (single ribosome) and polysome (arbitrary number) ribo-seq profiles enable us to determine all the kinetic rates in terms of the experimentally accessible mRNA degradation rate.

Horizontal Gene Transfer and Drug Resistance Involving Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb) acquires drug resistance at a rate comparable to that of bacterial pathogens that replicate much faster and have a higher mutation rate. One explanation for this rapid acquisition of drug resistance in Mtb is that drug resistance may evolve in other fast-replicating mycobacteria and then be transferred to Mtb through horizontal gene transfer (HGT). This paper aims to address three questions. First, does HGT occur between Mtb and other mycobacterial species? Second, what genes after HGT tend to survive in the recipient genome? Third, does HGT contribute to antibiotic resistance in Mtb? I present a conceptual framework for detecting HGT and analyze 39 ribosomal protein genes, 23S and 16S ribosomal RNA genes, as well as several genes targeted by antibiotics against Mtb, from 43 genomes representing all major groups within Mycobacterium. I also included mgtC and the insertion sequence IS6110 that were previously reported to be involved in HGT. The insertion sequence IS6110 shows clearly that the Mtb complex participates in HGT. However, the horizontal transferability of genes depends on gene function, as was previously hypothesized. HGT is not observed in functionally important genes such as ribosomal protein genes, rRNA genes, and other genes chosen as drug targets. This pattern can be explained by differential selection against functionally important and unimportant genes after HGT. Functionally unimportant genes such as IS6110 are not strongly selected against, so HGT events involving such genes are visible. For functionally important genes, a horizontally transferred diverged homologue from a different species may not work as well as the native counterpart, so the HGT event involving such genes is strongly selected against and eliminated, rendering them invisible to us. In short, while HGT involving the Mtb complex occurs, antibiotic resistance in the Mtb complex arose from mutations in those drug-targeted genes within the Mtb complex and was not gained through HGT.

Quantification of elongation stalls and impact on gene expression in yeast

Ribosomal pauses are a critical part of co-translational events including protein folding and localization. However, extended ribosome pauses can lead to ribosome collisions, resulting in the activation of ribosome rescue pathways and turnover of protein and mRNA. While this relationship has been known, the specific threshold between permissible pausing versus activation of rescue pathways has not been quantified. We have taken a method used to measure elongation time and adapted it for use in S. cerevisiae to quantify the impact of elongation stalls. We find, in transcripts containing Arg CGA codon repeat-induced stalls, a Hel2-mediated dose-dependent decrease in protein expression and mRNA level and an elongation delay on the order of minutes. In transcripts that contain synonymous substitutions to non-optimal Leu codons, there is a decrease in protein and mRNA levels, as well as similar elongation delay, but this occurs through a non-Hel2-mediated mechanism. Finally, we find that Dhh1 selectively increases protein expression, mRNA level, and elongation rate. This indicates that distinct poorly translated codons in an mRNA will activate different rescue pathways despite similar elongation stall durations. Taken together, these results provide new quantitative mechanistic insight into the surveillance of translation and the roles of Hel2 and Dhh1 in mediating ribosome pausing events.

Comparison of genomic and transcriptional microbiome analysis in gastric cancer patients and healthy individuals

BACKGROUND

Helicobacter pylori and the stomach microbiome play a crucial role in gastric carcinogenesis, and detailed characterization of the microbiome is necessary for a better understanding of the pathophysiology of the disease. There are two common modalities for microbiome analysis: DNA (16S rRNA gene) and RNA (16S rRNA transcript) sequencing. The implications from the use of one or another sequencing approach on the characterization and comparability of the mucosal microbiome in gastric cancer (GC) are poorly studied.

AIM

To characterize the microbiota of GC using 16S rRNA gene and its transcript and determine difference in the bacterial composition.

METHODS

In this study, 316 DNA and RNA samples extracted from 105 individual stomach biopsies were included. The study cohort consisted of 29 healthy control individuals and 76 patients with GC. Gastric tissue biopsy samples were collected from damaged mucosa and healthy mucosa at least 5 cm from the tumor tissue. From the controls, healthy stomach mucosa biopsies were collected. From all biopsies RNA and DNA were extracted. RNA was reverse transcribed into cDNA. V1-V2 region of bacterial 16S rRNA gene from all samples were amplified and sequenced on an Illumina MiSeq platform. Bray-Curtis algorithm was used to construct sample-similarity matrices abundances of taxonomic ranks in each sample type. For significant differences between groups permutational multivariate analysis of variance and Mann-Whitney test followed by false-discovery rate test were used.

RESULTS

Microbial analysis revealed that only a portion of phylotypes (18%-30%) overlapped between microbial profiles obtained from DNA and RNA samples. Detailed analysis revealed differences between GC and controls depending on the chosen modality, identifying 17 genera at the DNA level and 27 genera at the RNA level. Ten of those bacteria were found to be different from the control group at both levels. The key taxa showed congruent results in various tests used; however, differences in 7 bacteria taxa were found uniquely only at the DNA level, and 17 uniquely only at the RNA level. Furthermore, RNA sequencing was more sensitive for detecting differences in bacterial richness, as well as differences in the relative abundance of Reyranella and Sediminibacterium according to the type of GC. In each study group (control, tumor, and tumor adjacent) were found differences between DNA and RNA bacterial profiles.

CONCLUSION

Comprehensive microbial study provides evidence for the effect of choice of sequencing modality on the microbiota profile, as well as on the identified differences between case and control.

A dynamical stochastic model of yeast translation across the cell cycle

Translation is a central step in gene expression, however its quantitative and time-resolved regulation is poorly understood. We developed a discrete, stochastic model for protein translation in S. cerevisiae in a whole-transcriptome, single-cell context. A "base case" scenario representing an average cell highlights translation initiation rates as the main co-translational regulatory parameters. Codon usage bias emerges as a secondary regulatory mechanism through ribosome stalling. Demand for anticodons with low abundancy is shown to cause above-average ribosome dwelling times. Codon usage bias correlates strongly both with protein synthesis rates and elongation rates. Applying the model to a time-resolved transcriptome estimated by combining data from FISH and RNA-Seq experiments, it could be shown that increased total transcript abundance during the cell cycle decreases translation efficiency at single transcript level. Translation efficiency grouped by gene function shows highest values for ribosomal and glycolytic genes. Ribosomal proteins peak in S phase while glycolytic proteins rank highest in later cell cycle phases.

Single-tube isothermal label-free fluorescent sensor for pathogen detection based on genetic signatures

We report on a single-tube biosensor for real-time detection of bacterial pathogens with multiplex capabilities. The biosensor consists of two DNA probes, which bind to the complementary fragment of a bacterial RNA to form a three-way junction (3WJ) nucleic acid structure. One of the probes encodes a fluorescent light-up RNA aptamer under T7 promoter. It allows for generation of multiple aptamer copies due to elongation and transcription of the 3WJ structure in the presence of the complementary target. The aptamer coordinates and thereby enhances fluorescence of a cognate fluorogenic dye, allowing for fluorescent detection of the RNA target. Multiple aptamer copies can be produced from a single target-dependent 3WJ structure allowing for amplification and visual observation of the signal. The limit of detection depended on the assay time and was found to be 1.7 nM or 0.6 nM for 30-min or 60-min assay, respectively, when N-methylmesoporphyrin IX (NMM) was used as a fluorescent indicator. The sensor is excellent in analyzing folded RNA targets and differentiating between closely related sequences due to the multicomponent character of the target-interrogating probe. Response to unamplified samples of total bacterial RNA from Mycobacterium tuberculosis complex or Escherichia coli was observed with excellent selectivity within 30 min under isothermal conditions at 50°C in a one-tube one-step assay. Several bacterial species can be detected in multiplex by utilizing biosensors with the template strands encoding different light-up aptamers. The isothermal one-tube-one-step format of the assay and the possibility to monitor the signal visually makes it amenable to use in a point-of-care scenario.

Environment-specificity and universality of the microbial growth law

As the nutrient quality changes, the fractions of ribosomal proteins in the proteome are usually positively correlated with the growth rates due to the auto-catalytic nature of ribosomes. While this growth law is observed across multiple organisms, the relation between the ribosome fraction and growth rate is often more complex than linear, beyond models assuming a constant translation speed. Here, we propose a general framework of protein synthesis considering heterogeneous translation speeds and protein degradations. We demonstrate that the growth law curves are generally environment-specific, e.g., depending on the correlation between the translation speeds and ribosome allocations among proteins. Our predictions of ribosome fractions agree quantitatively with data of Saccharomyces cerevisiae. Interestingly, we find that the growth law curve of Escherichia coli nevertheless appears universal, which we prove must exhibit an upward bending in slow-growth conditions, in agreement with experiments. Our work provides insights on the connection between the heterogeneity among genes and the environment-specificity of cell behaviors.

Genome-wide transcription start site mapping in the facultative intracellular pathogen Brucella melitensis by Capping-seq

Brucella melitensis (B. melitensis) is an important facultative intracellular bacterium that causes global zoonotic diseases. Continuous intracellular survival and replication are the main obstruction responsible for the accessibility of prevention and treatment of brucellosis. Bacteria respond to complex environment by regulating gene expression. Many regulatory factors function at loci where RNA polymerase initiates messenger RNA synthesis. However, limited gene annotation is a current obstacle for the research on expression regulation in bacteria. To improve annotation and explore potential functional sites, we proposed a novel genome-wide method called Capping-seq for transcription start site (TSS) mapping in B. melitensis. This technique combines capture of capped primary transcripts with Single Molecule Real-Time (SMRT) sequencing technology. We identified 2,369 TSSs at single nucleotide resolution by Capping-seq. TSSs analysis of Brucella transcripts showed a preference of purine on the TSS positions. Our results revealed that -35 and -10 elements of promoter contained consensus sequences of TTGNNN and TATNNN, respectively. The 5' ends analysis showed that 57% genes are associated with more than one TSS and 47% genes contain long leader regions, suggested potential complex regulation at the 5' ends of genes in B. melitensis. Moreover, we identified 52 leaderless genes that are mainly involved in the metabolic processes. Overall, Capping-seq technology provides a unique solution for TSS determination in prokaryotes. Our findings develop a systematic insight into the primary transcriptome characterization of B. melitensis. This study represents a critical basis for investigating gene regulation and pathogenesis of Brucella.

Differential translation elongation directs protein synthesis in response to acute glucose deprivation in yeast

Protein synthesis is energetically expensive and its rate is influenced by factors such as cell type and environment. Suppression of translation is a canonical response to stressful changes in the cellular environment. In particular, inhibition of the initiation step of translation has been highlighted as the key control step in stress-induced translational suppression as mechanisms that quickly suppress initiation are well-conserved. However, cells have evolved complex regulatory means to control translation apart from initiation. Here, we examine the role of the elongation step of translation in yeast subjected to acute glucose deprivation. The use of ribosome profiling and in vivo reporter assays demonstrated elongation rates slow progressively following glucose removal. We observed that ribosome distribution broadly shifts towards the downstream ends of transcripts after both acute and gradual glucose deprivation but not in response to other stressors. Additionally, on assessed mRNAs, a correlation existed between ribosome occupancy and protein production pre-stress but was lost after stress. These results indicate that stress-induced elongation regulation causes ribosomes to slow down and build up on a considerable proportion of the transcriptome in response to glucose withdrawal. Finally, we report ribosomes that built up along transcripts are competent to resume elongation and complete protein synthesis after readdition of glucose to starved cells. This suggests that yeast has evolved mechanisms to slow translation elongation in response to glucose starvation which do not preclude continuation of protein production from those ribosomes, thereby averting a need for new initiation events to take place to synthesize proteins.

Abbreviations: AUG: start codon, bp: base pair(s), CDS: coding sequence, CHX: cycloheximide, eEF2: eukaryotic elongation factor 2, LTM: lactimidomycin, nt: nucleotide, PGK1: 3-phosphoglycerate kinase, ribosomal biogenesis: ribi, RO: ribosome occupancy, RPF: ribosome protected fragment, TE: translational efficiency

Rapid and accurate identification of ribosomal RNA sequences via deep learning

Advances in transcriptomic and translatomic techniques enable in-depth studies of RNA activity profiles and RNA-based regulatory mechanisms. Ribosomal RNA (rRNA) sequences are highly abundant among cellular RNA, but if the target sequences do not include polyadenylation, these cannot be easily removed in library preparation, requiring their post-hoc removal with computational techniques to accelerate and improve downstream analyses. Here, we describe RiboDetector, a novel software based on a Bi-directional Long Short-Term Memory (BiLSTM) neural network, which rapidly and accurately identifies rRNA reads from transcriptomic, metagenomic, metatranscriptomic, noncoding RNA, and ribosome profiling sequence data. Compared with state-of-the-art approaches, RiboDetector produced at least six times fewer misclassifications on the benchmark datasets. Importantly, the few false positives of RiboDetector were not enriched in certain Gene Ontology (GO) terms, suggesting a low bias for downstream functional profiling. RiboDetector also demonstrated a remarkable generalizability for detecting novel rRNA sequences that are divergent from the training data with sequence identities of <90%. On a personal computer, RiboDetector processed 40M reads in less than 6 min, which was ∼50 times faster in GPU mode and ∼15 times in CPU mode than other methods. RiboDetector is available under a GPL v3.0 license at https://github.com/hzi-bifo/RiboDetector.

Combating Antimicrobial Resistance via Single-Cell Diagnostic Technologies Powered by Droplet Microfluidics

Antimicrobial resistance is a global threat that if left unchecked could lead to 10 million annual mortalities by 2050. One factor contributing to the rise of multi-drug-resistant (MDR) pathogens is the reliance on traditional culture-based pathogen identification (ID) and antimicrobial susceptibility testing (AST) that typically takes several days. This delay of objective pathogen ID and AST information to inform clinical decision making results in clinicians treating patients empirically often using first-line, broad-spectrum antibiotics, contributing to the misuse/overuse of antibiotics. To combat the rise in MDR pathogens, there is a critical demand for rapid ID and AST technologies. Among the advances in ID and AST technologies in the past decade, single-cell diagnostic technologies powered by droplet microfluidics offer great promise due to their potential for high-sensitivity detection and rapid turnaround time. Our laboratory has been at the forefront of developing such technologies and applying them to diagnosing urinary tract infections (UTIs), one of the most common infections and a frequent reason for the prescription of antimicrobials. For pathogen ID, we first demonstrated the highly sensitive, amplification-free detection of single bacterial cells by confining them in picoliter-scale droplets and detection with fluorogenic peptide nucleic acid (PNA) probes that target their 16S rRNA (rRNA), a well-characterized marker for phylogenic classification. We subsequently improved the PNA probe design and enhanced detection sensitivity. For single-cell AST, we first employed a growth indicator dye and engineered an integrated device that allows us to detect growth from single bacterial cells under antibiotic exposure within 1 h, equivalent to two to three bacterial replications. To expand beyond testing a single antibiotic condition per device, a common limitation for droplet microfluidics, we developed an integrated programmable droplet microfluidic device for scalable single-cell AST. Using the scalable single-cell AST platform, we demonstrated the generation of up to 32 droplet groups in a single device with custom antibiotic titers and the capacity to scale up single-cell AST, and providing reliable pathogen categories beyond a binary call embodies a critical advance. Finally, we developed an integrated ID and AST platform. To this end, we developed a PNA probe panel that can identify nearly 90% of uropathogens and showed the quantitative detection of 16S rRNA from single bacterial cells in droplet-enabled AST after as little as 10 min of antibiotic exposure. This platform achieved both ID and AST from minimally processed urine samples in 30 min, representing one of the fastest turnaround times to date. In addition to tracing the development of our technologies, we compare them with contemporary research advances and offer our perspectives for future development, with the vision that single-cell ID and AST technologies powered by droplet microfluidics can indeed become a useful diagnostic tool for combating antimicrobial resistance.

Growth and Nutrient Uptake Characteristics of Heterosigma akashiwo (Raphidophyceae) under Nitrogen and Phosphorus Concentrations in the East China Sea

Heterosigma akashiwo is classified as a harmful algal bloom (HAB) species that frequently occurs in eutrophic coastal waters and results in the contamination and mortality of fish and shellfish. The growth of H. akashiwo in four phosphate and nitrate concentration scenarios, representing the observed nutrient concentration ranges in the East China Sea (ECS), was evaluated to further understand the effect of nutrient concentrations on H. akashiwo blooms. The specific growth rate in the exponential growth phase (µ′) and the maximum cell density were lower (17–21% and 41%, respectively) under low phosphorus concentration scenarios, compared to the rates observed under high phosphorus concentration scenarios. The cellular nitrogen-to-phosphorus ratios of H. akashiwo were influenced by the initially supplied N:P ratio and the allocation strategy employed. Phosphorus concentration had a greater influence on the total growth of H. akashiwo than nitrate did, within the natural nutrient conditions of the ECS. These results could serve as a reference for coastal water management and marine ecological management and may be useful for further studies on the simulation and prediction of H. akashiwo blooms, particularly in the ECS.

Expression Dysregulation as a Mediator of Fitness Costs in Antibiotic Resistance

Antimicrobial resistance (AMR) poses a threat to global health and the economy. Rifampicin-resistant Mycobacterium tuberculosis accounts for a third of the global AMR burden. Gaining the upper hand on AMR requires a deeper understanding of the physiology of resistance. AMR often results in a fitness cost in the absence of drug. Identifying the molecular mechanisms underpinning this cost could help strengthen future treatment regimens. Here, we used a collection of M. tuberculosis strains that provide an evolutionary and phylogenetic snapshot of rifampicin resistance and subjected them to genome-wide transcriptomic and proteomic profiling to identify key perturbations of normal physiology. We found that the clinically most common rifampicin resistance-conferring mutation, RpoB Ser450Leu, imparts considerable gene expression changes, many of which are mitigated by the compensatory mutation in RpoC Leu516Pro. However, our data also provide evidence for pervasive epistasis—the same resistance mutation imposed a different fitness cost and functionally distinct changes to gene expression in genetically unrelated clinical strains. Finally, we report a likely posttranscriptional modulation of gene expression that is shared in most of the tested strains carrying RpoB Ser450Leu, resulting in an increased abundance of proteins involved in central carbon metabolism. These changes contribute to a more general trend in which the disruption of the composition of the proteome correlates with the fitness cost of the RpoB Ser450Leu mutation in different strains.

A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics

Eukaryotic organisms play an important role in industrial biotechnology, from the production of fuels and commodity chemicals to therapeutic proteins. To optimize these industrial systems, a mathematical approach can be used to integrate the description of multiple biological networks into a single model for cell analysis and engineering. One of the most accurate models of biological systems include Expression and Thermodynamics FLux (ETFL), which efficiently integrates RNA and protein synthesis with traditional genome-scale metabolic models. However, ETFL is so far only applicable for E. coli. To adapt this model for Saccharomyces cerevisiae, we developed yETFL, in which we augmented the original formulation with additional considerations for biomass composition, the compartmentalized cellular expression system, and the energetic costs of biological processes. We demonstrated the ability of yETFL to predict maximum growth rate, essential genes, and the phenotype of overflow metabolism. We envision that the presented formulation can be extended to a wide range of eukaryotic organisms to the benefit of academic and industrial research.

A workflow to identify novel proteins based on the direct mapping of peptide-spectrum-matches to genomic locations

Background

Small Proteins have received increasing attention in recent years. They have in particular been implicated as signals contributing to the coordination of bacterial communities. In genome annotations they are often missing or hidden among large numbers of hypothetical proteins because genome annotation pipelines often exclude short open reading frames or over-predict hypothetical proteins based on simple models. The validation of novel proteins, and in particular of small proteins (sProteins), therefore requires additional evidence. Proteogenomics is considered the gold standard for this purpose. It extends beyond established annotations and includes all possible open reading frames (ORFs) as potential sources of peptides, thus allowing the discovery of novel, unannotated proteins. Typically this results in large numbers of putative novel small proteins fraught with large fractions of false-positive predictions.

Results

We observe that number and quality of the peptide-spectrum matches (PSMs) that map to a candidate ORF can be highly informative for the purpose of distinguishing proteins from spurious ORF annotations. We report here on a workflow that aggregates PSM quality information and local context into simple descriptors and reliably separates likely proteins from the large pool of false-positive, i.e., most likely untranslated ORFs. We investigated the artificial gut microbiome model SIHUMIx, comprising eight different species, for which we validate 5114 proteins that have previously been annotated only as hypothetical ORFs. In addition, we identified 37 non-annotated protein candidates for which we found evidence at the proteomic and transcriptomic level. Half (19) of these candidates have close functional homologs in other species. Another 12 candidates have homologs designated as hypothetical proteins in other species. The remaining six candidates are short (< 100 AA) and are most likely bona fide novel proteins.

Conclusions

The aggregation of PSM quality information for predicted ORFs provides a robust and efficient method to identify novel proteins in proteomics data. The workflow is in particular capable of identifying small proteins and frameshift variants. Since PSMs are explicitly mapped to genomic locations, it furthermore facilitates the integration of transcriptomics data and other sources of genome-level information.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04159-8.

Droplet-Based Single-Cell Measurements of 16S rRNA Enable Integrated Bacteria Identification and Pheno-Molecular Antimicrobial Susceptibility Testing from Clinical Samples in 30 min

Empiric broad-spectrum antimicrobial treatments of urinary tract infections (UTIs) have contributed to widespread antimicrobial resistance. Clinical adoption of evidence-based treatments necessitates rapid diagnostic methods for pathogen identification (ID) and antimicrobial susceptibility testing (AST) with minimal sample preparation. In response, a microfluidic droplet-based platform is developed for achieving both ID and AST from urine samples within 30 min. In this platform, fluorogenic hybridization probes are utilized to detect 16S rRNA from single bacterial cells encapsulated in picoliter droplets, enabling molecular identification of uropathogenic bacteria directly from urine in as little as 16 min. Moreover, in-droplet single-bacterial measurements of 16S rRNA provide a surrogate for AST, shortening the exposure time to 10 min for gentamicin and ciprofloxacin. A fully integrated device and screening workflow were developed to test urine specimens for one of seven unique diagnostic outcomes including the presence/absence of Gram-negative bacteria, molecular ID of the bacteriaas Escherichia coli, an Enterobacterales, or other organism, and assessment of bacterial susceptibility to ciprofloxacin. In a 50-specimen clinical comparison study, the platform demonstrates excellent performance compared to clinical standard methods (areas-under-curves, AUCs >0.95), within a small fraction of the turnaround time, highlighting its clinical utility.

Pliant pathogens: Estimating viral spread when confronted with new vector, host, and environmental conditions

Pathogen spread rates are determined, in part, by the performance of pathogens under altered environmental conditions and their ability to persist while switching among hosts and vectors.To determine the effects of new conditions (host, vector, and nutrient) on pathogen spread rate, we introduced a vector-borne viral plant pathogen, Barley Yellow Dwarf Virus PAV (BYDV-PAV) into hosts, vectors, and host nutrient supplies that it had not encountered for thousands of viral generations. We quantified pathogen prevalence over the course of two serial inoculations under the new conditions. Using individual-level transmission rates from this experiment, we parameterized a dynamical model of disease spread and projected spread across host populations through a growing season.A change in nutrient conditions (increased supply of phosphorus) reduced viral transmission whereas shifting to a new vector or host species had no effect on infection prevalence. However, the reduction in the new nutrient environment was only temporary; infection prevalence recovered after the second inoculation. Synthesis. These results highlight how robust the pathogen, BYDV-PAV, is to changes in its biotic and abiotic environment. Our study also highlights the need to quantify longitudinal infection information beyond snapshot assessments to project disease risk for pathogens in new environments.

Light and competition alter leaf stoichiometry of introduced species and native mangrove species

Ecological stoichiometry is the study of the balance of ecosystem energy and nutrient cycling, especially carbon (C), nitrogen (N), and phosphorus (P). C, N, and P are the key elements for plant growth and metabolism. Systematic research on leaf stoichiometry in mangrove forest ecosystems is still lacking. To understand the leaf stoichiometry of introduced species and native species in mangrove forests, we selected four species (one introduced species, Sonneratia apetala, and three native species, Avicennia marina, Aegiceras corniculatum, and Kandelia obovate) and measured leaf C, N, and P contents under different light conditions. The results showed that there were significant negative scaling relationships of leaf C versus N and C versus P but positive scaling relationships of leaf N versus P in the four mangrove species. Light and competition had significant effects on leaf stoichiometry, especially under the full light condition. S. apetala influenced leaf elements in a mixture with native species. Interspecific competition reduced leaf N and P contents in A. corniculatum and K. obovate but increased leaf N and P contents in A. marina. Leaf N and P contents of the four species showed similar responses to both intraspecific and interspecific competition. The ratio of leaf C:N:P (108:11:1) in the mangrove forests was lower than that in other ecosystems, and species with a higher growth rate had a higher leaf P content and lower N:P ratio, supporting the growth rate hypothesis. Leaf N:P was 11.04, indicating that there was N limitation in the mangrove forests. This systematic research of leaf stoichiometry of mangrove forests improves our understanding of mangrove growth and nutrient use strategies in response to different environmental stresses.

Interspecies Microbial Fusion and Large-Scale Exchange of Cytoplasmic Proteins and RNA in a Syntrophic Clostridium Coculture

Microbial syntrophy is universal in nature, profoundly affecting the composition and function of microbiomes. We have recently reported data suggesting direct cell-to-cell interactions leading to electron and material exchange between the two microbes in the syntrophy between Clostridium ljungdahlii and C. acetobutylicum Here, transmission electron microscopy and electron tomography demonstrated cell wall and membrane fusions between the two organisms, whereby C. ljungdahlii appears to invade C. acetobutylicum pole to pole. Correlative fluorescence transmission electron microscopy demonstrated large-scale exchange of proteins. Flow cytometry analysis captured the extent and dynamic persistence of these interactions. Dividing hybrid cells were identified containing stained proteins from both organisms, thus demonstrating persistence of cells with exchanged cellular components. Fluorescence microscopy and flow cytometry of one species with stained RNA and the other tagged with a fluorescent protein demonstrated extensive RNA exchange and identified hybrid cells, some of which continued to divide, while some were in an advanced C. acetobutylicum sporulation form. These data demonstrate that cell fusion enables large-scale cellular material exchange between the two organisms. Although unanticipated and never previously reported, these phenomena are likely widely distributed in nature, have profound implications for species evolution and the function of microbial communities, and could find utility in biotechnology. They may shed new light onto little-understood phenomena, such as antibiotic heteroresistance of pathogens, pathogen invasion of human tissues, and the evolutionary trajectory and persistence of unculturable bacteria.IMPORTANCE We report that two different bacterial organisms engage in heterologous cell fusion that leads to massive exchange of cellular material, including proteins and RNA, and the formation of persistent hybrid cells. The interspecies cell fusion observed here involves a syntrophic microbial system, but these heterologous cell fusions were observed even under nonstrict syntrophic conditions, leaving open the possibility that strict syntrophy may not be necessary for interspecies cell fusion and cellular material exchange. Formation of hybrid cells that contain proteins and RNA from both organisms is unexpected and unprecedented. Such fusion events are likely widely distributed in nature, but have gone undetected. The implications are profound and may shed light onto many unexplained phenomena in human health, natural environments, evolutionary biology, and biotechnology.

Impact of oxidative stress on the function, abundance, and turnover of the Arabidopsis 80S cytosolic ribosome

Abiotic stress in plants causes accumulation of reactive oxygen species (ROS) leading to the need for new protein synthesis to defend against ROS and to replace existing proteins that are damaged by oxidation. Functional plant ribosomes are critical for these activities, however we know little about the impact of oxidative stress on plant ribosome abundance, turnover, and function. Using Arabidopsis cell culture as a model system, we induced oxidative stress using 1 µm of H₂ O₂ or 5 µm menadione to more than halve cell growth rate and limit total protein content. We show that ribosome content on a total cell protein basis decreased in oxidatively stressed cells. However, overall protein synthesis rates on a ribosome abundance basis showed the resident ribosomes retained their function in oxidatively stressed cells. ¹⁵ N progressive labelling was used to calculate the rate of ribosome synthesis and degradation to track the fate of 62 r-proteins. The degradation rates and the synthesis rates of most r-proteins slowed following oxidative stress leading to an ageing population of ribosomes in stressed cells. However, there were exceptions to this trend; r-protein RPS14C doubled its degradation rate in both oxidative treatments. Overall, we show that ribosome abundance decreases and their age increases with oxidative stress in line with loss of cell growth rate and total cellular protein amount, but ribosome function of the ageing ribosomes appeared to be maintained concomittently with differences in the turnover rate and abundance of specific ribosomal proteins. Data are available via ProteomeXchange with identifier PXD012840.

SEQU-INTO: Early detection of impurities, contamination and off-targets (ICOs) in long read/MinION sequencing

The MinION sequencer by Oxford Nanopore Technologies turns DNA and RNA sequencing into a routine task in biology laboratories or in field research. For downstream analysis it is required to have a sufficient amount of target reads. Especially prokaryotic or bacteriophagic sequencing samples can contain a significant amount of off-target sequences in the processed sample, stemming from human DNA/RNA contamination, insufficient rRNA depletion, or remaining DNA/RNA from other organisms (e.g. host organism from bacteriophage cultivation). Such impurity, contamination and off-targets (ICOs) block read capacity, requiring to sequence deeper. In comparison to second-generation sequencing, MinION sequencing allows to reuse its chip after a (partial) run. This allows further usage of the same chip with more sample, even after adjusting the library preparation to reduce ICOs. The earlier a sample's ICOs are detected, the better the sequencing chip can be conserved for future use. Here we present sequ-into, a low-resource and user-friendly cross-platform tool to detect ICO sequences from a predefined ICO database in samples early during a MinION sequencing run. The data provided by sequ-into empowers the user to quickly take action to preserve sample material and chip capacity. sequ-into is available from https://github.com/mjoppich/sequ-into.

Compared to conventional, ecological intensive management promotes beneficial proteolytic soil microbial communities for agro-ecosystem functioning under climate change-induced rain regimes

Projected climate change and rainfall variability will affect soil microbial communities, biogeochemical cycling and agriculture. Nitrogen (N) is the most limiting nutrient in agroecosystems and its cycling and availability is highly dependent on microbial driven processes. In agroecosystems, hydrolysis of organic nitrogen (N) is an important step in controlling soil N availability. We analyzed the effect of management (ecological intensive vs. conventional intensive) on N-cycling processes and involved microbial communities under climate change-induced rain regimes. Terrestrial model ecosystems originating from agroecosystems across Europe were subjected to four different rain regimes for 263 days. Using structural equation modelling we identified direct impacts of rain regimes on N-cycling processes, whereas N-related microbial communities were more resistant. In addition to rain regimes, management indirectly affected N-cycling processes via modifications of N-related microbial community composition. Ecological intensive management promoted a beneficial N-related microbial community composition involved in N-cycling processes under climate change-induced rain regimes. Exploratory analyses identified phosphorus-associated litter properties as possible drivers for the observed management effects on N-related microbial community composition. This work provides novel insights into mechanisms controlling agro-ecosystem functioning under climate change.

An analytical theory of balanced cellular growth

The biological fitness of microbes is largely determined by the rate with which they replicate their biomass composition. Mathematical models that maximize this balanced growth rate while accounting for mass conservation, reaction kinetics, and limits on dry mass per volume are inevitably non-linear. Here, we develop a general theory for such models, termed Growth Balance Analysis (GBA), which provides explicit expressions for protein concentrations, fluxes, and growth rates. These variables are functions of the concentrations of cellular components, for which we calculate marginal fitness costs and benefits that are related to metabolic control coefficients. At maximal growth rate, the net benefits of all concentrations are equal. Based solely on physicochemical constraints, GBA unveils fundamental quantitative principles of cellular resource allocation and growth; it accurately predicts the relationship between growth rates and ribosome concentrations in E. coli and yeast and between growth rate and dry mass density in E. coli.

A chemical kinetic basis for measuring translation initiation and elongation rates from ribosome profiling data

Analysis methods based on simulations and optimization have been previously developed to estimate relative translation rates from next-generation sequencing data. Translation involves molecules and chemical reactions, hence bioinformatics methods consistent with the laws of chemistry and physics are more likely to produce accurate results. Here, we derive simple equations based on chemical kinetic principles to measure the translation-initiation rate, transcriptome-wide elongation rate, and individual codon translation rates from ribosome profiling experiments. Our methods reproduce the known rates from ribosome profiles generated from detailed simulations of translation. By applying our methods to data from S. cerevisiae and mouse embryonic stem cells, we find that the extracted rates reproduce expected correlations with various molecular properties, and we also find that mouse embryonic stem cells have a global translation speed of 5.2 AA/s, in agreement with previous reports that used other approaches. Our analysis further reveals that a codon can exhibit up to 26-fold variability in its translation rate depending upon its context within a transcript. This broad distribution means that the average translation rate of a codon is not representative of the rate at which most instances of that codon are translated, and it suggests that translational regulation might be used by cells to a greater degree than previously thought.

Non-Redfieldian dynamics driven by phytoplankton phosphate frugality explain nutrient and chlorophyll patterns in model simulations for the Mediterranean Sea

The relative abundance of nitrate (N) over phosphate (P) measured as a molar ratio (N:P) is typically considered to determine the macronutrient limiting marine primary production. In low-complexity biogeochemical models, a simple threshold value is usually applied based on the canonical Redfield ratio (N:P = 16). However, the N:P ratio is not constant in many oceanic areas, especially marginal, semi-enclosed seas, such as the Mediterranean basin. In this work, a flexible definition of the N:P ratio based on the capacity of phytoplankton to modulate phosphate uptake according to its availability in seawater, the so-called Line of Frugality, is incorporated into the biogeochemical model MedERGOM. This modification allows the acquisition of a more realistic representation of the stoichiometry of nutrients in the Mediterranean basin and allows to better reproduce the observed phytoplankton biomass in productive areas such as the Gulf of Gabes and the Adriatic Sea. This approach is, thus, especially suitable for coastal areas in which basin-scale biogeochemical models fail to reproduce patterns observed by remote sensing or in situ measurements. Our results show that implementation of the stoichiometric flexibility of phytoplankton in a low-complexity biogeochemical model enhances the reproducibility of ecosystem dynamics without increasing the computational demand, representing a simple approximation easily implemented in models aiming to describe regions with a Non-Redfieldian stoichiometry.

Rapid identification and phylogenetic classification of diverse bacterial pathogens in a multiplexed hybridization assay targeting ribosomal RNA

Rapid bacterial identification remains a critical challenge in infectious disease diagnostics. We developed a novel molecular approach to detect and identify a wide diversity of bacterial pathogens in a single, simple assay, exploiting the conservation, abundance, and rich phylogenetic content of ribosomal RNA in a rapid fluorescent hybridization assay that requires no amplification or enzymology. Of 117 isolates from 64 species across 4 phyla, this assay identified bacteria with >89% accuracy at the species level and 100% accuracy at the family level, enabling all critical clinical distinctions. In pilot studies on primary clinical specimens, including sputum, blood cultures, and pus, bacteria from 5 different phyla were identified.

Networks of ribosome flow models for modeling and analyzing intracellular traffic

The ribosome flow model with input and output (RFMIO) is a deterministic dynamical system that has been used to study the flow of ribosomes during mRNA translation. The input of the RFMIO controls its initiation rate and the output represents the ribosome exit rate (and thus the protein production rate) at the 3′ end of the mRNA molecule. The RFMIO and its variants encapsulate important properties that are relevant to modeling ribosome flow such as the possible evolution of “traffic jams” and non-homogeneous elongation rates along the mRNA molecule, and can also be used for studying additional intracellular processes such as transcription, transport, and more. Here we consider networks of interconnected RFMIOs as a fundamental tool for modeling, analyzing and re-engineering the complex mechanisms of protein production. In these networks, the output of each RFMIO may be divided, using connection weights, between several inputs of other RFMIOs. We show that under quite general feedback connections the network has two important properties: (1) it admits a unique steady-state and every trajectory converges to this steady-state; and (2) the problem of how to determine the connection weights so that the network steady-state output is maximized is a convex optimization problem. These mathematical properties make these networks highly suitable as models of various phenomena: property (1) means that the behavior is predictable and ordered, and property (2) means that determining the optimal weights is numerically tractable even for large-scale networks. For the specific case of a feed-forward network of RFMIOs we prove an additional useful property, namely, that there exists a spectral representation for the network steady-state, and thus it can be determined without any numerical simulations of the dynamics. We describe the implications of these results to several fundamental biological phenomena and biotechnological objectives.

Redox Signaling by Reactive Electrophiles and Oxidants

The concept of cell signaling in the context of nonenzyme-assisted protein modifications by reactive electrophilic and oxidative species, broadly known as redox signaling, is a uniquely complex topic that has been approached from numerous different and multidisciplinary angles. Our Review reflects on five aspects critical for understanding how nature harnesses these noncanonical post-translational modifications to coordinate distinct cellular activities: (1) specific players and their generation, (2) physicochemical properties, (3) mechanisms of action, (4) methods of interrogation, and (5) functional roles in health and disease. Emphasis is primarily placed on the latest progress in the field, but several aspects of classical work likely forgotten/lost are also recollected. For researchers with interests in getting into the field, our Review is anticipated to function as a primer. For the expert, we aim to stimulate thought and discussion about fundamentals of redox signaling mechanisms and nuances of specificity/selectivity and timing in this sophisticated yet fascinating arena at the crossroads of chemistry and biology.

Sequential activation of human signal recognition particle by the ribosome and signal sequence drives efficient protein targeting

Signal recognition particle (SRP) is a universally conserved targeting machine that mediates the targeted delivery of ∼30% of the proteome. The molecular mechanism by which eukaryotic SRP achieves efficient and selective protein targeting remains elusive. Here, we describe quantitative analyses of completely reconstituted human SRP (hSRP) and SRP receptor (SR). Enzymatic and fluorescence analyses showed that the ribosome, together with a functional signal sequence on the nascent polypeptide, are required to activate SRP for rapid recruitment of the SR, thereby delivering translating ribosomes to the endoplasmic reticulum. Single-molecule fluorescence spectroscopy combined with cross-complementation analyses reveal a sequential mechanism of activation whereby the ribosome unlocks the hSRP from an autoinhibited state and primes SRP to sample a variety of conformations. The signal sequence further preorganizes the mammalian SRP into the optimal conformation for efficient recruitment of the SR. Finally, the use of a signal sequence to activate SRP for receptor recruitment is a universally conserved feature to enable efficient and selective protein targeting, and the eukaryote-specific components confer upon the mammalian SRP the ability to sense and respond to ribosomes.

Computational analysis of the oscillatory behavior at the translation level induced by mRNA levels oscillations due to finite intracellular resources

Recent studies have demonstrated how the competition for the finite pool of available gene expression factors has important effect on fundamental gene expression aspects. In this study, based on a whole-cell model simulation of translation in S. cerevisiae, we evaluate for the first time the expected effect of mRNA levels fluctuations on translation due to the finite pool of ribosomes. We show that fluctuations of a single gene or a group of genes mRNA levels induce periodic behavior in all S. cerevisiae translation factors and aspects: the ribosomal densities and the translation rates of all S. cerevisiae mRNAs oscillate. We numerically measure the oscillation amplitudes demonstrating that fluctuations of endogenous and heterologous genes can cause a significant fluctuation of up to 50% in the steady-state translation rates of the rest of the genes. Furthermore, we demonstrate by synonymous mutations that oscillating the levels of mRNAs that experience high ribosomal occupancy (e.g. ribosomal “traffic jam”) induces the largest impact on the translation of the S. cerevisiae genome.

The results reported here should provide novel insights and principles related to the design of synthetic gene expression circuits and related to the evolutionary constraints shaping gene expression of endogenous genes.

Each cell contains a limited number of macromolecules and factors that participate in the gene expression process. These expression resources are shared between the different molecules that encode the genetic code, resulting in non-trivial couplings and competitions between the different gene expression stages. Such competitions should be considered when analyzing the cellular economy of the cell, the genome evolution, and the design of synthetic expression circuits. Here we study the effect of couplings and competitions for ribosomes by performing a whole-cell simulation of translation of S. cerevisiae, with parameters estimated from experimental data. We demonstrate that by periodically changing the mRNA levels of a single gene (endogenous or heterologous) or a set of genes, the translation of all S. cerevisiae genes are affected in a periodic manner. We numerically estimate the exact impact of the mRNA levels periodicity on the translation process dynamics, as well as on the dynamics of the free ribosomal pool and the way it is affected by parameters such as the codon composition of the oscillating gene, its initiation rate and mRNA levels. Furthermore, we show that the codon compositions of synthetically highly expressed heterologous genes that are expected to oscillate must be carefully considered. For example, synonymous mutations resulting in “traffic jams” of ribosomes along the fluctuated mRNAs may cause significant fluctuations of up to 50% in the steady-state translation rates of all genes.

A genomic perspective on stoichiometric regulation of soil carbon cycling

Similar to plant growth, soil carbon (C) cycling is constrained by the availability of nitrogen (N) and phosphorus (P). We hypothesized that stoichiometric control over soil microbial C cycling may be shaped by functional guilds with distinct nutrient substrate preferences. Across a series of rice fields spanning 5-25% soil C (N:P from 1:12 to 1:70), C turnover was best correlated with P availability and increased with experimental N addition only in lower C (mineral) soils with N:P⩽16. Microbial community membership also varied with soil stoichiometry but not with N addition. Shotgun metagenome data revealed changes in community functions with increasing C turnover, including a shift from aromatic C to carbohydrate utilization accompanied by lower N uptake and P scavenging. Similar patterns of C, N and P acquisition, along with higher ribosomal RNA operon copy numbers, distinguished that microbial taxa positively correlated with C turnover. Considering such tradeoffs in genomic resource allocation patterns among taxa strengthened correlations between microbial community composition and C cycling, suggesting simplified guilds amenable to ecosystem modeling. Our results suggest that patterns of soil C turnover may reflect community-dependent metabolic shifts driven by resource allocation strategies, analogous to growth rate-stoichiometry coupling in animal and plant communities.

Environmental Nutrient Supply Directly Alters Plant Traits but Indirectly Determines Virus Growth Rate

Ecological stoichiometry and resource competition theory both predict that nutrient rates and ratios can alter infectious disease dynamics. Pathogens such as viruses hijack nutrient rich host metabolites to complete multiple steps of their epidemiological cycle. As the synthesis of these molecules requires nitrogen (N) and phosphorus (P), environmental supply rates, and ratios of N and P to hosts can directly limit disease dynamics. Environmental nutrient supplies also may alter virus epidemiology indirectly by changing host phenotype or the dynamics of coinfecting pathogens. We tested whether host nutrient supplies and coinfection control pathogen growth within hosts and transmission to new hosts, either directly or through modifications of plant tissue chemistry (i.e., content and stoichiometric ratios of nutrients), host phenotypic traits, or among-pathogen interactions. We examined two widespread plant viruses (BYDV-PAV and CYDV-RPV) in cultivated oats (Avena sativa) grown along a range of N and of P supply rates. N and P supply rates altered plant tissue chemistry and phenotypic traits; however, environmental nutrient supplies and plant tissue content and ratios of nutrients did not directly alter virus titer. Infection with CYDV-RPV altered plant traits and resulted in thicker plant leaves (i.e., higher leaf mass per area) and there was a positive correlation between CYDV-RPV titer and leaf mass per area. CYDV-RPV titer was reduced by the presence of a competitor, BYDV-PAV, and higher CYDV-RPV titer led to more severe chlorotic symptoms. In our experimental conditions, virus transmission was unaffected by nutrient supply rates, co-infection, plant stoichiometry, or plant traits, although nutrient supply rates have been shown to increase infection and coinfection rates. This work provides a robust test of the role of plant nutrient content and ratios in the dynamics of globally important pathogens and reveals a more complex relationship between within-host virus growth and alterations of plant traits. A deeper understanding of the differential effects of environmental nutrient supplies on virus epidemiology and ecology is particularly relevant given the rapid increase of nutrients flowing into Earth's ecosystems as a result of human activities.

Ribosome flow model with extended objects

We study a deterministic mechanistic model for the flow of ribosomes along the mRNA molecule, called the ribosome flow model with extended objects (RFMEO). This model encapsulates many realistic features of translation including non-homogeneous transition rates along mRNA, the fact that every ribosome covers several codons, and the fact that ribosomes cannot overtake one another. The RFMEO is a mean-field approximation of an important model from statistical mechanics called the totally asymmetric simple exclusion process with extended objects (TASEPEO). We demonstrate that the RFMEO describes biophysical aspects of translation better than previous mean-field approximations, and that its predictions correlate well with those of TASEPEO. However, unlike TASEPEO, the RFMEO is amenable to rigorous analysis using tools from systems and control theory. We show that the ribosome density profile along the mRNA in the RFMEO converges to a unique steady-state density that depends on the length of the mRNA, the transition rates along it, and the number of codons covered by every ribosome, but not on the initial density of ribosomes along the mRNA. In particular, the protein production rate also converges to a unique steady state. Furthermore, if the transition rates along the mRNA are periodic with a common period T then the ribosome density along the mRNA and the protein production rate converge to a unique periodic pattern with period T, that is, the model entrains to periodic excitations in the transition rates. Analysis and simulations of the RFMEO demonstrate several counterintuitive results. For example, increasing the ribosome footprint may sometimes lead to an increase in the production rate. Also, for large values of the footprint the steady-state density along the mRNA may be quite complex (e.g. with quasi-periodic patterns) even for relatively simple (and non-periodic) transition rates along the mRNA. This implies that inferring the transition rates from the ribosome density may be non-trivial. We believe that the RFMEO could be useful for modelling, understanding and re-engineering translation as well as other important biological processes.

A deterministic mathematical model for bidirectional excluded flow with Langmuir kinetics

In many important cellular processes, including mRNA translation, gene transcription, phosphotransfer, and intracellular transport, biological "particles" move along some kind of "tracks". The motion of these particles can be modeled as a one-dimensional movement along an ordered sequence of sites. The biological particles (e.g., ribosomes or RNAPs) have volume and cannot surpass one another. In some cases, there is a preferred direction of movement along the track, but in general the movement may be bidirectional, and furthermore the particles may attach or detach from various regions along the tracks. We derive a new deterministic mathematical model for such transport phenomena that may be interpreted as a dynamic mean-field approximation of an important model from mechanical statistics called the asymmetric simple exclusion process (ASEP) with Langmuir kinetics. Using tools from the theory of monotone dynamical systems and contraction theory we show that the model admits a unique steady-state, and that every solution converges to this steady-state. Furthermore, we show that the model entrains (or phase locks) to periodic excitations in any of its forward, backward, attachment, or detachment rates. We demonstrate an application of this phenomenological transport model for analyzing ribosome drop off in mRNA translation.

Growth rate correlates negatively with protein turnover in Arabidopsis accessions

Previous studies with Arabidopsis accessions revealed that biomass correlates negatively to dusk starch content and total protein, and positively to the maximum activities of enzymes in photosynthesis. We hypothesized that large accessions have lower ribosome abundance and lower rates of protein synthesis, and that this is compensated by lower rates of protein degradation. This would increase growth efficiency and allow more investment in photosynthetic machinery. We analysed ribosome abundance and polysome loading in 19 accessions, modelled the rates of protein synthesis and compared them with the observed rate of growth. Large accessions contained less ribosomes than small accessions, due mainly to cytosolic ribosome abundance falling at night in large accessions. The modelled rates of protein synthesis resembled those required for growth in large accessions, but were up to 30% in excess in small accessions. We then employed ¹³ CO₂ pulse-chase labelling to measure the rates of protein synthesis and degradation in 13 accessions. Small accessions had a slightly higher rate of protein synthesis and much higher rates of protein degradation than large accessions. Protein turnover was negligible in large accessions but equivalent to up to 30% of synthesised protein day^-1 in small accessions. We discuss to what extent the decrease in growth in small accessions can be quantitatively explained by known costs of protein turnover and what factors may lead to the altered diurnal dynamics and increase of ribosome abundance in small accessions, and propose that there is a trade-off between protein turnover and maximisation of growth rate.

Optimal Down Regulation of mRNA Translation

Down regulation of mRNA translation is an important problem in various bio-medical domains ranging from developing effective medicines for tumors and for viral diseases to developing attenuated virus strains that can be used for vaccination. Here, we study the problem of down regulation of mRNA translation using a mathematical model called the ribosome flow model (RFM). In the RFM, the mRNA molecule is modeled as a chain of n sites. The flow of ribosomes between consecutive sites is regulated by n + 1 transition rates. Given a set of feasible transition rates, that models the outcome of all possible mutations, we consider the problem of maximally down regulating protein production by altering the rates within this set of feasible rates. Under certain conditions on the feasible set, we show that an optimal solution can be determined efficiently. We also rigorously analyze two special cases of the down regulation optimization problem. Our results suggest that one must focus on the position along the mRNA molecule where the transition rate has the strongest effect on the protein production rate. However, this rate is not necessarily the slowest transition rate along the mRNA molecule. We discuss some of the biological implications of these results.

Comparative evaluation of rRNA depletion procedures for the improved analysis of bacterial biofilm and mixed pathogen culture transcriptomes

Global transcriptomic analysis via RNA-seq is often hampered by the high abundance of ribosomal (r)RNA in bacterial cells. To remove rRNA and enrich coding sequences, subtractive hybridization procedures have become the approach of choice prior to RNA-seq, with their efficiency varying in a manner dependent on sample type and composition. Yet, despite an increasing number of RNA-seq studies, comparative evaluation of bacterial rRNA depletion methods has remained limited. Moreover, no such study has utilized RNA derived from bacterial biofilms, which have potentially higher rRNA:mRNA ratios and higher rRNA carryover during RNA-seq analysis. Presently, we evaluated the efficiency of three subtractive hybridization-based kits in depleting rRNA from samples derived from biofilm, as well as planktonic cells of the opportunistic human pathogen Pseudomonas aeruginosa. Our results indicated different rRNA removal efficiency for the three procedures, with the Ribo-Zero kit yielding the highest degree of rRNA depletion, which translated into enhanced enrichment of non-rRNA transcripts and increased depth of RNA-seq coverage. The results indicated that, in addition to improving RNA-seq sensitivity, efficient rRNA removal enhanced detection of low abundance transcripts via qPCR. Finally, we demonstrate that the Ribo-Zero kit also exhibited the highest efficiency when P. aeruginosa/Staphylococcus aureus co-culture RNA samples were tested.

Role of Proteome Physical Chemistry in Cell Behavior

We review how major cell behaviors, such as bacterial growth laws, are derived from the physical chemistry of the cell’s proteins. On one hand, cell actions depend on the individual biological functionalities of their many genes and proteins. On the other hand, the common physics among proteins can be as important as the unique biology that distinguishes them. For example, bacterial growth rates depend strongly on temperature. This dependence can be explained by the folding stabilities across a cell’s proteome. Such modeling explains how thermophilic and mesophilic organisms differ, and how oxidative damage of highly charged proteins can lead to unfolding and aggregation in aging cells. Cells have characteristic time scales. For example, E. coli can duplicate as fast as 2–3 times per hour. These time scales can be explained by protein dynamics (the rates of synthesis and degradation, folding, and diffusional transport). It rationalizes how bacterial growth is slowed down by added salt. In the same way that the behaviors of inanimate materials can be expressed in terms of the statistical distributions of atoms and molecules, some cell behaviors can be expressed in terms of distributions of protein properties, giving insights into the microscopic basis of growth laws in simple cells.

The Effect of Codon Mismatch on the Protein Translation System

Incorrect protein translation, caused by codon mismatch, is an important problem of living cells. In this work, a computational model was introduced to quantify the effects of codon mismatch and the model was used to study the protein translation of Saccharomyces cerevisiae. According to simulation results, the probability of codon mismatch will increase when the supply of amino acids is unbalanced, and the longer is the codon sequence, the larger is the probability for incorrect translation to occur, making the synthesis of long peptide chain difficult. By comparing to simulation results without codon mismatch effects taken into account, the fraction of mRNAs with bound ribosome decrease faster along the mRNAs, making the 5’ ramp phenomenon more obvious. It was also found in our work that the premature mechanism resulted from codon mismatch can reduce the proportion of incorrect translation when the amino acid supply is extremely unbalanced, which is one possible source of high fidelity protein synthesis after peptidyl transfer.

Understanding of polyhydroxybutyrate production under carbon and phosphorus-limited growth conditions in non-axenic continuous culture

In a waste into resource strategy, a selection of polyhydroxybutyrate (PHB)-accumulating organisms from activated sludge was achieved in an open continuous culture under acetic acid and phosphorus limitation. Once the microbial population was selected at a dilution rate (D), an increase in phosphorus limitation degree was applied in order to study the intracellular phosphorus plasticity of selected bacteria and the resulting capacity to produce PHB. Whatever D, all selected populations were able to produce PHB. At a D, the phosphorus availability determined the phosphorus-cell content which in turn fixed the amount of cell. All the remaining carbon was thus directed toward PHB. By decreasing D, microorganisms adapted more easily to higher phosphorus limitation leading to higher PHB content. A one-stage continuous reactor operated at D=0.023h(-)(1) gave reliable high PHB productivity with PHB content up to 80%. A two-stage reactor could ensure better productivity while allowing tuning product quality.

Novel Essential Role of Ethanol Oxidation Genes at Low Temperature Revealed by Transcriptome Analysis in the Antarctic Bacterium Pseudomonas extremaustralis

Temperature is one of the most important factors for bacterial growth and development. Cold environments are widely distributed on earth, and psychrotolerant and psychrophilic microorganisms have developed different adaptation strategies to cope with the stress derived from low temperatures. Pseudomonas extremaustralis is an Antarctic bacterium able to grow under low temperatures and to produce high amounts of polyhydroxyalkanoates (PHAs). In this work, we analyzed the genome-wide transcriptome by RNA deep-sequencing technology of early exponential cultures of P. extremaustralis growing in LB (Luria Broth) supplemented with sodium octanoate to favor PHA accumulation at 8°C and 30°C. We found that genes involved in primary metabolism, including tricarboxylic acid cycle (TCA) related genes, as well as cytochromes and amino acid metabolism coding genes, were repressed at low temperature. Among up-regulated genes, those coding for transcriptional regulatory and signal transduction proteins were over-represented at cold conditions. Remarkably, we found that genes involved in ethanol oxidation, exaA, exaB and exaC, encoding a pyrroloquinoline quinone (PQQ)-dependent ethanol dehydrogenase, the cytochrome c550 and an aldehyde dehydrogenase respectively, were up-regulated. Along with RNA-seq experiments, analysis of mutant strains for pqqB (PQQ biosynthesis protein B) and exaA were carried out. We found that the exaA and pqqB genes are essential for growth under low temperature in LB supplemented with sodium octanoate. Additionally, p-rosaniline assay measurements showed the presence of alcohol dehydrogenase activity at both 8°C and 30°C, while the activity was abolished in a pqqB mutant strain. These results together with the detection of ethanol by gas chromatography in P. extremaustralis cultures grown at 8°C support the conclusion that this pathway is important under cold conditions. The obtained results have led to the identification of novel components involved in cold adaptation mechanisms in this bacterium, suggesting for the first time a role of the ethanol oxidation pathway for bacterial growth at low temperatures.

Growth rate hypothesis and efficiency of protein synthesis under different sulphate concentrations in two green algae

The growth rate hypothesis (GRH) predicts a positive correlation between growth rate and RNA content because growth depends upon the protein synthesis machinery. The application of this hypothesis to photoautotrophic organisms has been questioned. We tested the GRH on one prasinophycean, Tetraselmis suecica, and one chlorophycean, Dunaliella salina, grown at three sulphate concentrations. Sulphate was chosen because its concentration in the oceans increased through geological time and apparently had a role in the evolutionary trajectories of phytoplankton. Cell protein content and P quota were positively related to the RNA content (r = 0.62 and r = 0.74, respectively). The correlation of the RNA content with growth rates (r = 0.95) indicates that the GRH was valid for these species when growth rates were below 0.82 d(-1) .

Community transcriptomics reveals unexpected high microbial diversity in acidophilic biofilm communities

A fundamental question in microbial ecology relates to community structure, and how this varies across environment types. It is widely believed that some environments, such as those at very low pH, host simple communities based on the low number of taxa, possibly due to the extreme environmental conditions. However, most analyses of species richness have relied on methods that provide relatively low ribosomal RNA (rRNA) sampling depth. Here we used community transcriptomics to analyze the microbial diversity of natural acid mine drainage biofilms from the Richmond Mine at Iron Mountain, California. Our analyses target deep pools of rRNA gene transcripts recovered from both natural and laboratory-grown biofilms across varying developmental stages. In all, 91.8% of the ∼ 254 million Illumina reads mapped to rRNA genes represented in the SILVA database. Up to 159 different taxa, including Bacteria, Archaea and Eukaryotes, were identified. Diversity measures, ordination and hierarchical clustering separate environmental from laboratory-grown biofilms. In part, this is due to the much larger number of rare members in the environmental biofilms. Although Leptospirillum bacteria generally dominate biofilms, we detect a wide variety of other Nitrospira organisms present at very low abundance. Bacteria from the Chloroflexi phylum were also detected. The results indicate that the primary characteristic that has enabled prior extensive cultivation-independent 'omic' analyses is not simplicity but rather the high dominance by a few taxa. We conclude that a much larger variety of organisms than previously thought have adapted to this extreme environment, although only few are selected for at any one time.