Proteome efficiency of metabolic pathways in Escherichia coli increases along the nutrient flow.

IMPORTANCE: Protein translation is the most expensive cellular process in fast-growing bacteria, and efficient proteome usage should thus be under strong natural selection. However, recent studies show that a considerable part of the proteome is unneeded for instantaneous cell growth in Escherichia coli. We still lack a systematic understanding of how this excess proteome is distributed across different pathways as a function of the growth conditions. We estimated the minimal required proteome across growth conditions in E. coli and compared the predictions with experimental data. We found that the proteome allocated to the most expensive internal pathways, including translation and the synthesis of amino acids and cofactors, is near the minimally required levels. In contrast, transporters and central carbon metabolism show much higher proteome levels than the predicted minimal abundance. Our analyses show that the proteome fraction unneeded for instantaneous cell growth decreases along the nutrient flow in E. coli.

Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning.

The turnover number kcat, a measure of enzyme efficiency, is central to understanding cellular physiology and resource allocation. As experimental kcat estimates are unavailable for the vast majority of enzymatic reactions, the development of accurate computational prediction methods is highly desirable. However, existing machine learning models are limited to a single, well-studied organism, or they provide inaccurate predictions except for enzymes that are highly similar to proteins in the training set. Here, we present TurNuP, a general and organism-independent model that successfully predicts turnover numbers for natural reactions of wild-type enzymes. We constructed model inputs by representing complete chemical reactions through differential reaction fingerprints and by representing enzymes through a modified and re-trained Transformer Network model for protein sequences. TurNuP outperforms previous models and generalizes well even to enzymes that are not similar to proteins in the training set. Parameterizing metabolic models with TurNuP-predicted kcat values leads to improved proteome allocation predictions. To provide a powerful and convenient tool for the study of molecular biochemistry and physiology, we implemented a TurNuP web server.

The Spatial Organization of Bacterial Transcriptional Regulatory Networks.

The transcriptional regulatory network (TRN) is the central pivot of a prokaryotic organism to receive, process and respond to internal and external environmental information. However, little is known about its spatial organization so far. In recent years, chromatin interaction data of bacteria such as Escherichia coli and Bacillus subtilis have been published, making it possible to study the spatial organization of bacterial transcriptional regulatory networks. By combining TRNs and chromatin interaction data of E. coli and B. subtilis, we explored the spatial organization characteristics of bacterial TRNs in many aspects such as regulation directions (positive and negative), central nodes (hubs, bottlenecks), hierarchical levels (top, middle, bottom) and network motifs (feed-forward loops and single input modules) of the TRNs and found that the bacterial TRNs have a variety of stable spatial organization features under different physiological conditions that may be closely related with biological functions. Our findings provided new insights into the connection between transcriptional regulation and the spatial organization of chromosome in bacteria and might serve as a factual foundation for trying spatial-distance-based gene circuit design in synthetic biology.

An optimal growth law for RNA composition and its partial implementation through ribosomal and tRNA gene locations in bacterial genomes.

The distribution of cellular resources across bacterial proteins has been quantified through phenomenological growth laws. Here, we describe a complementary bacterial growth law for RNA composition, emerging from optimal cellular resource allocation into ribosomes and ternary complexes. The predicted decline of the tRNA/rRNA ratio with growth rate agrees quantitatively with experimental data. Its regulation appears to be implemented in part through chromosomal localization, as rRNA genes are typically closer to the origin of replication than tRNA genes and thus have increasingly higher gene dosage at faster growth. At the highest growth rates in E. coli, the tRNA/rRNA gene dosage ratio based on chromosomal positions is almost identical to the observed and theoretically optimal tRNA/rRNA expression ratio, indicating that the chromosomal arrangement has evolved to favor maximal transcription of both types of genes at this condition.

The protein translation machinery is expressed for maximal efficiency in Escherichia coli.

Protein synthesis is the most expensive process in fast-growing bacteria. Experimentally observed growth rate dependencies of the translation machinery form the basis of powerful phenomenological growth laws; however, a quantitative theory on the basis of biochemical and biophysical constraints is lacking. Here, we show that the growth rate-dependence of the concentrations of ribosomes, tRNAs, mRNA, and elongation factors observed in Escherichia coli can be predicted accurately from a minimization of cellular costs in a mechanistic model of protein translation. The model is constrained only by the physicochemical properties of the molecules and has no adjustable parameters. The costs of individual components (made of protein and RNA parts) can be approximated through molecular masses, which correlate strongly with alternative cost measures such as the molecules' carbon content or the requirement of energy or enzymes for their biosynthesis. Analogous cost minimization approaches may facilitate similar quantitative insights also for other cellular subsystems.

Lack of associations between lactoferrin (LTF) and mannose-binding lectin 2 (MBL2) gene polymorphism and dental caries susceptibility.

OBJECTIVE: With the development of human genomics, the genetic factors associated with dental caries have receiving increasing attention. This study was performed to evaluate the relationship between lactoferrin (LTF) and mannose-binding lectin 2 (MBL2) gene single nucleotide polymorphisms (SNPs) and dental caries susceptibility in Chinese children. METHODS: This prospective case-control study included 360 unrelated children (aged 12-15 years) who received oral health examinations and questionnaire surveys. The children were divided into two groups by counting the numbers of decayed, missing, and filled teeth (DMFT/dmft): case group (n = 162, DMFT/dmft ≥ 1) and control group (n = 198, DMFT/dmft = 0); non-invasive saliva samples were collected to extract genomic DNA. Six SNPs (rs2073495C/G, rs1042073C/T, rs10865941C/T, and rs1126477A/G in LTF; rs7096206C/G and rs7095891G/A in MBL2) were tested by mass spectrometry. RESULTS: The study included 360 individuals with (85 boys and 77 girls) and without a history of caries (96 boys and 102 girls). There were no statistically significant differences in alleles and genotypes among the six SNPs between the two groups. CONCLUSION: There is no evidence that polymorphisms of LTF and MBL2 genes are associated with dental caries susceptibility in populations from northwest China; further confirmation is needed with larger sample sizes.

Association of ENAM, TUFT1, MMP13, IL1B, IL10 and IL1RN gene polymorphism and dental caries susceptibility in Chinese children.

OBJECTIVE: To investigate the association between single nucleotide polymorphisms (SNPs) in six candidate genes (enamelin [ ENAM]; tuftelin 1 [ TUFT1]; matrix metallopeptidase 13 [ MMP13]; interleukin 1 beta [ IL1B]; interleukin 10 [ IL10]; interleukin 1 receptor antagonist [ IL1RN]) and dental caries in children from northwest China. METHODS: This case-control study enrolled children (12-15 years) who underwent routine dental examinations. The children were divided into two groups based on the presence of dental caries. A saliva sample was collected and seven SNPs (rs3806804A/G in ENAM, rs3811411T/G in TUFT1, rs2252070A/G and rs597315A/T in MMP13, rs1143627C/T in IL1B, rs1800872A/C in IL10 and rs956730G/A in IL1RN) were genotyped. RESULTS: A total of 357 children were enrolled in the study: 161 with dental caries and 196 without dental caries. No significant difference was found in the alleles and genotypes of five genes ( ENAM, TUFT1, MMP13, IL10 and IL1RN) between those with and without dental caries. A significant relationship was found between the IL1B rs1143627C/T polymorphism and dental caries susceptibility with those carrying the rs1143627CT genotype having a lower risk of dental caries compared with those carrying the CC genotype (odds ratio 0.557; 95% confidence interval 0.326, 0.952). CONCLUSION: The IL1B rs1143627C/T polymorphism may be associated with dental caries susceptibility in children from northwest China.

Construction and simulation of the Bradyrhizobium diazoefficiens USDA110 metabolic network: a comparison between free-living and symbiotic states.

Bradyrhizobium diazoefficiens is a rhizobium able to convert atmospheric nitrogen into ammonium by establishing mutualistic symbiosis with soybean. It has been recognized as an important parent strain for microbial agents and is widely applied in agricultural and environmental fields. In order to study the metabolic properties of symbiotic nitrogen fixation and the differences between a free-living cell and a symbiotic bacteroid, a genome-scale metabolic network of B. diazoefficiens USDA110 was constructed and analyzed. The metabolic network, iYY1101, contains 1031 reactions, 661 metabolites, and 1101 genes in total. Metabolic models reflecting free-living and symbiotic states were determined by defining the corresponding objective functions and substrate input sets, and were further constrained by high-throughput transcriptomic and proteomic data. Constraint-based flux analysis was used to compare the metabolic capacities and the effects on the metabolic targets of genes and reactions between the two physiological states. The results showed that a free-living rhizobium possesses a steady state flux distribution for sustaining a complex supply of biomass precursors while a symbiotic bacteroid maintains a relatively condensed one adapted to nitrogen-fixation. Our metabolic models may serve as a promising platform for better understanding the symbiotic nitrogen fixation of this species.

Are protein hubs faster folders? Exploration based on Escherichia coli proteome.

Protein hubs in protein-protein interaction network are especially important due to their central roles in the entire network. Despite of their importance, the folding kinetics of hub proteins in comparison with non-hubs is still unknown. In this work, the folding rates for protein hubs and non-hubs were predicted and compared for the interactome of Escherichia coli K12, and the results showed that hub proteins fold faster than non-hub proteins. A possible explanation might be that protein hubs have more and fast-folding structural conformations than non-hubs, which leads to the notion of "hub of hubs" in the protein conformation space. It was found that the sequence and structure features relevant to protein folding rates are also different between hub and non-hub proteins. Moreover, the interacting proteins tend to have similar folding rates. These results gave insightful implications for understanding the interplay between the mechanisms of protein folding and interaction.

[Microbial Community Structure on the Root Surface of Patients with Periodontitis.]

OBJECTIVES: To study the microbial community structure on the root surface of patients with periodontitis. METHODS: Bacterial plaque and tissues from the root neck (RN group),root middle (RM group) and root tine (RT group) of six teeth with mobility 3 in one patient with periodontitis were sampled.The V3V4 region of 16S rRNA was sequenced on the Illumina MiSeq platform.The microbial community structure was analyzed by Mothur,Qiime and SPSS software. RESULTS: The principal component analysis (PCoA) results indicated that the RM samples had a similar microbial community structure as that of the RT samples,which was significant different from that of the RN samples.Thirteen phyla were detected in the three groups of samples,which included 7 dominant phyla.29 dominant genera were detected in 184 genera.The abundance of Bacteroidetes_[G-6] and Peptostre ptococcaceae_[XI][G-4] had a positive correlation with the depth of the collection site of samples (P<0.05),while the abundance of Prevotella,Selenomonas,Corynebacterium and Olsenella had a negative correlation with the depth of the collection site of samples (P<0.05). CONCLUSIONS: There is region-specificity of microbial community structure on the root surface of patients with periodontitis.

Amino Acid Flux from Metabolic Network Benefits Protein Translation: the Role of Resource Availability.

Protein translation is a central step in gene expression and affected by many factors such as codon usage bias, mRNA folding energy and tRNA abundance. Despite intensive previous studies, how metabolic amino acid supply correlates with protein translation efficiency remains unknown. In this work, we estimated the amino acid flux from metabolic network for each protein in Escherichia coli and Saccharomyces cerevisiae by using Flux Balance Analysis. Integrated with the mRNA expression level, protein abundance and ribosome profiling data, we provided a detailed description of the role of amino acid supply in protein translation. Our results showed that amino acid supply positively correlates with translation efficiency and ribosome density. Moreover, with the rank-based regression model, we found that metabolic amino acid supply facilitates ribosome utilization. Based on the fact that the ribosome density change of well-amino-acid-supplied genes is smaller than poorly-amino-acid-supply genes under amino acid starvation, we reached the conclusion that amino acid supply may buffer ribosome density change against amino acid starvation and benefit maintaining a relatively stable translation environment. Our work provided new insights into the connection between metabolic amino acid supply and protein translation process by revealing a new regulation strategy that is dependent on resource availability.

Sequence determinants of prokaryotic gene expression level under heat stress.

Prokaryotic gene expression is environment-dependent and temperature plays an important role in shaping the gene expression profile. Revealing the regulation mechanisms of gene expression pertaining to temperature has attracted tremendous efforts in recent years particularly owning to the yielding of transcriptome and proteome data by high-throughput techniques. However, most of the previous works concentrated on the characterization of the gene expression profile of individual organism and little effort has been made to disclose the commonality among organisms, especially for the gene sequence features. In this report, we collected the transcriptome and proteome data measured under heat stress condition from recently published literature and studied the sequence determinants for the expression level of heat-responsive genes on multiple layers. Our results showed that there indeed exist commonness and consistent patterns of the sequence features among organisms for the differentially expressed genes under heat stress condition. Some features are attributed to the requirement of thermostability while some are dominated by gene function. The revealed sequence determinants of bacterial gene expression level under heat stress complement the knowledge about the regulation factors of prokaryotic gene expression responding to the change of environmental conditions. Furthermore, comparisons to thermophilic adaption have been performed to reveal the similarity and dissimilarity of the sequence determinants for the response to heat stress and for the adaption to high habitat temperature, which elucidates the complex landscape of gene expression related to the same physical factor of temperature.