Biology subjects

Hu, X.-P.

Publications and source records attributed to Hu, X.-P..

7 recordsLinked to original sources

bioRxiv2023

Nearly half of all bacterial gene families are biased toward specific chromosomal positions

Many bacterial genes encoding components of the translation and transcription machinery occupy genomic positions close to the origin of genome replication. These positional biases are thought to result from selection for high expression levels, as genes close to the origin are the first to be duplicated after the initiation of DNA replication. However, recent work indicates that positional biases of RNA genes involved in translation have evolved to support optimal growth rate dependence of expression levels rather than high expression per se. We hypothesized that, more generally, natural selection may have favored the location of different genes at specific chromosomal positions to optimize the growth rate dependence of their relative gene copy numbers. Here we show that 49% of bacterial gene families are preferentially localized to specific chromosomal regions, with most families biased toward either the origin or the terminus of replication. From our hypothesis, we derive six specific predictions regarding the genes chromosomal positioning and functional categories, as well as the minimum cellular doubling times. All six predictions are robustly supported by comparative genomic analyses of 773 bacterial species and by proteomics data from Escherichia coli and Bacillus subtilis. Our findings reveal a complex relationship between bacterial growth, resource allocation and genome organization, and they provide new insights into the physiological significance and potential functions of a large number of gene families.

genomics↗

bioRxiv2022

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

Understanding the allocation of the cellular proteome to different cellular processes is central to unraveling the organizing principles of bacterial physiology. Proteome allocation to protein translation itself is maximally efficient, i.e., it represents the minimal allocation of dry mass able to sustain the observed protein production rate. In contrast, recent studies on bacteria have demonstrated that the concentrations of many proteins exceed the minimal level required to support the observed growth rate, indicating some heterogeneity across pathways in their proteome efficiency. Here, we systematically analyze the proteome efficiency of metabolic pathways, which together account for more than half of the E. coli proteome during exponential growth. Comparing the predicted minimal and the observed proteome allocation to different metabolic pathways across growth conditions, we find that the most costly biosynthesis pathways - those for amino acid biosynthesis and cofactor biosynthesis - are expressed for near optimal efficiency. Overall, proteome efficiency increases along the carbon flow through the metabolic network: proteins involved in pathways of nutrient uptake and central metabolism tend to be highly over-abundant, while proteins involved in anabolic pathways and in protein translation are much closer to the expected minimal abundance across conditions. Our work thus provides a birds-eye view of metabolic pathway efficiency, demonstrating systematic deviations from optimal cellular efficiency at the network level. ImportanceProtein 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 E. 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, are 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.

systems biology↗

bioRxiv2022

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 difference 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 at https://turnup.cs.hhu.de.

bioinformatics↗

bioRxiv2022

The Spatial Organization of Bacterial Transcriptional Regulatory Networks

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 which may be closely related with basic life activities. Our findings provided new insights into the connection between transcriptional regulation and the spatial organization of chromosome in bacteria, and might serve as a foundation for spatial-distance-based gene circuit design in synthetic biology.

systems biology↗

bioRxiv2021

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 ratio appears to be regulated entirely through this effect.

systems biology↗

bioRxiv2019

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

Protein synthesis is the most expensive process in fast-growing bacteria1,2. The economic aspects of protein synthesis at the cellular level have been investigated by estimating ribosome activity3-5 and the expression of ribosomes3,6, tRNA7-9, mRNA2, and elongation factors10,11. The observed growth-rate dependencies form the basis of powerful phenomenological bacterial growth laws5,12-16; however, a quantitative theory allowing us to understand these phenomena on the basis of fundamental biophysical and biochemical principles is currently lacking. Here, we show that the observed growth-rate dependence of the concentrations of ribosomes, tRNAs, mRNA, and elongation factors in Escherichia coli can be predicted accurately by minimizing cellular costs in a detailed mathematical model of protein translation; the mechanistic model is only constrained by the physicochemical properties of the molecules and requires no parameter fitting. We approximate the costs of molecule species through their masses, justified by the observation that cellular dry mass per volume is roughly constant across growth rates17 and hence represents a limited resource. Our results also account quantitatively for observed RNA/protein ratios and ribosome activities in E. coli across diverse growth conditions, including antibiotic stresses. Our prediction of active and free ribosome abundance facilitates an estimate of the deactivated ribosome reserve14,18,19, which reaches almost 50% at the lowest growth rates. We conclude that the growth rate dependent composition of E colis protein synthesis machinery is a consequence of natural selection for minimal total cost under physicochemical constraints, a paradigm that might generally be applied to the analysis of resource allocation in complex biological systems.

biophysics↗

bioRxiv2018

Identification of Miro as a mitochondrial receptor for myosin XIX

Mitochondrial distribution in cells is critical for cellular function and proper inheritance during cell division. In mammalian cells, mitochondria are transported predominantly along microtubules by kinesin and dynein and along actin filaments by myosin. Myosin XIX (Myo19) associates with the outer mitochondrial membrane, but no specific receptor has been identified. Using proximity BioID labeling, we identified Miro-1 and Miro-2 as potential binding partners of Myo19. Interaction studies show that Miro-1 binds to a C-terminal fragment of the Myo19 tail region and that Miro recruits the Myo19 tail in vivo. This recruitment is regulated by the nucleotide-state of the N-terminal Rho-like GTPase domain of Miro. Notably, Myo19 protein stability in cells depends on its association with Miro. Finally, Myo19 regulates the subcellular distribution of mitochondria. Downregulation, as well as overexpression, of Myo19 induces perinuclear collapse of mitochondria, phenocopying the loss of kinesin KIF5 or its mitochondrial receptor Miro. These results suggest that Miro coordinates microtubule- and actin-based mitochondrial movement.

cell biology↗