Model-driven insights into the effects of temperature on metabolism

Scaling metabolic model reconstruction up to the pan-genome level: A systematic review and prospective applications to photosynthetic organisms

Advances in genomics technologies have generated large data sets that provide tremendous insights into the genetic diversity of taxonomic groups. However, it remains challenging to pinpoint the effect of genetic diversity on different traits without performing resource-intensive phenotyping experiments. Pan-genome-scale metabolic models (panGEMs) extend traditional genome-scale metabolic models by considering the entire reaction repertoire that enables the prediction and comparison of metabolic capabilities within a taxonomic group. Here, we systematically review the state-of-the-art methodologies for constructing panGEMs, focusing on used tools, databases, experimental datasets, and orthology relationships. We highlight the unique advantages of panGEMs compared to single-species GEMs in predicting metabolic phenotypes and in guiding the experimental validation of genome annotations. In addition, we emphasize the disparity between the available (pan-)genomic data on photosynthetic organisms and their under-representation in current (pan)GEMs. Finally, we propose a perspective for tackling the reconstruction of panGEMs for photosynthetic eukaryotes that can help advance our understanding of the metabolic diversity in this taxonomic group.

Elevated temperature magnifies the toxicity of imidacloprid in the collembolan, Folsomia candida

Global warming subjects soil organisms to elevated temperature stress, while simultaneously altering the detoxification processes for pollutants within these organisms. The combined stressors of increased temperature and pollutants may impose synergistic stress on soil fauna, necessitating detailed investigation. Here, we exposed Collembola (Folsomia candida) to imidacloprid (a neonicotinoid pesticide) in combination with a range of constant temperatures in a full-factorial experimental design to assess the integrated impacts on survival, growth, and bioaccumulation. The results revealed that high temperatures and imidacloprid synergistically inhibited the survival of F. candida. Under 6.4 mg/kg imidacloprid exposure, survival rates decreased by 41.38 % at 30.2 °C and 68.75 % at 30.5 °C, compared to the same temperature treatments without imidacloprid exposure. Bayesian model analysis confirmed a significant synergistic interaction between imidacloprid and temperature on survival. Interestingly, at elevated temperatures, the internal concentration of imidacloprid in F. candida significantly decreased, while the soil concentration of the insecticide remained stable. This suggests that the observed synergistic effect is not due to increased pollutant accumulation within F. candida at higher temperatures, but rather the exhaustion of energy resources needed for detoxification and thermal stress management. This dual-stressor-induced energy competition underpins the synergistic interactions observed. Our findings highlight the significant synergistic effects of high temperatures and imidacloprid on Collembola, underscoring an increased ecological risk under such conditions.

Metabolic modeling identifies determinants of thermal growth responses in Arabidopsis thaliana

Temperature is a critical environmental factor affecting nearly all plant processes, including growth, development, and yield. Yet, despite decades of research, we lack the ability to predict plant performance at different temperatures, limiting the development of climate-resilient crops. Further, there is a pressing need to bridge the gap between the prediction of physiological and molecular traits to improve our understanding and manipulation of plant temperature responses. Here, we developed the first enzyme-constrained model of Arabidopsis thaliana's metabolism, facilitating predictions of growth-related phenotypes at different temperatures. We showed that the model can be employed for in silico identification of genes that affect plant growth at suboptimal growth temperature. Using mutant lines, we validated the genes predicted to affect plant growth, demonstrating the potential of metabolic modeling in accurately predicting plant thermal responses. The temperature-dependent enzyme-constrained metabolic model provides a template that can be used for developing sophisticated strategies to engineer climate-resilient crops.

Coexposure to ambient air pollution and temperature and its associations with birth outcomes in women undergoing assisted reproductive technology in Fujian, China: A retrospective cohort study

BACKGROUND

The interactions between pollutants and temperature coexposure, the mixing effects and their potential mechanisms remain uncertain.

METHODS

This retrospective cohort study included 11,766 women with infertility who received treatment at Fujian Hospital between 2015 and 2024. The daily mean concentrations of the six pollutants and the relative humidity and temperature data were acquired from the Fujian region. Data on genes were obtained from the Comparative Toxicogenomics Database.

RESULTS

O3 (aOR=0.80, 95 % CI=0.725--0.891) and temperature (aOR=0.936, 95 % CI=0.916--0.957) were negatively correlated with live birth rates. Moreover, PM10 (aOR=1.135, 95 % CI=1.028--1.252) and PM2.5 (aOR=1.146, 95 % CI=1.03--1.274) were positively associated with preterm birth. Among the effects on live births, PM2.5, PM10, NO2, CO, and SO2 had significant synergistic effects with temperature; in addition, O3 had significant antagonistic effects with temperature. A notable trend toward declining live birth rates with elevated concentrations of mixed pollutants was observed. Different infertility patients have different sensitivities to coexposure. Gene enrichment and cell experiments are associated mainly with cellular life activities.

CONCLUSIONS

Individual effects, interactions, and mixed effects between temperature and air pollutants and birth outcomes persist when air pollutant levels are relatively low. AAP may trigger miscarriage through cytotoxic effects.

Water-logged composting with sealed system enhances phosphorus availability and changes ecological attributes of bacterial community

Deciphering effects of sealed environment on phosphorus (P) availability and microbial community during water-logged composting is an essential but underestimated theme. Research targets are to unveil divergences in P fractions and bacterial landscapes between breathable and sealed systems using molecular and statistical tools. Water-logged composting with sealed system enhanced P availability, with soluble reactive P in overlying water notably increasing from 1.20 to 1.92 mg L-1 and available P in composting substrate significantly arising from 1.61 to 2.28 g kg-1. Higher abundances of organic P (Po)-mineralizing genes, including β-propeller phytase-encoding gene of bpp, acid phosphatase-encoding gene of phoC, alkaline phosphatase-encoding gene of phoD, and phosphonoacetaldehyde hydrolase-encoding gene of phnX, were found in sealed system than in breathable system. Bacterial community composition varied notably between sealed and breathable systems, with dominant bacterial phyla of Proteobacteria and Actinobacteriota in overlying water were notably more abundant in sealed and breathable systems, respectively. Bacteria in sealed system rather than breathable system displayed higher community complexity and stability, stronger migration potential and phylogenetic signal, and were affected more by determinism. Our findings highlight ecological consequences of water-logged composting with sealed system, and these findings might guide composting in a water-logged way to obtain P fertilizer.

Nanothermometry for cellular temperature monitoring and disease diagnostics

Body temperature variations, including the generation, transfer, and dissipation of heat, play an important role throughout life and participate in all biological events. Cellular temperature information is an indispensable link in the comprehensive understanding of life science processes, but traditional testing strategies cannot provide sufficient information due to their low precision and inefficient cellular‐entrance. In recent years, with the help of luminescent nanomaterials, a variety of new thermometers have been developed to achieve real‐time temperature measurement at the micro/nano scale. In this review, we summarized the latest advances in several nanoparticles for cellular temperature detection and their related applications in revealing cell metabolism and disease diagnosis. Furthermore, this review proposed a few challenges for the nano‐thermometry, expecting to spark novel thought to push forward its preclinical and translational uses.

Warm Cells, Hot Mitochondria: Achievements and Problems of Ultralocal Thermometry

Temperature is a crucial regulator of the rate and direction of biochemical reactions and cell processes. The recent data indicating the presence of local thermal gradients associated with the sites of high-rate thermogenesis, on the one hand, demonstrate the possibility for the existence of "thermal signaling" in a cell and, on the other, are criticized on the basis of thermodynamic calculations and models. Here, we review the main thermometric techniques and sensors developed for the determination of temperature inside living cells and diverse intracellular compartments. A comparative analysis is conducted of the results obtained using these methods for the cytosol, nucleus, endo-/sarcoplasmic reticulum, and mitochondria, as well as their biological consistency. Special attention is given to the limitations, possible sources of errors and ambiguities of the sensor's responses. The issue of biological temperature limits in cells and organelles is considered. It is concluded that the elaboration of experimental protocols for ultralocal temperature measurements that take into account both the characteristics of biological systems, as well as the properties and limitations of each type of sensor is of critical importance for the generation of reliable results and further progress in this field.

Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations

Environmental perturbations are encountered by microorganisms regularly and will require metabolic adaptations to ensure an organism can survive in the newly presenting conditions. In order to study the mechanisms of metabolic adaptation in such conditions, various experimental and computational approaches have been used. Genome-scale metabolic models (GEMs) are one of the most powerful approaches to study metabolism, providing a platform to study the systems level adaptations of an organism to different environments which could otherwise be infeasible experimentally. In this review, we are describing the application of GEMs in understanding how microbes reprogram their metabolic system as a result of environmental variation. In particular, we provide the details of metabolic model reconstruction approaches, various algorithms and tools for model simulation, consequences of genetic perturbations, integration of '-omics' datasets for creating context-specific models and their application in studying metabolic adaptation due to the change in environmental conditions.

PARROT: Prediction of enzyme abundances using protein-constrained metabolic models

Protein allocation determines the activity of cellular pathways and affects growth across all organisms. Therefore, different experimental and machine learning approaches have been developed to quantify and predict protein abundance and how they are allocated to different cellular functions, respectively. Yet, despite advances in protein quantification, it remains challenging to predict condition-specific allocation of enzymes in metabolic networks. Here, using protein-constrained metabolic models, we propose a family of constrained-based approaches, termed PARROT, to predict how much of each enzyme is used based on the principle of minimizing the difference between a reference and an alternative growth condition. To this end, PARROT variants model the minimization of enzyme reallocation using four different (combinations of) distance functions. We demonstrate that the PARROT variant that minimizes the Manhattan distance between the enzyme allocation of a reference and an alternative condition outperforms existing approaches based on the parsimonious distribution of fluxes or enzymes for both Escherichia coli and Saccharomyces cerevisiae. Further, we show that the combined minimization of flux and enzyme allocation adjustment leads to inconsistent predictions. Together, our findings indicate that minimization of protein allocation rather than flux redistribution is a governing principle determining steady-state pathway activity for microorganism grown in alternative growth conditions.

Uses of Multi-Objective Flux Analysis for Optimization of Microbial Production of Secondary Metabolites

Secondary metabolites are not essential for the growth of microorganisms, but they play a critical role in how microbes interact with their surroundings. In addition to this important ecological role, secondary metabolites also have a variety of agricultural, medicinal, and industrial uses, and thus the examination of secondary metabolism of plants and microbes is a growing scientific field. While the chemical production of certain secondary metabolites is possible, industrial-scale microbial production is a green and economically attractive alternative. This is even more true, given the advances in bioengineering that allow us to alter the workings of microbes in order to increase their production of compounds of interest. This type of engineering requires detailed knowledge of the "chassis" organism's metabolism. Since the resources and the catalytic capacity of enzymes in microbes is finite, it is important to examine the tradeoffs between various bioprocesses in an engineered system and alter its working in a manner that minimally perturbs the robustness of the system while allowing for the maximum production of a product of interest. The in silico multi-objective analysis of metabolism using genome-scale models is an ideal method for such examinations.