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Abstract Recent advances in gene editing and precise regulation of gene expression based on CRISPR technologies have provided powerful tools for the understanding and manipulation of gene functions. Fusing RNA aptamers to the sgRNA of CRISPR can recruit cognate RNA-binding protein (RBP) effectors to target genomic sites, and the expression of sgRNA containing different RNA aptamers permit simultaneous multiplexed and multifunctional gene regulations. Here, we report an intracellular directed evolution platform for RNA aptamers against intracellularly expressed RBPs. We optimize a bacterial CRISPR-hybrid system coupled with FACS, and identified high affinity RNA aptamers orthogonal to existing aptamer-RBP pairs. Application of orthogonal aptamer-RBP pairs in multiplexed CRISPR allows effective simultaneous transcriptional activation and repression of endogenous genes in mammalian cells.

Aldehydes with bulky substituents in the ortho-positions have been historically difficult in porphyrin synthesis, presumably owing to steric hindrance around the reactive site. We have used mechanochemistry for the simple, room-temperature synthesis of tetra-meso-substituted porphyrins. In the present study, mesitaldehyde undergoes acid-catalyzed mechanochemical condensation with pyrrole to give meso-tetrakis[2,4,6-(trimethyl)phenyl]porphyrin (TMP) after oxidation in solution. Yields are similar to those obtained using high-temperature porphyrin synthesis, although they remain significantly lower than some optimized room-temperature, solution-based methods. Yields of the mechanochemical synthesis were found to increase slightly upon longer exposure to an organic oxidizing agent in solution. This indicates that the mechanochemical condensation step may be more successful than initially realized. This work shows that mechanochemistry is a successful, simple, room-temperature method for producing tetra-meso-substituted porphyrins with bulky substituents.

Although atmospheric CO2 concentrations collected by satellites play a crucial role in understanding global greenhouse gases, the sparse geographic distribution greatly affects their widespread application. In this paper, a hybrid CNN and spatiotemporal Kriging (CNN-STK) model is proposed to generate a monthly spatiotemporal continuous XCO2 dataset over China at 0.25° grid-scale from 2015 to 2020, utilizing OCO-2 XCO2 and geographic covariates. The validations against observation samples, CAMS XCO2 and TCCON measurements indicate the CNN-STK model is effective, robust, and reliable with high accuracy (validation set metrics: R2 = 0.936, RMSE = 1.3 ppm, MAE = 0.946 ppm; compared with TCCON: R2 = 0.954, RMSE = 0.898 ppm and MAE = 0.741 ppm). The accuracy of CNN-STK XCO2 exhibits spatial inhomogeneity, with higher accuracy in northern China during spring, autumn, and winter and lower accuracy in northeast China during summer. XCO2 in low-value-clustering areas is notably influenced by biological activities. Moreover, relatively high uncertainties are observed in the Qinghai-Tibet Plateau and Sichuan Basin. This study innovatively integrates deep learning with the geostatistical method, providing a stable and cost-effective approach for other countries and regions to obtain regional scales of atmospheric CO2 concentrations, thereby supporting policy formulation and actions to address climate change.

We investigate a mosquito population suppression model, which includes the release of Wolbachia-infected males causing incomplete cytoplasmic incompatibility (CI). The model consists of two sub-equations by considering the density-dependent birth rate of wild mosquitoes. By assuming the release waiting period T is larger than the sexual lifespan $ \bar {T} $ of Wolbachia-infected males, we derive four thresholds: the CI intensity threshold $ s_h^* $ , the release amount thresholds $ g^* $ and $ c^* $ , and the waiting period threshold $ T^* $ . From a biological view, we assume $ s_h \gt s_h^* $ throughout the paper. When $ g^* \lt c \lt c^* $ , we prove the origin $ E_0 $ is locally asymptotically stable iff $ T \lt T^* $ , and the model admits a unique T-periodic solution iff $ T\geq T^* $ , which is globally asymptotically stable. When $ c\geq c^* $ , we show the origin $ E_0 $ is globally asymptotically stable iff $ T\leq T^* $ , and the model has a unique T-periodic solution iff $ T \gt T^* $ , which is globally asymptotically stable. Our theoretical results are confirmed by numerical simulations.

As an atom economy technology, the direct esterification reaction of olefins plays a significant role in the production of organic esters. In this work, a series of resin-supported HPW catalysts were prepared using D101, D152, RM027, and A15 as the carriers, and they were used to prepare 1-methylpentyl acetate by the esterification of 1-hexene and acetic acid. The composition, structure, morphology, acid content, and other parameters of catalysts were characterized. In the esterification reaction, the activity order of catalysts is as follows: 20.0%HPW/A15 > 20.0%HPW/D101 ≈ 20.0%HPW/D152 > 20.0%HPW/RM027. It depends on the acid strength of the carrier, the interaction between the carrier and HPW, and the HPW content. Moreover, it can be concluded that the weak acid resin carrier is completely inactive for the esterification reaction of 1-hexene, and the ratio of acetic acid to 1-hexene has a significant influence on the product selectivity. When n (acetic acid)/n (1-hexene) = 2, the optimum 1-methylpentyl acetate selectivity is 38.2%. Results of repeated experiments confirm that the stability of the active constituent is related to the type of functional groups on the carrier.

We consider a mosquito population suppression model with time delay. We show that, in the absence of sterile mosquitoes released, the model solutions oscillate with respect to its unique non-zero equilibrium. With the releases of sterile mosquitoes, we then determine an oscillation threshold, denoted by $\hat{b}$, for the constant release rate of the sterile mosquitoes such that all non-trivial positive solutions oscillate when the release rate of the sterile mosquitoes is less than $\hat{b}$, and the oscillation disappears as the release rate exceeds $\hat{b}$. We also provide some numerical simulations to validate our theoretical results.

To date, an innovative strategy to control dengue is to release Wolbachia-infected male mosquitoes into wild areas to sterilize wild female mosquito vectors by cytoplasmic incompatibility (CI). To investigate the efficacy of Wolbachia in blocking dengue virus transmission, we develop a deterministic mathematical model of human and mosquito populations in which one dengue serotype circulates. The delay differential equation model captures the respective extrinsic and intrinsic incubation periods (EIP and IIP) in the mosquito and human, as well as the maturation delay between mating and emergence of adult mosquitoes, which have received relatively little attention. We analyze the existence and stability of disease-free equilibria, and obtain a sufficient and necessary condition on the existence of the disease-endemic equilibrium. We also determine two threshold values of the release ratio $\theta$, denoted by $\theta_1^*$ and $\theta_2^*$ with $\theta_1^*>\theta_2^*$. When $\theta>\theta_1^*$, the mosquito population will be eradicated eventually. When $\theta_2^* < \theta < \theta_1^*$, a complete mosquito eradication becomes impossible, but virus eradication is ensured at the meantime. When $\theta < \theta_2^*$, the disease-endemic equilibrium emerges that allows dengue virus to circulate between humans and mosquitoes. We carry out sensitivity analysis of the threshold values in terms of the model parameters, and simulate several possible control strategies with different release ratios, which confirm the public awareness that reducing mosquito bites and killing adult mosquitoes are the most effective strategy to control the epidemic. Our model provides new insights on the effectiveness of Wolbachia in reducing dengue at a population level.

COVID-19 has been prevalent for the last two years. The transmission capacity of SARS-CoV-2 differs under the influence of different epidemic prevention policies, making it difficult to measure the infectivity of the virus itself. In order to evaluate the infectivity of SARS-CoV-2 in patients with different diseases, we constructed a viral kinetic model by adding the effects of T cells and antibodies. To analyze and compare the delay time of T cell action in patients with different symptoms, we constructed a delay differential equation model. Through the first model, we found that the basic reproduction number of severe patients is greater than that of mild patients, and accordingly, we constructed classification criteria for severe and mild patients. Through the second model, we found that the delay time of T cell action in severe patients is much longer than that in mild patients, and accordingly, we present suggestions for the prevention, diagnosis, and treatment of different patients.

Background: Gene transcription is a random bursting process that leads to large variability in mRNA numbers in single cells. The main cause is largely attributed to random switching between periods of active and inactive gene transcription. In some experiments, it has been observed that variation in the number of active transcription sites causes the initiation rate to vary during elongation. Results: We established a mathematical model based on the molecular reaction mechanism in single cells and studied a stochastic transcription system consisting of two active states and one inactive state, in which mRNA molecules are produced with two different synthesis rates. Conclusions: By calculation, we obtained the average mRNA expression level, the noise strength, and the skewness of transcripts. We gave a necessary and sufficient condition that causes the average mRNA level to peak at a limited time. The model could help us to distinguish an appropriate mechanism that may be employed by cells to transcribe mRNA molecules. Our simulations were in agreement with some experimental data and showed that the skewness can measure the deviation of the distribution of transcripts from the mean value. Especially for mature mRNAs, their distributions were almost able to be determined by the mean, the noise (or the noise strength), and the skewness.

In this paper, we use a similar approach to the one proposed by Chen and Jiao to calculate the mathematical formulas of the generating function V(z,t) and the mass function Pm(t) of a cross-talking pathways model in large parameter regions. Together with kinetic rates from yeast and mouse genes, our numerical examples reveal novel bimodal mRNA distributions for intermediate times, whereby the mode of distribution Pm(t) displays unimodality with the peak at m=0 for initial and long times, which has not been obtained in previous works. Such regulation of mRNA distribution exactly matches the transcriptional dynamics for the osmosensitive genes in Saccharomyces cerevisiae, which has not been generated by those models with one single pathway or feedback loops. This paper may provide us with a novel observation on transcriptional distribution dynamics regulated by multiple signaling pathways in response to environmental changes and genetic perturbations.

Gene transcription is a noisy process, and cell division cycle is an important source of gene transcription noise. In this work, we develop a mathematical approach by coupling transcription kinetics with cell division cycles to delineate how they are combined to regulate transcription output and noise. In view of gene dosage, a cell cycle is divided into an early stage [Formula: see text] and a late stage [Formula: see text]. The analytical forms for the mean and the noise of mRNA numbers are given in each stage. The analysis based on these formulas predicts precisely the fold change r* of mRNA numbers from [Formula: see text] to [Formula: see text] measured in a mouse embryonic stem cell line. When transcription follows similar kinetics in both stages, r* buffers against DNA dosage variation and r* ∈ (1, 2). Numerical simulations suggest that increasing cell cycle durations up-regulates transcription with less noise, whereas rapid stage transitions induce highly noisy transcription. A minimization of the transcription noise is observed when transcription homeostasis is attained by varying a single kinetic rate. When the transcription level scales with cellular volume, either by reducing the transcription burst frequency or by increasing the burst size in [Formula: see text], the noise shows only a minor variation over a wide range of cell cycle stage durations. The reduction level in the burst frequency is nearly a constant, whereas the increase in the burst size is conceivably sensitive, when responding to a large random variation of the cell cycle durations and the gene duplication time.

The transcription of inducible genes involves signalling pathways that induce DNA binding of the downstream transcription factors to form functional promoter states. How the transcription dynamics is linked to the temporal variations of activation signals is far from being fully understood. In this work, we develop a mathematical model with multiple promoter states to address this question. Each promoter state has its own activation and inactivation rates and is selected randomly with a probability that may change in time. Under the activation of constant signals, our analysis shows that if only the activation rates differ among the promoter states, then the mean transcription level m(t) displays only a monotone or monophasic growth pattern. In a sharp contrast, if the inactivation rates change with the promoter states, then m(t) may display multiphasic growth patterns. Upon the activation of signals that oscillate periodically, m(t) also oscillates later, almost periodically at the same frequency, but the magnitude decreases with frequency and is almost completely attenuated at high frequencies. This gives a surprising indication that multiple promoter states could filter out the signal oscillation and the noise in the random promoter state selection, as observed in the transcription of a gene activated by p53 in breast carcinoma cells. Our approach may help develop a theoretical framework to integrate coherently the genetic circuit with the promoter states to elucidate the linkage from the activation signal to the temporal profile of transcription outputs.