Quantitative single-cell splicing analysis reveals an ‘economy of scale’ filter for gene expression

In eukaryotic cells, splicing affects the fate of each pre-mRNA transcript, helping to determine whether it is ultimately processed into an mRNA, or degraded. The efficiency of splicing plays a key role in gene expression. However, because it depends on the levels of multiple isoforms at the same transcriptional active site (TAS) in the same cell, splicing efficiency has been challenging to measure. Here, we introduce a quantitative single-molecule FISH-based method that enables determination of the absolute abundances of distinct RNA isoforms at individual TASs. Using this method, we discovered that splicing efficiency behaves in an unexpected ‘economy of scale’ manner, increasing, rather than decreasing, with gene expression levels, opposite to a standard enzymatic process. This behavior could result from an observed correlation between splicing efficiency and spatial proximity to nuclear speckles. Economy of scale splicing represents a non-linear filter that amplifies the expression of genes when they are more strongly transcribed. This method will help to reveal the roles of splicing in the quantitative control of gene expression

Constitutive splicing and economies of scale in gene expression

In eukaryotic cells, many introns are constitutively, rather than alternatively, spliced and therefore do not contribute to isoform diversification. It has remained unclear what functional roles such constitutive splicing provides. To explore this issue, we asked how splicing affects the efficiency with which individual pre-messenger RNA transcripts are productively processed across different gene expression levels. We developed a quantitative single-molecule fluorescence in situ hybridization-based method to quantify splicing efficiency at transcription active sites in single cells. We found that both natural and synthetic genes in mouse and human cells exhibited an unexpected ‘economy of scale’ behavior in which splicing efficiency increased with transcription rate. Correlations between splicing efficiency and spatial proximity to nuclear speckles could explain this counterintuitive behavior. Functionally, economy of scale splicing represents a non-linear filter that amplifies the expression of genes when they are more strongly transcribed. These results indicate that constitutive splicing plays an active functional role in modulating gene expression

Quantitative single-cell splicing analysis reveals an ‘economy of scale’ filter for gene expression

In eukaryotic cells, splicing affects the fate of each pre-mRNA transcript, helping to determine whether it is ultimately processed into an mRNA, or degraded. The efficiency of splicing plays a key role in gene expression. However, because it depends on the levels of multiple isoforms at the same transcriptional active site (TAS) in the same cell, splicing efficiency has been challenging to measure. Here, we introduce a quantitative single-molecule FISH-based method that enables determination of the absolute abundances of distinct RNA isoforms at individual TASs. Using this method, we discovered that splicing efficiency behaves in an unexpected ‘economy of scale’ manner, increasing, rather than decreasing, with gene expression levels, opposite to a standard enzymatic process. This behavior could result from an observed correlation between splicing efficiency and spatial proximity to nuclear speckles. Economy of scale splicing represents a non-linear filter that amplifies the expression of genes when they are more strongly transcribed. This method will help to reveal the roles of splicing in the quantitative control of gene expression

Constitutive splicing and economies of scale in gene expression

In eukaryotic cells, many introns are constitutively, rather than alternatively, spliced and therefore do not contribute to isoform diversification. It has remained unclear what functional roles such constitutive splicing provides. To explore this issue, we asked how splicing affects the efficiency with which individual pre-messenger RNA transcripts are productively processed across different gene expression levels. We developed a quantitative single-molecule fluorescence in situ hybridization-based method to quantify splicing efficiency at transcription active sites in single cells. We found that both natural and synthetic genes in mouse and human cells exhibited an unexpected ‘economy of scale’ behavior in which splicing efficiency increased with transcription rate. Correlations between splicing efficiency and spatial proximity to nuclear speckles could explain this counterintuitive behavior. Functionally, economy of scale splicing represents a non-linear filter that amplifies the expression of genes when they are more strongly transcribed. These results indicate that constitutive splicing plays an active functional role in modulating gene expression

Dynamics and functional roles of splicing factor autoregulation

Non-spliceosomal splicing factors are essential, conserved regulators of alternative splicing. They provide concentration-dependent control of diverse pre-mRNAs. Many splicing factors direct unproductive splicing of their own pre-mRNAs through negative autoregulation. However, the impact of such feedback loops on splicing dynamics at the single cell level remains unclear. We developed a system to dynamically, quantitatively analyze negative autoregulatory splicing by the SF2 splicing factor in response to perturbations in single HEK293 cells. Here, we show that negative autoregulatory splicing provides critical functions for gene regulation, establishing a ceiling of SF2 protein concentration, reducing cell-cell heterogeneity in SF2 levels, and buffering variation in SF2 transcription. Most importantly, it adapts SF2 splicing activity to variations in demand from other pre-mRNA substrates. A minimal mathematical model of autoregulatory splicing explains these experimentally observed features, and provides values for effective biochemical parameters. These results reveal the unique functional roles that splicing negative autoregulation plays in homeostatically regulating transcriptional programs

Dynamics and functional roles of splicing factor autoregulation

Non-spliceosomal splicing factors are essential, conserved regulators of alternative splicing. They provide concentration-dependent control of diverse pre-mRNAs. Many splicing factors direct unproductive splicing of their own pre-mRNAs through negative autoregulation. However, the impact of such feedback loops on splicing dynamics at the single cell level remains unclear. We developed a system to dynamically, quantitatively analyze negative autoregulatory splicing by the SF2 splicing factor in response to perturbations in single HEK293 cells. Here, we show that negative autoregulatory splicing provides critical functions for gene regulation, establishing a ceiling of SF2 protein concentration, reducing cell-cell heterogeneity in SF2 levels, and buffering variation in SF2 transcription. Most importantly, it adapts SF2 splicing activity to variations in demand from other pre-mRNA substrates. A minimal mathematical model of autoregulatory splicing explains these experimentally observed features, and provides values for effective biochemical parameters. These results reveal the unique functional roles that splicing negative autoregulation plays in homeostatically regulating transcriptional programs

Collaborative coupling between polymerase and helicase for leading-strand synthesis

Rapid and processive leading-strand DNA synthesis in the bacteriophage T4 system requires functional coupling between the helicase and the holoenzyme, consisting of the polymerase and trimeric clamp loaded by the clamp loader. We investigated the mechanism of this coupling on a DNA hairpin substrate manipulated by a magnetic trap. In stark contrast to the isolated enzymes, the coupled system synthesized DNA at the maximum rate without exhibiting fork regression or pauses. DNA synthesis and unwinding activities were coupled at low forces, but became uncoupled displaying separate activities at high forces or low dNTP concentration. We propose a collaborative model in which the helicase releases the fork regression pressure on the holoenzyme allowing it to adopt a processive polymerization conformation and the holoenzyme destabilizes the first few base pairs of the fork thereby increasing the efficiency of helicase unwinding. The model implies that both enzymes are localized at the fork, but does not require a specific interaction between them. The model quantitatively reproduces homologous and heterologous coupling results under various experimental conditions

Disaster cassification net: A disaster classification algorithm on remote sensing imagery

As we all know, natural disasters have a great impact on people’s lives and properties, and it is very necessary to deal with disaster categories in a timely and effective manner. In light of this, we propose using tandem stitching to create a new Disaster Cassification network D-Net (Disaster Cassification Net) using the D-Conv, D-Linear, D-model, and D-Layer modules. During the experiment, we compared the proposed method with “CNN” and “Transformer”, we found that disaster cassification net compared to CNN algorithm Params decreased by 26–608 times, FLOPs decreased by up to 21 times, Precision increased by 1.6%–43.5%; we found that disaster cassification net compared to Transformer algorithm Params decreased by 23–149 times, FLOPs decreased by 1.7–10 times, Precision increased by 3.9%–25.9%. Precision increased by 3.9%–25.9%. And found that disaster cassification net achieves the effect of SOTA(State-Of-The-Art) on the disaster dataset; After that, we compared the above-mentioned MobileNet_v2 with the best performance on the classification dataset and CCT network are compared with disaster cassification net on fashion_mnist and CIFAR_100 public datasets, respectively, and the results show that disaster cassification net can still achieve the state-of-the-art classification effect. Therefore, our proposed algorithm can be applied not only to disaster tasks, but also to other classification tasks

Mechanism of strand displacement synthesis by DNA replicative polymerases

Replicative holoenzymes exhibit rapid and processive primer extension DNA synthesis, but inefficient strand displacement DNA synthesis. We investigated the bacteriophage T4 and T7 holoenzymes primer extension activity and strand displacement activity on a DNA hairpin substrate manipulated by a magnetic trap. Holoenzyme primer extension activity is moderately hindered by the applied force. In contrast, the strand displacement activity is strongly stimulated by the applied force; DNA polymerization is favoured at high force, while a processive exonuclease activity is triggered at low force. We propose that the DNA fork upstream of the holoenzyme generates a regression pressure which inhibits the polymerization-driven forward motion of the holoenzyme. The inhibition is generated by the distortion of the template strand within the polymerization active site thereby shifting the equilibrium to a DNA-protein exonuclease conformation. We conclude that stalling of the holoenzyme induced by the fork regression pressure is the basis for the inefficient strand displacement synthesis characteristic of replicative polymerases. The resulting processive exonuclease activity may be relevant in replisome disassembly to reset a stalled replication fork to a symmetrical situation. Our findings offer interesting applications for single-molecule DNA sequencing

The value of Apolipoprotein B/Apolipoprotein A1 ratio for metabolic syndrome diagnosis in a Chinese population: a cross-sectional study

BACKGROUND: The apoB/apoA1 ratio has been reported to be associated with the metabolic syndrome (MetS), and it may be a more convenient biomarker in MetS predicting. However, whether apoB/apoA1 ratio is a better indicator of metabolic syndrome than other biomarkers and what is the optimal cut-off value of apoB/apoA1 ratio as an indicator of metabolic syndrome in Chinese population remain unknown. Thus, we carried out the current study to assess the predictive value of apoB/apoA1 ratio and determine the optimal cut-off value of apoB/apoA1 ratio for diagnosing MetS in a Chinese population. METHOD: We selected 1,855 subjects with MetS and 6,265 individuals without MetS based on the inclusion and exclusion criteria from the China Health Nutrition Survey (CHNS) in 2009. MetS was identified based on the diagnostic criteria of International Diabetes Federation (2005). Logistic regression was used to estimate the association between the apoB/apoA1 ratio and risk of MetS, and receiver operating characteristics (ROC) curve analysis was performed to test the predictive value of apoB/apoA1 ratio and calculate the appropriate cut-off value. RESULTS: Compared with the lowest quartile of apoB/apoA1 ratio, subjects in the fourth quartile had a higher risk of MetS in both men [odds ratio (OR) = 2.64, 95% confidence interval (CI) =1.82-3.83] and women (OR = 5.18, 95% CI = 3.87-6.92) after adjustment for potential confounders. The optimal cut-off value of apoB/apoA1 ratio for MetS detection was 0.85 in men and 0.80 in women. Comparisons of ROC curves indicated that apoB/apoA1 ratio was better than traditional biomarkers in predicting MetS. CONCLUSION: Our results suggest that, apoB/apoA1 ratio has a promising predictive effectiveness in detection of MetS. An apoB/apoA1 ratio higher than 0.85 in men and 0.80 in women may be a promising and convenient marker of MetS