Phase separation provides a mechanism to drive phenotype switching.

Phenotypic switching plays a crucial role in cell fate determination across various organisms. Recent experimental findings highlight the significance of protein compartmentalization via liquid-liquid phase separation in influencing such decisions. However, the precise mechanism through which phase separation regulates phenotypic switching remains elusive. To investigate this, we established a mathematical model that couples a phase separation process and a gene expression process with feedback. We used the chemical master equation theory and mean-field approximation to study the effects of phase separation on the gene expression products. We found that phase separation can cause bistability and bimodality. Furthermore, phase separation can control the bistable properties of the system, such as bifurcation points and bistable ranges. On the other hand, in stochastic dynamics, the droplet phase exhibits double peaks within a more extensive phase separation threshold range than the dilute phase, indicating the pivotal role of the droplet phase in cell fate decisions. These findings propose an alternative mechanism that influences cell fate decisions through the phase separation process. As phase separation is increasingly discovered in gene regulatory networks, related modeling research can help build biomolecular systems with desired properties and offer insights into explaining cell fate decisions.

Power-law behavior of transcriptional bursting regulated by enhancer-promoter communication.

Revealing how transcriptional bursting kinetics are genomically encoded is challenging because genome structures are stochastic at the organization level and are suggestively linked to gene transcription. To address this challenge, we develop a generic theoretical framework that integrates chromatin dynamics, enhancer-promoter (E-P) communication, and gene-state switching to study transcriptional bursting. The theory predicts that power law can be a general rule to quantitatively describe bursting modulations by E-P spatial communication. Specifically, burst frequency and burst size are up-regulated by E-P communication strength, following power laws with positive exponents. Analysis of the scaling exponents further reveals that burst frequency is preferentially regulated. Bursting kinetics are down-regulated by E-P genomic distance with negative power-law exponents, and this negative modulation desensitizes at large distances. The mutual information between burst frequency (or burst size) and E-P spatial distance further reveals essential characteristics of the information transfer from E-P communication to transcriptional bursting kinetics. These findings, which are in agreement with experimental observations, not only reveal fundamental principles of E-P communication in transcriptional bursting but also are essential for understanding cellular decision-making.

Phase separation reduces cell-to-cell variability of transcriptional bursting.

Gene expression is a stochastic and noisy process often occurring in "bursts". Experiments have shown that the compartmentalization of proteins by liquid-liquid phase separation is conducive to reducing the noise of gene expression. Therefore, an important goal is to explore the role of bursts in phase separation noise reduction processes. We propose a coupled model that includes phase separation and a two-state gene expression process. Using the timescale separation method, we obtain approximate solutions for the expectation, variance, and noise strength of the dilute phase. We find that a higher burst frequency weakens the ability of noise reduction by phase separation, but as the burst size increases, this ability first increases and then decreases. This study provides a deeper understanding of phase separation to reduce noise in the stochastic gene expression with burst kinetics.

4D nucleome equation predicts gene expression controlled by long-range enhancer-promoter interaction.

Recent experimental evidence strongly supports that three-dimensional (3D) long-range enhancer-promoter (E-P) interactions have important influences on gene-expression dynamics, but it is unclear how the interaction information is translated into gene expression over time (4D). To address this question, we developed a general theoretical framework (named as a 4D nucleome equation), which integrates E-P interactions on chromatin and biochemical reactions of gene transcription. With this equation, we first present the distribution of mRNA counts as a function of the E-P genomic distance and then reveal a power-law scaling of the expression level in this distance. Interestingly, we find that long-range E-P interactions can induce bimodal and trimodal mRNA distributions. The 4D nucleome equation also allows for model selection and parameter inference. When this equation is applied to the mouse embryonic stem cell smRNA-FISH data and the E-P genomic-distance data, the predicted E-P contact probability and mRNA distribution are in good agreement with experimental results. Further statistical inference indicates that the E-P interactions prefer to modulate the mRNA level by controlling promoter activation and transcription initiation rates. Our model and results provide quantitative insights into both spatiotemporal gene-expression determinants (i.e., long-range E-P interactions) and cellular fates during development.

Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model.

Gene expression has inherent stochasticity resulting from transcription's burst manners. Single-cell snapshot data can be exploited to rigorously infer transcriptional burst kinetics, using mathematical models as blueprints. The classical telegraph model (CTM) has been widely used to explain transcriptional bursting with Markovian assumptions. However, growing evidence suggests that the gene-state dwell times are generally non-exponential, as gene-state switching is a multi-step process in organisms. Therefore, interpretable non-Markovian mathematical models and efficient statistical inference methods are urgently required in investigating transcriptional burst kinetics. We develop an interpretable and tractable model, the generalized telegraph model (GTM), to characterize transcriptional bursting that allows arbitrary dwell-time distributions, rather than exponential distributions, to be incorporated into the ON and OFF switching process. Based on the GTM, we propose an inference method for transcriptional bursting kinetics using an approximate Bayesian computation framework. This method demonstrates an efficient and scalable estimation of burst frequency and burst size on synthetic data. Further, the application of inference to genome-wide data from mouse embryonic fibroblasts reveals that GTM would estimate lower burst frequency and higher burst size than those estimated by CTM. In conclusion, the GTM and the corresponding inference method are effective tools to infer dynamic transcriptional bursting from static single-cell snapshot data.

Genome-wide inference reveals that feedback regulations constrain promoter-dependent transcriptional burst kinetics.

Gene expression in mammalian cells is highly variable and episodic, resulting in a series of discontinuous bursts of mRNAs. A challenge is to understand how static promoter architecture and dynamic feedback regulations dictate bursting on a genome-wide scale. Although single-cell RNA sequencing (scRNA-seq) provides an opportunity to address this challenge, effective analytical methods are scarce. We developed an interpretable and scalable inference framework, which combined experimental data with a mechanistic model to infer transcriptional burst kinetics (sizes and frequencies) and feedback regulations. Applying this framework to scRNA-seq data generated from embryonic mouse fibroblast cells, we found Simpson's paradoxes, i.e. genome-wide burst kinetics exhibit different characteristics in two cases without and with distinguishing feedback regulations. We also showed that feedbacks differently modulate burst frequencies and sizes and conceal the effects of transcription start site distributions on burst kinetics. Notably, only in the presence of positive feedback, TATA genes are expressed with high burst frequencies and enhancer-promoter interactions mainly modulate burst frequencies. The developed inference method provided a flexible and efficient way to investigate transcriptional burst kinetics and the obtained results would be helpful for understanding cell development and fate decision.

Silent transcription intervals and translational bursting lead to diverse phenotypic switching.

Gene-expression bimodality, as a potential mechanism generating phenotypic cell diversity, can enhance the survival of cells in a fluctuating environment. Previous studies have shown that intrinsic or extrinsic regulations could induce bimodal gene expressions, but it is unclear whether this bimodality can occur without regulation. Here we develop an interpretable and tractable model, namely a generalized telegraph model (GTM), which considers silent transcription intervals and translational bursting, each being characterized by a general distribution. Using the queuing theory, we derive the analytical expressions of protein distributions, and show that non-exponential inactive times and translational bursting can lead to two peaks of the protein distribution away from the origin, which are different from those occurring in classical telegraph models. We also find that both silent-interval noise and translational burst-size noise can amplify gene-expression noise and induce diverse dynamic expression patterns. Our results not only provide an alternative mechanism of phenotypic switching but also could be used in explaining the bimodal phenomenon in experimental observations.

Exact results for queuing models of stochastic transcription with memory and crosstalk.

Gene transcription is a complex multistep biochemical process, which can create memory between individual reaction events. On the other hand, many inducible genes, when activated by external cues, are often coregulated by several competitive pathways with crosstalk. This raises an unexplored question: how do molecular memory and crosstalk together affect gene expressions? To address this question, we introduce a queuing model of stochastic transcription, where two crossing signaling pathways are used to direct gene activation in response to external signals and memory functions to model multistep reaction processes involved in transcription. We first establish, based on the total probability principle, the chemical master equation for this queuing model, and then we derive, based on the binomial moment approach, exact expressions for statistical quantities (including distributions) of mRNA, which provide insights into the roles of crosstalk and memory in controlling the mRNA level and noise. We find that molecular memory of gene activation decreases the mRNA level but increases the mRNA noise, and double activation pathways always reduce the mRNA noise in contrast to a single pathway. In addition, we find that molecular memory can make the mRNA bimodality disappear.

Analytical results for non-Markovian models of bursty gene expression.

Modeling stochastic gene expression has long relied on Markovian hypothesis. In recent years, however, this hypothesis is challenged by the increasing availability of time-resolved data. Correspondingly, there is considerable interest in understanding how non-Markovian reaction kinetics of gene expression impact protein variations across a population of genetically identical cells. Here, we analyze a stochastic model of gene expression with arbitrary waiting-time distributions, which includes existing gene models as its special cases. We find that stationary probabilistic behavior of this non-Markovian system is exactly the same as that of an equivalent Markovian system with the same substrates. Based on this fact, we derive analytical results, which provide insight into the roles of feedback regulation and molecular memory in controlling the protein noise and properties of the steady states, which are inaccessible via existing methodology. Our results also provide quantitative insight into diverse cellular processes involving stochastic sources of gene expression and molecular memory.

Epidemiological analysis of primary liver cancer in the early 21st century in Guangxi province of China.

BACKGROUND AND OBJECTIVE: In Guangxi province, from 1970s to 1990s, the mortality of primary liver cancer (PLC) ranked the first among a variety of malignant tumors. Investigating the epidemiological characteristics of PLC is very important for developing reasonable and effective treatment strategy, allocating health resources rationally, and evaluating the quality of PLC prevention and control. This study was to analyze the mortality and epidemiological characteristics of PLC in Guangxi province between 2004 and 2005. METHODS: Multi stage stratified cluster random sampling method was used to select 9 counties (cities or urban areas) as sample points. The residents' death causes between 2004 and 2005 were analyzed, and the epidemiological characteristics of PLC were investigated. RESULTS: In the period of 2004-2005, the crude mortality of PLC was 34.39/100,000 in Guangxi province population (55.30/100,000 in men and 13.21/100,000 in women). The national population standardized mortality in 1964 was 22.17/100,000. The man to woman ratio of mortality was 4.19:1. PLC ranked as the first death cause among a variety of malignant tumors, and PLC related death accounted for 30.70% of all tumor related death cases. Age specific mortality of PLC was increased with age, rising significantly from 30 year old (from 25 year old in men and from 40 year old in women), and reached a peak at 75 year old. CONCLUSIONS: The mortality of PLC shows a decreasing trend in Guangxi province in the early 21st century, and the starting age of PLC death peak postpones about 10 years than that in 1990s. It shows that the comprehensive prevention and control measures of PLC implemented in Guangxi province are fruitful. However, the PLC mortality in Guangxi province is still significantly higher than the national average level, and it still ranks as the first death cause in a variety of malignant tumors in Guangxi province. PLC mainly occurs in middle aged and elderly people. The prevention and treatment research of PLC still has a long way to go.

[Study on the relationship between familial clustering of hepatocellular carcinoma and polymorphism of cytochrome P450 2E1 gene in Zhuang population, Guangxi].

OBJECTIVE: To study the relationship between familial clustering of hepatocellular carcinoma (HCC) and the polymorphism of cytochrome P450 2El gene (CYP2E1) as well as of other relevant risk factors to the cancer. METHODS: Peripheral blood samples were collected from 91 members of 10 HCC clustering families and 102 of 10 control families, among Zhuang population, in Guangxi. The area had been with high incidence rate of HCC. Genotypes and allele frequencies of CYP2E1 Rsa I site were determined by polymerase chain reaction, combined with restriction fragment length polymorphism method (PCR-RFLP). Serum HBsAg was tested by means of ELISA. Data on relevant risk factors of the cancer were collected as well, through a unique questionnaire. RESULTS: Frequencies of c1/c1 and c1/c2 genetypes of CYP2E1 Rsa I site were 63.7% and 36.3%, respectively, in the members of families with cancer clustering phenomena. In the members of the control families, these two rates were 48.0% and 52.0%, respectively (OR = 1.901, 95% CI: 1.067-3.387). Difference of genotypes frequencies of CYP2E1 Rsa I site between the members in these two groups was statistically significant (chi2 = 4.797, P = 0.029). According to the results from non-condition logistic regression analysis, the major risk factors on familial clustering of HCC could be listed as: intake of corns, HBsAg carrying status and CYP2E1 c1/c1 genotype. CONCLUSION: The relationship seemed to exist between familial clustering of HCC and the frequencies of polymorphism of cytochrome P450 2E1 gene (CYP2E1). The frequencies of CYP2E1 Rsa I site were neither the only nor the major factor, causing the familial clustering phenomenon of cancer. More possible, it was the affect of syntheses with the involvement of multiple factors.

[A cross-sectional study on liver diseases in the rural residents in southern Guangxi, China].

OBJECTIVE: To study the epidemiological characteristics of liver diseases in a rural population in Southern Guangxi, China. METHODS: The enzyme immunoassays was used to detect of HBsAg and AFP. AFP positive serum samples were further examined for concentration of AFP by using a radio immunoassays. Liver morphological changes were measured with ultrasonography of type B. RESULTS: The positive rates of HBsAg in the studied population was 17.8% (2800/15,701). The prevalence rates of viral hepatitis B, cirrhosis, primary liver cancer, clonorchiasis, fatty liver disease, alcoholic liver disease were 1.1% (173/15,701), 0.4% (63/15,701), 299.3 per 100,000 (47/15,701), 6.6% (1036/15,701), 4.8% (754/15,701) and 0.3% (47/15,701), respectively. The positive rates of HBsAg and the prevalence rates of viral hepatitis B, cirrhosis, primary liver cancer, clonorchiasis, fatty liver disease in male were significantly higher as compared with those in female (5.98 < or = chi(2) < or = 394.78, P < 0.01). No difference was observed in the prevalence rates of liver cavernous hemangioma and hepatic cysts between male and female. The prevalence rates of intrahepatic bile duct stones was significantly higher in female than in male (chi(2) = 30.80, P < 0.01). The positive rates of HBsAg and the prevalence rates of viral hepatitis B and clonorchiasis were decreased with age. But the prevalence rates of cirrhosis, primary liver cancer, fatty liver disease, alcoholic liver disease, liver cavernous hemangioma, hepatic cysts and intrahepatic bile duct stones were increased with age. CONCLUSION: The rural areas in the southern Guangxi are high prevalence regions of liver illness, and the male resident are even at high risk.

Genetic polymorphisms in the methylenetetrahydrofolate reductase and thymidylate synthase genes and risk of hepatocellular carcinoma.

UNLABELLED: Methylenetetrahydrofolate reductase (MTHFR) and thymidylate synthase (TYMS) are known to play a role in DNA methylation, synthesis, and repair. The genetic mutations in MTHFR and TYMS genes may have influences on their respective enzyme activities. Data on the association studies of the MTHFR and TYMS genetic polymorphisms and risk of hepatocellular carcinoma (HCC) are sparse. MTHFR and TYMS genotypes were determined on 365 HCC cases and 457 healthy control subjects among Hispanic and non-Hispanic whites and African-Americans in Los Angeles County, California, and among Chinese in the city of Nanning, Guangxi, China. Relative to the high-activity genotype, each low-activity genotype of MTHFR was associated with a statistically nonsignificant 30% to 50% reduction in risk of HCC. Relative to the TYMS3'UTR +6/+6 genotype, individuals with 1 or 2 copies of the deletion allele had a statistically significant 50% reduction in risk of HCC. When we examined HCC risk by the total number of mutant alleles in the 3 polymorphic loci of MTHFR/TYMS (range, 0-4), there was a monotonic decrease in risk with increasing number of mutant alleles (P for trend = 0.003). Individuals possessing the maximum number of mutant alleles (i.e., 4) had an odds ratio of 0.46 (95% confidence interval = 0.23-0.93) for HCC compared with those with no or only 1 mutant allele. CONCLUSION: This study supports the hypothesis that reduced MTHFR activity and enhanced TYMS activity, both of which are essential elements in minimizing uracil misincorporation into DNA, may protect against the development of HCC.

Phase IIa chemoprevention trial of green tea polyphenols in high-risk individuals of liver cancer: modulation of urinary excretion of green tea polyphenols and 8-hydroxydeoxyguanosine.

Modulation of urinary excretion of green tea polyphenols (GTPs) and oxidative DNA damage biomarker, 8-hydroxydeoxyguanosine (8-OHdG), were assessed in urine samples collected from a randomized, double-blinded and placebo-controlled phase IIa chemoprevention trial with GTP in 124 individuals. These individuals were sero-positive for both HBsAg and aflatoxin-albumin adducts, and took GTP capsules daily at doses of 500 mg, 1000 mg or a placebo for 3 months. Twenty-four hour urine samples were collected before the intervention and at the first and third month of the study. Urinary excretion of 8-OHdG and GTP components was measured by HPLC-CoulArray electrochemical detection. The baseline levels of 8-OHdG and GTP components among the three groups showed homogeneity (P > 0.70), and a non-significant fluctuation was observed in the placebo group over the 3 months (P > 0.30). In GTP-treated groups, epigallocatechin (EGC) and epicatechin (EC) levels displayed significant and dose-dependent increases in both the 500 mg group and 1000 mg group (P < 0.05). The 8-OHdG levels did not differ between the three groups at the 1 month collection, with medians of 1.83, 2.08 and 1.86 ng/mg-creatinine for placebo, 500 and 1000 mg group, respectively (P = 0.999). At the end of the 3 months' intervention, 8-OHdG levels decreased significantly in both GTP-treated groups, with medians of 2.02, 1.03 and 1.15 ng/mg-creatinine for placebo, 500 mg and 1000 mg group, respectively (P = 0.007). These results suggest that urinary excretions of EGC and EC can serve as practical biomarkers for green tea consumption in human populations. The results also suggest that chemoprevention with GTP is effective in diminishing oxidative DNA damage.

Effect of cytokine genotypes on the hepatitis B virus-hepatocellular carcinoma association.

BACKGROUND: In Southern Guangxi, China, chronic infection with the hepatitis B virus (HBV) acquired during the perinatal period from carrier mothers is a primary cause of hepatocellular carcinoma. However, only a minority of HBV carriers eventually develop hepatocellular carcinoma. The authors hypothesized that cytokine genotypes may be important codeterminants of the risk of HBV-related hepatocellular carcinoma. METHODS: The authors examined the correlation between polymorphisms in T-helper 1 (Th1) and Th2 cytokine genes among a group of 250 patients with incident hepatocellular carcinoma (cases) and a group of 250 hospital controls who were matched individually to the index case by age, gender, ethnicity, residence, and month of hospital admission in the city of Nanning, Guangxi, China. RESULTS: Relative to the putative high-activity genotypes, each individual low-activity genotype of interferon gamma, interleukin 12 (IL12), and IL18 was associated with a statistically nonsignificant increase (40-60%) in the risk of hepatocellular carcinoma. This risk increased with increasing numbers of low-activity Th1 genotypes after adjusting for potential confounders (2-sided P value for trend=0.04). Conversely, individual Th2 (IL4, IL10) low-activity genotypes were associated with a statistically nonsignificant reduced risk of hepatocellular carcinoma. This risk decreased with increasing number of low-activity Th2 genotypes after adjusting for potential confounders (2-sided P value for trend=0.01). Individuals who had the maximum number (i.e., 3) of low-activity Th1 genes and the minimum number (i.e., 0) of low-activity Th2 genes showed a relative risk of 20.0 (95% confidence interval, 1.7-235.0). CONCLUSIONS: Diminished cell-mediated immune response, which is controlled genetically, appeared to be an important risk determinant of HBV-related hepatocellular carcinogenesis.