Increased gene expression noise in human cancers is correlated with low p53 and immune activities as well as late stage cancer

Multimodal screen identifies noise-regulatory proteins

Gene-expression noise can influence cell-fate choices across pathology and physiology. However, a crucial question persists: do regulatory proteins or pathways exist that control noise independently of mean expression levels? Our integrative approach, combining single-cell RNA sequencing with proteomics and regulator enrichment analysis, identifies 32 putative noise regulators. SON, a nuclear speckle-associated protein, alters transcriptional noise without changing mean expression levels. Furthermore, SON's noise control can propagate to the protein level. Long-read and total RNA sequencing shows that SON's noise control does not significantly change isoform usage or splicing efficiency. Moreover, SON depletion reduces state switching in pluripotent mouse embryonic stem cells and impacts their fate choice during differentiation. Collectively, we demonstrate a class of proteins that control noise orthogonally to mean expression levels. This work serves as a proof of concept that can identify other functional noise regulators throughout development and disease progression.

Cis-regulatory control of transcriptional timing and noise in response to estrogen

Cis-regulatory elements control transcription levels, temporal dynamics, and cell-cell variation or transcriptional noise. However, the combination of regulatory features that control these different attributes is not fully understood. Here, we used single-cell RNA-seq during an estrogen treatment time course and machine learning to identify predictors of expression timing and noise. We found that genes with multiple active enhancers exhibit faster temporal responses. We verified this finding by showing that manipulation of enhancer activity changes the temporal response of estrogen target genes. Analysis of transcriptional noise uncovered a relationship between promoter and enhancer activity, with active promoters associated with low noise and active enhancers linked to high noise. Finally, we observed that co-expression across single cells is an emergent property associated with chromatin looping, timing, and noise. Overall, our results indicate a fundamental tradeoff between a gene's ability to quickly respond to incoming signals and maintain low variation across cells.

Cis-regulatory control of transcriptional timing and noise in response to estrogen

Cis-regulatory elements control transcription levels, temporal dynamics, and cell-cell variation or transcriptional noise. However, the combination of regulatory features that control these different attributes is not fully understood. Here, we used single cell RNA-seq during an estrogen treatment time course and machine learning to identify predictors of expression timing and noise. We find that genes with multiple active enhancers exhibit faster temporal responses. We verified this finding by showing that manipulation of enhancer activity changes the temporal response of estrogen target genes. Analysis of transcriptional noise uncovered a relationship between promoter and enhancer activity, with active promoters associated with low noise and active enhancers linked to high noise. Finally, we observed that co-expression across single cells is an emergent property associated with chromatin looping, timing, and noise. Overall, our results indicate a fundamental tradeoff between a gene's ability to quickly respond to incoming signals and maintain low variation across cells.

Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression

The majority of gene expression studies focus on the search for genes whose mean expression is different between two or more populations of samples in the so-called "differential expression analysis" approach. However, a difference in variance in gene expression may also be biologically and physiologically relevant. In the classical statistical model used to analyze RNA-sequencing (RNA-seq) data, the dispersion, which defines the variance, is only considered as a parameter to be estimated prior to identifying a difference in mean expression between conditions of interest. Here, we propose to evaluate four recently published methods, which detect differences in both the mean and dispersion in RNA-seq data. We thoroughly investigated the performance of these methods on simulated datasets and characterized parameter settings to reliably detect genes with a differential expression dispersion. We applied these methods to The Cancer Genome Atlas datasets. Interestingly, among the genes with an increased expression dispersion in tumors and without a change in mean expression, we identified some key cellular functions, most of which were related to catabolism and were overrepresented in most of the analyzed cancers. In particular, our results highlight autophagy, whose role in cancerogenesis is context-dependent, illustrating the potential of the differential dispersion approach to gain new insights into biological processes and to discover new biomarkers.

Localization of Noise in Biochemical Networks

Noise, or uncertainty in biochemical networks, has become an important aspect of many biological problems. Noise can arise and propagate from external factors and probabilistic chemical reactions occurring in small cellular compartments. For species survival, it is important to regulate such uncertainties in executing vital cell functions. Regulated noise can improve adaptability, whereas uncontrolled noise can cause diseases. Simulation can provide a detailed analysis of uncertainties, but parameters such as rate constants and initial conditions are usually unknown. A general understanding of noise dynamics from the perspective of network structure is highly desirable. In this study, we extended the previously developed law of localization for characterizing noise in terms of (co)variances and developed noise localization theory. With linear noise approximation, we can expand a biochemical network into an extended set of differential equations representing a fictitious network for pseudo-components consisting of variances and covariances, together with chemical species. Through localization analysis, perturbation responses at the steady state of pseudo-components can be summarized into a sensitivity matrix that only requires knowledge of network topology. Our work allows identification of buffering structures at the level of species, variances, and covariances and can provide insights into noise flow under non-steady-state conditions in the form of a pseudo-chemical reaction. We tested noise localization in various systems, and here we discuss its implications and potential applications. Results show that this theory is potentially applicable in discriminating models, scanning network topologies with interesting noise behavior, and designing and perturbing networks with the desired response.

Convergent Usage of Amino Acids in Human Cancers as A Reversed Process of Tissue Development

Genome- and transcriptome-wide amino acid usage preference across different species is a well-studied phenomenon in molecular evolution, but its characteristics and implication in cancer evolution and therapy remain largely unexplored. Here, we analyzed large-scale transcriptome/proteome profiles, such as The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx), and the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and found that compared to normal tissues, different cancer types showed a convergent pattern toward using biosynthetically low-cost amino acids. Such a pattern can be accurately captured by a single index based on the average biosynthetic energy cost of amino acids, termed energy cost per amino acid (ECPA). With this index, we further compared the trends of amino acid usage and the contributing genes in cancer and tissue development, and revealed their reversed patterns. Finally, focusing on the liver, a tissue with a dramatic increase in ECPA during development, we found that ECPA represents a powerful biomarker that could distinguish liver tumors from normal liver samples consistently across 11 independent patient cohorts and outperforms any index based on single genes. Our study reveals an important principle underlying cancer evolution and suggests the global amino acid usage as a system-level biomarker for cancer diagnosis.

Translational control of gene expression noise and its relationship to ageing in yeast

Gene expression noise influences organism evolution and fitness but is poorly understood. There is increasing evidence that the functional roles of components of the translation machinery influence noise intensity. In addition, modulation of the activities of at least some of these same components affects the replicative lifespan of a broad spectrum of organisms. In a novel comparative approach, we modulate the activities of the translation initiation factors eIFG1 and eIF4G2, both of which are involved in the process of recruiting ribosomal 43S preinitiation complexes to the 5' end of eukaryotic mRNAs. We show that tagging of the cell wall using a fluorescent dye allows us to follow gene expression noise as different yeast strains progress through successive cycles of replicative ageing. This procedure reveals a relationship between global protein synthesis rate and gene expression noise (cell-to-cell heterogeneity), which is accompanied by a parallel correlation between gene expression noise and the replicative age of mother cells. An alternative approach, based on microfluidics, confirms the interdependence between protein synthesis rate, gene expression noise and ageing. We additionally show that it is important to characterize the influence of the design of the microfluidic device on the nutritional state of the cells during such experiments. Analysis of the noise data derived from flow cytometry and fluorescence microscopy measurements indicates that both the intrinsic and the extrinsic noise components increase as a function of ageing.

Long non‑coding RNA PCAT6 promotes the development of osteosarcoma by increasing MDM2 expression

Osteosarcoma is a severe malignant tumor. Several studies indicated that lncRNA prostate cancer-associated transcript 6 (PCAT6) promoted the development of multiple types of cancers. Studies have also revealed that MDM2 could aggravate tumor symptoms inhibiting P53 expression. However, whether lncRNA PCAT6 could affect the proliferation and metastasis of osteosarcoma cells by regulating P53 expression is unclear. The present study established lncRNA PCAT6-overexpressing osteosarcoma cells. Cell Counting Kit-8, wound healing and Transwell assays were performed to detect the change in proliferation, migration and invasion of these cells, respectively. Subsequently, E3 ubiquitin-protein ligase Mdm2 (MDM2), P53 and P21 expression were determined using western blotting. Finally, MDM2 expression was inhibited and the proliferation, migration and invasion of these cells was determined again. The present study found that the proliferation, migration and invasion of osteosarcoma cells increased following overexpression of lncRNA PCAT6. MDM2 expression was upregulated while the levels of P53 and P21 decreased following overexpression of lncRNA PCAT6. However, the proliferation, migration and invasion of osteosarcoma cells were inhibited following MDM2 knockdown. Additionally, P53 and P21 was rescued following MDM2 knockdown. To conclude, lncRNA PCAT6 promoted the proliferation, migration and invasion of osteosarcoma cells by promoting the expression of MDM2 and suppressing the expression of P53 and P21.

Natural Selection on Exonic SNPs Shapes Allelic Expression Imbalance (AEI) Adaptability in Lung Cancer Progression

Tumors are driven by a sequence of genetic and epigenetic alterations. Previous studies have mostly focused on the roles of somatic mutations in tumorigenesis, but how germline variants act is largely unknown. In this study, we hypothesized that allelic expression imbalance (AEI) participated in the process of germline variants on tumorigenesis. We screened single-nucleotide polymorphisms (SNPs) as representative germline variants. By using 127 patients’ RNA sequencing data from paired lung cancer and adjacent normal tissues from public databases, we analyzed the effects of the functional consequence of SNPs, function and conservativeness on genes with AEI. We found that natural selection can affect AEI. Functional adaptability of genes with a high frequency of AEI and a correlation of the incidence of AEI with conservativeness were observed in both adjacent tissues and tumor tissues. Moreover, we observed a higher incidence of AEI in genes with non-synonymous SNPs than in those with synonymous SNPs. However, we also found that AEI was affected by allele expression noise, especially in tumor tissues, which led to an increased proportion of AEI, weakened the effect of natural selection and eliminated the influence of the functional consequence of SNPs on AEI. We unveiled a previously unknown adaptive regulatory mechanism in which the effect of natural selection on SNPs can be reflected in allelic expression, which provides insight into a better understanding of cancer evolution.

Expression of HTRA Genes and Its Association with Microsatellite Instability and Survival of Patients with Colorectal Cancer

HtrA proteases regulate cellular homeostasis and cell death. Their dysfunctions have been correlated with oncogenesis and response to therapeutic treatment. We investigated the relation between HtrA1-3 expression and clinicopathological, and survival data, as well as the microsatellite status of tumors. Sixty-five colorectal cancer patients were included in the study. The expression of HTRA1-3 was estimated at the mRNA and protein levels by quantitative PCR and immunoblotting. Microsatellite status was determined by high-resolution-melting PCR. We found that the HTRA1 mRNA level was higher in colorectal cancer tissue as compared to the unchanged mucosa, specifically in primary lesions of metastasizing cancer. The levels of HtrA1 and HtrA2 proteins were reduced in tumor tissue when compared to unchanged mucosa, specifically in primary lesions of metastasizing disease. Moreover, a decrease in HTRA1 and HTRA2 transcripts' levels in cancers with a high level of microsatellite instability compared to microsatellite stable ones has been observed. A low level of HtrA1 or/and HtrA2 in cancer tissue correlated with poorer patient survival. The expression of HTRA1 and HTRA2 changes during colorectal carcinogenesis and microsatellite instability may be, at least partially, associated with these changes. The alterations in the HTRA1/2 genes' expression are connected with metastatic potential of colorectal cancer and may affect patient survival.

Molecular Biology and Evolution of Cancer: From Discovery to Action

Cancer progression is an evolutionary process. During this process, evolving cancer cell populations encounter restrictive ecological niches within the body, such as the primary tumor, circulatory system, and diverse metastatic sites. Efforts to prevent or delay cancer evolution-and progression-require a deep understanding of the underlying molecular evolutionary processes. Herein we discuss a suite of concepts and tools from evolutionary and ecological theory that can inform cancer biology in new and meaningful ways. We also highlight current challenges to applying these concepts, and propose ways in which incorporating these concepts could identify new therapeutic modes and vulnerabilities in cancer.

The Coral Hospital

Herein we propose an ambitious confrontation of the current coral reef crisis through the establishment of a "Coral Hospital." In an analogous manner to a human hospital, "sick" corals will first be diagnosed either in situ or in the hospital's diagnostic "clinic" such that the root cause of illness can be discerned (e.g., disease, high temperatures, or pollutant stress). Then, corals will be "treated" (when necessary) and allowed to "convalesce" in precisely controlled coral husbandry facilities. Upon "rehabilitation," the recovered corals will be returned to their home reef (if this reef was not found to have degraded), or, alternatively, to a site featuring oceanographic conditions favoring a high level of health, as determined by husbandry experiments performed in other hospital "wards." When possible, diagnostic data from the sick corals (i.e., the underlying cause of sickness) will be used to guide environmental remediation schemes aimed at promoting coral resilience in the ocean. If the home reef improves to an appreciable extent during the time the corals are "hospitalized," these corals could be replanted there upon rehabilitation. Regardless of the site of outplanting, recuperated corals will be monitored over time to validate the "quality of care" in the hospital. In the event that the home reefs suffer to such an extent that environmental mitigation is no longer possible, coral gametes will be collected and cryopreserved such that they may be fertilized, reared in officinarum, and later reseeded once/if global marine conditions again permit coral survival.

MicroRNA, Noise, and Gene Expression Regulation

Gene regulatory network that determines the cellular functions exhibits stochastic fluctuations, or "noise," in different layers. Noise has begun to be appreciated for many previously unrecognized functions in important cellular activities. In fact, molecular noise is unavoidable in both microbial and eukaryotic cells, the feedback system is established evolutionally to reduce noise or optimize the noise for cellular homeostasis. The small noncoding RNAs, particularly, microNRAs, post-transcriptionally and negatively regulate gene expressions. MicroRNAs function as a novel layer to buffer noise level, and stabilize mRNA and protein level to maintain normal cellular function. Furthermore, the changing of microRNA expression levels may increase the stochastic fluctuation leading to abnormal cellular function, even diseases.

Molecular subtyping of nasopharyngeal carcinoma (NPC) and a microRNA-based prognostic model for distant metastasis

BACKGROUND

Nasopharyngeal carcinoma (NPC) is a highly invasive and metastatic cancer, with diverse molecular characteristics and clinical outcomes. This study aims to dissect the molecular heterogeneity of NPC, followed by the construction of a microRNA (miRNA)-based prognostic model for prediction of distant metastasis.

METHODS

We retrieved two NPC datasets: GSE32960 and GSE70970 as training and validation cohorts, respectively. Consensus clustering was employed for cluster discovery, and support vector machine was used to build a classifier. Finally, Cox regression analysis was applied to constructing a prognostic model for predicting risk of distant metastasis.

RESULTS

Three NPC subtypes (immunogenic, classical and mesenchymal) were identified that are molecularly distinct and clinically relevant, of which mesenchymal subtype (~ 36%) is associated with poor prognosis, characterized by suppressing tumor suppressor miRNAs and the activation of epithelial--mesenchymal transition. Out of the 25 most differentially expressed miRNAs in mesenchymal subtype, miR-142, miR-26a, miR-141 and let-7i have significant prognostic power (P < 0.05).

CONCLUSIONS

We proposed for the first time that NPC can be stratified into three subtypes. Using a panel of 4 miRNAs, we established a prognostic model that can robustly stratify NPC patients into high- and low- risk groups of distant metastasis.