Specificity of RNA Folding and Its Association with Evolutionarily Adaptive mRNA Secondary Structures

The secondary structure is a fundamental feature of both noncoding and messenger RNAs. However, our understanding of the secondary structure of mRNA, especially that of the coding regions, remains elusive, likely due to translation and the lack of RNA-binding proteins that sustain the consensus structure, such as those that bind to noncoding RNA. Indeed, mRNA has recently been found to adopt diverse alternative structures, the overall functional significance of which remains untested. We hereby approached this problem by estimating the folding specificity, i.e., the probability that a fragment of RNA folds back to the same partner once refolded. We showed that the folding specificity of mRNA is lower than that of noncoding RNA and exhibits moderate evolutionary conservation. Notably, we found that specific rather than alternative folding is likely evolutionarily adaptive since specific folding is frequently associated with functionally important genes or sites within a gene. Additional analysis in combination with ribosome density suggests the ability to modulate ribosome movement as one potential functional advantage provided by specific folding. Our findings revealed a novel facet of the RNA structurome with important functional and evolutionary implications and indicated a potential method for distinguishing the mRNA secondary structures maintained by natural selection from molecular noise.

Distinct codon usage bias evolutionary patterns between weakly and strongly virulent respiratory viruses

Human respiratory viruses are of vastly different virulence, giving rise to symptoms ranging from common cold to severe pneumonia or even death. Although this most likely impacts molecular evolution of the corresponding viruses, the specific differences in their evolutionary patterns remain largely unknown. By comparing structural and nonstructural genes within respiratory viruses, greater similarities in codon usage bias (CUB) between nonstructural genes and humans were observed in weakly virulent viruses, whereas in strongly virulent viruses, it was structural genes whose CUBs were more similar to that of humans. Further comparisons between genomes of weakly and strongly virulent coronaviruses revealed greater similarities in CUBs between strongly virulent viruses and humans. Finally, using phylogenetic independent contrasts, dissimilation of viral CUB from that of humans was observed in SARS-CoV-2. Our work revealed distinct CUB evolutionary patterns between weakly and strongly virulent viruses, a previously unrecognized interaction between CUB and virulence in respiratory viruses.

Genomic variations of COVID-19 suggest multiple outbreak sources of transmission

Summary We examined 169 genomes of SARS-CoV-2 and found that they can be classified into two major genotypes, Type I and Type II. Type I can be further divided into Type IA and IB. Our phylogenetic analysis showed that the Type IA resembles the ancestral SARS-CoV-2 most. Type II was likely evolved from Type I and predominant in the infections. Our results suggest that Type II SARS-CoV-2 was the source of the outbreak in the Wuhan Huanan market and it was likely originated from a super-spreader. The outbreak caused by the Type I virus should have occurred somewhere else, because the patients had no direct link to the market. Furthermore, by analyzing three genomic sites that distinguish Type I and Type II strains, we found that synonymous changes at two of the three sites confer higher protein translational efficiencies in Type II strains than in Type I strains, which might explain why Type II strains are predominant, implying that Type II is more contagious (transmissible) than Type I. These findings could be valuable for the current epidemic prevention and control.

Alignment of Cell Lineage Trees Elucidates Genetic Programs for the Development and Evolution of Cell Types

A full understanding of the developmental process requires fine-scale characterization of cell divisions and cell types, which are naturally organized as the developmental cell lineage tree (CLT). Technological breakthroughs facilitated determination of more CLTs, but complete comprehension of the data remains difficult without quantitative comparison among CLTs. We hereby quantified phenotypic similarity between CLTs using a novel computational method that exhaustively searches for optimal correspondence between individual cells meanwhile retaining their topological relationships. The revealed CLT similarities allowed us to infer functional similarity at the transcriptome level, identify cell fate transformations, predict functional relationships between mutants, and find evolutionary correspondence between cell types of different species. By allowing quantitative comparison between CLTs, our work is expected to greatly enhance the interpretability of relevant data and help answer the myriad of questions surrounding the developmental process.

Transcriptome Analysis in Yeast Reveals the Externality of Position Effect

ABSTRACT When a gene is integrated into the chromosome, its activity depends on the genomic context. Although this phenomenon of “position effect” was widely reported, how the integration event affects the local environment, or the “externality” of position effect, remained largely unexplored, let alone the mechanism or phenotypic consequence of such externality. Here, we examined the transcriptome profiles of ∼250 Saccharomyces cerevisiae strains, each with GFP inserted into a different locus of the wild-type strain. We found that the GFP expression level and the change of expression of genes near the integration site decreases in genomic regions with high density of essential genes. This observation was found associated with H3K4me2 by further joint-analysis with public genome-wide histone modification profiles. More importantly, we found that the expression changes of neighboring genes, but not the GFP expression, exerted a significant impact on cellular growth rate. As a result, genomic loci that grant higher GFP expression immediately after the integration will have lower total yield of GFP in the long run. Our results, which were consistent with the competition for transcriptional resources among neighboring genes, revealed a previously unappreciated facet of the position effect, and highlighted its impact on the fate of genomic integration of exogenous genes, which has profound implications for biological engineering and pathology of virus integrative to host genome.

Dissimilation of synonymous codon usage bias in virus–host coevolution due to translational selection

Eighteen of the 20 amino acids are each encoded by more than one synonymous codon. Due to differential transfer RNA supply within the cell, synonymous codons are not used with equal frequency, a phenomenon termed codon usage bias (CUB). Previous studies have demonstrated that CUB of endogenous genes trans-regulates the translational efficiency of other genes. We hypothesized similar effects for CUB of exogenous genes on host translation, and tested it in the case of viral infection, a common form of naturally occurring exogenous gene translation. We analysed public Ribo-Seq datasets from virus-infected yeast and human cells and showed that virus CUB trans-regulated tRNA availability, and therefore the relative decoding time of codons. Manipulative experiments in yeast using 37 synonymous fluorescent proteins confirmed that an exogenous gene with CUB more similar to that of the host would apply decreased translational load on the host per unit of expression, whereas expression of the exogenous gene was elevated. The combination of these two effects was that exogenous genes with CUB overly similar to that of the host severely impeded host translation. Finally, using a manually curated list of viruses and natural and symptomatic hosts, we found that virus CUB tended to be more similar to that of symptomatic hosts than that of natural hosts, supporting a general deleterious effect of excessive CUB similarity between virus and host. Our work revealed repulsion between virus and host CUBs when they are overly similar, a previously unrecognized complexity in the coevolution of virus and host.

The Evolution of Sex Chromosome Dosage Compensation in Animals

Bidirectional genetic control of phenotypic heterogeneity and its implication for cancer drug resistance

Abstract Negative genetic regulators of phenotypic heterogeneity, or phenotypic capacitors/stabilizers, elevate population-average fitness by limiting deviation from the optimal phenotype and increase the efficacy of natural selection by enhancing the phenotypic differences among genotypes. Stabilizers can presumably be switched off to release phenotypic heterogeneity in the face of extreme or fluctuating environments to ensure population survival. This task could, however, also be achieved by positive genetic regulators of phenotypic heterogeneity, or “phenotypic diversifiers”, as shown by recently reported evidence that a bacterial divisome factor enhances antibiotic resistance. We hypothesized that such active creation of phenotypic heterogeneity by diversifiers, which is functionally independent of stabilizers, is more common than previously recognized. Using morphological phenotypic data from 4,718 single-gene-knockout strains of Saccharomyces cerevisiae, we systematically identified 324 stabilizers and 160 diversifiers and constructed a bipartite network between these genes and the morphological traits they control. Further analyses showed that, compared with stabilizers, diversifiers tended to be weaker and more promiscuous (regulating more traits) regulators targeting traits unrelated to fitness. Moreover, there is a general division of labor between stabilizers and diversifiers. Finally, by incorporating NCI-60 human cancer cell line anticancer drug screening data, we found that human one-to-one orthologs of yeast diversifiers/stabilizers likely regulate the anticancer drug resistance of human cancer cell lines, suggesting that these orthologs are potential targets for auxiliary treatments. Our study therefore highlights stabilizers and diversifiers as the genetic regulators for the bidirectional control of phenotypic heterogeneity, as well as their distinct evolutionary roles and functional independence.

In vitro and in vivo growth inhibition of human cervical cancer cells via human papillomavirus E6/E7 mRNAs’ cleavage by CRISPR/Cas13a system

Sustained infection of high-risk human papillomavirus (HR-HPVs), especially HPV16 and HPV18, is a major cause of cervical cancer. E6 and E7 oncoproteins, encoded by the HPV genome, are critical for transformation and maintenance of malignant phenotypes of cervical cancer. Here, we used an emerging programmable clustered regularly interspaced short palindromic repeat (CRISPR)/Cas13a system to cleave HPV 16/18 E6/E7 messenger RNAs (mRNAs). The results showed that customized CRISPR/Cas13a system effectively and specifically knocked down HPV 16/18 E6/E7 mRNAs, inducing growth inhibition and apoptosis in HPV16-positive SiHa and HPV18-positive HeLa Cell lines, but not in HPV-negative C33A cell line. Simultaneously, we detected downregulation of E6/E7 oncoproteins and upregulation of tumor suppressor P53 and RB proteins. In addition, we used subcutaneous xenograft tumor growth assays to find that the weight and volume of tumors in the SiHa-16E6CR1 group knocked down by the CRISPR/Cas13a system were significantly lower than those in the SiHa-VECTOR group lacking crRNA. Our study demonstrated that targeting HPV E6/E7 mRNAs by the CRISPR/Cas13a system may be a candidate therapeutic strategy for HPV-related cervical cancer.

The expression tractability of biological traits shaped by natural selection

Risk stratification of cervical lesions using capture sequencing and machine learning method based on HPV and human integrated genomic profiles

Abstract From initial human papillomavirus (HPV) infection and precursor stages, the development of cervical cancer takes decades. High-sensitivity HPV DNA testing is currently recommended as primary screening method for cervical cancer, whereas better triage methodologies are encouraged to provide accurate risk management for HPV-positive women. Given that virus-driven genomic variation accumulates during cervical carcinogenesis, we designed a 39 Mb custom capture panel targeting 17 HPV types and 522 mutant genes related to cervical cancer. Using capture-based next-generation sequencing, HPV integration status, somatic mutation and copy number variation were analyzed on 34 paired samples, including 10 cases of HPV infection (HPV+), 10 cases of cervical intraepithelial neoplasia (CIN) grade and 14 cases of CIN2+ (CIN2: n = 1; CIN2-3: n = 3; CIN3: n = 9; squamous cell carcinoma: n = 1). Finally, the machine learning algorithm (Random Forest) was applied to build the risk stratification model for cervical precursor lesions based on CIN2+ enriched biomarkers. Generally, HPV integration events (11 in HPV+, 25 in CIN1 and 56 in CIN2+), non-synonymous mutations (2 in CIN1, 12 in CIN2+) and copy number variations (19.1 in HPV+, 29.4 in CIN1 and 127 in CIN2+) increased from HPV+ to CIN2+. Interestingly, ‘common’ deletion of mitochondrial chromosome was significantly observed in CIN2+ (P = 0.009). Together, CIN2+ enriched biomarkers, classified as HPV information, mutation, amplification, deletion and mitochondrial change, successfully predicted CIN2+ with average accuracy probability score of 0.814, and amplification and deletion ranked as the most important features. Our custom capture sequencing combined with machine learning method effectively stratified the risk of cervical lesions and provided valuable integrated triage strategies.

Computational comparison of developmental cell lineage trees by alignments

ABSTRACT The developmental cell lineage tree, which records every cell division event and the terminal developmental state of each single cell, is one of the most important traits of multicellular organisms, as well as key to many significant unresolved questions in biology. Recent technological breakthroughs are paving the way for direct determination of cell lineage trees, yet a general framework for the computational analysis of lineage trees, in particular an algorithm to compare two lineage trees, is still lacking. Based on previous findings that the same developmental program can be invoked by different cells on the lineage tree to produce highly similar subtrees, we designed D evelopmental C e ll L ineage T ree A lignment (DELTA), an algorithm that exhaustively searches for lineage trees with phenotypic resemblance in lineal organization of terminal cells, meanwhile resolving detailed correspondence between individual cells. Using simulated and nematode lineage trees, we demonstrated DELTA’s capability of revealing similarities of developmental programs by lineal resemblances. Moreover, DELTA successfully identifies gene deletion-triggered homeotic cell fate transformations, reveals functional relationship between mutants by quantifying their lineal similarities, and finds the evolutionary correspondence between cell types defined non-uniformly for different species. DELTA establishes novel foundation for comparative study of lineage trees, much like sequence alignment algorithm for biological sequences, and along with the increase of lineage tree data, will likely bring unique insights for the myriads of important questions surrounding cell lineage trees.

The expression tractability of a biological trait

Abstract Understanding how gene expression is translated to phenotype is central to modern molecular biology, but the success is contingent on the intrinsic tractability of the specific traits under examination. However, an a priori estimate of trait tractability from the perspective of gene expression is unavailable. Motivated by the concept of entropy in a thermodynamic system, we here propose such an estimate ( S T ) by gauging the number ( N ) of different expression states that underlie the same trait abnormality, with large S T corresponding to large N . By analyzing over 200 yeast morphological traits we show that S T is constrained by natural selection, which builds co-regulated gene modules to minimize the total number of possible expression states. We further show that S T is a good measure of the titer of recurrent patterns of an expression-trait relationship, predicting the extent to which the trait could be deterministically understood with gene expression data.

Dosage sensitivity of X-linked genes in human embryonic single cells

Fifty years ago, Susumu Ohno proposed that the expression levels of X-linked genes have doubled as dosage compensation for autosomal genes due to degeneration of Y-linked homologs during evolution of mammalian sex chromosomes. Recent studies have nevertheless shown that the X to autosome expression ratio equals ~1 in haploid human parthenogenetic embryonic stem (pES) cells and ~0.5 in diploid pES cells, thus refuting Ohno's hypothesis. Here, by reanalyzing a RNA-seq-based single-cell transcriptome dataset of human embryos (Petropoulos, et al. 2016), we found that from the 8-cell stage until the time-point just prior to implantation, the expression levels of X-linked genes are not two-fold upregulated in male cells and gradually decrease from two-fold in female cells. This observation suggests that the expression levels of X-linked genes are imbalanced, with autosomal genes starting from the early 8-cell stage, and that the dosage conversion is fast, such that the X:AA expression ratio reaches ~0.5 in no more than a week. Additional analyses of gene expression noise further suggest that the dosage sensitivity of X-linked genes is weaker than that of autosomal genes in differentiated female cells, which contradicts a key assumption of Ohno’s hypothesis. Moreover, the dosage-sensitive housekeeping genes are preferentially located on autosomes, implying selection against X-linkage for dosage-sensitive genes. Our results collectively suggest an alternative to Ohno’s hypothesis that X-linked genes are less likely to be dosage sensitive than autosomal genes.

Does mRNA structure contain genetic information for regulating co-translational protein folding?

Deciphering the Genic Basis of Yeast Fitness Variation by Simultaneous Forward and Reverse Genetics

Intra and Interspecific Variations of Gene Expression Levels in Yeast Are Largely Neutral: (Nei Lecture, SMBE 2016, Gold Coast)