Genetic Interaction Network as an Important Determinant of Gene Order in Genome Evolution

Co-transfer of functionally interdependent genes contributes to genome mosaicism in lambdoid phages

Lambdoid (or Lambda-like) phages are a group of related temperate phages that can infect Escherichia coli and other gut bacteria. A key characteristic of these phages is their mosaic genome structure, which served as the basis for the 'modular genome hypothesis'. Accordingly, lambdoid phages evolve by transferring genomic regions, each of which constitutes a functional unit. Nevertheless, it is unknown which genes are preferentially transferred together and what drives such co-transfer events. Here we aim to characterize genome modularity by studying co-transfer of genes among 95 distantly related lambdoid (pro-)phages. Based on gene content, we observed that the genomes cluster into 12 groups, which are characterized by a highly similar gene content within the groups and highly divergent gene content across groups. Highly similar proteins can occur in genomes of different groups, indicating that they have been transferred. About 26 % of homologous protein clusters in the four known operons (i.e. the early left, early right, immunity and late operon) engage in gene transfer, which affects all operons to a similar extent. We identified pairs of genes that are frequently co-transferred and observed that these pairs tend to be near one another on the genome. We find that frequently co-transferred genes are involved in related functions and highlight interesting examples involving structural proteins, the cI repressor and Cro regulator, proteins interacting with DNA, and membrane-interacting proteins. We conclude that epistatic effects, where the functioning of one protein depends on the presence of another, play an important role in the evolution of the modular structure of these genomes.

Neighbours matter: Effects of genomic organization on gene expression plasticity in response to environmental stresses during biological invasions

Gene expression regulation has been widely recognized as an important molecular mechanism underlying phenotypic plasticity in environmental adaptation. However, it remains largely unexplored on the effects of genomic organization on gene expression plasticity under environmental stresses during biological invasions. Here, we use an invasive model ascidian, Ciona robusta, to investigate how genomic organization affects gene expression in response to salinity stresses during range expansions. Our study showed that neighboring genes were co-expressed and approximately 30% of stress responsive genes were physically clustered on chromosomes. Such coordinated expression was substantially affected by the physical distance and orientation of genes. Interestingly, the overall expression correlation of neighboring genes was significantly decreased under high salinity stresses, illustrating that the co-expression regulation could be disrupted by salinity challenges. Furthermore, the clustering of genes was associated with their function constraints and expression patterns - operon genes enriched in gene expression machinery had the highest transcriptional activity and expression stability. Notably, our analyses showed that the tail-to-tail genes, mainly involved in biological functions related to phosphorylation, homeostatic process, and ion transport, exhibited higher intrinsic expression variability and greater response to salinity challenges. Altogether, the results obtained here provide new insights into the effects of gene organization on gene expression plasticity under environmental challenges, hence improving our knowledge on mechanisms of rapid environmental adaptation during biological invasions.

A novel classification method for NSCLC based on the background interaction network and the edge-perturbation matrix

The biological functional network of tumor tissues is relatively stable for a period of time and under different conditions, so the impact of tumor heterogeneity is effectively avoided. Based on edge perturbation, functional gene interaction networks were used to reveal the pathological environment of patients with non-small cell carcinoma at the individual level, and to identify cancer subtypes with the same or similar status, and then a multi-dimensional and multi-omics comprehensive analysis was put into practice. Two edge perturbation subtypes were identified through the construction of the background interaction network and the edge-perturbation matrix (EPM). Further analyses revealed clear differences between those two clusters in terms of prognostic survival, stemness indices, immune cell infiltration, immune checkpoint molecular expression, copy number alterations, mutation load, homologous recombination defects (HRD), neoantigen load, and chromosomal instability. Additionally, a risk prediction model based on TCGA for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) was successfully constructed and validated using the independent data set (GSE50081).

Transcriptome Analysis in Yeast Reveals the Externality of Position Effects

The activity of a gene newly integrated into a chromosome depends on the genomic context of the integration site. This “position effect” has been widely reported, although the other side of the coin, that is, how integration affects the local chromosomal environment, has remained largely unexplored, as have the mechanism and phenotypic consequences of this “externality” of the position effect. Here, we examined the transcriptome profiles of approximately 250 Saccharomyces cerevisiae strains, each with GFP integrated into a different locus of the wild-type strain. We found that in genomic regions enriched in essential genes, GFP expression tended to be lower, and the genes near the integration site tended to show greater expression reduction. Further joint analysis with public genome-wide histone modification profiles indicated that this effect was associated with H3K4me2. More importantly, we found that changes in the expression of neighboring genes, but not GFP expression, significantly altered the cellular growth rate. As a result, genomic loci that showed high GFP expression immediately after integration were associated with growth disadvantages caused by elevated expression of neighboring genes, ultimately leading to a low total yield of GFP in the long run. Our results were consistent with competition for transcriptional resources among neighboring genes and revealed a previously unappreciated facet of position effects. This study highlights the impact of position effects on the fate of exogenous gene integration and has significant implications for biological engineering and the pathology of viral integration into the host genome.

Predicting Evolution Using Regulatory Architecture

The limits of evolution have long fascinated biologists. However, the causes of evolutionary constraint have remained elusive due to a poor mechanistic understanding of studied phenotypes. Recently, a range of innovative approaches have leveraged mechanistic information on regulatory networks and cellular biology. These methods combine systems biology models with population and single-cell quantification and with new genetic tools, and they have been applied to a range of complex cellular functions and engineered networks. In this article, we review these developments, which are revealing the mechanistic causes of epistasis at different levels of biological organization-in molecular recognition, within a single regulatory network, and between different networks-providing first indications of predictable features of evolutionary constraint.

Gene-gene interaction: the curse of dimensionality

Identified genetic variants from genome wide association studies frequently show only modest effects on the disease risk, leading to the "missing heritability" problem. An avenue, to account for a part of this "missingness" is to evaluate gene-gene interactions (epistasis) thereby elucidating their effect on complex diseases. This can potentially help with identifying gene functions, pathways, and drug targets. However, the exhaustive evaluation of all possible genetic interactions among millions of single nucleotide polymorphisms (SNPs) raises several issues, otherwise known as the "curse of dimensionality". The dimensionality involved in the epistatic analysis of such exponentially growing SNPs diminishes the usefulness of traditional, parametric statistical methods. With the immense popularity of multifactor dimensionality reduction (MDR), a non-parametric method, proposed in 2001, that classifies multi-dimensional genotypes into one- dimensional binary approaches, led to the emergence of a fast-growing collection of methods that were based on the MDR approach. Moreover, machine-learning (ML) methods such as random forests and neural networks (NNs), deep-learning (DL) approaches, and hybrid approaches have also been applied profusely, in the recent years, to tackle this dimensionality issue associated with whole genome gene-gene interaction studies. However, exhaustive searching in MDR based approaches or variable selection in ML methods, still pose the risk of missing out on relevant SNPs. Furthermore, interpretability issues are a major hindrance for DL methods. To minimize this loss of information, Python based tools such as PySpark can potentially take advantage of distributed computing resources in the cloud, to bring back smaller subsets of data for further local analysis. Parallel computing can be a powerful resource that stands to fight this "curse". PySpark supports all standard Python libraries and C extensions thus making it convenient to write codes to deliver dramatic improvements in processing speed for extraordinarily large sets of data.

Modification of the genome topology network and its application to the comparison of group B Streptococcus genomes

Background

The genome topology network (GTN) is a new approach for studying the phylogenetics of bacterial genomes by analysing their gene order. The previous GTN tool gives a phylogenetic tree and calculate the different degrees (DD) of various adjacent gene families with complete genome data, but it is limited to the gene family level.

Result

In this study, we collected 51 published complete and draft group B Streptococcus (GBS) genomes from the NCBI database as the case study data. The phylogenetic tree obtained from the GTN method assigned the genomes into six main clades. Compared with single nucleotide polymorphism (SNP)-based method, the GTN method exhibited a higher resolution in two clades. The gene families located at unique node connections in these clades were associated with the clusters of orthologous groups (COG) functional categories of “[G] Carbohydrate transport and metabolism,”, “[L] Replication, recombination, and repair” and “[J] translation, ribosomal structure and biogenesis”. Thus, these genes were the major factors affecting the differentiation of these six clades in the phylogenetic tree obtained from the GTN.

Conclusion

The modified GTN analyzes draft genomic data and exhibits greater functionality than the previous version. The gene family clustering algorithm embedded in the GTN tool is optimized by introducing the Markov cluster algorithm (MCL) tool to assign genes to functional gene families. A bootstrap test is performed to verify the credibility of the clades when allowing users to adjust the relationships of the clades accordingly. The GTN tool gives additional evolutionary information that is a useful complement to the SNP-based method. Information on the differences in the connections between a gene and its adjacent genes in species or clades is easily obtained. The modified GTN tool can be downloaded from https://github.com/0232/Genome_topology_network

Synchronized replication of genes encoding the same protein complex in fast-proliferating cells

DNA replication perturbs the dosage balance among genes; at mid-S phase, early-replicating genes have doubled their copies while late-replicating ones have not. Dosage imbalance among genes, especially within members of a protein complex, is toxic to cells. However, the molecular mechanisms that cells use to deal with such imbalance remain not fully understood. Here, we validate at the genomic scale that the dosage between early- and late-replicating genes is imbalanced in HeLa cells. We propose the synchronized replication hypothesis that genes sensitive to stoichiometric relationships will be replicated simultaneously to maintain stoichiometry. In support of this hypothesis, we observe that genes encoding the same protein complex have similar replication timing but mainly in fast-proliferating cells such as embryonic stem cells and cancer cells. We find that the synchronized replication observed in cancer cells, but not in slow-proliferating differentiated cells, is due to convergent evolution during tumorigenesis that restores synchronized replication timing within protein complexes. Taken together, our study reveals that the demand for dosage balance during S phase plays an important role in the optimization of the replication-timing program; this selection is relaxed during differentiation as the cell cycle prolongs and is restored during tumorigenesis as the cell cycle shortens.

Gene Repositioning Is Under Constraints After Evolutionary Conserved Gene Neighborhood Separate

Genes are not randomly distributed on eukaryotic chromosomes. Some neighboring genes show order conservation among species, while some neighboring genes separate during evolution even though their neighborhoods are conserved in some species. Here, I investigated whether after-separation gene repositioning is under natural selection for evolutionary conserved gene neighborhoods compared with nonconserved neighborhoods. After separation, genes with conserved neighborhoods show low-expression divergence between the after-separation species and the before-separation species. After genes separate from their conserved gene neighbors, their after-separation gene neighbors tend to show coexpression and coprotein complex with their before-separation gene neighbors. These results indicate evolutionary constraints on the selection of neighboring genes after evolutionary conserved gene neighborhoods separate.

Recombination drives the evolution of mutational robustness

Recombination can impose fitness costs as beneficial parental combinations of alleles are broken apart, a phenomenon known as recombination load. Computational models suggest that populations may evolve a reduced recombination load by reducing either the likelihood of recombination events (bring interacting loci in physical proximity) or the strength of interactions between loci (make loci more independent of one another). We review evidence for each of these possibilities and their consequences for the genotype-fitness relationship. In particular, we expect that reducing interaction strengths between loci will lead to genomes that are also robust to mutational perturbations, but reducing recombination rates alone will not. We note that both mechanisms most likely played a role in the evolution of extant populations, and that both can result in the frequently-observed pattern of physical linkage between interacting loci.

Natural selection interacts with recombination to shape the evolution of hybrid genomes

To investigate the consequences of hybridization between species, we studied three replicate hybrid populations that formed naturally between two swordtail fish species, estimating their fine-scale genetic map and inferring ancestry along the genomes of 690 individuals. In all three populations, ancestry from the "minor" parental species is more common in regions of high recombination and where there is linkage to fewer putative targets of selection. The same patterns are apparent in a reanalysis of human and archaic admixture. These results support models in which ancestry from the minor parental species is more likely to persist when rapidly uncoupled from alleles that are deleterious in hybrids. Our analyses further indicate that selection on swordtail hybrids stems predominantly from deleterious combinations of epistatically interacting alleles.

Screening of Genes Related to Early and Late Flowering in Tree Peony Based on Bulked Segregant RNA Sequencing and Verification by Quantitative Real-Time PCR

Tree peony (Paeonia suffruticosa Andrews) is a perennial woody shrub bearing large and colorful flowers. However, the flowering period is short and relatively uniform, which to an important extent hinders the cultivation and exploitation of ornamental peonies. In this study, the segregation of an F₁ population derived from P. ostti ‘Feng Dan’ (an early-flowering cultivar) × P. suffruticosa ‘Xin Riyuejin’ (a late-flowering cultivar) was used to screen and analyze candidate genes associated with flowering period of the two parents. Extreme early- and late-flowering genotypes of the F₁ population at full-bloom stage were sampled to establish an early-flowering mixed pool (T03), a late-flowering mixed pool (T04), a late-flowering male pool (T01), and an early-flowering female pool (T02), using the Sequencing By Synthesis (SBS) technology on the Illumina HiSeq TM2500 platform. A total of 56.51 Gb of clean reads data, comprising at least 87.62% of Quality30 (Q30), was generated, which was then combined into 173,960 transcripts (N50 = 1781) and 78,645 (N50 = 1282) unigenes, with a mean length of 1106.76 and 732.27 base pairs (bp), respectively. Altogether, 58,084 genes were annotated by comparison with public databases, based on an E-value parameter of less than 10⁻⁵ and 10⁻¹⁰ for BLAST and HMMER, respectively. In total, 291 unigene sequences were finally screened out by BSR-seq (bulked segregant RNA-seq) association analysis. To validate these unigenes, we finally confirmed seven unigenes that were related to early and late flowering, which were then verified by quantitative real-time PCR (qRT-PCR). This is the first reported study to screen genes associated with early and late flowering of tree peony by the BSA (bulked sample analysis) method of transcriptome sequencing, and to construct a high-quality transcriptome database. A set of candidate functional genes related to flowering time was successfully obtained, providing an important genetic resource for further studies of flowering in peony and the mechanism of regulation of flowering time in tree peony.