Phenotypic robustness and the assortativity signature of human transcription factor networks.

Many developmental, physiological, and behavioral processes depend on the precise expression of genes in space and time. Such spatiotemporal gene expression phenotypes arise from the binding of sequence-specific transcription factors (TFs) to DNA, and from the regulation of nearby genes that such binding causes. These nearby genes may themselves encode TFs, giving rise to a transcription factor network (TFN), wherein nodes represent TFs and directed edges denote regulatory interactions between TFs. Computational studies have linked several topological properties of TFNs - such as their degree distribution - with the robustness of a TFN's gene expression phenotype to genetic and environmental perturbation. Another important topological property is assortativity, which measures the tendency of nodes with similar numbers of edges to connect. In directed networks, assortativity comprises four distinct components that collectively form an assortativity signature. We know very little about how a TFN's assortativity signature affects the robustness of its gene expression phenotype to perturbation. While recent theoretical results suggest that increasing one specific component of a TFN's assortativity signature leads to increased phenotypic robustness, the biological context of this finding is currently limited because the assortativity signatures of real-world TFNs have not been characterized. It is therefore unclear whether these earlier theoretical findings are biologically relevant. Moreover, it is not known how the other three components of the assortativity signature contribute to the phenotypic robustness of TFNs. Here, we use publicly available DNaseI-seq data to measure the assortativity signatures of genome-wide TFNs in 41 distinct human cell and tissue types. We find that all TFNs share a common assortativity signature and that this signature confers phenotypic robustness to model TFNs. Lastly, we determine the extent to which each of the four components of the assortativity signature contributes to this robustness.

The influence of assortativity on the robustness and evolvability of gene regulatory networks upon gene birth.

Gene regulatory networks (GRNs) represent the interactions between genes and gene products, which drive the gene expression patterns that produce cellular phenotypes. GRNs display a number of characteristics that are beneficial for the development and evolution of organisms. For example, they are often robust to genetic perturbation, such as mutations in regulatory regions or loss of gene function. Simultaneously, GRNs are often evolvable as these genetic perturbations are occasionally exploited to innovate novel regulatory programs. Several topological properties, such as degree distribution, are known to influence the robustness and evolvability of GRNs. Assortativity, which measures the propensity of nodes of similar connectivity to connect to one another, is a separate topological property that has recently been shown to influence the robustness of GRNs to point mutations in cis-regulatory regions. However, it remains to be seen how assortativity may influence the robustness and evolvability of GRNs to other forms of genetic perturbation, such as gene birth via duplication or de novo origination. Here, we employ a computational model of genetic regulation to investigate whether the assortativity of a GRN influences its robustness and evolvability upon gene birth. We find that the robustness of a GRN generally increases with increasing assortativity, while its evolvability generally decreases. However, the rate of change in robustness outpaces that of evolvability, resulting in an increased proportion of assortative GRNs that are simultaneously robust and evolvable. By providing a mechanistic explanation for these observations, this work extends our understanding of how the assortativity of a GRN influences its robustness and evolvability upon gene birth.

The influence of assortativity on the robustness of signal-integration logic in gene regulatory networks.

Gene regulatory networks (GRNs) drive the cellular processes that sustain life. To do so reliably, GRNs must be robust to perturbations, such as gene deletion and the addition or removal of regulatory interactions. GRNs must also be robust to genetic changes in regulatory regions that define the logic of signal-integration, as these changes can affect how specific combinations of regulatory signals are mapped to particular gene expression states. Previous theoretical analyses have demonstrated that the robustness of a GRN is influenced by its underlying topological properties, such as degree distribution and modularity. Another important topological property is assortativity, which measures the propensity with which nodes of similar connectivity are connected to one another. How assortativity influences the robustness of the signal-integration logic of GRNs remains an open question. Here, we use computational models of GRNs to investigate this relationship. We separately consider each of the three dynamical regimes of this model for a variety of degree distributions. We find that in the chaotic regime, robustness exhibits a pronounced increase as assortativity becomes more positive, while in the critical and ordered regimes, robustness is generally less sensitive to changes in assortativity. We attribute the increased robustness to a decrease in the duration of the gene expression pattern, which is caused by a reduction in the average size of a GRN's in-components. This study provides the first direct evidence that assortativity influences the robustness of the signal-integration logic of computational models of GRNs, illuminates a mechanistic explanation for this influence, and furthers our understanding of the relationship between topology and robustness in complex biological systems.

Association mapping and marker-assisted selection of the lettuce dieback resistance gene Tvr1.

BACKGROUND: Lettuce (Lactuca saliva L.) is susceptible to dieback, a soilborne disease caused by two viruses from the family Tombusviridae. Susceptibility to dieback is widespread in romaine and leaf-type lettuce, while modern iceberg cultivars are resistant to this disease. Resistance in iceberg cultivars is conferred by Tvr1 - a single, dominant gene that provides durable resistance. This study describes fine mapping of the resistance gene, analysis of nucleotide polymorphism and linkage disequilibrium in the Tvr1 region, and development of molecular markers for marker-assisted selection. RESULTS: A combination of classical linkage mapping and association mapping allowed us to pinpoint the location of the Tvr1 resistance gene on chromosomal linkage group 2. Nine molecular markers, based on expressed sequence tags (EST), were closely linked to Tvr1 in the mapping population, developed from crosses between resistant (Salinas and Salinas 88) and susceptible (Valmaine) cultivars. Sequencing of these markers from a set of 68 cultivars revealed a relatively high level of nucleotide polymorphism (theta = 6.7 x 10-3) and extensive linkage disequilibrium (r(2) = 0.124 at 8 cM) in this region. However, the extent of linkage disequilibrium was affected by population structure and the values were substantially larger when the analysis was performed only for romaine (r(2) = 0.247) and crisphead (r(2) = 0.345) accessions. The association mapping approach revealed that one of the nine markers (Cntg10192) in the Tvr1 region matched exactly with resistant and susceptible phenotypes when tested on a set of 200 L. sativa accessions from all horticultural types of lettuce. The marker-trait association was also confirmed on two accessions of Lactuca serriola - a wild relative of cultivated lettuce. The combination of three single-nucleotide polymorphisms (SNPs) at the Cntg10192 marker identified four haplotypes. Three of the haplotypes were associated with resistance and one of them was always associated with susceptibility to the disease. CONCLUSION: We have successfully applied high-resolution DNA melting (HRM) analysis to distinguish all four haplotypes of the Cntg10192 marker in a single analysis. Marker-assisted selection for dieback resistance with HRM is now an integral part of our breeding program that is focused on the development of improved lettuce cultivars.

Hypoxia-Induced VISTA Promotes the Suppressive Function of Myeloid-Derived Suppressor Cells in the Tumor Microenvironment.

Tumor hypoxia is a negative prognostic factor that is implicated in oncogenic signal activation, immune escape, and resistance to treatment. Identifying the mechanistic role of hypoxia in immune escape and resistance to immune-checkpoint inhibitors may aid the identification of therapeutic targets. We and others have shown that V-domain Ig suppressor of T-cell activation (VISTA), a negative checkpoint regulator in the B7 family, is highly expressed in the tumor microenvironment in tumor models and primary human cancers. In this study, we show that VISTA and HIF1α activity are correlated in a cohort of colorectal cancer patients. High VISTA expression was associated with worse overall survival. We used the CT26 colon cancer model to investigate the regulation of VISTA by hypoxia. Compared with less hypoxic tumor regions or draining lymph nodes, regions of profound hypoxia in the tumor microenvironment were associated with increased VISTA expression on tumor-infiltrating myeloid-derived suppressor cells (MDSC). Using chromatin immunoprecipitation and genetic silencing, we show that hypoxia-inducible factor (HIF)-1α binding to a conserved hypoxia response element in the VISTA promoter upregulated VISTA on myeloid cells. Further, antibody targeting or genetic ablation of VISTA under hypoxia relieved MDSC-mediated T-cell suppression, revealing VISTA as a mediator of MDSC function. Collectively, these data suggest that targeting VISTA may mitigate the deleterious effects of hypoxia on antitumor immunity.