Construction of a hierarchical gene regulatory network centered around a transcription factor

Reanalysis of single-cell data reveals macrophage subsets associated with the immunotherapy response and prognosis of patients with endometrial cancer

Endometrial cancer (EC) is an aggressive gynecological malignancy with an increased incidence rate. The immune landscape crucially affects immunotherapy efficacy and prognosis in EC patients. Here, we characterized the distinct tumor microenvironment signatures of EC tumors by analyzing single-cell RNA sequencing data from Gene Expression Omnibus and bulk RNA sequencing data from The Cancer Genome Atlas, which were compared with normal endometrium. Three macrophage subsets were identified, and two of them showed tissue-specific distribution. One of the macrophage subsets was dominant in macrophages derived from EC and exhibited characteristic behaviors such as promoting tumor growth and metastasis. One of the other macrophage subsets was mainly found in normal endometrium and served functions related to antigen presentation. We also identified a macrophage subset that was found in both EC and normal endometrial tissue. However, the pathway and cellular cross-talk of this subset were completely different based on the respective origin, suggesting a tumor-related differentiation mechanism of macrophages. Additionally, the tumor-enriched macrophage subset was found to predict immunotherapy responses in EC. Notably, we selected six genes from macrophage subset markers that could predict the survival of EC patients, SCL8A1, TXN, ANXA5, CST3, CD74 and NANS, and constructed a prognostic signature. To verify the signature, we identified immunohistochemistry for the tumor samples of 83 EC patients based on the selected genes and further followed up with the survival of the patients. Our results provide strong evidence that the signature can effectively predict the prognosis of EC patients.

TGPred: efficient methods for predicting target genes of a transcription factor by integrating statistics, machine learning and optimization

Four statistical selection methods for inferring transcription factor (TF)-target gene (TG) pairs were developed by coupling mean squared error (MSE) or Huber loss function, with elastic net (ENET) or least absolute shrinkage and selection operator (Lasso) penalty. Two methods were also developed for inferring pathway gene regulatory networks (GRNs) by combining Huber or MSE loss function with a network (Net)-based penalty. To solve these regressions, we ameliorated an accelerated proximal gradient descent (APGD) algorithm to optimize parameter selection processes, resulting in an equally effective but much faster algorithm than the commonly used convex optimization solver. The synthetic data generated in a general setting was used to test four TF-TG identification methods, ENET-based methods performed better than Lasso-based methods. Synthetic data generated from two network settings was used to test Huber-Net and MSE-Net, which outperformed all other methods. The TF-TG identification methods were also tested with SND1 and gl3 overexpression transcriptomic data, Huber-ENET and MSE-ENET outperformed all other methods when genome-wide predictions were performed. The TF-TG identification methods fill the gap of lacking a method for genome-wide TG prediction of a TF, and potential for validating ChIP/DAP-seq results, while the two Net-based methods are instrumental for predicting pathway GRNs.

Bacterium-enabled transient gene activation by artificial transcription factors for resolving gene regulation in maize

Understanding gene regulatory networks is essential to elucidate developmental processes and environmental responses. Here, we studied regulation of a maize (Zea mays) transcription factor gene using designer transcription activator-like effectors (dTALes), which are synthetic Type III TALes of the bacterial genus Xanthomonas and serve as inducers of disease susceptibility gene transcription in host cells. The maize pathogen Xanthomonas vasicola pv. vasculorum was used to introduce 2 independent dTALes into maize cells to induced expression of the gene glossy3 (gl3), which encodes a MYB transcription factor involved in biosynthesis of cuticular wax. RNA-seq analysis of leaf samples identified, in addition to gl3, 146 genes altered in expression by the 2 dTALes. Nine of the 10 genes known to be involved in cuticular wax biosynthesis were upregulated by at least 1 of the 2 dTALes. A gene previously unknown to be associated with gl3, Zm00001d017418, which encodes aldehyde dehydrogenase, was also expressed in a dTALe-dependent manner. A chemically induced mutant and a CRISPR-Cas9 mutant of Zm00001d017418 both exhibited glossy leaf phenotypes, indicating that Zm00001d017418 is involved in biosynthesis of cuticular waxes. Bacterial protein delivery of dTALes proved to be a straightforward and practical approach for the analysis and discovery of pathway-specific genes in maize.

Two high hierarchical regulators, PuMYB40 and PuWRKY75, control the low phosphorus driven adventitious root formation in Populus ussuriensis

Adventitious rooting is an essential biological process in the vegetative propagation of economically important horticultural and forest tree species. It enables utilization of the elite genotypes in breeding programmes and production. Promotion of adventitious root (AR) formation has been associated with starvation of inorganic phosphate and some factors involved in low phosphorus (LP) signalling. However, the regulatory mechanism underlying LP-mediated AR formation remains largely elusive. We established an efficient experimental system that guaranteed AR formation through short-term LP treatment in Populus ussuriensis. We then generated a time-course RNA-seq data set to recognize key regulatory genes and regulatory cascades positively regulating AR formation through data analysis and gene network construction, which were followed by experimental validation and characterization. We constructed a multilayered hierarchical gene regulatory network, from which PuMYB40, a typical R2R3-type MYB transcription factor (TF), and its interactive partner, PuWRKY75, as well as their direct targets, PuLRP1 and PuERF003, were identified to function upstream of the known adventitious rooting genes. These regulatory genes were functionally characterized and proved their roles in promoting AR formation in P. ussuriensis. In conclusion, our study unveiled a new hierarchical regulatory network that promoted AR formation in P. ussuriensis, which was activated by short-term LP stimulus and primarily governed by PuMYB40 and PuWRKY75.

Characterization of Expression and Epigenetic Features of Core Genes in Common Wheat

The availability of multiple wheat genome sequences enables us to identify core genes and characterize their genetic and epigenetic features, thereby advancing our understanding of their biological implications within individual plant species. It is, however, largely understudied in wheat. To this end, we reanalyzed genome sequences from 16 different wheat varieties and identified 62,299 core genes. We found that core and non-core genes have different roles in subgenome differentiation. Meanwhile, according to their expression profiles, these core genes can be classified into genes related to tissue development and stress responses, including 3376 genes highly expressed in both spikelets and at high temperatures. After associating with six histone marks and open chromatin, we found that these core genes can be divided into eight sub-clusters with distinct epigenomic features. Furthermore, we found that ca. 51% of the expressed transcription factors (TFs) were marked with both H3K27me3 and H3K4me3, indicative of the bivalency feature, which can be involved in tissue development through the TF-centered regulatory network. Thus, our study provides a valuable resource for the functional characterization of core genes in stress responses and tissue development in wheat.

Genome-wide association studies reveal the coordinated regulatory networks underlying photosynthesis and wood formation in Populus

Photosynthesis and wood formation underlie the ability of trees to provide renewable resources and perform ecological functions; however, the genetic basis and regulatory pathways coordinating these two linked processes remain unclear. Here, we used a systems genetics strategy, integrating genome-wide association studies, transcriptomic analyses, and transgenic experiments, to investigate the genetic architecture of photosynthesis and wood properties among 435 unrelated individuals of Populus tomentosa, and unravel the coordinated regulatory networks resulting in two trait categories. We detected 222 significant single-nucleotide polymorphisms, annotated to 177 candidate genes, for 10 traits of photosynthesis and wood properties. Epistasis uncovered 74 epistatic interactions for phenotypes. Strikingly, we deciphered the coordinated regulation patterns of pleiotropic genes underlying phenotypic variations for two trait categories. Furthermore, expression quantitative trait nucleotide mapping and coexpression analysis were integrated to unravel the potential transcriptional regulatory networks of candidate genes coordinating photosynthesis and wood properties. Finally, heterologous expression of two pleiotropic genes, PtoMYB62 and PtoMYB80, in Arabidopsis thaliana demonstrated that they control regulatory networks balancing photosynthesis and stem secondary cell wall components, respectively. Our study provides insights into the regulatory mechanisms coordinating photosynthesis and wood formation in poplar, and should facilitate genetic breeding in trees via molecular design.

Identification of biological pathway and process regulators using sparse partial least squares and triple-gene mutual interaction

Identification of biological process- and pathway-specific regulators is essential for advancing our understanding of regulation and formation of various phenotypic and complex traits. In this study, we applied two methods, triple-gene mutual interaction (TGMI) and Sparse Partial Least Squares (SPLS), to identify the regulators of multiple metabolic pathways in Arabidopsis thaliana and Populus trichocarpa using high-throughput gene expression data. We analyzed four pathways: (1) lignin biosynthesis pathway in A. thaliana and P. trichocarpa; (2) flavanones, flavonol and anthocyannin biosynthesis in A. thaliana; (3) light reaction pathway and Calvin cycle in A. thaliana. (4) light reaction pathway alone in A. thaliana. The efficiencies of two methods were evaluated by examining the positive known regulators captured, the receiver operating characteristic (ROC) curves and the area under ROC curves (AUROC). Our results showed that TGMI is in general more efficient than SPLS in identifying true pathway regulators and ranks them to the top of candidate regulatory gene lists, but the two methods are to some degree complementary because they could identify some different pathway regulators. This study identified many regulators that potentially regulate the above pathways in plants and are valuable for genetic engineering of these pathways.

Growth-regulating factor 5 (GRF5)-mediated gene regulatory network promotes leaf growth and expansion in poplar

Although polyploid plants have larger leaves than their diploid counterparts, the molecular mechanisms underlying this difference (or trait) remain elusive. Differentially expressed genes (DEGs) between triploid and full-sib diploid poplar trees were identified from two transcriptomic data sets followed by a gene association study among DEGs to identify key leaf growth regulators. Yeast one-hybrid system, electrophoretic mobility shift assay, and dual-luciferase assay were employed to substantiate that PpnGRF5-1 directly regulated PpnCKX1. The interactions between PpnGRF5-1 and growth-regulating factor (GRF)-interacting factors (GIFs) were experimentally validated and a multilayered hierarchical regulatory network (ML-hGRN)-mediated by PpnGRF5-1 was constructed with top-down graphic Gaussian model (GGM) algorithm by combining RNA-sequencing data from its overexpression lines and DAP-sequencing data. PpnGRF5-1 is a negative regulator of PpnCKX1. Overexpression of PpnGRF5-1 in diploid transgenic lines resulted in larger leaves resembling those of triploids, and significantly increased zeatin and isopentenyladenine in the apical buds and third leaves. PpnGRF5-1 also interacted with GIFs to increase its regulatory diversity and capacity. An ML-hGRN-mediated by PpnGRF5-1 was obtained and could largely elucidate larger leaves. PpnGRF5-1 and the ML-hGRN-mediated by PpnGRF5-1 were underlying the leaf growth and development.

Comparative proteomic analysis provides insight into the molecular mechanism of vegetative growth advantage in allotriploid Populus

Though allotriploid poplar shows a salient vegetative growth advantage that impacts biomass and lumber yield, the proteomic data of Populus allotriploids have not been scrutinized for identifying the underlying molecular mechanisms. We conducted a large-scale label-free proteomics profiling of the 5th, 10th, and 25th leaves of allotriploids and diploids, and identified 4587 protein groups. Among 932 differentially expressed proteins (DEPs), 22 are transcription factors (TFs) that could regulate vegetative growth advantage in allotriploids. The DEPs involved in light reaction, Calvin cycle, and photorespiration, protein synthesis, sucrose synthesis, starch synthesis, and starch decomposition displayed elevated expression in Populus allotriploids. However, the DEPs functioning in sucrose decomposition, tricarboxylic acid (TCA) cycle, and protein degradation exhibited significantly downregulated expression. The alternations of these DEPs augmented efficiency of photosynthesis, carbon fixation, sucrose and starch accumulation, and decreased capacity of carbohydrate consumption, leading to larger volume of biomass and vigorous growth in Populus allotriploids.

PuHox52-mediated hierarchical multilayered gene regulatory network promotes adventitious root formation in Populus ussuriensis

Adventitious root (AR) formation is critically important in vegetative propagation through cuttings in some plants, especially woody species. However, the underlying molecular mechanisms remain elusive. Here, we report the identification of a poplar homeobox gene, PuHox52, which was induced rapidly (within 15 min) at the basal ends of stems upon cutting and played a key regulatory role in adventitious rooting. We demonstrated that overexpression of PuHox52 significantly increased the number of ARs while suppression of PuHox52 had the opposite effect. A multilayered hierarchical gene regulatory network (ML-hGRN) mediated by PuHox52 was reverse-engineered and demonstrated to govern AR formation. PuHox52 regulated AR formation through upregulation of nine hub regulators, including a jasmonate signaling pathway gene, PuMYC2, and an auxin signaling pathway gene, PuAGL12. We also identified coherent type 4 feed-forward loops within this ML-hGRN; PuHox52 repressed PuHDA9, which encodes a histone deacetylase, and led to an increase in acetylation and presumably expression of three hub regulators, PuWRKY51, PuLBD21 and PuIAA7. Our results indicate that the ML-hGRN mediated by PuHox52 governs AR formation at the basal ends of stem cuttings from poplar trees.

Neither biological nor symptomatology reductionism: A call for integration in psychopathology research

We agree with Borsboom et al. in challenging neurobiological reductionism, and underscore some specific strengths of a network approach. However, they do not acknowledge that a similar problem is present in current psychosocial frameworks. We discuss this challenge as well as describe valuable parallels between symptom and neurobiological network theories that will substantially augment psychopathological research when integrated.

TGMI: an efficient algorithm for identifying pathway regulators through evaluation of triple-gene mutual interaction

Despite their important roles, the regulators for most metabolic pathways and biological processes remain elusive. Presently, the methods for identifying metabolic pathway and biological process regulators are intensively sought after. We developed a novel algorithm called triple-gene mutual interaction (TGMI) for identifying these regulators using high-throughput gene expression data. It first calculated the regulatory interactions among triple gene blocks (two pathway genes and one transcription factor (TF)), using conditional mutual information, and then identifies significantly interacted triple genes using a newly identified novel mutual interaction measure (MIM), which was substantiated to reflect strengths of regulatory interactions within each triple gene block. The TGMI calculated the MIM for each triple gene block and then examined its statistical significance using bootstrap. Finally, the frequencies of all TFs present in all significantly interacted triple gene blocks were calculated and ranked. We showed that the TFs with higher frequencies were usually genuine pathway regulators upon evaluating multiple pathways in plants, animals and yeast. Comparison of TGMI with several other algorithms demonstrated its higher accuracy. Therefore, TGMI will be a valuable tool that can help biologists to identify regulators of metabolic pathways and biological processes from the exploded high-throughput gene expression data in public repositories.