The SEEL motif and members of the MYB-related REVEILLE transcription factor family are important for the expression of LORELEI in the synergid cells of the Arabidopsis female gametophyte.

Synergid cells in the micropylar end of the female gametophyte are required for critical cell-cell signaling interactions between the pollen tube and the ovule that precede double fertilization and seed formation in flowering plants. LORELEI (LRE) encodes a putative GPI-anchored protein that is expressed primarily in the synergid cells, and together with FERONIA, a receptor-like kinase, it controls pollen tube reception by the receptive synergid cell. Still, how LRE expression is controlled in synergid cells remains poorly characterized. We identified candidate cis-regulatory elements enriched in LRE and other synergid cell-expressed genes. One of the candidate motifs ('TAATATCT') in the LRE promoter was an uncharacterized variant of the Evening Element motif that we named as the Short Evening Element-like (SEEL) motif. Deletion or point mutations in the SEEL motif of the LRE promoter resulted in decreased reporter expression in synergid cells, demonstrating that the SEEL motif is important for expression of LRE in synergid cells. Additionally, we found that LRE expression is decreased in the loss of function mutants of REVEILLE (RVE) transcription factors, which are clock genes known to bind the SEEL and other closely related motifs. We propose that RVE transcription factors regulate LRE expression in synergid cells by binding to the SEEL motif in the LRE promoter. Identification of cis-regulatory elements and transcription factors involved in the expression of LRE will serve as a foundation to characterize the gene regulatory networks in synergid cells.

Optimising the use of gene expression data to predict plant metabolic pathway memberships.

Plant metabolites from diverse pathways are important for plant survival, human nutrition and medicine. The pathway memberships of most plant enzyme genes are unknown. While co-expression is useful for assigning genes to pathways, expression correlation may exist only under specific spatiotemporal and conditional contexts. Utilising > 600 tomato (Solanum lycopersicum) expression data combinations, three strategies for predicting memberships in 85 pathways were explored. Optimal predictions for different pathways require distinct data combinations indicative of pathway functions. Naive prediction (i.e. identifying pathways with the most similarly expressed genes) is error prone. In 52 pathways, unsupervised learning performed better than supervised approaches, possibly due to limited training data availability. Using gene-to-pathway expression similarities led to prediction models that outperformed those based simply on expression levels. Using 36 experimental validated genes, the pathway-best model prediction accuracy is 58.3%, significantly better compared with that for predicting annotated genes without experimental evidence (37.0%) or random guess (1.2%), demonstrating the importance of data quality. Our study highlights the need to extensively explore expression-based features and prediction strategies to maximise the accuracy of metabolic pathway membership assignment. The prediction framework outlined here can be applied to other species and serves as a baseline model for future comparisons.

Phase II Trial of Neoadjuvant Carboplatin and Nab-Paclitaxel in Patients with Triple-Negative Breast Cancer.

BACKGROUND: In this phase II clinical trial, we evaluated the efficacy of the nonanthracycline combination of carboplatin and nab-paclitaxel in early stage triple-negative breast cancer (TNBC). PATIENTS AND METHODS: Patients with newly diagnosed stage II-III TNBC (n = 69) were treated with neoadjuvant carboplatin (area under the curve 6) every 28 days for four cycles plus nab-paclitaxel (100 mg/m2 ) weekly for 16 weeks. Pathological complete response (pCR) and residual cancer burden (RCB) were analyzed with germline mutation status, tumor-infiltrating lymphocytes (TILs), TNBC molecular subtype, and GeparSixto immune signature (GSIS). RESULTS: Sixty-seven patients were evaluable for safety and response. Fifty-three (79%) patients experienced grade 3/4 adverse events, including grade 3 anemia (43%), neutropenia (39%), leukopenia (15%), thrombocytopenia (12%), fatigue (7%), peripheral neuropathy (7%), neutropenia (16%), and leukopenia (1%). Twenty-four patients (35%) had at least one dose delay, and 50 patients (72%) required dose reduction. Sixty-three (94%) patients completed scheduled treatment. The responses were as follows: 32 of 67 patients (48%) had pCR (RCB 0), 10 of 67 (15%) had RCB I, 19 of 67 (28%) had RCB II, 5 of 67 (7%) had RCB III, and 1 of 67 (2%) progressed and had no surgery. Univariate analysis showed that immune-hot GSIS and DNA repair defect (DRD) were associated with higher pCR with odds ratios of 4.62 (p = .005) and 4.76 (p = .03), respectively, and with RCB 0/I versus RCB II/III with odds ratio 4.80 (p = .01). Immune-hot GSIS was highly correlated with DRD status (p = .03), TIL level (p < .001), and TNBC molecular subtype (p < .001). After adjusting for age, race, stage, and grade, GSIS remained associated with higher pCR and RCB class 0/I versus II/III with odds ratios 7.19 (95% confidence interval [CI], 2.01-25.68; p = .002) and 8.95 (95% CI, 2.09-38.23; p = .003), respectively. CONCLUSION: The combination of carboplatin and nab-paclitaxel for early stage high-risk TNBC showed manageable toxicity and encouraging antitumor activity. Immune-hot GSIS is associated with higher pCR rate and RCB class 0/1. This study provides an additional rationale for using nonanthracycline platinum-based therapy for future neoadjuvant trials in early stage TNBCs. Clinical trial identification number: NCT01525966 IMPLICATIONS FOR PRACTICE: Platinum is an important neoadjuvant chemotherapy agent for treatment of early stage triple-negative breast cancer (TNBC). In this study, carboplatin and nab-paclitaxel were well tolerated and highly effective in TNBC, resulting in pathological complete response of 48%. In univariate and multivariate analyses adjusting for age, race, tumor stage and grade, "immune-hot" GeparSixto immune signature (GSIS) and DNA repair defect (DRD) were associated with higher pathological complete response (pCR) and residual cancer burden class 0/1. The association of immune-hot GSIS with higher pCR holds promise for de-escalating neoadjuvant chemotherapy for patients with early stage TNBC. Although GSIS is not routinely used in clinic, further development of this immune signature into a clinically applicable assay is indicated.

Cis-Regulatory Code for Predicting Plant Cell-Type Transcriptional Response to High Salinity.

Multicellular organisms have diverse cell types with distinct roles in development and responses to the environment. At the transcriptional level, the differences in the environmental response between cell types are due to differences in regulatory programs. In plants, although cell-type environmental responses have been examined, it is unclear how these responses are regulated. Here, we identify a set of putative cis-regulatory elements (pCREs) enriched in the promoters of genes responsive to high-salinity stress in six Arabidopsis (Arabidopsis thaliana) root cell types. We then use these pCREs to establish cis-regulatory codes (i.e. models predicting whether a gene is responsive to high salinity for each cell type with machine learning). These pCRE-based models outperform models using in vitro binding data of 758 Arabidopsis transcription factors. Surprisingly, organ pCREs identified based on the whole-root high-salinity response can predict cell-type responses as well as pCREs derived from cell-type data, because organ and cell-type pCREs predict complementary subsets of high-salinity response genes. Our findings not only advance our understanding of the regulatory mechanisms of the plant spatial transcriptional response through cis-regulatory codes but also suggest broad applicability of the approach to any species, particularly those with little or no trans-regulatory data.

Regulatory Divergence in Wound-Responsive Gene Expression between Domesticated and Wild Tomato.

The evolution of transcriptional regulatory mechanisms is central to how stress response and tolerance differ between species. However, it remains largely unknown how divergence in cis-regulatory sites and, subsequently, transcription factor (TF) binding specificity contribute to stress-responsive expression divergence, particularly between wild and domesticated species. By profiling wound-responsive gene transcriptomes in wild Solanum pennellii and domesticated S. lycopersicum, we found extensive wound response divergence and identified 493 S. lycopersicum and 278 S. pennellii putative cis-regulatory elements (pCREs) that were predictive of wound-responsive gene expression. Only 24-52% of these wound response pCREs (depending on wound response patterns) were consistently enriched in the putative promoter regions of wound-responsive genes across species. In addition, between these two species, their differences in pCRE site sequences were significantly and positively correlated with differences in wound-responsive gene expression. Furthermore, ∼11-39% of pCREs were specific to only one of the species and likely bound by TFs from different families. These findings indicate substantial regulatory divergence in these two plant species that diverged ∼3-7 million years ago. Our study provides insights into the mechanistic basis of how the transcriptional response to wounding is regulated and, importantly, the contribution of cis-regulatory components to variation in wound-responsive gene expression between a wild and a domesticated plant species.

Recovery from N Deprivation Is a Transcriptionally and Functionally Distinct State in Chlamydomonas.

Facing adverse conditions such as nitrogen (N) deprivation, microalgae enter cellular quiescence, a reversible cell cycle arrest with drastic changes in metabolism allowing cells to remain viable. Recovering from N deprivation and quiescence is an active and orderly process as we are showing here for Chlamydomonas reinhardtii We conducted comparative transcriptomics on this alga to discern processes relevant to quiescence in the context of N deprivation and recovery following refeeding. A mutant with slow recovery from N deprivation, compromised hydrolysis of triacylglycerols7 (cht7), was included to better define the regulatory processes governing the respective transitions. We identified an ordered set of biological processes with expression patterns that showed sequential reversal following N resupply and uncovered acclimation responses specific to the recovery phase. Biochemical assays and microscopy validated selected inferences made based on the transcriptional analyses. These comprise (1) the restoration of N source preference and cellular bioenergetics during the early stage of recovery; (2) flagellum-based motility in the mid to late stage of recovery; and (3) recovery phase-specific gene groups cooperating in the rapid replenishment of chloroplast proteins. In the cht7 mutant, a large number of programmed responses failed to readjust in a timely manner. Finally, evidence is provided for the involvement of the cAMP-protein kinase A pathway in gating the recovery. We conclude that the recovery from N deprivation represents not simply a reversal of processes directly following N deprivation, but a distinct cellular state.

Predictive Models of Spatial Transcriptional Response to High Salinity.

Plants are exposed to a variety of environmental conditions, and their ability to respond to environmental variation depends on the proper regulation of gene expression in an organ-, tissue-, and cell type-specific manner. Although our knowledge of how stress responses are regulated is accumulating, a genome-wide model of how plant transcription factors (TFs) and cis-regulatory elements control spatially specific stress response has yet to emerge. Using Arabidopsis (Arabidopsis thaliana) as a model, we identified a set of 1,894 putative cis-regulatory elements (pCREs) that are associated with high-salinity (salt) up-regulated genes in the root or the shoot. We used these pCREs to develop computational models that can better predict salt up-regulated genes in the root and shoot compared with models based on known TF binding motifs. In addition, we incorporated TF binding sites identified via large-scale in vitro assays, chromatin accessibility, evolutionary conservation, and pCRE combinatorial relationships in machine learning models and found that only consideration of pCRE combinations led to better performance in salt up-regulation prediction in the root and shoot. Our results suggest that the plant organ transcriptional response to high salinity is regulated by a core set of pCREs and provide a genome-wide view of the cis-regulatory code of plant spatial transcriptional responses to environmental stress.

Diversity, expansion, and evolutionary novelty of plant DNA-binding transcription factor families.

Plant transcription factors (TFs) that interact with specific sequences via DNA-binding domains are crucial for regulating transcriptional initiation and are fundamental to plant development and environmental response. In addition, expansion of TF families has allowed functional divergence of duplicate copies, which has contributed to novel, and in some cases adaptive, traits in plants. Thus, TFs are central to the generation of the diverse plant species that we see today. Major plant agronomic traits, including those relevant to domestication, have also frequently arisen through changes in TF coding sequence or expression patterns. Here our goal is to provide an overview of plant TF evolution by first comparing the diversity of DNA-binding domains and the sizes of these domain families in plants and other eukaryotes. Because TFs are among the most highly expanded gene families in plants, the birth and death process of TFs as well as the mechanisms contributing to their retention are discussed. We also provide recent examples of how TFs have contributed to novel traits that are important in plant evolution and in agriculture.This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.

Utility and Limitations of Using Gene Expression Data to Identify Functional Associations.

Gene co-expression has been widely used to hypothesize gene function through guilt-by association. However, it is not clear to what degree co-expression is informative, whether it can be applied to genes involved in different biological processes, and how the type of dataset impacts inferences about gene functions. Here our goal is to assess the utility and limitations of using co-expression as a criterion to recover functional associations between genes. By determining the percentage of gene pairs in a metabolic pathway with significant expression correlation, we found that many genes in the same pathway do not have similar transcript profiles and the choice of dataset, annotation quality, gene function, expression similarity measure, and clustering approach significantly impacts the ability to recover functional associations between genes using Arabidopsis thaliana as an example. Some datasets are more informative in capturing coordinated expression profiles and larger data sets are not always better. In addition, to recover the maximum number of known pathways and identify candidate genes with similar functions, it is important to explore rather exhaustively multiple dataset combinations, similarity measures, clustering algorithms and parameters. Finally, we validated the biological relevance of co-expression cluster memberships with an independent phenomics dataset and found that genes that consistently cluster with leucine degradation genes tend to have similar leucine levels in mutants. This study provides a framework for obtaining gene functional associations by maximizing the information that can be obtained from gene expression datasets.

Molecular Evidence for Functional Divergence and Decay of a Transcription Factor Derived from Whole-Genome Duplication in Arabidopsis thaliana.

Functional divergence between duplicate transcription factors (TFs) has been linked to critical events in the evolution of land plants and can result from changes in patterns of expression, binding site divergence, and/or interactions with other proteins. Although plant TFs tend to be retained post polyploidization, many are lost within tens to hundreds of million years. Thus, it can be hypothesized that some TFs in plant genomes are in the process of becoming pseudogenes. Here, we use a pair of salt tolerance-conferring transcription factors, DWARF AND DELAYED FLOWERING1 (DDF1) and DDF2, that duplicated through paleopolyploidy 50 to 65 million years ago, as examples to illustrate potential mechanisms leading to duplicate retention and loss. We found that the expression patterns of Arabidopsis thaliana (At)DDF1 and AtDDF2 have diverged in a highly asymmetric manner, and AtDDF2 has lost most inferred ancestral stress responses. Consistent with promoter disablement, the AtDDF2 promoter has fewer predicted cis-elements and a methylated repetitive element. Through comparisons of AtDDF1, AtDDF2, and their Arabidopsis lyrata orthologs, we identified significant differences in binding affinities and binding site preference. In particular, an AtDDF2-specific substitution within the DNA-binding domain significantly reduces binding affinity. Cross-species analyses indicate that both AtDDF1 and AtDDF2 are under selective constraint, but among A. thaliana accessions, AtDDF2 has a higher level of nonsynonymous nucleotide diversity compared with AtDDF1. This may be the result of selection in different environments or may point toward the possibility of ongoing functional decay despite retention for millions of years after gene duplication.

The Impact of the Branched-Chain Ketoacid Dehydrogenase Complex on Amino Acid Homeostasis in Arabidopsis.

The branched-chain amino acids (BCAAs) Leu, Ile, and Val are among nine essential amino acids that must be obtained from the diet of humans and other animals, and can be nutritionally limiting in plant foods. Despite genetic evidence of its importance in regulating seed amino acid levels, the full BCAA catabolic network is not completely understood in plants, and limited information is available regarding its regulation. In this study, transcript coexpression analyses revealed positive correlations among BCAA catabolism genes in stress, development, diurnal/circadian, and light data sets. A core subset of BCAA catabolism genes, including those encoding putative branched-chain ketoacid dehydrogenase subunits, is highly expressed during the night in plants on a diel cycle and in prolonged darkness. Mutants defective in these subunits accumulate higher levels of BCAAs in mature seeds, providing genetic evidence for their function in BCAA catabolism. In addition, prolonged dark treatment caused the mutants to undergo senescence early and overaccumulate leaf BCAAs. These results extend the previous evidence that BCAAs can be catabolized and serve as respiratory substrates at multiple steps. Moreover, comparison of amino acid profiles between mature seeds and dark-treated leaves revealed differences in amino acid accumulation when BCAA catabolism is perturbed. Together, these results demonstrate the consequences of blocking BCAA catabolism during both normal growth conditions and under energy-limited conditions.

Measuring semantic similarities by combining gene ontology annotations and gene co-function networks.

BACKGROUND: Gene Ontology (GO) has been used widely to study functional relationships between genes. The current semantic similarity measures rely only on GO annotations and GO structure. This limits the power of GO-based similarity because of the limited proportion of genes that are annotated to GO in most organisms. RESULTS: We introduce a novel approach called NETSIM (network-based similarity measure) that incorporates information from gene co-function networks in addition to using the GO structure and annotations. Using metabolic reaction maps of yeast, Arabidopsis, and human, we demonstrate that NETSIM can improve the accuracy of GO term similarities. We also demonstrate that NETSIM works well even for genomes with sparser gene annotation data. We applied NETSIM on large Arabidopsis gene families such as cytochrome P450 monooxygenases to group the members functionally and show that this grouping could facilitate functional characterization of genes in these families. CONCLUSIONS: Using NETSIM as an example, we demonstrated that the performance of a semantic similarity measure could be significantly improved after incorporating genome-specific information. NETSIM incorporates both GO annotations and gene co-function network data as a priori knowledge in the model. Therefore, functional similarities of GO terms that are not explicitly encoded in GO but are relevant in a taxon-specific manner become measurable when GO annotations are limited. Supplementary information and software are available at http://www.msu.edu/~jinchen/NETSIM .