Marker-Assisted Development and Evaluation of Monogenic Lines of Rice cv. Kaohsiung 145 Carrying Blast Resistance Genes.

Rice blast is a serious threat to global rice production. Large-scale and long-term cultivation of rice varieties with a single blast resistance gene usually leads to breakdown of resistance. To effectively control rice blast in Taiwan, marker-assisted backcrossing was conducted to develop monogenic lines carrying different blast resistance genes in the genetic background of an elite japonica rice cultivar, Kaohsiung 145 (KH145). Eleven International Rice Research Institute (IRRI)-bred blast-resistant lines (IRBLs) showing broad-spectrum resistance to local Pyricularia oryzae isolates were used as resistance donors. Sequencing analysis revealed that the recurrent parent, KH145, does not carry known resistance alleles at the target Pi2/9, Pik, Pita, and Ptr loci. For each IRBL × KH145 cross, we screened 21 to 370 (average of 108) plants per generation from the BC1F1 to BC3F1/BC4F1 generation. A total of 1,499 BC3F2/BC4F2 lines carrying homozygous resistance alleles were selected and self-crossed for four to six successive generations. The derived lines were also evaluated for background genotype using genotyping by sequencing, for blast resistance under artificial inoculation and natural infection conditions, and for agronomic performance in multiple field trials. In Chiayi and Taitung blast nurseries in 2018 to 2020, Pi2, Pi9, and Ptr conferred high resistance, Pi20 and Pik-h moderate resistance, and Pi1, Pi7, Pik-p, and Pik susceptibility to leaf blast; only Pi2, Pi9, and Ptr conferred effective resistance against panicle blast. The monogenic lines showed agronomic traits, yield, and grain quality similar to those of KH145, suggesting the potential of growing a mixture of lines to achieve durable resistance in the field.

SWATH-MS-based quantitative proteomics reveals a uniquely intricate defense response in Cnaphalocrocis medinalis-resistant rice.

Cnaphalocrocis medinalis is a major insect pest of rice in Asia. A few defensive enzymes were reported to show higher activities in a resistant rice line (Qingliu) than in a susceptible rice line (TN1) upon leaffolder infestation. However, the overall molecular regulation of the rice defense response against leaffolder herbivory is unknown. Here, differential proteomic analysis by SWATH-MS was performed to identify differentially expressed proteins between the two rice varieties, Qingliu and TN1, at four time points of leaffolder herbivory, 0, 6, 24, and 72 h. Gene Ontology (GO) enrichment of the differentially expressed proteins indicated overrepresentation of (1) photosynthesis, (2) amino acid and derivative metabolic process, and (3) secondary metabolic process. Phenylalanine ammonia lyase and chalcone synthase, which catalyze flavonoid biosynthesis, and lipoxygenase, which catalyzes jasmonic acid biosynthesis, exhibited higher expression in Qingliu than in TN1 even before insect herbivory. Momentary activation of the light reaction and Calvin cycle was detected in Qingliu at 6 h and 24 h of insect herbivory, respectively. At 72 h of insect herbivory, amino acid biosynthesis and glutathione-mediated antioxidation were activated in Qingliu. A defense response involving jasmonic acid signaling, carbon remobilization, and the production of flavonoids and glutathione could underlie the resistance of Qingliu to leaffolder.

In Vitro and in Planta Evaluation of Trichoderma asperellum TA as a Biocontrol Agent Against Phellinus noxius, the Cause of Brown Root Rot Disease of Trees.

Brown root rot (BRR), caused by the white rot fungus Phellinus noxius, is an epidemic disease of diverse broadleaved and coniferous tree species in many tropical and subtropical regions. Flooding and trenching control measures are difficult to implement, and chemical controls can have an adverse impact on ecosystems. Previous studies have provided in vitro evidence for the potential use of Trichoderma spp. for biocontrol of BRR. Here, we analyzed the in vitro antagonistic and mycoparasitic abilities of four Trichoderma spp. isolates against four P. noxius isolates in dual culture and Ficus microcarpa wood blocks. A convenient inoculation system based on root inoculation of a highly susceptible loquat (Eriobotrya japonica) with P. noxius-colonized wheat-oat grains was developed to examine the effect of Trichoderma treatment in planta. Preventive application of Trichoderma asperellum TA, the isolate showing high antagonistic activity in vitro, was effective in preventing and delaying the wilting of P. noxius-inoculated loquat cuttings in greenhouse trials. To understand the specific niche in which T. asperellum TA interacts with P. noxius, KOH-aniline blue fluorescence microscopy was used to investigate the colonization of loquat roots by P. noxius and/or T. asperellum TA. Dilution plating assays were also conducted to quantify Trichoderma populations in the rhizosphere and potting mix. T. asperellum TA was able to robustly establish in the rhizosphere and potting mix but with scarce root penetration limited to the superficial layer. We discuss the timing and strategy for applying antagonistic Trichodema sp. on living trees or in BRR-infested areas for BRR management.

The Major Prognostic Features of Nuclear Receptor NR5A2 in Infiltrating Ductal Breast Carcinomas.

Background. Gene expression profiles of 181 breast cancer samples were analyzed to identify prognostic features of nuclear receptors NR5A1 and NR5A2 based upon their associated transcriptional networks. Methods. A supervised network analysis approach was used to build the NR5A-mediated transcriptional regulatory network. Other bioinformatic tools and statistical methods were utilized to confirm and extend results from the network analysis methodology. Results. NR5A2 expression is a negative factor in breast cancer prognosis in both ER(-) and ER(-)/ER(+) mixed cohorts. The clinical and cohort significance of NR5A2-mediated transcriptional activities indicates that it may have a significant role in attenuating grade development and cancer related signal transduction pathways. NR5A2 signature that conditions poor prognosis was identified based upon results from 15 distinct probes. Alternatively, the expression of NR5A1 predicts favorable prognosis when concurrent NR5A2 expression is low. A favorable signature of eight transcription factors mediated by NR5A1 was also identified. Conclusions. Correlation of poor prognosis and NR5A2 activity is identified by NR5A2-mediated 15-gene signature. NR5A2 may be a potential drug target for treating a subset of breast cancer tumors across breast cancer subtypes, especially ER(-) breast tumors. The favorable prognostic feature of NR5A1 is predicted by NR5A1-mediated 8-gene signature.

Correction to: Prognostic Features of Signal Transducer and Activator of Transcription 3 in an ER(+) Breast Cancer Model System.

[This corrects the article on p. 21 in vol. 13, PMID: 24526833.].

A supervised network analysis on gene expression profiles of breast tumors predicts a 41-gene prognostic signature of the transcription factor MYB across molecular subtypes.

BACKGROUND: MYB is predicted to be a favorable prognostic predictor in a breast cancer population. We proposed to find the inferred mechanism(s) relevant to the prognostic features of MYB via a supervised network analysis. METHODS: Both coefficient of intrinsic dependence (CID) and Galton Pierson's correlation coefficient (GPCC) were combined and designated as CIDUGPCC. It is for the univariate network analysis. Multivariate CID is for the multivariate network analysis. Other analyses using bioinformatic tools and statistical methods are included. RESULTS: ARNT2 is predicted to be the essential gene partner of MYB. We classified four prognostic relevant gene subpools in three breast cancer cohorts as feature types I-IV. Only the probes in feature type II are the potential prognostic feature of MYB. Moreover, we further validated 41 prognosis relevant probes to be the favorable prognostic signature. Surprisingly, two additional family members of MYB are elevated to promote poor prognosis when both levels of MYB and ARNT2 decline. Both MYBL1 and MYBL2 may partially decrease the tumor suppressive activities that are predicted to be up-regulated by MYB and ARNT2. CONCLUSIONS: The major prognostic feature of MYB is predicted to be determined by the MYB subnetwork (41 probes) that is relevant across subtypes.

Prognostic features of signal transducer and activator of transcription 3 in an ER(+) breast cancer model system.

The aberrantly expressed signal transducer and activator of transcription 3 (STAT3) predicts poor prognosis, primarily in estrogen receptor positive (ER(+)) breast cancers. Activated STAT3 is overexpressed in luminal A subtype cells. The mechanisms contributing to the prognosis and/or subtype relevant features of STAT3 in ER(+) breast cancers are through multiple interacting regulatory pathways, including STAT3-MYC, STAT3-ERα, and STAT3-MYC-ERα interactions, as well as the direct action of activated STAT3. These data predict malignant events, treatment responses and a novel enhancer of tamoxifen resistance. The inferred crosstalk between ERα and STAT3 in regulating their shared target gene-METAP2 is partially validated in the luminal B breast cancer cell line-MCF7. Taken together, we identify a poor prognosis relevant gene set within the STAT3 network and a robust one in a subset of patients. VEGFA, ABL1, LYN, IGF2R and STAT3 are suggested therapeutic targets for further study based upon the degree of differential expression in our model.

In Silico Prediction for Regulation of Transcription Factors onTheir Shared Target Genes Indicates Relevant Clinical Implications in a Breast Cancer Population.

Aberrant transcriptional activities have been documented in breast cancers. Studies often find some transcription factors to be inappropriately regulated and enriched in certain pathological states. The promoter regions of most target genes have binding sites for their transcription factors. An ample of evidence supports their combinatorial effect on their shared target gene expressions. Here, we used a new statistic method, bivariate CID, to predict combinatorial interaction activity between ERα and a transcription factor (E2F1or GATA3 or ERRα) in regulating target gene expression via four regulatory mechanisms. We identified gene sets in three signal transduction pathways perturbed in breast tumors: cell cycle, VEGF, and PDGFRB. Bivariate network analysis revealed several target genes previously implicated in tumor angiogenesis are among the predicted shared targets, including VEGFA, PDGFRB. In summary, our analysis suggests the importance for the multivariate space of an inferred ERα transcriptional regulatory network in breast cancer diagnostic and therapeutic development.

Statistical identification of gene association by CID in application of constructing ER regulatory network.

BACKGROUND: A variety of high-throughput techniques are now available for constructing comprehensive gene regulatory networks in systems biology. In this study, we report a new statistical approach for facilitating in silico inference of regulatory network structure. The new measure of association, coefficient of intrinsic dependence (CID), is model-free and can be applied to both continuous and categorical distributions. When given two variables X and Y, CID answers whether Y is dependent on X by examining the conditional distribution of Y given X. In this paper, we apply CID to analyze the regulatory relationships between transcription factors (TFs) (X) and their downstream genes (Y) based on clinical data. More specifically, we use estrogen receptor alpha (ERalpha) as the variable X, and the analyses are based on 48 clinical breast cancer gene expression arrays (48A). RESULTS: The analytical utility of CID was evaluated in comparison with four commonly used statistical methods, Galton-Pearson's correlation coefficient (GPCC), Student's t-test (STT), coefficient of determination (CoD), and mutual information (MI). When being compared to GPCC, CoD, and MI, CID reveals its preferential ability to discover the regulatory association where distribution of the mRNA expression levels on X and Y does not fit linear models. On the other hand, when CID is used to measure the association of a continuous variable (Y) against a discrete variable (X), it shows similar performance as compared to STT, and appears to outperform CoD and MI. In addition, this study established a two-layer transcriptional regulatory network to exemplify the usage of CID, in combination with GPCC, in deciphering gene networks based on gene expression profiles from patient arrays. CONCLUSION: CID is shown to provide useful information for identifying associations between genes and transcription factors of interest in patient arrays. When coupled with the relationships detected by GPCC, the association predicted by CID are applicable to the construction of transcriptional regulatory networks. This study shows how information from different data sources and learning algorithms can be integrated to investigate whether relevant regulatory mechanisms identified in cell models can also be partially re-identified in clinical samples of breast cancers. AVAILABILITY: the implementation of CID in R codes can be freely downloaded from (http://homepage.ntu.edu.tw/~lyliu/BC/).

Vascularity change and tumor response to neoadjuvant chemotherapy for advanced breast cancer.

For advanced breast cancer with severe local disease (ABC) (stage III/IV), neoadjuvant chemotherapy improves local control and surgical outcome. However, about approximately 20 to 30% of advanced cancers show either no or poor response to chemotherapy. To prevent unnecessary treatment, a capability of predicting clinical response to neoadjuvant chemotherapy of ABC is highly desirable. Vascularity index (VI) of breast cancers was derived from the quantification results in 30 ABC patients by using power Doppler sonography. Power Doppler sonography evaluation was performed every one to two weeks during chemotherapy. The overall response rate for 30 advanced patients tested was 70%, when 50% or more reduction in tumor size was the objective clinical response. Chemotherapy response was unrelated to the original tumor size (p = 0.563) or chemotherapy agents used (p = 0.657). The median VI for all 30 patients was 4.99%. The response rates for hypervascular tumors vs. hypovascular tumors, based on initial median value, were 86.7% and 53.3%, respectively (p = 0.109). The average VIs in responders and nonresponders were 7.67 +/- 4.77% and 4.01 +/- 3.82% (p = 0.052). There was a tendency for responders who have a relatively high initial vascularity. The VI change in responder group shows a pattern of initial increasing in vascularity followed by decreasing in vascularity. All patients (17/17) with a VI increment of >5% during chemotherapy had good chemotherapy response, whereas in patients with a VI increment of <5%, the response rate was 30.8% (4/13) (p < 0.001). For patients with a peak VI of >10% during chemotherapy, the response rate was 94.1% (16/17). However, in patients with a peak VI of <10%, the response rate was 38.5% (5/13) (p = 0.001). This prediction was made mostly within one month (25.47 +/- 12.96 d for VI increments >5% and 25.44 +/- 12.41 d for VI increased to >10%). In the meantime, the differences in size reduction shown in B-mode sonography were insignificant between responders and nonresponders (patient group with VI increment >5%, p = 0.308; patient group with peak VI >10%, p = 0.396). In conclusion, we propose that VI as determined by using power Doppler sonography is a good and inexpensive clinical tool for monitoring vascularity changes during neoadjuvant chemotherapy in ABC patients. Two parameters--VI increment >5% and peak VI >10%--are potential early predictors for good responses to neoadjuvant chemotherapy within one month in patients with ABC.

A two-stage normalization method for partially degraded mRNA microarray data.

MOTIVATION: The goal of the study is to obtain genetic information from exfoliated colonocytes in the fecal stream rather than directly from mucosa cells within the colon. The latter is obtained through invasive procedures. The difficulties encountered by this procedure are that certain probe information may be compromised due to partially degraded mRNA. Proper normalization is essential to obtaining useful information from these fecal array data. RESULTS: We propose a new two-stage semiparametric normalization method motivated by the features observed in fecal microarray data. A location-scale transformation and a robust inclusion step were used to roughly align arrays within the same treatment. A non-parametric estimated non-linear transformation was then used to remove the potential intensity-based biases. We compared the performance of the new method in analyzing a fecal microarray dataset with those achieved by two existing normalization approaches: global median transformation and quantile normalization. The new method favorably compared with the global median and quantile normalization methods. AVAILABILITY: The R codes implementing the two-stage method may be obtained from the corresponding author.