Li-Yu Daisy Liu

MicroRNA396-Targeted SHORT VEGETATIVE PHASE Is Required to Repress Flowering and Is Related to the Development of Abnormal Flower Symptoms by the Phyllody Symptoms1 Effector

A PHYL1 effector protein interferes with miR396-mediated transcriptional regulator mRNA decay, enhancing the transcription factor for abnormal flower development. Leafy flowers are the major symptoms of peanut witches’ broom (PnWB) phytoplasma infection in Catharanthus roseus. The orthologs of the phyllody symptoms1 (PHYL1) effector of PnWB from other species of phytoplasma can trigger the proteasomal degradation of several MADS box transcription factors, resulting in leafy flower formation. In contrast, the flowering negative regulator gene SHORT VEGETATIVE PHASE (SVP) was up-regulated in PnWB-infected C. roseus plants, but most microRNA (miRNA) genes had repressed expression. Coincidentally, transgenic Arabidopsis (Arabidopsis thaliana) plants expressing the PHYL1 gene of PnWB (PHYL1 plants), which show leafy flower phenotypes, up-regulate SVP of Arabidopsis (AtSVP) but repress a putative regulatory miRNA of AtSVP, miR396. However, the mechanism by which PHYL1 regulates AtSVP and miR396 is unknown, and the evidence of miR396-mediated AtSVP degradation is lacking. Here, we show that miR396 triggers AtSVP messenger RNA (mRNA) decay using genetic approaches, a reporter assay, and high-throughput degradome profiles. Genetic evidence indicates that PHYL1 plants and atmir396a-1 mutants have higher AtSVP accumulation, whereas the transgenic plants overexpressing MIR396 display lower AtSVP expression. The reporter assay indicated that target-site mutation results in decreasing the miR396-mediated repression efficiency. Moreover, degradome profiles revealed that miR396 triggers AtSVP mRNA decay rather than miRNA-mediated cleavage, implying that AtSVP caused miR396-mediated translation inhibition. We hypothesize that PHYL1 directly or indirectly interferes with miR396-mediated AtSVP mRNA decay and synergizes with other effects (e.g. MADS box transcription factor degradation), resulting in abnormal flower formation. We anticipate our findings to be a starting point for studying the posttranscriptional regulation of PHYL1 effectors in symptom development.

Multi-scale symbolic entropy analysis provides prognostic prediction in patients receiving extracorporeal life support

IntroductionExtracorporeal life support (ECLS) can temporarily support cardiopulmonary function, and is occasionally used in resuscitation. Multi-scale entropy (MSE) derived from heart rate variability (HRV) is a powerful tool in outcome prediction of patients with cardiovascular diseases. Multi-scale symbolic entropy analysis (MSsE), a new method derived from MSE, mitigates the effect of arrhythmia on analysis. The objective is to evaluate the prognostic value of MSsE in patients receiving ECLS. The primary outcome is death or urgent transplantation during the index admission.MethodsFifty-seven patients receiving ECLS less than 24 hours and 23 control subjects were enrolled. Digital 24-hour Holter electrocardiograms were recorded and three MSsE parameters (slope 5, Area 6–20, Area 6–40) associated with the multiscale correlation and complexity of heart beat fluctuation were calculated.ResultsPatients receiving ECLS had significantly lower value of slope 5, area 6 to 20, and area 6 to 40 than control subjects. During the follow-up period, 29 patients met primary outcome. Age, slope 5, Area 6 to 20, Area 6 to 40, acute physiology and chronic health evaluation II score, multiple organ dysfunction score (MODS), logistic organ dysfunction score (LODS), and myocardial infarction history were significantly associated with primary outcome. Slope 5 showed the greatest discriminatory power. In a net reclassification improvement model, slope 5 significantly improved the predictive power of LODS; Area 6 to 20 and Area 6 to 40 significantly improved the predictive power in MODS. In an integrated discrimination improvement model, slope 5 added significantly to the prediction power of each clinical parameter. Area 6 to 20 and Area 6 to 40 significantly improved the predictive power in sequential organ failure assessment.ConclusionsMSsE provides additional prognostic information in patients receiving ECLS.

Comparison the prognostic value of galectin-3 and serum markers of cardiac extracellular matrix turnover in patients with chronic systolic heart failure.

Background: Galectin-3 (Gal-3) shows the ability of survival prediction in heart failure (HF) patients. However, Gal-3 is strongly associated with serum markers of cardiac extracellular matrix (ECM) turnover. The aim of this study is to compare the impact of Gal-3 and serum markers of cardiac ECM turnover on prognostic prediction of chronic systolic HF patients. Methods: Serum Gal-3, brain natriuretic peptide (BNP), extracellular matrix including type I and III aminoterminal propeptide of procollagen (PINP and PIIINP), matrix metalloproteinase-2, 9 (MMP-2, 9), and tissue inhibitor of metalloproteinase-1 (TIMP-1) were analyzed. Cox regression analysis was used for survival analysis. Results: A total of 105 (81 male) patients were enrolled. During 980±346 days follow-up, 17 patients died and 36 episodes of HF admission happened. Mortality of these patients was significantly associated with the log PIIINP (β= 15.380; P=0.042), log TIMP-1(β= 44.530; P=0.003), log MMP-2 (β= 554.336; P<0.001), log BNP (β= 28.273; P=0.034). Log Gal-3 (β= 7.484; P=0.066) is borderline associated with mortality. Mortality or first HF admission of these patients was significantly associated with the log TIMP-1(β= 16.496; P=0.006), log MMP-2 (β= 221.864; P<0.001), log BNP (β= 5.999; P=0.034). Log Gal-3 (β= 4.486; P=0.095) only showed borderline significance. In several models adjusting clinical parameters, log MMP-2 was significantly associated with clinical outcome. In contrast, log Gal-3 was not. Conclusion: The prognostic strength of MMP-2 to clinical outcome prediction in HF patients is stronger than Gal-3.

Ds transposon is biased towards providing splice donor sites for exonization in transgenic tobacco

Insertion of transposed elements into introns can lead to their activation as alternatively spliced cassette exons, an event called exonization, which can enrich the complexity of transcriptomes and proteomes. In this study, the first exonization event was detected when the modified rice EPSPS marker gene was inserted with the Ac transposon 5′ end, which provided a splice donor site to yield abundant novel transcripts. To assess the contribution of splice donor and acceptor sites of transposon sequences, we inserted a Ds element into each intron of the EPSPS marker gene. This process yielded 14 constructs, with the Ds transposon inserted in the forward and reverse direction in each of the 7 introns of the EPSPS marker gene. The constructs were transformed into tobacco plants, and novel transcripts were identified by RT-PCR with specific primers. Exonization of Ds in EPSPS was biased towards providing splice donor sites of the inserted Ds sequence. Additionally, when the Ds inserted in reverse direction, a continuous splice donor consensus region was determined by offering 4 donor sites in the same intron. Information on these exonization events may help enhance gene divergence and functional genomic studies.

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.

Major Functional Transcriptome of an Inferred Center Regulator of an ER(-) Breast Cancer Model System

We aimed to find clinically relevant gene activities ruled by the signal transducer and activator of transcription 3 (STAT3) proteins in an ER(–) breast cancer population via network approach. STAT3 is negatively associated with both lymph nodal category and stage. MYC is a component of STAT3 network. MYC and STAT3 may co-regulate gene expressions for Warburg effect, stem cell like phenotype, cell proliferation and angiogenesis. We identified a STAT3 network in silico showing its ability in predicting its target gene expressions primarily for specific tumor subtype, tumor progression, treatment options and prognostic features. The aberrant expressions of MYC and STAT3 are enriched in triple negatives (TN). They promote histological grade, vascularity, metastasis and tumor anti-apoptotic activities. VEGFA, STAT3, FOXM1 and METAP2 are druggable targets. High levels of METAP2, MMP7, IGF2 and IGF2R are unfavorable prognostic factors. STAT3 is an inferred center regulator at early cancer development predominantly in TN.

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

BackgroundA 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 α (ERα) as the variable X, and the analyses are based on 48 clinical breast cancer gene expression arrays (48A).ResultsThe analytical utility of CID was evaluated in comparison with four commonly used statistical methods, Galton-Pearsons correlation coefficient (GPCC), Students 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.ConclusionCID 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.Availabilitythe implementation of CID in R codes can be freely downloaded from http://homepage.ntu.edu.tw/~lyliu/BC/.

Heart rhythm complexity impairment in patients undergoing peritoneal dialysis.

Cardiovascular disease is one of the leading causes of death in patients with advanced renal disease. The objective of this study was to investigate impairments in heart rhythm complexity in patients with end-stage renal disease. We prospectively analyzed 65 patients undergoing peritoneal dialysis (PD) without prior cardiovascular disease and 72 individuals with normal renal function as the control group. Heart rhythm analysis including complexity analysis by including detrended fractal analysis (DFA) and multiscale entropy (MSE) were performed. In linear analysis, the PD patients had a significantly lower standard deviation of normal RR intervals (SDRR) and percentage of absolute differences in normal RR intervals greater than 20 ms (pNN20). Of the nonlinear analysis indicators, scale 5, area under the MSE curve for scale 1 to 5 (area 1–5) and 6 to 20 (area 6–20) were significantly lower than those in the control group. In DFA anaylsis, both DFA α1 and DFA α2 were comparable in both groups. In receiver operating characteristic curve analysis, scale 5 had the greatest discriminatory power for two groups. In both net reclassification improvement model and integrated discrimination improvement models, MSE parameters significantly improved the discriminatory power of SDRR, pNN20, and pNN50. In conclusion, PD patients had worse cardiac complexity parameters. MSE parameters are useful to discriminate PD patients from patients with normal renal function.

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 Piersons 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.

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.