Identification of transcription-factor genes expressed in the Arabidopsis female gametophyte

Dongfang Wang, Changqing Zhang, David J. Hearn, Il-HO Kang, megan I. Skaggs, Karen S. Schumaker, and Ramin Yadegari are with the School of Plant Sciences, University of Arizona, Tucson, Arizona 85721-0036, USA -- Il-Ho Kang, Jayson A. Punwani, and Gary N. Drews are with the Department of Biology, University of Utah, Salt Lake City, Utah 84112-0840, USA -- Changqing Zhang is with The Section of Molecular, Cell and Developmental Biology, University of Texas at Austin, Austin, Texas 78712-0159, USA -- David J. Hearn is with the Department of Biological Sciences, Towson University, Towson, Maryland 21252-0001, USA -- Il-Ho Kang is with the Department of Horticulture, Iowa State University, Ames, Iowa 50011-1100, USA --Jayson A. Punwani is with the Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-3280, USABackground In flowering plants, the female gametophyte is typically a seven-celled structure with four cell types: the egg cell, the central cell, the synergid cells, and the antipodal cells. These cells perform essential functions required for double fertilization and early seed development. Differentiation of these distinct cell types likely involves coordinated changes in gene expression regulated by transcription factors. Therefore, understanding female gametophyte cell differentiation and function will require dissection of the gene regulatory networks operating in each of the cell types. These efforts have been hampered because few transcription factor genes expressed in the female gametophyte have been identified. To identify such genes, we undertook a large-scale differential expression screen followed by promoter-fusion analysis to detect transcription-factor genes transcribed in the Arabidopsis female gametophyte. Results Using quantitative reverse-transcriptase PCR, we analyzed 1,482 Arabidopsis transcription-factor genes and identified 26 genes exhibiting reduced mRNA levels in determinate infertile 1 mutant ovaries, which lack female gametophytes, relative to ovaries containing female gametophytes. Spatial patterns of gene transcription within the mature female gametophyte were identified for 17 transcription-factor genes using promoter-fusion analysis. Of these, ten genes were predominantly expressed in a single cell type of the female gametophyte including the egg cell, central cell and the antipodal cells whereas the remaining seven genes were expressed in two or more cell types. After fertilization, 12 genes were transcriptionally active in the developing embryo and/or endosperm. Conclusions We have shown that our quantitative reverse-transcriptase PCR differential-expression screen is sufficiently sensitive to detect transcription-factor genes transcribed in the female gametophyte. Most of the genes identified in this study have not been reported previously as being expressed in the female gametophyte. Therefore, they might represent novel regulators and provide entry points for reverse genetic and molecular approaches to uncover the gene regulatory networks underlying female gametophyte development.Cellular and Molecular [email protected]

Direct parametric reconstruction from undersampled (k, t)-space data in dynamic contrast enhanced MRI

The Magnetic Resonance Imaging (MRI) signal can be made sensitive to functional parameters that provide information about tissues. In dynamic contrast enhanced (DCE) MRI these functional parameters are related to the microvasculature environment and the concentration changes that occur rapidly after the injection of a contrast agent. Typically DCE images are reconstructed individually and kinetic parameters are estimated by fitting a pharmacokinetic model to the time-enhancement response; these methods can be denoted as "indirect". If undersampling is present to accelerate the acquisition, techniques such as kt-FOCUSS can be employed in the reconstruction step to avoid image degradation. This paper suggests a Bayesian inference framework to estimate functional parameters directly from the measurements at high temporal resolution. The current implementation estimates pharmacokinetic parameters (related to the extended Tofts model) from undersampled (k, t)-space DCE MRI. The proposed scheme is evaluated on a simulated abdominal DCE phantom and prostate DCE data, for fully sampled, 4 and 8-fold undersampled (k, t)-space data. Direct kinetic parameters demonstrate better correspondence (up to 70% higher mutual information) to the ground truth kinetic parameters (of the simulated abdominal DCE phantom) than the ones derived from the indirect methods. For the prostate DCE data, direct kinetic parameters depict the morphology of the tumour better. To examine the impact on cancer diagnosis, a peripheral zone prostate cancer diagnostic model was employed to calculate a probability map for each method

Feasibility of vocal fold abduction and adduction assessment using cine-MRI

OBJECTIVE: Determine feasibility of vocal fold (VF) abduction and adduction assessment by cine magnetic resonance imaging (cine-MRI) METHODS: Cine-MRI of the VF was performed on five healthy and nine unilateral VF paralysis (UVFP) participants using an axial gradient echo acquisition with temporal resolution of 0.7 s. VFs were continuously imaged with cine-MRI during a 10-s period of quiet respiration and phonation. Scanning was repeated twice within an individual session and then once again at a 1-week interval. Asymmetry of VF position during phonation (VF phonation asymmetry, VFPa) and respiration (VF respiration asymmetry, VFRa) was determined. Percentage reduction in total glottal area between respiration and phonation (VF abduction potential, VFAP) was derived to measure overall mobility. An un-paired t-test was used to compare differences between groups. Intra-session, inter-session and inter-reader repeatability of the quantitative metrics was evaluated using intraclass correlation coefficient (ICC). RESULTS: VF position asymmetry (VFPa and VFRa) was greater (p=0.012; p=0.001) and overall mobility (VFAP) was lower (p=0.008) in UVFP patients compared with healthy participants. ICC of repeatability of all metrics was good, ranged from 0.82 to 0.95 except for the inter-session VFPa (0.44). CONCLUSION: Cine-MRI is feasible for assessing VF abduction and adduction. Derived quantitative metrics have good repeatability. KEY POINTS: • Cine-MRI is used to assess vocal folds (VFs) mobility: abduction and adduction. • New quantitative metrics are derived from VF position and abduction potential. • Cine-MRI able to depict the difference between normal and abnormal VF mobility. • Cine-MRI derived quantitative metrics have good repeatability

Respiratory motion correction in dynamic MRI using robust data decomposition registration - Application to DCE-MRI.

Motion correction in Dynamic Contrast Enhanced (DCE-) MRI is challenging because rapid intensity changes can compromise common (intensity based) registration algorithms. In this study we introduce a novel registration technique based on robust principal component analysis (RPCA) to decompose a given time-series into a low rank and a sparse component. This allows robust separation of motion components that can be registered, from intensity variations that are left unchanged. This Robust Data Decomposition Registration (RDDR) is demonstrated on both simulated and a wide range of clinical data. Robustness to different types of motion and breathing choices during acquisition is demonstrated for a variety of imaged organs including liver, small bowel and prostate. The analysis of clinically relevant regions of interest showed both a decrease of error (15-62% reduction following registration) in tissue time-intensity curves and improved areas under the curve (AUC60) at early enhancement

Tumour growth rates of prostate cancer during active surveillance: is there a difference between MRI-visible low and intermediate-risk disease?

OBJECTIVES: The aim of this study was to evaluate the changes in lesion volume on serial multiparametric magnetic resonance (mpMRI) during active surveillance for prostate cancer. METHODS: A total of 160 patients with a targeted biopsy-confirmed visible lesion on mpMRI, stratified by low- and intermediate-risk disease (Gleason Grade Group 1 vs Gleason Grade Group 2), were analysed. The % change per year was calculated using the formula: [(final volume/initial volume) exp (1/interval between scans in years)]-1. RESULTS: There was no significant difference in the annual median percentage change between Gleason Grade Group 1 (18%) and Gleason Grade Group 2 (23%) disease (p = 0.16), and between ≤ 10% (23%) and > 10% (22%) of Gleason pattern 4 (p = 0.78).Assuming a spherical lesion, these changes corresponded to annual increases in mean tumour diameter of 6% and 7% for Gleason Grade Group 1 and Gleason Grade Group 2 respectively, which may be less than the interscan variability of serial mpMRI. CONCLUSION: In an active surveillance cohort, we did not see a significant difference in the annual growth rate of Gleason Grade Group 1 and 2 tumours. ADVANCES IN KNOWLEDGE: In patients on active surveillance, the measured growth rates for visible tumours in Gleason Grade Groups 1 and 2 were similar. The annual growth rate was small in most cases and this may have implications for the MRI follow-up interval in active surveillance

The ReIMAGINE Multimodal Warehouse: Using Artificial Intelligence for Accurate Risk Stratification of Prostate Cancer

Introduction. Prostate cancer (PCa) is the most frequent cancer diagnosis in men worldwide. Our ability to identify those men whose cancer will decrease their lifespan and/or quality of life remains poor. The ReIMAGINE Consortium has been established to improve PCa diagnosis. / Materials and methods. MRI will likely become the future cornerstone of the risk-stratification process for men at risk of early prostate cancer. We will, for the first time, be able to combine the underlying molecular changes in PCa with the state-of-the-art imaging. ReIMAGINE Screening invites men for MRI and PSA evaluation. ReIMAGINE Risk includes men at risk of prostate cancer based on MRI, and includes biomarker testing. / Results. Baseline clinical information, genomics, blood, urine, fresh prostate tissue samples, digital pathology and radiomics data will be analysed. Data will be de-identified, stored with correlated mpMRI disease endotypes and linked with long term follow-up outcomes in an instance of the Philips Clinical Data Lake, consisting of cloud-based software. The ReIMAGINE platform includes application programming interfaces and a user interface that allows users to browse data, select cohorts, manage users and access rights, query data, and more. Connection to analytics tools such as Python allows statistical and stratification method pipelines to run profiling regression analyses. / Discussion. The ReIMAGINE Multimodal Warehouse comprises a unique data source for PCa research, to improve risk stratification for PCa and inform clinical practice. The de-identified dataset characterized by clinical, imaging, genomics and digital pathology PCa patient phenotypes will be a valuable resource for the scientific and medical community