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]

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

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

Test-retest repeatability of ADC in prostate using the multi b-Value VERDICT acquisition

Purpose: VERDICT (Vascular, Extracellular, Restricted Diffusion for Cytometry in Tumours) MRI is a multi b-value, variable diffusion time DWI sequence that allows generation of ADC maps from different b-value and diffusion time combinations. The aim was to assess precision of prostate ADC measurements from varying b-value combinations using VERDICT and determine which protocol provides the most repeatable ADC. // Materials and Methods: Forty-one men (median age: 67.7 years) from a prior prospective VERDICT study (April 2016–October 2017) were analysed retrospectively. Men who were suspected of prostate cancer and scanned twice using VERDICT were included. ADC maps were formed using 5b-value combinations and the within-subject standard deviations (wSD) were calculated per ADC map. Three anatomical locations were analysed per subject: normal TZ (transition zone), normal PZ (peripheral zone), and index lesions. Repeated measures ANOVAs showed which b-value range had the lowest wSD, Spearman correlation and generalized linear model regression analysis determined whether wSD was related to ADC magnitude and ROI size. // Results: The mean lesion ADC for b0 b1500 had the lowest wSD in most zones (0.18–0.58x10-4 mm2/s). The wSD was unaffected by ADC magnitude (Lesion: p = 0.064, TZ: p = 0.368, PZ: p = 0.072) and lesion Likert score (p = 0.95). wSD showed a decrease with ROI size pooled over zones (p = 0.019, adjusted regression coefficient = -1.6x10-3, larger ROIs for TZ versus PZ versus lesions). ADC maps formed with a maximum b-value of 500 s/mm2 had the largest wSDs (1.90–10.24x10-4 mm2/s). // Conclusion: ADC maps generated from b0 b1500 have better repeatability in normal TZ, normal PZ, and index lesions

Natural history of prostate cancer on active surveillance: stratification by MRI using the PRECISE recommendations in a UK cohort

Objectives: The PRECISE recommendations for magnetic resonance imaging (MRI) in patients on active surveillance (AS) for prostate cancer (PCa) include repeated measurement of each lesion, and attribution of a PRECISE radiological progression score for the likelihood of clinically significant change over time. We aimed to compare the PRECISE score with clinical progression in patients who are managed using an MRI-led AS protocol. Methods: A total of 553 patients on AS for low- and intermediate-risk PCa (up to Gleason score 3 + 4) who had two or more MRI scans performed between December 2005 and January 2020 were included. Overall, 2161 scans were retrospectively re-reported by a dedicated radiologist to give a PI-RADS v2 score for each scan and assess the PRECISE score for each follow-up scan. Clinical progression was defined by histological progression to ≥ Gleason score 4 + 3 (Gleason Grade Group 3) and/or initiation of active treatment. Progression-free survival was assessed using Kaplan-Meier curves and log-rank test was used to assess differences between curves. Results: Overall, 165/553 (30%) patients experienced the primary outcome of clinical progression (median follow-up, 74.5 months; interquartile ranges, 53–98). Of all patients, 313/553 (57%) did not show radiological progression on MRI (PRECISE 1–3), of which 296/313 (95%) had also no clinical progression. Of the remaining 240/553 patients (43%) with radiological progression on MRI (PRECISE 4–5), 146/240 (61%) experienced clinical progression (p < 0.0001). Patients with radiological progression on MRI (PRECISE 4-5) showed a trend to an increase in PSA density. Conclusions: Patients without radiological progression on MRI (PRECISE 1-3) during AS had a very low likelihood of clinical progression and many could avoid routine re-biopsy. Key Points: • Patients without radiological progression on MRI (PRECISE 1–3) during AS had a very low likelihood of clinical progression and many could avoid routine re-biopsy. • Clinical progression was almost always detectable in patients with radiological progression on MRI (PRECISE 4–5) during AS. • Patients with radiological progression on MRI (PRECISE 4–5) during AS showed a trend to an increase in PSA density

Multiparametric MR characterisation of a high-fat, high-cholesterol diet rodent model of liver disease

There is a growing interest in the development of new animal models of non-alcoholic fatty liver disease. In this study, we use T1, proton density fat fraction (PDFF) and R2* mapping to characterise hepatic parenchymal tissue and the evolution of MR properties over time in a high-fat, high-cholesterol diet model of fatty liver disease

Accuracy of Transperineal Targeted Prostate Biopsies, Visual Estimation and Image Fusion in Men Needing Repeat Biopsy in the PICTURE Trial

PURPOSE: To evaluate detection of clinically significant prostate cancer (csPCa) using MRI-targeted biopsies, and compare visual-estimation to image-fusion targeting, in patients requiring repeat prostate biopsies. MATERIALS AND METHODS: Prospective, ethics-committee approved, registered PICTURE trial enrolling 249 consecutive patients (11th/January/2012-29th/January/2014). Men underwent an mpMRI and were blinded to its results. All underwent transperineal template prostate mapping (TTPM) biopsies. In 200 with a lesion, this was preceded by visual-estimation and image-fusion targeted biopsies. For the primary endpoint, csPCa was defined as Gleason >/=4+3 and/or any grade of cancer length >/=6mm. Other definitions of csPCa were also evaluated. RESULTS: Mean (SD) age was 62.6 (7) years, median (IQR) PSA 7.17ng/ml (5.25, 10.09), mean primary lesion size 0.37cc (SD1.52), with mean 4.3 (SD2.3) targeted cores per lesion (visual-estimation and image-fusion combined) and mean 48.7 (SD12.3) TTPM-biopsy cores. TTPM-biopsies detected 97 (48.5%) cases of csPCa and 85 (42.5%) insignificant cancers. Overall, mpMRI-targeted biopsies detected 81 (40.5%) csPCa and 63 (31.5%) insignificant cancers. Eighteen (9%) with csPCa on MRI-targeted biopsies were benign or clinically insignificant on TTPM-biopsy. Thirty-four (17%) had csPCa detected on TTPM-biopsy but not on MRI-targeted biopsies; approximately half of these were present in non-targeted areas. csPCa was found with visual-estimation and image-fusion in 53/169 (31.3%) and 48/169 (28.4%) (McNemar's test, p=0.5322). Visual-estimation missed 23 (13.6%) csPCa detected by image-fusion; image-fusion missed 18 (10.8%) csPCa that visual-estimation detected. CONCLUSIONS: MRI-targeted biopsies are accurate at detection of csPCa and reducing over-diagnosis of insignificant cancers. To maximise detection both visual-estimation and image-fusion targeted biopsies are required

CT colonography: external clinical validation of an algorithm for computer-assisted prone and supine registration

Purpose To perform external validation of a computer-assisted registration algorithm for prone and supine computed tomographic (CT) colonography and to compare the results with those of an existing centerline method. Materials and Methods All contributing centers had institutional review board approval; participants provided informed consent. A validation sample of CT colonographic examinations of 51 patients with 68 polyps (6–55 mm) was selected from a publicly available, HIPAA compliant, anonymized archive. No patients were excluded because of poor preparation or inadequate distension. Corresponding prone and supine polyp coordinates were recorded, and endoluminal surfaces were registered automatically by using a computer algorithm. Two observers independently scored three-dimensional endoluminal polyp registration success. Results were compared with those obtained by using the normalized distance along the colonic centerline (NDACC) method. Pairwise Wilcoxon signed rank tests were used to compare gross registration error and McNemar tests were used to compare polyp conspicuity. Results Registration was possible in all 51 patients, and 136 paired polyp coordinates were generated (68 polyps) to test the algorithm. Overall mean three-dimensional polyp registration error (mean ± standard deviation, 19.9 mm ± 20.4) was significantly less than that for the NDACC method (mean, 27.4 mm ± 15.1; P = .001). Accuracy was unaffected by colonic segment (P = .76) or luminal collapse (P = .066). During endoluminal review by two observers (272 matching tasks, 68 polyps, prone to supine and supine to prone coordinates), 223 (82%) polyp matches were visible (120° field of view) compared with just 129 (47%) when the NDACC method was used (P < .001). By using multiplanar visualization, 48 (70%) polyps were visible after scrolling ± 15 mm in any multiplanar axis compared with 16 (24%) for NDACC (P < .001). Conclusion Computer-assisted registration is more accurate than the NDACC method for mapping the endoluminal surface and matching the location of polyps in corresponding prone and supine CT colonographic acquisitions