Publications by authors named "Dmytro Lituiev"

18 Publications

  • Page 1 of 1

Opal: an implementation science tool for machine learning clinical decision support in anesthesia.

J Clin Monit Comput 2021 Nov 27. Epub 2021 Nov 27.

Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA.

Opal is the first published example of a full-stack platform infrastructure for an implementation science designed for ML in anesthesia that solves the problem of leveraging ML for clinical decision support. Users interact with a secure online Opal web application to select a desired operating room (OR) case cohort for data extraction, visualize datasets with built-in graphing techniques, and run in-client ML or extract data for external use. Opal was used to obtain data from 29,004 unique OR cases from a single academic institution for pre-operative prediction of post-operative acute kidney injury (AKI) based on creatinine KDIGO criteria using predictors which included pre-operative demographic, past medical history, medications, and flowsheet information. To demonstrate utility with unsupervised learning, Opal was also used to extract intra-operative flowsheet data from 2995 unique OR cases and patients were clustered using PCA analysis and k-means clustering. A gradient boosting machine model was developed using an 80/20 train to test ratio and yielded an area under the receiver operating curve (ROC-AUC) of 0.85 with 95% CI [0.80-0.90]. At the default probability decision threshold of 0.5, the model sensitivity was 0.9 and the specificity was 0.8. K-means clustering was performed to partition the cases into two clusters and for hypothesis generation of potential groups of outcomes related to intraoperative vitals. Opal's design has created streamlined ML functionality for researchers and clinicians in the perioperative setting and opens the door for many future clinical applications, including data mining, clinical simulation, high-frequency prediction, and quality improvement.
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http://dx.doi.org/10.1007/s10877-021-00774-1DOI Listing
November 2021

Empowering study of breast cancer data with application of artificial intelligence technology: promises, challenges, and use cases.

Clin Exp Metastasis 2021 Oct 26. Epub 2021 Oct 26.

Department of Clinical Sciences, College of Medicine, University of Central Florida, Orlando, USA.

In healthcare, artificial intelligence (AI) technologies have the potential to create significant value by improving time-sensitive outcomes while lowering error rates for each patient. Diagnostic images, clinical notes, and reports are increasingly generated and stored in electronic medical records. This heterogeneous data presenting us with challenges in data analytics and reusability that is by nature has high complexity, thereby necessitating novel ways to store, manage and process, and reuse big data. This presents an urgent need to develop new, scalable, and expandable AI infrastructure and analytical methods that can enable healthcare providers to access knowledge for individual patients, yielding better decisions and outcomes. In this review article, we briefly discuss the nature of data in breast cancer study and the role of AI for generating "smart data" which offer actionable information that supports the better decision for personalized medicine for individual patients. In our view, the biggest challenge is to create a system that makes data robust and smart for healthcare providers and patients that can lead to more effective clinical decision-making, improved health outcomes, and ultimately, managing the healthcare outcomes and costs. We highlight some of the challenges in using breast cancer data and propose the need for an AI-driven environment to address them. We illustrate our vision with practical use cases and discuss a path for empowering the study of breast cancer databases with the application of AI and future directions.
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http://dx.doi.org/10.1007/s10585-021-10125-8DOI Listing
October 2021

Machine Learning Prediction of Liver Allograft Utilization From Deceased Organ Donors Using the National Donor Management Goals Registry.

Transplant Direct 2021 Oct 27;7(10):e771. Epub 2021 Sep 27.

Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA.

Early prediction of whether a liver allograft will be utilized for transplantation may allow better resource deployment during donor management and improve organ allocation. The national donor management goals (DMG) registry contains critical care data collected during donor management. We developed a machine learning model to predict transplantation of a liver graft based on data from the DMG registry.

Methods: Several machine learning classifiers were trained to predict transplantation of a liver graft. We utilized 127 variables available in the DMG dataset. We included data from potential deceased organ donors between April 2012 and January 2019. The outcome was defined as liver recovery for transplantation in the operating room. The prediction was made based on data available 12-18 h after the time of authorization for transplantation. The data were randomly separated into training (60%), validation (20%), and test sets (20%). We compared the performance of our models to the Liver Discard Risk Index.

Results: Of 13 629 donors in the dataset, 9255 (68%) livers were recovered and transplanted, 1519 recovered but used for research or discarded, 2855 were not recovered. The optimized gradient boosting machine classifier achieved an area under the curve of the receiver operator characteristic of 0.84 on the test set, outperforming all other classifiers.

Conclusions: This model predicts successful liver recovery for transplantation in the operating room, using data available early during donor management. It performs favorably when compared to existing models. It may provide real-time decision support during organ donor management and transplant logistics.
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http://dx.doi.org/10.1097/TXD.0000000000001212DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8478404PMC
October 2021

Assessment of a Deep Learning Model Based on Electronic Health Record Data to Forecast Clinical Outcomes in Patients With Rheumatoid Arthritis.

JAMA Netw Open 2019 03 1;2(3):e190606. Epub 2019 Mar 1.

Bakar Computational Health Sciences Institute, University of California, San Francisco.

Importance: Knowing the future condition of a patient would enable a physician to customize current therapeutic options to prevent disease worsening, but predicting that future condition requires sophisticated modeling and information. If artificial intelligence models were capable of forecasting future patient outcomes, they could be used to aid practitioners and patients in prognosticating outcomes or simulating potential outcomes under different treatment scenarios.

Objective: To assess the ability of an artificial intelligence system to prognosticate the state of disease activity of patients with rheumatoid arthritis (RA) at their next clinical visit.

Design, Setting, And Participants: This prognostic study included 820 patients with RA from rheumatology clinics at 2 distinct health care systems with different electronic health record platforms: a university hospital (UH) and a public safety-net hospital (SNH). The UH and SNH had substantially different patient populations and treatment patterns. The UH has records on approximately 1 million total patients starting in January 2012. The UH data for this study were accessed on July 1, 2017. The SNH has records on 65 000 unique individuals starting in January 2013. The SNH data for the study were collected on February 27, 2018.

Exposures: Structured data were extracted from the electronic health record, including exposures (medications), patient demographics, laboratories, and prior measures of disease activity. A longitudinal deep learning model was used to predict disease activity for patients with RA at their next rheumatology clinic visit and to evaluate interhospital performance and model interoperability strategies.

Main Outcomes And Measures: Model performance was quantified using the area under the receiver operating characteristic curve (AUROC). Disease activity in RA was measured using a composite index score.

Results: A total of 578 UH patients (mean [SD] age, 57 [15] years; 477 [82.5%] female; 296 [51.2%] white) and 242 SNH patients (mean [SD] age, 60 [15] years; 195 [80.6%] female; 30 [12.4%] white) were included in the study. Patients at the UH compared with those at the SNH were seen more frequently (median time between visits, 100 vs 180 days) and were more frequently prescribed higher-class medications (biologics) (364 [63.0%] vs 70 [28.9%]). At the UH, the model reached an AUROC of 0.91 (95% CI, 0.86-0.96) in a test cohort of 116 patients. The UH-trained model had an AUROC of 0.74 (95% CI, 0.65-0.83) in the SNH test cohort (n = 117) despite marked differences in the patient populations. In both settings, baseline prediction using each patients' most recent disease activity score had statistically random performance.

Conclusions And Relevance: The findings suggest that building accurate models to forecast complex disease outcomes using electronic health record data is possible and these models can be shared across hospitals with diverse patient populations.
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http://dx.doi.org/10.1001/jamanetworkopen.2019.0606DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6484652PMC
March 2019

Automatic Labeling of Special Diagnostic Mammography Views from Images and DICOM Headers.

J Digit Imaging 2019 04;32(2):228-233

Institute for Computational Health Sciences, University of California, San Francisco, 550 16th Street, San Francisco, CA, USA.

Applying state-of-the-art machine learning techniques to medical images requires a thorough selection and normalization of input data. One of such steps in digital mammography screening for breast cancer is the labeling and removal of special diagnostic views, in which diagnostic tools or magnification are applied to assist in assessment of suspicious initial findings. As a common task in medical informatics is prediction of disease and its stage, these special diagnostic views, which are only enriched among the cohort of diseased cases, will bias machine learning disease predictions. In order to automate this process, here, we develop a machine learning pipeline that utilizes both DICOM headers and images to predict such views in an automatic manner, allowing for their removal and the generation of unbiased datasets. We achieve AUC of 99.72% in predicting special mammogram views when combining both types of models. Finally, we apply these models to clean up a dataset of about 772,000 images with expected sensitivity of 99.0%. The pipeline presented in this paper can be applied to other datasets to obtain high-quality image sets suitable to train algorithms for disease detection.
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http://dx.doi.org/10.1007/s10278-018-0154-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6456464PMC
April 2019

A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using F-FDG PET of the Brain.

Radiology 2019 02 6;290(2):456-464. Epub 2018 Nov 6.

From the Department of Radiology and Biomedical Imaging (Y.D., J.H.S., H.T., R.H., N.W.J., T.P.C., M.S.A., C.M.A., S.C.B., R.R.F., S.Y.H., Y.S., R.A.H., M.H.P., B.L.F.) and Institute for Computational Health Sciences (J.H.S., M.G.K., H.T., D.L., K.A.Z., D.H.), University of California, San Francisco, 550 Parnassus Ave, San Francisco, CA 94143; Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, Calif (Y.D.); and Department of Radiology, University of California, Davis, Sacramento, Calif (L.N.).

Purpose To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither at fluorine 18 (F) fluorodeoxyglucose (FDG) PET of the brain and compare its performance to that of radiologic readers. Materials and Methods Prospective F-FDG PET brain images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (2109 imaging studies from 2005 to 2017, 1002 patients) and retrospective independent test set (40 imaging studies from 2006 to 2016, 40 patients) were collected. Final clinical diagnosis at follow-up was recorded. Convolutional neural network of InceptionV3 architecture was trained on 90% of ADNI data set and tested on the remaining 10%, as well as the independent test set, with performance compared to radiologic readers. Model was analyzed with sensitivity, specificity, receiver operating characteristic (ROC), saliency map, and t-distributed stochastic neighbor embedding. Results The algorithm achieved area under the ROC curve of 0.98 (95% confidence interval: 0.94, 1.00) when evaluated on predicting the final clinical diagnosis of AD in the independent test set (82% specificity at 100% sensitivity), an average of 75.8 months prior to the final diagnosis, which in ROC space outperformed reader performance (57% [four of seven] sensitivity, 91% [30 of 33] specificity; P < .05). Saliency map demonstrated attention to known areas of interest but with focus on the entire brain. Conclusion By using fluorine 18 fluorodeoxyglucose PET of the brain, a deep learning algorithm developed for early prediction of Alzheimer disease achieved 82% specificity at 100% sensitivity, an average of 75.8 months prior to the final diagnosis. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Larvie in this issue.
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http://dx.doi.org/10.1148/radiol.2018180958DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6358051PMC
February 2019

Large Scale Semi-Automated Labeling of Routine Free-Text Clinical Records for Deep Learning.

J Digit Imaging 2019 02;32(1):30-37

Institute for Computational Health Sciences, University of California, San Francisco, CA, USA.

Breast cancer is a leading cause of cancer death among women in the USA. Screening mammography is effective in reducing mortality, but has a high rate of unnecessary recalls and biopsies. While deep learning can be applied to mammography, large-scale labeled datasets, which are difficult to obtain, are required. We aim to remove many barriers of dataset development by automatically harvesting data from existing clinical records using a hybrid framework combining traditional NLP and IBM Watson. An expert reviewer manually annotated 3521 breast pathology reports with one of four outcomes: left positive, right positive, bilateral positive, negative. Traditional NLP techniques using seven different machine learning classifiers were compared to IBM Watson's automated natural language classifier. Techniques were evaluated using precision, recall, and F-measure. Logistic regression outperformed all other traditional machine learning classifiers and was used for subsequent comparisons. Both traditional NLP and Watson's NLC performed well for cases under 1024 characters with weighted average F-measures above 0.96 across all classes. Performance of traditional NLP was lower for cases over 1024 characters with an F-measure of 0.83. We demonstrate a hybrid framework using traditional NLP techniques combined with IBM Watson to annotate over 10,000 breast pathology reports for development of a large-scale database to be used for deep learning in mammography. Our work shows that traditional NLP and IBM Watson perform extremely well for cases under 1024 characters and can accelerate the rate of data annotation.
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http://dx.doi.org/10.1007/s10278-018-0105-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6382632PMC
February 2019

The Protein Phosphatases ATUNIS1 and ATUNIS2 Regulate Cell Wall Integrity in Tip-Growing Cells.

Plant Cell 2018 08 10;30(8):1906-1923. Epub 2018 Jul 10.

University of Cologne, Biocenter, 50674 Cologne, Germany

Fast tip-growing plant cells such as pollen tubes (PTs) and root hairs (RHs) require a robust coordination between their internal growth machinery and modifications of their extracellular rigid, yet extensible, cell wall (CW). Part of this essential coordination is governed by members of the receptor-like kinase1-like (RLK1L) subfamily of RLKs with FERONIA (FER) and its closest homologs, ANXUR1 (ANX1) and ANX2, controlling CW integrity during RH and PT growth, respectively. Recently, Leucine-Rich Repeat Extensin 8 (LRX8) to LRX11 were also shown to be important for CW integrity in PTs. We previously reported an suppressor screen in that revealed MARIS (MRI) as a positive regulator of both FER- and ANX1/2-dependent CW integrity pathways. Here, we characterize a suppressor that exhibits a weak rescue of the PT bursting phenotype and a short RH phenotype. The corresponding suppressor mutation causes a D94N substitution in a Type One Protein Phosphatase we named ATUNIS1 (AUN1). We show that AUN1 and its closest homolog, AUN2, are nucleocytoplasmic negative regulators of tip growth. Moreover, we demonstrate that AUN1 and AUN1 harboring mutations in key amino acids of the conserved catalytic site of phosphoprotein phosphatases function as dominant amorphic variants that repress PT growth. Finally, genetic interaction studies using the hypermorph MRI and amorph AUN1 dominant variants indicate that LRX8-11 and ANX1/2 function in distinct but converging pathways to fine-tune CW integrity during tip growth.
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http://dx.doi.org/10.1105/tpc.18.00284DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6139677PMC
August 2018

Genetic determinants of co-accessible chromatin regions in activated T cells across humans.

Nat Genet 2018 08 9;50(8):1140-1150. Epub 2018 Jul 9.

Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Over 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, we analyzed ATAC-seq and RNA-seq profiles from stimulated primary CD4 T cells in up to 105 healthy donors. We found that regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution, consistent with the three-dimensional chromatin organization measured by in situ Hi-C in T cells. Fifteen percent of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak (local-ATAC-QTLs). Local-ATAC-QTLs have the largest effects on co-accessible peaks, are associated with gene expression and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis-regulatory elements, in isolation or in concert, to influence gene expression.
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http://dx.doi.org/10.1038/s41588-018-0156-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6097927PMC
August 2018

Author Correction: Discovery of stimulation-responsive immune enhancers with CRISPR activation.

Nature 2018 07;559(7715):E13

Illumina Inc., 5200 Illumina Way, San Diego, California, 92122, USA.

In this Letter, analysis of steady-state regulatory T (Treg) cell percentages from Il2ra enhancer deletion (EDEL) and wild-type (WT) mice revealed no differences between them (Extended Data Fig. 9d). This analysis included two mice whose genotypes were incorrectly assigned. Even after correction of the genotypes, no significant differences in Treg cell percentages were seen when data across experimental cohorts were averaged (as was done in Extended Data Fig. 9d). However, if we normalize the corrected data to account for variation among experimental cohorts, a subtle decrease in EDEL Treg cell percentages is revealed and, using the corrected and normalized data, we have redrawn Extended Data Fig. 9d in Supplementary Fig. 1. The Supplementary Information to this Amendment contains the corrected and reanalysed Extended Data Fig. 9d. The sentence "This enhancer deletion (EDEL) strain also had no obvious T cell phenotypes at steady state (Extended Data Fig. 9)." should read: "This enhancer deletion (EDEL) strain had a small decrease in the percentage of Treg cells (Extended Data Fig. 9).". This error does not affect any of the main figures in the Letter or the data from mice with the human autoimmune-associated single nucleotide polymorphism (SNP) knocked in or with a 12-base-pair deletion at the site (12DEL). In addition, we stated in the Methods that we observed consistent immunophenotypes of EDEL mice across three founders, but in fact, we observed consistent phenotypes in mice from two founders. This does not change any of our conclusions and the original Letter has not been corrected.
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http://dx.doi.org/10.1038/s41586-018-0227-7DOI Listing
July 2018

Discovery of stimulation-responsive immune enhancers with CRISPR activation.

Nature 2017 09 30;549(7670):111-115. Epub 2017 Aug 30.

Illumina Inc., 5200 Illumina Way, San Diego, California 92122, USA.

The majority of genetic variants associated with common human diseases map to enhancers, non-coding elements that shape cell-type-specific transcriptional programs and responses to extracellular cues. Systematic mapping of functional enhancers and their biological contexts is required to understand the mechanisms by which variation in non-coding genetic sequences contributes to disease. Functional enhancers can be mapped by genomic sequence disruption, but this approach is limited to the subset of enhancers that are necessary in the particular cellular context being studied. We hypothesized that recruitment of a strong transcriptional activator to an enhancer would be sufficient to drive target gene expression, even if that enhancer was not currently active in the assayed cells. Here we describe a discovery platform that can identify stimulus-responsive enhancers for a target gene independent of stimulus exposure. We used tiled CRISPR activation (CRISPRa) to synthetically recruit a transcriptional activator to sites across large genomic regions (more than 100 kilobases) surrounding two key autoimmunity risk loci, CD69 and IL2RA. We identified several CRISPRa-responsive elements with chromatin features of stimulus-responsive enhancers, including an IL2RA enhancer that harbours an autoimmunity risk variant. Using engineered mouse models, we found that sequence perturbation of the disease-associated Il2ra enhancer did not entirely block Il2ra expression, but rather delayed the timing of gene activation in response to specific extracellular signals. Enhancer deletion skewed polarization of naive T cells towards a pro-inflammatory T helper (T17) cell state and away from a regulatory T cell state. This integrated approach identifies functional enhancers and reveals how non-coding variation associated with human immune dysfunction alters context-specific gene programs.
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http://dx.doi.org/10.1038/nature23875DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5675716PMC
September 2017

Receptor-like cytoplasmic kinase MARIS functions downstream of CrRLK1L-dependent signaling during tip growth.

Proc Natl Acad Sci U S A 2015 Sep 16;112(39):12211-6. Epub 2015 Sep 16.

Institute of Plant Biology, University of Zurich, 8008 Zurich, Switzerland; Zurich-Basel Plant Science Center, University of Zurich, 8008 Zurich, Switzerland.

Growing plant cells need to rigorously coordinate external signals with internal processes. For instance, the maintenance of cell wall (CW) integrity requires the coordination of CW sensing with CW remodeling and biosynthesis to avoid growth arrest or integrity loss. Despite the involvement of receptor-like kinases (RLKs) of the Catharanthus roseus RLK1-like (CrRLK1L) subfamily and the reactive oxygen species-producing NADPH oxidases, it remains largely unknown how this coordination is achieved. ANXUR1 (ANX1) and ANX2, two redundant members of the CrRLK1L subfamily, are required for tip growth of the pollen tube (PT), and their closest homolog, FERONIA, controls root-hair tip growth. Previously, we showed that ANX1 overexpression mildly inhibits PT growth by oversecretion of CW material, whereas pollen tubes of anx1 anx2 double mutants burst spontaneously after germination. Here, we report the identification of suppressor mutants with improved fertility caused by the rescue of anx1 anx2 pollen tube bursting. Mapping of one these mutants revealed an R240C nonsynonymous substitution in the activation loop of a receptor-like cytoplasmic kinase (RLCK), which we named MARIS (MRI). We show that MRI is a plasma membrane-localized member of the RLCK-VIII subfamily and is preferentially expressed in both PTs and root hairs. Interestingly, mri-knockout mutants display spontaneous PT and root-hair bursting. Moreover, expression of the MRI(R240C) mutant, but not its wild-type form, partially rescues the bursting phenotypes of anx1 anx2 PTs and fer root hairs but strongly inhibits wild-type tip growth. Thus, our findings identify a novel positive component of the CrRLK1L-dependent signaling cascade that coordinates CW integrity and tip growth.
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http://dx.doi.org/10.1073/pnas.1512375112DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4593096PMC
September 2015

A calcium dialog mediated by the FERONIA signal transduction pathway controls plant sperm delivery.

Dev Cell 2014 May 8;29(4):491-500. Epub 2014 May 8.

Institute of Plant Biology and Zürich-Basel Plant Science Center, University of Zürich, Zollikerstrasse 107, 8008 Zürich, Switzerland. Electronic address:

Sperm delivery for double fertilization of flowering plants relies on interactions between the pollen tube (PT) and two synergids, leading to programmed cell death (PCD) of the PT and one synergid. The mechanisms underlying the communication among these cells during PT reception is unknown. We discovered that the synergids control this process by coordinating their distinct calcium signatures in response to the calcium dynamics and growth behavior of the PT. Induced and spontaneous aberrant calcium responses in the synergids abolish the two coordinated PCD events. Components of the FERONIA (FER) signaling pathway are required for initiating and modulating these calcium responses and for coupling the PCD events. Intriguingly, the calcium signatures are interchangeable between the two synergids, implying that their fates of death and survival are determined by reversible interactions with the PT. Thus, complex intercellular interactions involving a receptor kinase pathway and calcium-mediated signaling control sperm delivery in plants.
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http://dx.doi.org/10.1016/j.devcel.2014.04.008DOI Listing
May 2014

Patterning of the angiosperm female gametophyte through the prism of theoretical paradigms.

Biochem Soc Trans 2014 Apr;42(2):332-9

*Institute of Plant Biology and Zürich-Basel Plant Science Centre, University of Zürich, CH-8008 Zürich, Switzerland.

The FG (female gametophyte) of flowering plants (angiosperms) is a simple highly polar structure composed of only a few cell types. The FG develops from a single cell through mitotic divisions to generate, depending on the species, four to 16 nuclei in a syncytium. These nuclei are then partitioned into three or four distinct cell types. The mechanisms underlying the specification of the nuclei in the FG has been a focus of research over the last decade. Nevertheless, we are far from understanding the patterning mechanisms that govern cell specification. Although some results were previously interpreted in terms of static positional information, several lines of evidence now show that local interactions are important. In the present article, we revisit the available data on developmental mutants and cell fate markers in the light of theoretical frameworks for biological patterning. We argue that a further dissection of the mechanisms may be impeded by the combinatorial and dynamical nature of developmental cues. However, accounting for these properties of developing systems is necessary to disentangle the diversity of the phenotypic manifestations of the underlying molecular interactions.
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http://dx.doi.org/10.1042/BST20140036DOI Listing
April 2014

ANXUR receptor-like kinases coordinate cell wall integrity with growth at the pollen tube tip via NADPH oxidases.

PLoS Biol 2013 Nov 26;11(11):e1001719. Epub 2013 Nov 26.

Institute of Plant Biology, University of Zürich, Zürich, Switzerland ; Zürich-Basel Plant Science Center, ETH Zürich, Zürich, Switzerland.

It has become increasingly apparent that the extracellular matrix (ECM), which in plants corresponds to the cell wall, can influence intracellular activities in ways that go far beyond their supposedly passive mechanical support. In plants, growing cells use mechanisms sensing cell wall integrity to coordinate cell wall performance with the internal growth machinery to avoid growth cessation or loss of integrity. How this coordination precisely works is unknown. Previously, we reported that in the tip-growing pollen tube the ANXUR receptor-like kinases (RLKs) of the CrRLK1L subfamily are essential to sustain growth without loss of cell wall integrity in Arabidopsis. Here, we show that over-expression of the ANXUR RLKs inhibits growth by over-activating exocytosis and the over-accumulation of secreted cell wall material. Moreover, the characterization of mutations in two partially redundant pollen-expressed NADPH oxidases coupled with genetic interaction studies demonstrate that the ANXUR RLKs function upstream of these NADPH oxidases. Using the H₂O₂-sensitive HyPer and the Ca²⁺-sensitive YC3.60 sensors in NADPH oxidase-deficient mutants, we reveal that NADPH oxidases generate tip-localized, pulsating H₂O₂ production that functions, possibly through Ca²⁺ channel activation, to maintain a steady tip-focused Ca²⁺ gradient during growth. Our findings support a model where ECM-sensing receptors regulate reactive oxygen species production, Ca²⁺ homeostasis, and exocytosis to coordinate ECM-performance with the internal growth machinery.
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http://dx.doi.org/10.1371/journal.pbio.1001719DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3841104PMC
November 2013

Theoretical and experimental evidence indicates that there is no detectable auxin gradient in the angiosperm female gametophyte.

Development 2013 Nov;140(22):4544-53

Institute of Plant Biology and Zürich-Basel Plant Science Center, University of Zürich, Zollikerstrasse 107, CH-8008 Zürich, Switzerland.

The plant life cycle alternates between a diploid sporophytic and a haploid gametophytic generation. The female gametophyte (FG) of flowering plants is typically formed through three syncytial mitoses, followed by cellularisation that forms seven cells belonging to four cell types. The specification of cell fates in the FG has been suggested to depend on positional information provided by an intrinsic auxin concentration gradient. The goal of this study was to develop mathematical models that explain the formation of this gradient in a syncytium. Two factors were proposed to contribute to the maintenance of the auxin gradient in Arabidopsis FGs: polar influx at early stages and localised auxin synthesis at later stages. However, no gradient could be generated using classical, one-dimensional theoretical models under these assumptions. Thus, we tested other hypotheses, including spatial confinement by the large central vacuole, background efflux and localised degradation, and investigated the robustness of cell specification under different parameters and assumptions. None of the models led to the generation of an auxin gradient that was steep enough to allow sufficiently robust patterning. This led us to re-examine the response to an auxin gradient in developing FGs using various auxin reporters, including a novel degron-based reporter system. In agreement with the predictions of our models, auxin responses were not detectable within the FG of Arabidopsis or maize, suggesting that the effects of manipulating auxin production and response on cell fate determination might be indirect.
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http://dx.doi.org/10.1242/dev.098301DOI Listing
November 2013

A robust and sensitive synthetic sensor to monitor the transcriptional output of the cytokinin signaling network in planta.

Plant Physiol 2013 Mar 25;161(3):1066-75. Epub 2013 Jan 25.

Institute of Plant Biology, Zurich-Basel Plant Science Center, University of Zurich, CH-8008 Zurich, Switzerland.

Cytokinins are classic plant hormones that orchestrate plant growth, development, and physiology. They affect gene expression in target cells by activating a multistep phosphorelay network. Type-B response regulators, acting as transcriptional activators, mediate the final step in the signaling cascade. Previously, we have introduced a synthetic reporter, Two Component signaling Sensor (TCS)::green fluorescent protein (GFP), which reflects the transcriptional activity of type-B response regulators. TCS::GFP was instrumental in uncovering roles of cytokinin and deepening our understanding of existing functions. However, TCS-mediated expression of reporters is weak in some developmental contexts where cytokinin signaling has a documented role, such as in the shoot apical meristem or in the vasculature of Arabidopsis (Arabidopsis thaliana). We also observed that GFP expression becomes rapidly silenced in TCS::GFP transgenic plants. Here, we present an improved version of the reporter, TCS new (TCSn), which, compared with TCS, is more sensitive to phosphorelay signaling in Arabidopsis and maize (Zea mays) cellular assays while retaining its specificity. Transgenic Arabidopsis TCSn::GFP plants exhibit strong and dynamic GFP expression patterns consistent with known cytokinin functions. In addition, GFP expression has been stable over generations, allowing for crosses with different genetic backgrounds. Thus, TCSn represents a significant improvement to report the transcriptional output profile of phosphorelay signaling networks in Arabidopsis, maize, and likely other plants that display common response regulator DNA-binding specificities.
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http://dx.doi.org/10.1104/pp.112.211763DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3585579PMC
March 2013

Generation of monoclonal antibodies against tumor-associated antigen MX35/sodium-dependent phosphate transporter NaPi2b.

Hybridoma (Larchmt) 2011 Feb;30(1):37-42

Department of Cell Signalling, Institute of Molecular Biology and Genetics, NAS of Ukraine, 150 Zabolotnogo Str., Kyiv, Ukraine.

Tumor-associated antigen MX35, which is overexpressed in 70-90% of epithelial ovarian cancers, has been recently identified as phosphate transporter NaPi2b. This finding has raised significant interest in understanding NaPi2b function under physiological conditions and its deregulation in human pathologies, such as cancer. As a member of the sodium-dependent phosphate transporter family, NaPi2b is primarily involved in the maintenance of phosphate homeostasis in the human body. The role of NaPi2b in oncogenic transformation and malignant growth is not well understood. To date, several monoclonal antibodies specific to NaPi2b have been reported. However, available monoclonal antibodies are not very efficient in recognizing endogenous NaPi2b under reducing conditions. In addition, these antibodies could not recognize the mutant form of transporter (NaPi2b-T330V). In this study we describe the production of monoclonal antibodies raised against the N-terminal region of NaPi2b. One of them, designated N-NaPi2b(15/1), possesses very useful immunological characteristics. We found that N-NaPi2b(15/1) specifically recognizes NaPi2b protein in immunohistochemical analysis and immunoprecipitation assay. Importantly, N-NaPi2b(15/1) antibody detects very efficiently endogenous and expressed wild-type and mutant forms of NaPi2b under both reducing and non-reducing conditions in Western blot analysis. These features make N-NaPi2b(15/1) antibody a very useful tool for studying the pattern of NaPi2b expression in health and pathologies.
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http://dx.doi.org/10.1089/hyb.2010.0064DOI Listing
February 2011
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