The mutual interaction of glycolytic enzymes and RNA in post-transcriptional regulation.

About three decades ago, researchers suggested that metabolic enzymes participate in cellular processes that are unrelated to their catalytic activity, and the term "moonlighting functions" was proposed. Recently developed advanced technologies in the field of RNA interactome capture now unveil the unexpected RNA binding activity of many metabolic enzymes, as exemplified here for the enzymes of glycolysis. Although for most of these proteins a precise binding mechanism, binding conditions, and physiological relevance of the binding events still await in-depth clarification, several well explored examples demonstrate that metabolic enzymes hold crucial functions in post-transcriptional regulation of protein synthesis. This widely conserved RNA-binding function of glycolytic enzymes plays major roles in controlling cell activities. The best explored examples are glyceraldehyde 3-phosphate dehydrogenase, enolase, phosphoglycerate kinase, and pyruvate kinase. This review summarizes current knowledge about the RNA-binding activity of the ten core enzymes of glycolysis in plant, yeast, and animal cells, its regulation and physiological relevance. Apparently, a tight bidirectional regulation connects core metabolism and RNA biology, forcing us to rethink long established functional singularities.

Reprogramming the translatome during daily light transitions as affected by cytosolic glyceraldehyde-3-phosphate dehydrogenases GAPC1/C2.

Dark-light and light-dark transitions during the day are switching points of leaf metabolism that strongly affect the regulatory state of the cells, and this change is hypothesized to affect the translatome. The cytosolic glyceraldehyde-3-phosphate dehydrogenases GAPC1 and GAPC2 function in glycolysis, and carbohydrate and energy metabolism, but GAPC1/C2 also shows moonlighting functions in gene expression and post-transcriptional regulation. In this study we examined the rapid reprogramming of the translatome that occurs within 10 min at the end of the night and the end of the day in wild-type (WT) Arabidopsis and a gapc1/c2 double-knockdown mutant. Metabolite profiling compared to the WT showed that gapc1/c2 knockdown led to increases in a set of metabolites at the start of day, particularly intermediates of the citric acid cycle and linked pathways. Differences in metabolite changes were also detected at the end of the day. Only small sets of transcripts changed in the total RNA pool; however, RNA-sequencing revealed major alterations in polysome-associated transcripts at the light-transition points. The most pronounced difference between the WT and gapc1/c2 was seen in the reorganization of the translatome at the start of the night. Our results are in line with the proposed hypothesis that GAPC1/C2 play a role in the control of the translatome during light/dark transitions.

Strategies of Molecular Signal Integration for Optimized Plant Acclimation to Stress Combinations.

Plant growth and survival in their natural environment require versatile mitigation of diverse threats. The task is especially challenging due to the largely unpredictable interaction of countless abiotic and biotic factors. To resist an unfavorable environment, plants have evolved diverse sensing, signaling, and adaptive molecular mechanisms. Recent stress studies have identified molecular elements like secondary messengers (ROS, Ca2+, etc.), hormones (ABA, JA, etc.), and signaling proteins (SnRK, MAPK, etc.). However, major gaps remain in understanding the interaction between these pathways, and in particular under conditions of stress combinations. Here, we highlight the challenge of defining "stress" in such complex natural scenarios. Therefore, defining stress hallmarks for different combinations is crucial. We discuss three examples of robust and dynamic plant acclimation systems, outlining specific plant responses to complex stress overlaps. (a) The high plasticity of root system architecture is a decisive feature in sustainable crop development in times of global climate change. (b) Similarly, broad sensory abilities and apparent control of cellular metabolism under adverse conditions through retrograde signaling make chloroplasts an ideal hub. Functional specificity of the chloroplast-associated molecular patterns (ChAMPs) under combined stresses needs further focus. (c) The molecular integration of several hormonal signaling pathways, which bring together all cellular information to initiate the adaptive changes, needs resolving.

An Efficient Deep Neural Network to Classify Large 3D Images With Small Objects.

3D imaging enables accurate diagnosis by providing spatial information about organ anatomy. However, using 3D images to train AI models is computationally challenging because they consist of 10x or 100x more pixels than their 2D counterparts. To be trained with high-resolution 3D images, convolutional neural networks resort to downsampling them or projecting them to 2D. We propose an effective alternative, a neural network that enables efficient classification of full-resolution 3D medical images. Compared to off-the-shelf convolutional neural networks, our network, 3D Globally-Aware Multiple Instance Classifier (3D-GMIC), uses 77.98%-90.05% less GPU memory and 91.23%-96.02% less computation. While it is trained only with image-level labels, without segmentation labels, it explains its predictions by providing pixel-level saliency maps. On a dataset collected at NYU Langone Health, including 85,526 patients with full-field 2D mammography (FFDM), synthetic 2D mammography, and 3D mammography, 3D-GMIC achieves an AUC of 0.831 (95% CI: 0.769-0.887) in classifying breasts with malignant findings using 3D mammography. This is comparable to the performance of GMIC on FFDM (0.816, 95% CI: 0.737-0.878) and synthetic 2D (0.826, 95% CI: 0.754-0.884), which demonstrates that 3D-GMIC successfully classified large 3D images despite focusing computation on a smaller percentage of its input compared to GMIC. Therefore, 3D-GMIC identifies and utilizes extremely small regions of interest from 3D images consisting of hundreds of millions of pixels, dramatically reducing associated computational challenges. 3D-GMIC generalizes well to BCS-DBT, an external dataset from Duke University Hospital, achieving an AUC of 0.848 (95% CI: 0.798-0.896).

Can an Artificial Intelligence Decision Aid Decrease False-Positive Breast Biopsies?

ABSTRACT: This study aimed to evaluate the effect of an artificial intelligence (AI) support system on breast ultrasound diagnostic accuracy.In this Health Insurance Portability and Accountability Act-compliant, institutional review board-approved retrospective study, 200 lesions (155 benign, 45 malignant) were randomly selected from consecutive ultrasound-guided biopsies (June 2017-January 2019). Two readers, blinded to clinical history and pathology, evaluated lesions with and without an Food and Drug Administration-approved AI software. Lesion features, Breast Imaging Reporting and Data System (BI-RADS) rating (1-5), reader confidence level (1-5), and AI BI-RADS equivalent (1-5) were recorded. Statistical analysis was performed for diagnostic accuracy, negative predictive value, positive predictive value (PPV), sensitivity, and specificity of reader versus AI BI-RADS. Generalized estimating equation analysis was used for reader versus AI accuracy regarding lesion features and AI impact on low-confidence score lesions. Artificial intelligence effect on false-positive biopsy rate was determined. Statistical tests were conducted at a 2-sided 5% significance level.There was no significant difference in accuracy (73 vs 69.8%), negative predictive value (100% vs 98.5%), PPV (45.5 vs 42.4%), sensitivity (100% vs 96.7%), and specificity (65.2 vs 61.9; P = 0.118-0.409) for AI versus pooled reader assessment. Artificial intelligence was more accurate than readers for irregular shape (74.1% vs 57.4%, P = 0.002) and less accurate for round shape (26.5% vs 50.0%, P = 0.049). Artificial intelligence improved diagnostic accuracy for reader-rated low-confidence lesions with increased PPV (24.7% AI vs 19.3%, P = 0.004) and specificity (57.8% vs 44.6%, P = 0.008).Artificial intelligence decision support aid may help improve sonographic diagnostic accuracy, particularly in cases with low reader confidence, thereby decreasing false-positives.

Toward an Integrated Understanding of Retrograde Control of Photosynthesis.

SIGNIFICANCE: Photosynthesis takes place in the chloroplast of eukaryotes, which occupies a large portion of the photosynthetic cell. The chloroplast function and integrity depend on intensive material and signal exchange between all genetic compartments and conditionally secure efficient photosynthesis and high fitness. Recent Advances: During the last two decades, the concept of mutual control of plastid performance by extraplastidic anterograde signals acting on the chloroplast and the feedback from the chloroplast to the extraplastidic space by retrograde signals has been profoundly revised and expanded. It has become clear that a complex set of diverse signals is released from the chloroplast and exceeds the historically proposed small number of information signals. Thus, it is also recognized that redox compounds and reactive oxygen species play a decisive role in retrograde signaling. CRITICAL ISSUES: The diversity of processes controlled or modulated by the retrograde network covers all molecular levels, including RNA fate and translation, and also includes subcellular heterogeneity, indirect gating of other organelles' metabolism, and specific signaling routes and pathways, previously not considered. All these processes must be integrated for optimal adjustment of the chloroplast processes. Thus, evidence is presented suggesting that retrograde signaling affects translation, stress granule, and processing body (P-body) dynamics. FUTURE DIRECTIONS: Redundancy of signal transduction elements, parallelisms of pathways, and conditionally alternative mechanisms generate a robust network and system that only tentatively can be assessed by use of single-site mutants.

Magnetic Tracking of Protein Synthesis in Microfluidic Environments-Challenges and Perspectives.

A novel technique to study protein synthesis is proposed that uses magnetic nanoparticles in combination with microfluidic devices to achieve new insights into translational regulation. Cellular protein synthesis is an energy-demanding process which is tightly controlled and is dependent on environmental and developmental requirements. Processivity and regulation of protein synthesis as part of the posttranslational nano-machinery has now moved back into the focus of cell biology, since it became apparent that multiple mechanisms are in place for fine-tuning of translation and conditional selection of transcripts. Recent methodological developments, such as ribosome foot printing, propel current research. Here we propose a strategy to open up a new field of labelling, separation, and analysis of specific polysomes using superparamagnetic particles following pharmacological arrest of translation during cell lysis and subsequent analysis. Translation occurs in polysomes, which are assemblies of specific transcripts, associated ribosomes, nascent polypeptides, and other factors. This supramolecular structure allows for unique approaches to selection of polysomes by targeting the specific transcript, ribosomes, or nascent polypeptides. Once labeled with functionalized superparamagnetic particles, such assemblies can be separated in microfluidic devices or magnetic ratchets and quantified. Insights into the dynamics of translation is obtained through quantifying large numbers of ribosomes along different locations of the polysome. Thus, an entire new concept for in vitro, ex vivo, and eventually single cell analysis will be realized and will allow for magnetic tracking of protein synthesis.

The Relative Value Unit: History, Current Use, and Controversies.

The relative value unit (RVU) is an important measuring tool for the work performed by physicians, and is currently used in the United States to calculate physician reimbursement. An understanding of radiology RVUs and current procedural terminology codes is important for radiologists, trainees, radiology managers, and administrators, as this knowledge would help them to understand better their current productivity and reimbursement, as well as controversies regarding reimbursement, and permit them to adapt to reimbursement changes that may occur in the future. This article reviews the components of the RVU and how radiology payment is calculated, highlights trends in RVUs and resultant payment for diagnostic and therapeutic imaging and examinations, and discusses current issues involving RVU and current procedural terminology codes.