ThyroidPrint®: clinical utility for indeterminate thyroid cytology

Molecular testing contributes to improving the diagnosis of indeterminate thyroid nodules (ITNs). ThyroidPrint® is a ten-gene classifier aimed to rule out malignancy in ITN. Post-validation studies are necessary to determine the real-world clinical benefit of ThyroidPrint® in patients with ITN. A single-center, prospective, noninterventional clinical utility study was performed, analyzing the impact of ThyroidPrint® in the physicians' clinical decisions for ITN. Demographics, nodule characteristics, benign call rates (BCRs), and surgical outcomes were measured. Histopathological data were collected from surgical biopsies of resected nodules. Of 1272 fine-needle aspirations, 109 (8.6%) were Bethesda III and 135 (10.6%) were Bethesda IV. Molecular testing was performed in 155 of 244 ITN (63.5%), of which 104 were classified as benign (BCR of 67.1%). After a median follow-up of 15 months, 103 of 104 (99.0%) patients with a benign ThyroidPrint® remained under surveillance and one patient underwent surgery which was a follicular adenoma. Surgery was performed in all 51 patients with a suspicious for malignancy as per ThyroidPrint® result and in 56 patients who did not undergo testing, with a rate of malignancy of 70.6% and 32.1%, respectively. A higher BCR was observed in follicular lesion of undetermined significance (87%) compared to atypia of undetermined significance (58%) (P < 0.05). False-positive cases included four benign follicular nodules and six follicular and four oncocytic adenomas. Our results show that, physicians chose active surveillance instead of diagnostic surgery in all patients with a benign ThyroidPrint® result, reducing the need for diagnostic surgery in 67% of patients with preoperative diagnosis of ITN.

DNA Input Classification by a Riboregulator-Based Cell-Free Perceptron

The ability to recognize molecular patterns is essential for the continued survival of biological organisms, allowing them to sense and respond to their immediate environment. The design of synthetic gene-based classifiers has been explored previously; however, prior strategies have focused primarily on DNA strand-displacement reactions. Here, we present a synthetic in vitro transcription and translation (TXTL)-based perceptron consisting of a weighted sum operation (WSO) coupled to a downstream thresholding function. We demonstrate the application of toehold switch riboregulators to construct a TXTL-based WSO circuit that converts DNA inputs into a GFP output, the concentration of which correlates to the input pattern and the corresponding weights. We exploit the modular nature of the WSO circuit by changing the output protein to the Escherichia coli σ28-factor, facilitating the coupling of the WSO output to a downstream reporter network. The subsequent introduction of a σ28 inhibitor enabled thresholding of the WSO output such that the expression of the downstream reporter protein occurs only when the produced σ28 exceeds this threshold. In this manner, we demonstrate a genetically implemented perceptron capable of binary classification, i.e., the expression of a single output protein only when the desired minimum number of inputs is exceeded.

Assessing COVID-19 susceptibility through analysis of the genetic and epigenetic diversity of ACE2-mediated SARS-CoV-2 entry

There is considerable variation in disease course among individuals infected with SARS-CoV-2. Many of them do not exhibit any symptoms, while some others proceed to develop COVID-19; however, severity of COVID-19 symptoms greatly differs among individuals. Focusing on the early events related to SARS-CoV-2 entry to cells through the ACE2 pathway, we describe how variability in (epi)genetic factors can conceivably explain variability in disease course. We specifically focus on variations in ACE2, TMPRSS2 and FURIN genes, as central components for SARS-CoV-2 infection, and on other molecules that modulate their expression such as CALM, ADAM-17, AR and ESRs. We propose a genetic classifier for predicting SARS-CoV-2 infectivity potential as a preliminary tool for identifying the at-risk-population. This tool can serve as a dynamic scaffold being updated and adapted to validated (epi)genetic data. Overall, the proposed approach holds potential for better personalization of COVID-19 handling.

Rationally Designed MicroRNA-Based Genetic Classifiers Target Specific Neurons in the Brain

Targeting transgene expression to specific cell types in vivo has proven instrumental in characterizing the functional role of defined cell populations. Genetic classifiers, synthetic transgene constructs designed to restrict expression to particular classes of cells, commonly rely on transcriptional promoters to define cellular specificity. However, the large size of many natural promoters complicates their use in viral vectors, an important mode of transgene delivery in the brain and in human gene therapy. Here, we expanded upon an emerging classifier platform, orthogonal to promoter-based strategies, that exploits endogenous microRNA regulation to target gene expression. Such classifiers have been extensively explored in other tissues; however, their use in the nervous system has thus far been limited to targeting gene expression between neurons and supporting cells. Here, we tested the possibility of using combinatory microRNA regulation to specify gene targeting between neuronal subtypes, and successfully targeted inhibitory cells in the neocortex. These classifiers demonstrate the feasibility of designing a new generation of microRNA-based neuron-type- and brain-region-specific gene expression targeting neurotechnologies.