TGF-ß1 Drives Integrin-Dependent Pericyte Migration and Microvascular Destabilization in Fibrotic Disease.

Interactions between endothelial cells (ECs) and mural pericytes (PCs) are critical in maintaining the stability and function of the microvascular wall. Abnormal interactions between these two cell types are a hallmark of progressive fibrotic diseases such as systemic sclerosis (also known as scleroderma). However, the role of PCs in signaling microvascular dysfunction remains underexplored. We hypothesized that integrin-matrix interactions contribute to PC migration from the vascular wall and conversion into interstitial myofibroblasts. Herein, pro-inflammatory tumor necrosis factor α (TNFα) or a fibrotic growth factor [transforming growth factor ß1 (TGF-ß1)] were used to evaluate human PC inflammatory and fibrotic phenotypes by assessing their migration, matrix deposition, integrin expression, and subsequent effects on endothelial dysfunction. Both TNFα and TGF-ß1 treatment altered integrin expression and matrix protein deposition, but only fibrotic TGF-ß1 drove PC migration in an integrin-dependent manner. In addition, integrin-dependent PC migration was correlated to changes in EC angiopoietin-2 levels, a marker of vascular instability. Finally, there was evidence of changes in vascular stability corresponding to disease state in human systemic sclerosis skin. This work shows that TNFα and TGF-ß1 induce changes in PC integrin expression and matrix deposition that facilitate migration and reduce vascular stability, providing evidence that microvascular destabilization can be an early indicator of tissue fibrosis.

Early immune factors associated with the development of post-acute sequelae of SARS-CoV-2 infection in hospitalized and non-hospitalized individuals.

Background: Infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can lead to post-acute sequelae of SARS-CoV-2 (PASC) that can persist for weeks to years following initial viral infection. Clinical manifestations of PASC are heterogeneous and often involve multiple organs. While many hypotheses have been made on the mechanisms of PASC and its associated symptoms, the acute biological drivers of PASC are still unknown. Methods: We enrolled 494 patients with COVID-19 at their initial presentation to a hospital or clinic and followed them longitudinally to determine their development of PASC. From 341 patients, we conducted multi-omic profiling on peripheral blood samples collected shortly after study enrollment to investigate early immune signatures associated with the development of PASC. Results: During the first week of COVID-19, we observed a large number of differences in the immune profile of individuals who were hospitalized for COVID-19 compared to those individuals with COVID-19 who were not hospitalized. Differences between individuals who did or did not later develop PASC were, in comparison, more limited, but included significant differences in autoantibodies and in epigenetic and transcriptional signatures in double-negative 1 B cells, in particular. Conclusions: We found that early immune indicators of incident PASC were nuanced, with significant molecular signals manifesting predominantly in double-negative B cells, compared with the robust differences associated with hospitalization during acute COVID-19. The emerging acute differences in B cell phenotypes, especially in double-negative 1 B cells, in PASC patients highlight a potentially important role of these cells in the development of PASC.

Compact RNA sensors for increasingly complex functions of multiple inputs.

Designing single molecules that compute general functions of input molecular partners represents a major unsolved challenge in molecular design. Here, we demonstrate that high-throughput, iterative experimental testing of diverse RNA designs crowdsourced from Eterna yields sensors of increasingly complex functions of input oligonucleotide concentrations. After designing single-input RNA sensors with activation ratios beyond our detection limits, we created logic gates, including challenging XOR and XNOR gates, and sensors that respond to the ratio of two inputs. Finally, we describe the OpenTB challenge, which elicited 85-nucleotide sensors that compute a score for diagnosing active tuberculosis, based on the ratio of products of three gene segments. Building on OpenTB design strategies, we created an algorithm Nucleologic that produces similarly compact sensors for the three-gene score based on RNA and DNA. These results open new avenues for diverse applications of compact, single molecule sensors previously limited by design complexity.

Distinct temporal trajectories and risk factors for Post-acute sequelae of SARS-CoV-2 infection.

Background: The understanding of Post-acute sequelae of SARS-CoV-2 infection (PASC) can be improved by longitudinal assessment of symptoms encompassing the acute illness period. To gain insight into the various disease trajectories of PASC, we assessed symptom evolution and clinical factors associated with the development of PASC over 3 months, starting with the acute illness period. Methods: We conducted a prospective cohort study to identify parameters associated with PASC. We performed cluster and case control analyses of clinical data, including symptomatology collected over 3 months following infection. Results: We identified three phenotypic clusters associated with PASC that could be characterized as remittent, persistent, or incident based on the 3-month change in symptom number compared to study entry: remittent (median; min, max: -4; -17, 3), persistent (-2; -14, 7), or incident (4.5; -5, 17) (p = 0.041 remittent vs. persistent, p < 0.001 remittent vs. incident, p < 0.001 persistent vs. incident). Despite younger age and lower hospitalization rates, the incident phenotype had a greater number of symptoms (15; 8, 24) and a higher proportion of participants with PASC (63.2%) than the persistent (6; 2, 9 and 52.2%) or remittent clusters (1; 0, 6 and 18.7%). Systemic corticosteroid administration during acute infection was also associated with PASC at 3 months [OR (95% CI): 2.23 (1.14, 4.36)]. Conclusion: An incident disease phenotype characterized by symptoms that were absent during acute illness and the observed association with high dose steroids during acute illness have potential critical implications for preventing PASC.

Multi-omic profiling reveals early immunological indicators for identifying COVID-19 Progressors.

We sought to better understand the immune response during the immediate post-diagnosis phase of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by identifying molecular associations with longitudinal disease outcomes. Multi-omic analyses identified differences in immune cell composition, cytokine levels, and cell subset-specific transcriptomic and epigenomic signatures between individuals on a more serious disease trajectory (Progressors) as compared to those on a milder course (Non-progressors). Higher levels of multiple cytokines were observed in Progressors, with IL-6 showing the largest difference. Blood monocyte cell subsets were also skewed, showing a comparative decrease in non-classical CD14-CD16+ and intermediate CD14+CD16+ monocytes. In lymphocytes, the CD8+ T effector memory cells displayed a gene expression signature consistent with stronger T cell activation in Progressors. These early stage observations could serve as the basis for the development of prognostic biomarkers of disease risk and interventional strategies to improve the management of severe COVID-19. BACKGROUND: Much of the literature on immune response post-SARS-CoV-2 infection has been in the acute and post-acute phases of infection. TRANSLATIONAL SIGNIFICANCE: We found differences at early time points of infection in approximately 160 participants. We compared multi-omic signatures in immune cells between individuals progressing to needing more significant medical intervention and non-progressors. We observed widespread evidence of a state of increased inflammation associated with progression, supported by a range of epigenomic, transcriptomic, and proteomic signatures. The signatures we identified support other findings at later time points and serve as the basis for prognostic biomarker development or to inform interventional strategies.

Multi-omic Profiling Reveals Early Immunological Indicators for Identifying COVID-19 Progressors.

The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a rapid response by the scientific community to further understand and combat its associated pathologic etiology. A focal point has been on the immune responses mounted during the acute and post-acute phases of infection, but the immediate post-diagnosis phase remains relatively understudied. We sought to better understand the immediate post-diagnosis phase by collecting blood from study participants soon after a positive test and identifying molecular associations with longitudinal disease outcomes. Multi-omic analyses identified differences in immune cell composition, cytokine levels, and cell subset-specific transcriptomic and epigenomic signatures between individuals on a more serious disease trajectory (Progressors) as compared to those on a milder course (Non-progressors). Higher levels of multiple cytokines were observed in Progressors, with IL-6 showing the largest difference. Blood monocyte cell subsets were also skewed, showing a comparative decrease in non-classical CD14-CD16+ and intermediate CD14+CD16+ monocytes. Additionally, in the lymphocyte compartment, CD8+ T effector memory cells displayed a gene expression signature consistent with stronger T cell activation in Progressors. Importantly, the identification of these cellular and molecular immune changes occurred at the early stages of COVID-19 disease. These observations could serve as the basis for the development of prognostic biomarkers of disease risk and interventional strategies to improve the management of severe COVID-19.

Crowdsourced RNA design discovers diverse, reversible, efficient, self-contained molecular switches.

Internet-based scientific communities promise a means to apply distributed, diverse human intelligence toward previously intractable scientific problems. However, current implementations have not allowed communities to propose experiments to test all emerging hypotheses at scale or to modify hypotheses in response to experiments. We report high-throughput methods for molecular characterization of nucleic acids that enable the large-scale video game­based crowdsourcing of RNA sensor design, followed by high-throughput functional characterization. Iterative design testing of thousands of crowdsourced RNA sensor designs produced near­thermodynamically optimal and reversible RNA switches that act as self-contained molecular sensors and couple five distinct small molecule inputs to three distinct protein binding and fluorogenic outputs. This work suggests a paradigm for widely distributed experimental bioscience.

Biocompatibility of nanomaterials and their immunological properties.

Nanomaterials (NMs) have revolutionized multiple aspects of medicine by enabling novel sensing, diagnostic, and therapeutic approaches. Advancements in processing and fabrication have also allowed significant expansion in the applications of the major classes of NMs based on polymer, metal/metal oxide, carbon, liposome, or multi-scale macro-nano bulk materials. Concomitantly, concerns regarding the nanotoxicity and overall biocompatibility of NMs have been raised. These involve putative negative effects on both patients and those subjected to occupational exposure during manufacturing. In this review, we describe the current state of testing of NMs including those that are in clinical use, in clinical trials, or under development. We also discuss the cellular and molecular interactions that dictate their toxicity and biocompatibility. Specifically, we focus on the reciprocal interactions between NMs and host proteins, lipids, and sugars and how these induce responses in immune and other cell types leading to topical and/or systemic effects.

Automated Design of Diverse Stand-Alone Riboswitches.

Riboswitches that couple binding of ligands to conformational changes offer sensors and control elements for RNA synthetic biology and medical biotechnology. However, design of these riboswitches has required expert intuition or software specialized to transcription or translation outputs; design has been particularly challenging for applications in which the riboswitch output cannot be amplified by other molecular machinery. We present a fully automated design method called RiboLogic for such "stand-alone" riboswitches and test it via high-throughput experiments on 2875 molecules using RNA-MaP (RNA on a massively parallel array) technology. These molecules consistently modulate their affinity to the MS2 bacteriophage coat protein upon binding of flavin mononucleotide, tryptophan, theophylline, and microRNA miR-208a, achieving activation ratios of up to 20 and significantly better performance than control designs. By encompassing a wide diversity of stand-alone switches and highly quantitative data, the resulting ribologic-solves experimental data set provides a rich resource for further improvement of riboswitch models and design methods.

Lasso Peptide Benenodin-1 Is a Thermally Actuated [1]Rotaxane Switch.

Mechanically interlocked molecules that change their conformation in response to stimuli have been developed by synthetic chemists as building blocks for molecular machines. Here we describe a natural product, the lasso peptide benenodin-1, which exhibits conformational switching between two distinct threaded conformers upon actuation by heat. We have determined the structures of both conformers and have characterized the kinetics and energetics of the conformational switch. Single amino acid substitutions to benenodin-1 generate peptides that are biased to a single conformer, showing that the switching behavior is potentially an evolvable trait in these peptides. Lasso peptides such as benenodin-1 can be recognized and cleaved by enzymes called lasso peptide isopeptidases. We show that only the native conformer of benenodin-1 is cleaved by its cognate isopeptidase. Thus, thermally induced conformational switching of benenodin-1 may also be relevant to the biological function of these molecules.

High-throughput biochemical profiling reveals sequence determinants of dCas9 off-target binding and unbinding.

The bacterial adaptive immune system CRISPR-Cas9 has been appropriated as a versatile tool for editing genomes, controlling gene expression, and visualizing genetic loci. To analyze Cas9's ability to bind DNA rapidly and specifically, we generated multiple libraries of potential binding partners for measuring the kinetics of nuclease-dead Cas9 (dCas9) interactions. Using a massively parallel method to quantify protein-DNA interactions on a high-throughput sequencing flow cell, we comprehensively assess the effects of combinatorial mismatches between guide RNA (gRNA) and target nucleotides, both in the seed and in more distal nucleotides, plus disruption of the protospacer adjacent motif (PAM). We report two consequences of PAM-distal mismatches: reversal of dCas9 binding at long time scales, and synergistic changes in association kinetics when other gRNA-target mismatches are present. Together, these observations support a model for Cas9 specificity wherein gRNA-DNA mismatches at PAM-distal bases modulate different biophysical parameters that determine association and dissociation rates. The methods we present decouple aspects of kinetic and thermodynamic properties of the Cas9-DNA interaction and broaden the toolkit for investigating off-target binding behavior.

PRISM: A DATA-DRIVEN PLATFORM FOR MONITORING MENTAL HEALTH.

Neuropsychiatric disorders are the leading cause of disability worldwide and there is no gold standard currently available for the measurement of mental health. This issue is exacerbated by the fact that the information physicians use to diagnose these disorders is episodic and often subjective. Current methods to monitor mental health involve the use of subjective DSM-5 guidelines, and advances in EEG and video monitoring technologies have not been widely adopted due to invasiveness and inconvenience. Wearable technologies have surfaced as a ubiquitous and unobtrusive method for providing continuous, quantitative data about a patient. Here, we introduce PRISM-Passive, Real-time Information for Sensing Mental Health. This platform integrates motion, light and heart rate data from a smart watch application with user interactions and text entries from a web application. We have demonstrated a proof of concept by collecting preliminary data through a pilot study of 13 subjects. We have engineered appropriate features and applied both unsupervised and supervised learning to develop models that are predictive of user-reported ratings of their emotional state, demonstrating that the data has the potential to be useful for evaluating mental health. This platform could allow patients and clinicians to leverage continuous streams of passive data for early and accurate diagnosis as well as constant monitoring of patients suffering from mental disorders.