Stabilization of G-Quadruplex Structures of the SARS-CoV-2 Genome by TMPyP4, BRACO19, and PhenDC3.

The G-quadruplex is one of the non-canonical structures formed by nucleic acids, which can be formed by guanine-rich sequences. They became the focus of much research when they were found in several oncogene promoter regions and also in the telomeres. Later on, they were discovered in viruses as well. Various ligands have been developed in order to stabilize DNA G-quadruplexes, which were believed to have an anti-cancer or antiviral effect. We investigated three of these ligands, and whether they can also affect the stability of the G-quadruplex-forming sequences of the RNA genome of SARS-CoV-2. All three investigated oligonucleotides showed the G-quadruplex form. We characterized their stability and measured their thermodynamic parameters using the Förster resonance energy transfer method. The addition of the ligands caused an increase in the unfolding temperature, but this effect was smaller compared to that found earlier in the case of G-quadruplexes of the hepatitis B virus, which has a DNA genome.

SuperCUT, an unsupervised multimodal image registration with deep learning for biomedical microscopy.

Numerous imaging techniques are available for observing and interrogating biological samples, and several of them can be used consecutively to enable correlative analysis of different image modalities with varying resolutions and the inclusion of structural or molecular information. Achieving accurate registration of multimodal images is essential for the correlative analysis process, but it remains a challenging computer vision task with no widely accepted solution. Moreover, supervised registration methods require annotated data produced by experts, which is limited. To address this challenge, we propose a general unsupervised pipeline for multimodal image registration using deep learning. We provide a comprehensive evaluation of the proposed pipeline versus the current state-of-the-art image registration and style transfer methods on four types of biological problems utilizing different microscopy modalities. We found that style transfer of modality domains paired with fully unsupervised training leads to comparable image registration accuracy to supervised methods and, most importantly, does not require human intervention.

Learning representations for image-based profiling of perturbations.

Measuring the phenotypic effect of treatments on cells through imaging assays is an efficient and powerful way of studying cell biology, and requires computational methods for transforming images into quantitative data. Here, we present an improved strategy for learning representations of treatment effects from high-throughput imaging, following a causal interpretation. We use weakly supervised learning for modeling associations between images and treatments, and show that it encodes both confounding factors and phenotypic features in the learned representation. To facilitate their separation, we constructed a large training dataset with images from five different studies to maximize experimental diversity, following insights from our causal analysis. Training a model with this dataset successfully improves downstream performance, and produces a reusable convolutional network for image-based profiling, which we call Cell Painting CNN. We evaluated our strategy on three publicly available Cell Painting datasets, and observed that the Cell Painting CNN improves performance in downstream analysis up to 30% with respect to classical features, while also being more computationally efficient.

Central Ring Puckering Enhances the Stokes Shift of Xanthene Dyes.

Small-molecule dyes are generally designed based on well-understood electronic effects. However, steric hindrance can promote excited-state geometric relaxation, increasing the difference between the positions of absorption and emission bands (the Stokes shift). Accordingly, we hypothesized that sterically induced central ring puckering in xanthene dyes could be used to systematically increase their Stokes shift. Through a combined experimental/quantum-chemical approach, we screened a group of (9-acylimino)-pyronin dyes with a perturbed central ring geometry. Our results showed that an atom with sp3 hybridization in position 10 of (9-acylimino)-pyronins induces central ring puckering and facilitates excited-state geometric relaxation, thereby markedly enhancing their Stokes shifts (by up to ~2000 cm-1). Thus, we prepared fluorescent (9-acylimino)-pyronin pH sensors, which showed a Stokes shift disparity between acid and base forms of up to ~8700 cm-1. Moreover, the concept of ring puckering-enhanced Stokes shift can be applied to a wide range of xanthene analogues found in the literature. Therefore, central ring puckering may be reliably used as a strategy for enhancing Stokes shifts in the rational design of dyes.

Role of the kisspeptin-KISS1R axis in the pathogenesis of chronic kidney disease and uremic cardiomyopathy.

The prevalence of chronic kidney disease (CKD) is increasing globally, especially in elderly patients. Uremic cardiomyopathy is a common cardiovascular complication of CKD, characterized by left ventricular hypertrophy (LVH), diastolic dysfunction, and fibrosis. Kisspeptins and their receptor, KISS1R, exert a pivotal influence on kidney pathophysiology and modulate age-related pathologies across various organ systems. KISS1R agonists, including kisspeptin-13 (KP-13), hold promise as novel therapeutic agents within age-related biological processes and kidney-related disorders. Our investigation aimed to elucidate the impact of KP-13 on the trajectory of CKD and uremic cardiomyopathy. Male Wistar rats (300-350 g) were randomized into four groups: (I) sham-operated, (II) 5/6 nephrectomy-induced CKD, (III) CKD subjected to a low dose of KP-13 (intraperitoneal 13 µg/day), and (IV) CKD treated with a higher KP-13 dose (intraperitoneal 26 µg/day). Treatments were administered daily from week 3 for 10 days. After 13 weeks, KP-13 increased systemic blood pressure, accentuating diastolic dysfunction's echocardiographic indicators and intensifying CKD-associated markers such as serum urea levels, glomerular hypertrophy, and tubular dilation. Notably, KP-13 did not exacerbate circulatory uremic toxin levels, renal inflammation, or fibrosis markers. In contrast, the higher KP-13 dose correlated with reduced posterior and anterior wall thickness, coupled with diminished cardiomyocyte cross-sectional areas and concurrent elevation of inflammatory (Il6, Tnf), fibrosis (Col1), and apoptosis markers (Bax/Bcl2) relative to the CKD group. In summary, KP-13's influence on CKD and uremic cardiomyopathy encompassed heightened blood pressure and potentially activated inflammatory and apoptotic pathways in the left ventricle.

Segmentation metric misinterpretations in bioimage analysis.

Quantitative evaluation of image segmentation algorithms is crucial in the field of bioimage analysis. The most common assessment scores, however, are often misinterpreted and multiple definitions coexist with the same name. Here we present the ambiguities of evaluation metrics for segmentation algorithms and show how these misinterpretations can alter leaderboards of influential competitions. We also propose guidelines for how the currently existing problems could be tackled.

RuX: A Novel, Flexible, and Sensitive Mifepristone-Induced Transcriptional Regulation System.

Inducible gene regulation methods are indispensable in diverse biological applications, yet many of them have severe limitations in their applicability. These include inducer toxicity, a limited variety of organisms the given system can be used in, and side effects of the induction method. In this study, a novel inducible system, the RuX system, was created using a mutant ligand-binding domain of the glucocorticoid receptor (CS1/CD), used together with various genetic elements such as the Gal4 DNA-binding domain or Cre recombinase. The RuX system is shown to be capable of over 1000-fold inducibility, has flexible applications, and is offered for use in cell cultures.

Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumor-specificity and predictive potential of extracellular vesicles for cell invasion and proliferation - A meta-analysis.

BACKGROUND: Although interest in the role of extracellular vesicles (EV) in oncology is growing, not all potential aspects have been investigated. In this meta-analysis, data regarding (i) the EV proteome and (ii) the invasion and proliferation capacity of the NCI-60 tumor cell lines (60 cell lines from nine different tumor types) were analyzed using machine learning methods. METHODS: On the basis of the entire proteome or the proteins shared by all EV samples, 60 cell lines were classified into the nine tumor types using multiple logistic regression. Then, utilizing the Least Absolute Shrinkage and Selection Operator, we constructed a discriminative protein panel, upon which the samples were reclassified and pathway analyses were performed. These panels were validated using clinical data (n = 4,665) from Human Protein Atlas. RESULTS: Classification models based on the entire proteome, shared proteins, and discriminative protein panel were able to distinguish the nine tumor types with 49.15%, 69.10%, and 91.68% accuracy, respectively. Invasion and proliferation capacity of the 60 cell lines were predicted with R2 = 0.68 and R2 = 0.62 (p < 0.0001). The results of the Reactome pathway analysis of the discriminative protein panel suggest that the molecular content of EVs might be indicative of tumor-specific biological processes. CONCLUSION: Integrating in vitro EV proteomic data, cell physiological characteristics, and clinical data of various tumor types illuminates the diagnostic, prognostic, and therapeutic potential of EVs. Video Abstract.

Neuregulin-1ß Improves Uremic Cardiomyopathy and Renal Dysfunction in Rats.

Chronic kidney disease is a global health problem affecting 10% to 12% of the population. Uremic cardiomyopathy is often characterized by left ventricular hypertrophy, fibrosis, and diastolic dysfunction. Dysregulation of neuregulin-1ß signaling in the heart is a known contributor to heart failure. The systemically administered recombinant human neuregulin-1ß for 10 days in our 5/6 nephrectomy-induced model of chronic kidney disease alleviated the progression of uremic cardiomyopathy and kidney dysfunction in type 4 cardiorenal syndrome. The currently presented positive preclinical data warrant clinical studies to confirm the beneficial effects of recombinant human neuregulin-1ß in patients with chronic kidney disease.

Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2.

We present a miniaturized immunofluorescence assay (mini-IFA) for measuring antibody response in patient blood samples. The method utilizes machine learning-guided image analysis and enables simultaneous measurement of immunoglobulin M (IgM), IgA, and IgG responses against different viral antigens in an automated and high-throughput manner. The assay relies on antigens expressed through transfection, enabling use at a low biosafety level and fast adaptation to emerging pathogens. Using severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the model pathogen, we demonstrate that this method allows differentiation between vaccine-induced and infection-induced antibody responses. Additionally, we established a dedicated web page for quantitative visualization of sample-specific results and their distribution, comparing them with controls and other samples. Our results provide a proof of concept for the approach, demonstrating fast and accurate measurement of antibody responses in a research setup with prospects for clinical diagnostics.

Structure preserving adversarial generation of labeled training samples for single-cell segmentation.

We introduce a generative data augmentation strategy to improve the accuracy of instance segmentation of microscopy data for complex tissue structures. Our pipeline uses regular and conditional generative adversarial networks (GANs) for image-to-image translation to construct synthetic microscopy images along with their corresponding masks to simulate the distribution and shape of the objects and their appearance. The synthetic samples are then used for training an instance segmentation network (for example, StarDist or Cellpose). We show on two single-cell-resolution tissue datasets that our method improves the accuracy of downstream instance segmentation tasks compared with traditional training strategies using either the raw data or basic augmentations. We also compare the quality of the object masks with those generated by a traditional cell population simulation method, finding that our synthesized masks are closer to the ground truth considering Fréchet inception distances.

Polymorphic amyloid nanostructures of hormone peptides involved in glucose homeostasis display reversible amyloid formation.

A large group of hormones are stored as amyloid fibrils in acidic secretion vesicles before they are released into the bloodstream and readopt their functional state. Here, we identify an evolutionarily conserved hexapeptide sequence as the major aggregation-prone region (APR) of gastrointestinal peptides of the glucagon family: xFxxWL. We determine nine polymorphic crystal structures of the APR segments of glucagon-like peptides 1 and 2, and exendin and its derivatives. We follow amyloid formation by CD, FTIR, ThT assays, and AFM. We propose that the pH-dependent changes of the protonation states of glutamate/aspartate residues of APRs initiate switching between the amyloid and the folded, monomeric forms of the hormones. We find that pH sensitivity diminishes in the absence of acidic gatekeepers and amyloid formation progresses over a broad pH range. Our results highlight the dual role of short aggregation core motifs in reversible amyloid formation and receptor binding.

The kisspeptin-1 receptor antagonist peptide-234 aggravates uremic cardiomyopathy in a rat model.

Uremic cardiomyopathy is characterized by diastolic dysfunction, left ventricular hypertrophy (LVH), and fibrosis. Dysregulation of the kisspeptin receptor (KISS1R)-mediated pathways are associated with the development of fibrosis in cancerous diseases. Here, we investigated the effects of the KISS1R antagonist peptide-234 (P234) on the development of uremic cardiomyopathy. Male Wistar rats (300-350 g) were randomized into four groups: (i) Sham, (ii) chronic kidney disease (CKD) induced by 5/6 nephrectomy, (iii) CKD treated with a lower dose of P234 (ip. 13 µg/day), (iv) CKD treated with a higher dose of P234 (ip. 26 µg/day). Treatments were administered daily from week 3 for 10 days. At week 13, the P234 administration did not influence the creatinine clearance and urinary protein excretion. However, the higher dose of P234 led to reduced anterior and posterior wall thicknesses, more severe interstitial fibrosis, and overexpression of genes associated with left ventricular remodeling (Ctgf, Tgfb, Col3a1, Mmp9), stretch (Nppa), and apoptosis (Bax, Bcl2, Casp7) compared to the CKD group. In contrast, no significant differences were found in the expressions of apoptosis-associated proteins between the groups. Our results suggest that the higher dose of P234 hastens the development and pathophysiology of uremic cardiomyopathy by activating the fibrotic TGF-ß-mediated pathways.

Analysis and comparison of SARS-CoV-2 variant antibodies and neutralizing activity for 6 months after a booster mRNA vaccine in a healthcare worker population.

Introduction: In the context of recurrent surges of SARS-CoV-2 infections, a detailed characterization of antibody persistence over a 6-month period following vaccine booster dose is necessary to crafting effective public health policies on repeat vaccination. Methods: To characterize the SARS-CoV-2 antibody profile of a healthcare worker population over a 6-month period following mRNA vaccination and booster dose. 323 healthcare workers at an academic medical center in Orange County, California who had completed primary vaccination and booster dose against SARS-CoV-2 were recruited for the study. A total of 690 blood specimens over a 6-month period were collected via finger-stick blood and analyzed for the presence of antibodies against 9 SARS-CoV-2 antigens using a coronavirus antigen microarray. Results: The primary outcome of this study was the average SARS-CoV-2 antibody level as measured using a novel coronavirus antigen microarray. Additional outcomes measured include levels of antibodies specific to SARS-CoV-2 variants including Delta, Omicron BA.1, and BA.2. We also measured SARS-CoV-2 neutralization capacity for a subset of the population to confirm correlation with antibody levels. Although antibodies against SARS-CoV-2 wane throughout the 6-month period following a booster dose, antibody levels remain higher than pre-boost levels. However, a booster dose of vaccine based on the original Wuhan strain generates approximately 3-fold lower antibody reactivity against Omicron variants BA.1 and BA.2 as compared to the vaccine strain. Despite waning antibody levels, neutralization activity against the vaccine strain is maintained throughout the 6-month period. Discussion: In the context of recurrent surges of SARS-CoV-2 infections, our data indicate that breakthrough infections are likely driven by novel variants with different antibody specificity and not by time since last dose of vaccination, indicating that development of vaccinations specific to these novel variants is necessary to prevent future surges of SARS-CoV-2 infections.

Climate-ecosystem modelling made easy: The Land Sites Platform.

Dynamic Global Vegetation Models (DGVMs) provide a state-of-the-art process-based approach to study the complex interplay between vegetation and its physical environment. For example, they help to predict how terrestrial plants interact with climate, soils, disturbance and competition for resources. We argue that there is untapped potential for the use of DGVMs in ecological and ecophysiological research. One fundamental barrier to realize this potential is that many researchers with relevant expertize (ecology, plant physiology, soil science, etc.) lack access to the technical resources or awareness of the research potential of DGVMs. Here we present the Land Sites Platform (LSP): new software that facilitates single-site simulations with the Functionally Assembled Terrestrial Ecosystem Simulator, an advanced DGVM coupled with the Community Land Model. The LSP includes a Graphical User Interface and an Application Programming Interface, which improve the user experience and lower the technical thresholds for installing these model architectures and setting up model experiments. The software is distributed via version-controlled containers; researchers and students can run simulations directly on their personal computers or servers, with relatively low hardware requirements, and on different operating systems. Version 1.0 of the LSP supports site-level simulations. We provide input data for 20 established geo-ecological observation sites in Norway and workflows to add generic sites from public global datasets. The LSP makes standard model experiments with default data easily achievable (e.g., for educational or introductory purposes) while retaining flexibility for more advanced scientific uses. We further provide tools to visualize the model input and output, including simple examples to relate predictions to local observations. The LSP improves access to land surface and DGVM modelling as a building block of community cyberinfrastructure that may inspire new avenues for mechanistic ecosystem research across disciplines.

Serum Progranulin Level Might Differentiate Non-IPF ILD from IPF.

Diagnosing interstitial lung disease (ILD) can be a challenging process. New biomarkers may support diagnostic decisions. Elevated serum progranulin (PGRN) levels have been reported in liver fibrosis and dermatomyositis-associated acute interstitial pneumonia. Our aim was to assess the role of PGRN in the differential diagnosis of idiopathic pulmonary fibrosis (IPF) and other ILDs. Serum levels of PGRN were measured by enzyme-linked immunosorbent assay in stable IPF (n = 40), non-IPF ILD (n = 48) and healthy controls (n = 17). Patient characteristics, lung function, CO diffusion (DLCO), arterial blood gases, 6-min walk test, laboratory parameters and high-resolution (HR)CT pattern were assessed. In stable IPF, PGRN levels did not differ from healthy controls; however, serum PGRN levels were significantly higher in non-IPF ILD patients compared to healthy subjects and IPF (53.47 ± 15.38 vs. 40.99 ± 5.33 vs. 44.66 ± 7.77 ng/mL respectively; p < 0.01). The HRCT pattern of usual interstitial pneumonia (UIP) was associated with normal PGRN level, while for non-UIP patterns, significantly elevated PGRN level was measured. Elevated serum PGRN levels may be associated with non-IPF ILD, especially non-UIP patterns and might be helpful in cases of unclear radiological patterns in the differentiation between IPF and other ILDs.

The link between the sphingolipid rheostat and obstructive sleep apnea.

Chronic inflammation induced by hypoxia during sleep is an important mechanism of microvascular damage in OSA patients. In this study, we investigated the role of the sphingosine rheostat, which has diverse inflammatory effects. Thirty-seven healthy subjects and 31 patients with OSA were recruited. We collected data on demographics and comorbidities. Plasma sphingosine-1-phosphate and ceramide antibody concentrations were measured by ELISA. The results were compared between the OSA and control groups, and the correlations between these measurements and markers of disease severity and comorbidities were explored. Ceramide antibody levels were significantly elevated in OSA patients (892.17 ng/ml) vs. controls (209.55 ng/ml). S1P levels were also significantly higher in patients with OSA (1760.0 pg/ml) than in controls (290.35 pg/ml, p < 0.001). The ceramide antibody concentration showed correlations with BMI (ρ = 0.25, p = 0.04), CRP (ρ = 0.36, p = 0.005), AHI (ρ = 0.43, p < 0.001), ODI (ρ = 0.43, p < 0.001), TST90% (ρ = 0.35, p = 0.004) and the lowest oxygen saturation (ρ =  0.37, p = 0.001) in the whole study population but not when patients with OSA were analyzed separately. The elevated ceramide antibody and sphingosine-1-phosphate concentrations in patients suffering from OSA suggests their involvement in the pathomechanism of OSA and its comorbidities.

Risk factors for SARS-CoV-2 seropositivity in a health care worker population during the early pandemic.

BACKGROUND: While others have reported severe acute respiratory syndrome-related coronavirus 2(SARS-CoV-2) seroprevalence studies in health care workers (HCWs), we leverage the use of a highly sensitive coronavirus antigen microarray to identify a group of seropositive health care workers who were missed by daily symptom screening that was instituted prior to any epidemiologically significant local outbreak. Given that most health care facilities rely on daily symptom screening as the primary method to identify SARS-CoV-2 among health care workers, here, we aim to determine how demographic, occupational, and clinical variables influence SARS-CoV-2 seropositivity among health care workers. METHODS: We designed a cross-sectional survey of HCWs for SARS-CoV-2 seropositivity conducted from May 15th to June 30th 2020 at a 418-bed academic hospital in Orange County, California. From an eligible population of 5,349 HCWs, study participants were recruited in two ways: an open cohort, and a targeted cohort. The open cohort was open to anyone, whereas the targeted cohort that recruited HCWs previously screened for COVID-19 or work in high-risk units. A total of 1,557 HCWs completed the survey and provided specimens, including 1,044 in the open cohort and 513 in the targeted cohort. Demographic, occupational, and clinical variables were surveyed electronically. SARS-CoV-2 seropositivity was assessed using a coronavirus antigen microarray (CoVAM), which measures antibodies against eleven viral antigens to identify prior infection with 98% specificity and 93% sensitivity. RESULTS: Among tested HCWs (n = 1,557), SARS-CoV-2 seropositivity was 10.8%, and risk factors included male gender (OR 1.48, 95% CI 1.05-2.06), exposure to COVID-19 outside of work (2.29, 1.14-4.29), working in food or environmental services (4.85, 1.51-14.85), and working in COVID-19 units (ICU: 2.28, 1.29-3.96; ward: 1.59, 1.01-2.48). Amongst 1,103 HCWs not previously screened, seropositivity was 8.0%, and additional risk factors included younger age (1.57, 1.00-2.45) and working in administration (2.69, 1.10-7.10). CONCLUSION: SARS-CoV-2 seropositivity is significantly higher than reported case counts even among HCWs who are meticulously screened. Seropositive HCWs missed by screening were more likely to be younger, work outside direct patient care, or have exposure outside of work.

Predicting compound activity from phenotypic profiles and chemical structures.

Predicting assay results for compounds virtually using chemical structures and phenotypic profiles has the potential to reduce the time and resources of screens for drug discovery. Here, we evaluate the relative strength of three high-throughput data sources-chemical structures, imaging (Cell Painting), and gene-expression profiles (L1000)-to predict compound bioactivity using a historical collection of 16,170 compounds tested in 270 assays for a total of 585,439 readouts. All three data modalities can predict compound activity for 6-10% of assays, and in combination they predict 21% of assays with high accuracy, which is a 2 to 3 times higher success rate than using a single modality alone. In practice, the accuracy of predictors could be lower and still be useful, increasing the assays that can be predicted from 37% with chemical structures alone up to 64% when combined with phenotypic data. Our study shows that unbiased phenotypic profiling can be leveraged to enhance compound bioactivity prediction to accelerate the early stages of the drug-discovery process.