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Abstract
Convolutional neural networks (CNNs) and other deep-learning models have proven to be transformative tools for the automated analysis of microscopy images, particularly in the domain of cellular and tissue imaging. These computer-vision models have primarily been applied with traditional microscopy imaging modalities (e.g. brightfield and fluorescence), likely due to the availability of large datasets in these regimes. However, more advanced microscopy imaging techniques could, potentially, allow for improved model performance in various computational histopathology tasks. In this work, we demonstrate that CNNs can achieve high accuracy in cell detection and classification without large amounts of data when applied to histology images acquired by fluorescence lifetime imaging microscopy (FLIM). This accuracy is higher than what would be achieved with regular single or dual-channel fluorescence images under the same settings, particularly for CNNs pretrained on publicly available fluorescent cell or general image datasets. Additionally, generated FLIM images could be predicted from just the fluorescence image data by using a dense U-Net CNN model trained on a subset of ground-truth FLIM images. These U-Net CNN generated FLIM images demonstrated high similarity to ground truth and improved accuracy in cell detection and classification over fluorescence alone when used as input to a variety of commonly used CNNs. This improved accuracy was maintained even when the FLIM images were generated by a U-Net CNN trained on only a few example FLIM images.

Abstract
Multistage disease processes are often characterized by a linear relationship between the log of incidence rates and the log of age. Examples include sequences of somatic mutations, that can cause cancer, and have recently been linked with a range of non-malignant diseases. Using a Weibull distribution to model diseases that occur through an ordered sequence of stages, and another model where stages can occur in any order, we characterized the age-related onset of disease in UK Biobank data. Despite their different underlying assumptions, both models accurately described the incidence of over 450 diseases, demonstrating that multistage disease processes cannot be inferred from this data alone. The parametric models provided unique insights into age-related disease, that conventional studies of relative risks cannot. The rate at which disease risk increases with age was used to distinguish between "sporadic" diseases, with an initially low and slowly increasing risk, and "late-onset" diseases whose negligible risk when young rapidly increases with age. "Relative aging rates" were introduced to quantify how risk factors modify age-related risk, finding the effective age-at-risk of sporadic diseases is strongly modified by common risk factors. Relative aging rates are ideal for risk-stratification, allowing the identification of ages with equivalent-risk in groups with different exposures. Most importantly, our results suggest that a substantial burden of sporadic diseases can be substantially delayed or avoided by early lifestyle interventions.

Abstract
In the midst of the COVID-19 experience, we learned an important scientific lesson: knowledge acquisition and information quality in medicine depends more on "data quality" rather than "data quantity." The large number of COVID-19 reports, published in a very short time, demonstrated that the most advanced statistical and computational tools cannot properly overcome the poor quality of acquired data. The main evidence for this observation comes from the poor reproducibility of results. Indeed, understanding the data generation process is fundamental when investigating scientific questions such as prevalence, immunity, transmissibility, and susceptibility. Most of COVID-19 studies are case reports based on "non probability" sampling and do not adhere to the general principles of controlled experimental designs. Such collected data suffers from many limitations when used to derive clinical conclusions. These include confounding factors, measurement errors and bias selection effects. Each of these elements represents a source of uncertainty, which is often ignored or assumed to provide an unbiased random contribution. Inference retrieved from large data in medicine is also affected by data protection policies that, while protecting patients' privacy, are likely to reduce consistently usefulness of big data in achieving fundamental goals such as effective and efficient data-integration. This limits the degree of generalizability of scientific studies and leads to paradoxical and conflicting conclusions. We provide such examples from assessing the role of risks factors. In conclusion, new paradigms and new designs schemes are needed in order to reach inferential conclusions that are meaningful and informative when dealing with data collected during emergencies like COVID-19.

Abstract
DNA supercoiling is a key regulatory mechanism that orchestrates DNA readout, recombination, and genome maintenance. DNA-binding proteins often mediate these processes by bringing two distant DNA sites together, thereby inducing (transient) topological domains. In order to understand the dynamics and molecular architecture of protein-induced topological domains in DNA, quantitative and time-resolved approaches are required. Here, we present a methodology to determine the size and dynamics of topological domains in supercoiled DNA in real time and at the single-molecule level. Our approach is based on quantifying the extension fluctuations—in addition to the mean extension—of supercoiled DNA in magnetic tweezers (MT). Using a combination of high-speed MT experiments, Monte Carlo simulations, and analytical theory, we map out the dependence of DNA extension fluctuations as a function of supercoiling density and external force. We find that in the plectonemic regime, the extension variance increases linearly with increasing supercoiling density and show how this enables us to determine the formation and size of topological domains. In addition, we demonstrate how the transient (partial) dissociation of DNA-bridging proteins results in the dynamic sampling of different topological states, which allows us to deduce the torsional stiffness of the plectonemic state and the kinetics of protein-plectoneme interactions. We expect our results to further the understanding and optimization of magnetic tweezer measurements and to enable quantification of the dynamics and reaction pathways of DNA processing enzymes in the context of physiologically relevant forces and supercoiling densities.

Abstract
The efficient and specific delivery of functional cargos such as small-molecule drugs, proteins, or nucleic acids across lipid membranes and into subcellular compartments is a significant unmet need in nanomedicine and molecular biology. Systematic Evolution of Ligands by EXponential enrichment (SELEX) exploits vast combinatorial nucleic acid libraries to identify short, nonimmunogenic single-stranded DNA molecules (aptamers) capable of recognizing specific targets based on their 3D structures and molecular interactions. While SELEX has previously been applied to identify aptamers that bind specific cell types or gain cellular uptake, selection of aptamers capable of carrying cargos to specific subcellular compartments is challenging. Here, we describe peroxidase proximity selection (PPS), a generalizable subcellular SELEX approach. We implement local expression of engineered ascorbate peroxidase APEX2 to biotinylate naked DNA aptamers capable of gaining access to the cytoplasm of living cells without assistance. We discovered DNA aptamers that are preferentially taken up into endosomes by macropinocytosis, with a fraction apparently accessing APEX2 in the cytoplasm. One of these selected aptamers is capable of endosomal delivery of an IgG antibody.

Abstract
The new variant of concern (VOC) of SARS-CoV-2, Omicron (B.1.1.529), is genetically very different from other VOCs. We compared Omicron with the preceding VOC Delta (B.1.617.2) and the wildtype (wt) strain (B.1) with respect to their interactions with the antiviral interferon (IFN-alpha/beta) response in infected cells. Our data indicate that IFN induction by Omicron is low and comparable to the wt, whereas Delta showed an increased IFN induction. However, Omicron exceeded both the wt and the Delta strain with respect to the ability to withstand the antiviral state imposed by IFN-alpha.

Abstract
Rubella is a highly contagious viral infection that usually causes a mild disease in children and adults. However, infection during pregnancy can result in a fetal or newborn death or congenital rubella syndrome (CRS), a constellation of permanent birth defects including cataracts, heart defects, and sensorineural deafness. The live-attenuated rubella vaccine has been highly effective, with the Americas declared free of endemic rubella transmission in 2015. However, rubella remains a significant problem worldwide and the leading cause of vaccine-preventable birth defects globally. Thus, elimination of rubella and CRS is a goal of the World Health Organization. No specific therapeutics are approved for the rubella virus. Therefore, we set out to identify whether existing small molecules may be repurposed for use against rubella virus infection. Thus, we performed a high-throughput screen for small molecules active against rubella virus in human respiratory cells and identified two nucleoside analogs, NM107 and AT-527, with potent antiviral activity. Furthermore, we found that combining these nucleoside analogs with inhibitors of host nucleoside biosynthesis had synergistic antiviral activity. These studies open the door to new potential approaches to treat rubella infections.

Abstract
Inherited retinal diseases (IRDs) are a group of ocular conditions characterized by an elevated genetic and clinical heterogeneity. They are transmitted almost invariantly as monogenic traits. However, with more than 280 disease genes identified so far, association of clinical phenotypes with genotypes can be very challenging, and molecular diagnosis is essential for genetic counseling and correct management of the disease. In addition, the prevalence and the assortment of IRD mutations are often population-specific. In this work, we examined 230 families from Portugal, with individuals suffering from a variety of IRD diagnostic classes (270 subjects in total). Overall, we identified 157 unique mutations (34 previously unreported) in 57 distinct genes, with a diagnostic rate of 76%. The IRD mutational landscape was, to some extent, different from those reported in other European populations, including Spanish cohorts. For instance, the EYS gene appeared to be the most frequently mutated, with a prevalence of 10% among all IRD cases. This was, in part, due to the presence of a recurrent and seemingly founder mutation involving the deletion of exons 13 and 14 of this gene. Moreover, our analysis highlighted that as many as 51% of our cases had mutations in a homozygous state. To our knowledge, this is the first study assessing a cross-sectional genotype–phenotype landscape of IRDs in Portugal. Our data reveal a rather unique distribution of mutations, possibly shaped by a small number of rare ancestral events that have now become prevalent alleles in patients.

Abstract
SARS-CoV-2 viral-load measurements from a single-specimen type are used to establish diagnostic strategies, interpret clinical-trial results for vaccines and therapeutics, model viral transmission, and understand virus–host interactions. However, measurements from a single-specimen type are implicitly assumed to be representative of other specimen types. We quantified viral-load timecourses from individuals who began daily self-sampling of saliva, anterior-nares (nasal), and oropharyngeal (throat) swabs before or at the incidence of infection with the Omicron variant. Viral loads in different specimen types from the same person at the same timepoint exhibited extreme differences, up to 109 copies/mL. These differences were not due to variation in sample self-collection, which was consistent. For most individuals, longitudinal viral-load timecourses in different specimen types did not correlate. Throat-swab and saliva viral loads began to rise as many as 7 days earlier than nasal-swab viral loads in most individuals, leading to very low clinical sensitivity of nasal swabs during the first days of infection. Individuals frequently exhibited presumably infectious viral loads in one specimen type while viral loads were low or undetectable in other specimen types. Therefore, defining an individual as infectious based on assessment of a single-specimen type underestimates the infectious period, and overestimates the ability of that specimen type to detect infectious individuals. For diagnostic COVID-19 testing, these three single-specimen types have low clinical sensitivity, whereas a combined throat–nasal swab, and assays with high analytical sensitivity, was inferred to have significantly better clinical sensitivity to detect presumed pre-infectious and infectious individuals.

Abstract
Dental calculus preserves oral microbes, enabling comparative studies of the oral microbiome and health through time. However, small sample sizes and limited dental health metadata have hindered health-focused investigations to date. Here, we investigate the relationship between tobacco pipe smoking and dental calculus microbiomes. Dental calculus from 75 individuals from the 19th century Middenbeemster skeletal collection (Netherlands) were analyzed by metagenomics. Demographic and dental health parameters were systematically recorded, including the presence/number of pipe notches. Comparative data sets from European populations before and after the introduction of tobacco were also analyzed. Calculus species profiles were compared with oral pathology to examine associations between microbiome community, smoking behavior, and oral health status. The Middenbeemster individuals exhibited relatively poor oral health, with a high prevalence of periodontal disease, caries, heavy calculus deposits, and antemortem tooth loss. No associations between pipe notches and dental pathologies, or microbial species composition, were found. Calculus samples before and after the introduction of tobacco showed highly similar species profiles. Observed interindividual microbiome differences were consistent with previously described variation in human populations from the Upper Paleolithic to the present. Dental calculus may not preserve microbial indicators of health and disease status as distinctly as dental plaque.

Abstract
Microbial specialized metabolites are an important source of and inspiration for many pharmaceuticals, biotechnological products and play key roles in ecological processes. Untargeted metabolomics using liquid chromatography coupled with tandem mass spectrometry is an efficient technique to access metabolites from fractions and even environmental crude extracts. Nevertheless, metabolomics is limited in predicting structures or bioactivities for cryptic metabolites. Efficiently linking the biosynthetic potential inferred from (meta)genomics to the specialized metabolome would accelerate drug discovery programs by allowing metabolomics to make use of genetic predictions. Here, we present a k-nearest neighbor classifier to systematically connect mass spectrometry fragmentation spectra to their corresponding biosynthetic gene clusters (independent of their chemical class). Our new pattern-based genome mining pipeline links biosynthetic genes to metabolites that they encode for, as detected via mass spectrometry from bacterial cultures or environmental microbiomes. Using paired datasets that include validated genes-mass spectral links from the Paired Omics Data Platform, we demonstrate this approach by automatically linking 18 previously known mass spectra (17 for which the biosynthesis gene clusters can be found at the MIBiG database plus palmyramide A) to their corresponding previously experimentally validated biosynthetic genes (e.g., via nuclear magnetic resonance or genetic engineering). We illustrated a computational example of how to use our Natural Products Mixed Omics (NPOmix) tool for siderophore mining that can be reproduced by the users. We conclude that NPOmix minimizes the need for culturing (it worked well on microbiomes) and facilitates specialized metabolite prioritization based on integrative omics mining.