A synthetic methylotrophic Escherichia coli as a chassis for bioproduction from methanol.

Methanol synthesized from captured greenhouse gases is an emerging renewable feedstock with great potential for bioproduction. Recent research has raised the prospect of methanol bioconversion to value-added products using synthetic methylotrophic Escherichia coli, as its metabolism can be rewired to enable growth solely on the reduced one-carbon compound. Here we describe the generation of an E. coli strain that grows on methanol at a doubling time of 4.3 h-comparable to many natural methylotrophs. To establish bioproduction from methanol using this synthetic chassis, we demonstrate biosynthesis from four metabolic nodes from which numerous bioproducts can be derived: lactic acid from pyruvate, polyhydroxybutyrate from acetyl coenzyme A, itaconic acid from the tricarboxylic acid cycle and p-aminobenzoic acid from the chorismate pathway. In a step towards carbon-negative chemicals and valorizing greenhouse gases, our work brings synthetic methylotrophy in E. coli within reach of industrial applications.

A polygenic risk score for the QT interval is an independent predictor of drug-induced QT prolongation.

Drug-induced QT prolongation (diLQTS), and subsequent risk of torsade de pointes, is a major concern with use of many medications, including for non-cardiac conditions. The possibility that genetic risk, in the form of polygenic risk scores (PGS), could be integrated into prediction of risk of diLQTS has great potential, although it is unknown how genetic risk is related to clinical risk factors as might be applied in clinical decision-making. In this study, we examined the PGS for QT interval in 2500 subjects exposed to a known QT-prolonging drug on prolongation of the QT interval over 500ms on subsequent ECG using electronic health record data. We found that the normalized QT PGS was higher in cases than controls (0.212±0.954 vs. -0.0270±1.003, P = 0.0002), with an unadjusted odds ratio of 1.34 (95%CI 1.17-1.53, P<0.001) for association with diLQTS. When included with age and clinical predictors of QT prolongation, we found that the PGS for QT interval provided independent risk prediction for diLQTS, in which the interaction for high-risk diagnosis or with certain high-risk medications (amiodarone, sotalol, and dofetilide) was not significant, indicating that genetic risk did not modify the effect of other risk factors on risk of diLQTS. We found that a high-risk cutoff (QT PGS ≥ 2 standard deviations above mean), but not a low-risk cutoff, was associated with risk of diLQTS after adjustment for clinical factors, and provided one method of integration based on the decision-tree framework. In conclusion, we found that PGS for QT interval is an independent predictor of diLQTS, but that in contrast to existing theories about repolarization reserve as a mechanism of increasing risk, the effect is independent of other clinical risk factors. More work is needed for external validation in clinical decision-making, as well as defining the mechanism through which genes that increase QT interval are associated with risk of diLQTS.

Dashing Growth Curves: a web application for rapid and interactive analysis of microbial growth curves.

BACKGROUND: Recording and analyzing microbial growth is a routine task in the life sciences. Microplate readers that record dozens to hundreds of growth curves simultaneously are increasingly used for this task raising the demand for their rapid and reliable analysis. RESULTS: Here, we present Dashing Growth Curves, an interactive web application ( http://dashing-growth-curves.ethz.ch/ ) that enables researchers to quickly visualize and analyze growth curves without the requirement for coding knowledge and independent of operating system. Growth curves can be fitted with parametric and non-parametric models or manually. The application extracts maximum growth rates as well as other features such as lag time, length of exponential growth phase and maximum population size among others. Furthermore, Dashing Growth Curves automatically groups replicate samples and generates downloadable summary plots for of all growth parameters. CONCLUSIONS: Dashing Growth Curves is an open-source web application that reduces the time required to analyze microbial growth curves from hours to minutes.

FCM marker importance for MRD assessment in T-cell acute lymphoblastic leukemia: An AIEOP-BFM-ALL-FLOW study group report.

T-lineage acute lymphoblastic leukemia (T-ALL) accounts for about 15% of pediatric and about 25% of adult ALL cases. Minimal/measurable residual disease (MRD) assessed by flow cytometry (FCM) is an important prognostic indicator for risk stratification. In order to assess the MRD a limited number of antibodies directed against the most discriminative antigens must be selected. We propose a pipeline for evaluating the influence of different markers for cell population classification in FCM data. We use linear support vector machine, fitted to each sample individually to avoid issues with patient and laboratory variations. The best separating hyperplane direction as well as the influence of omitting specific markers is considered. Ninety-one bone marrow samples of 43 pediatric T-ALL patients from five reference laboratories were analyzed by FCM regarding marker importance for blast cell identification using combinations of eight different markers. For all laboratories, CD48 and CD99 were among the top three markers with strongest contribution to the optimal hyperplane, measured by median separating hyperplane coefficient size for all samples per center and time point (diagnosis, Day 15, Day 33). Based on the available limited set tested (CD3, CD4, CD5, CD7, CD8, CD45, CD48, CD99), our findings prove that CD48 and CD99 are useful markers for MRD monitoring in T-ALL. The proposed pipeline can be applied for evaluation of other marker combinations in the future.

Exploring Dual-Energy CT Spectral Information for Machine Learning-Driven Lesion Diagnosis in Pre-Log Domain.

In this study, we proposed a computer-aided diagnosis (CADx) framework under dual-energy spectral CT (DECT), which operates directly on the transmission data in the pre-log domain, called CADxDE, to explore the spectral information for lesion diagnosis. The CADxDE includes material identification and machine learning (ML) based CADx. Benefits from DECT's capability of performing virtual monoenergetic imaging with the identified materials, the responses of different tissue types (e.g., muscle, water, and fat) in lesions at each energy can be explored by ML for CADx. Without losing essential factors in the DECT scan, a pre-log domain model-based iterative reconstruction is adopted to obtain decomposed material images, which are then used to generate the virtual monoenergetic images (VMIs) at selected n energies. While these VMIs have the same anatomy, their contrast distribution patterns contain rich information along with the n energies for tissue characterization. Thus, a corresponding ML-based CADx is developed to exploit the energy-enhanced tissue features for differentiating malignant from benign lesions. Specifically, an original image-driven multi-channel three-dimensional convolutional neural network (CNN) and extracted lesion feature-based ML CADx methods are developed to show the feasibility of CADxDE. Results from three pathologically proven clinical datasets showed 4.01% to 14.25% higher AUC (area under the receiver operating characteristic curve) scores than the scores of both the conventional DECT data (high and low energy spectrum separately) and the conventional CT data. The mean gain >9.13% in AUC scores indicated that the energy spectral-enhanced tissue features from CADxDE have great potential to improve lesion diagnosis performance.

Machine Learned Texture Prior From Full-Dose CT Database via Multi-Modality Feature Selection for Bayesian Reconstruction of Low-Dose CT.

In our earlier study, we proposed a regional Markov random field type tissue-specific texture prior from previous full-dose computed tomography (FdCT) scan for current low-dose CT (LdCT) imaging, which showed clinical benefits through task-based evaluation. Nevertheless, two assumptions were made for early study. One assumption is that the center pixel has a linear relationship with its nearby neighbors and the other is previous FdCT scans of the same subject are available. To eliminate the two assumptions, we proposed a database assisted end-to-end LdCT reconstruction framework which includes a deep learning texture prior model and a multi-modality feature based candidate selection model. A convolutional neural network-based texture prior is proposed to eliminate the linear relationship assumption. And for scenarios in which the concerned subject has no previous FdCT scans, we propose to select one proper prior candidate from the FdCT database using multi-modality features. Features from three modalities are used including the subjects' physiological factors, the CT scan protocol, and a novel feature named Lung Mark which is deliberately proposed to reflect the z-axial property of human anatomy. Moreover, a majority vote strategy is designed to overcome the noise effect from LdCT scans. Experimental results showed the effectiveness of Lung Mark. The selection model has accuracy of 84% testing on 1,470 images from 49 subjects. The learned texture prior from FdCT database provided reconstruction comparable to the subjects having corresponding FdCT. This study demonstrated the feasibility of bringing clinically relevant textures from available FdCT database to perform Bayesian reconstruction of any current LdCT scan.

TLR4/CD14/MD2 Revealed as the Limited Toll-like Receptor Complex for Chlamydia trachomatis-Induced NF-κB Signaling.

Chlamydia trachomatis (Ct) is the most common cause of genital tract infections as well as preventable blindness worldwide. Pattern recognition receptors such as toll-like receptors (TLRs) represent the initial step in recognizing pathogenic microorganisms and are crucial for the initiation of an appropriate immune response. However, our understanding of TLR-signaling in Chlamydia-infected immune cells is incomplete. For a better comprehension of pathological inflammatory responses, robust models for interrogating TLR-signaling upon chlamydial infections are needed. To analyze the TLR response, we developed and utilized a highly sensitive and selective fluorescent transcriptional cellular reporter system to measure the activity of the transcription factor NF-κB. Upon incubation of the reporter cells with different preparations of Ct, we were able to pinpoint which components of TLRs are involved in the recognition of Ct. We identified CD14 associated with unique characteristics of different serovars as the crucial factor of the TLR4/CD14/MD2 complex for Ct-mediated activation of the NF-κB pathway. Furthermore, we found the TLR4/CD14/MD2 complex to be decisive for the uptake of Ct-derived lipopolysaccharides but not for infection and replication of Ct. Imaging flow cytometry provided information about inclusion formation in myeloid- as well as lymphocytic cells and was highest for Ct L2 with at least 25% of inclusion forming cells. Ct E inclusion formation was eminent in Jurkat cells without CD14 expression (11.1%). Thus, our model enables to determine Ct uptake and signal induction by pinpointing individual components of the recognition and signaling pathways to better understand the immune response towards infectious pathogens.

Engineering Endosymbiotic Growth of E. coli in Mammalian Cells.

Endosymbioses are cellular mergers in which one cell lives within another cell and have led to major evolutionary transitions, most prominently to eukaryogenesis. Generation of synthetic endosymbioses aims to provide a defined starting point for studying fundamental processes in emerging endosymbiotic systems and enable the engineering of cells with novel properties. Here, we tested the potential of different bacteria for artificial endosymbiosis in mammalian cells. To this end, we adopted the fluidic force microscopy technology to inject diverse bacteria directly into the cytosol of HeLa cells and examined the endosymbiont-host interactions by real-time fluorescence microscopy. Among them, Escherichia coli grew exponentially within the cytoplasm, however, at a faster pace than its host cell. To slow down the intracellular growth of E. coli, we introduced auxotrophies in E. coli and demonstrated that the intracellular growth rate can be reduced by limiting the uptake of aromatic amino acids. In consequence, the survival of the endosymbiont-host pair was prolonged. The presented experimental framework enables studying endosymbiotic candidate systems at high temporal resolution and at the single cell level. Our work represents a starting point for engineering a stable, vertically inherited endosymbiosis.

Generation of an Escherichia coli strain growing on methanol via the ribulose monophosphate cycle.

Methanol is a liquid with high energy storage capacity that holds promise as an alternative substrate to replace sugars in the biotechnology industry. It can be produced from CO2 or methane and its use does not compete with food and animal feed production. However, there are currently only limited biotechnological options for the valorization of methanol, which hinders its widespread adoption. Here, we report the conversion of the industrial platform organism Escherichia coli into a synthetic methylotroph that assimilates methanol via the energy efficient ribulose monophosphate cycle. Methylotrophy is achieved after evolution of a methanol-dependent E. coli strain over 250 generations in continuous chemostat culture. We demonstrate growth on methanol and biomass formation exclusively from the one-carbon source by 13C isotopic tracer analysis. In line with computational modeling, the methylotrophic E. coli strain optimizes methanol oxidation by upregulation of an improved methanol dehydrogenase, increasing ribulose monophosphate cycle activity, channeling carbon flux through the Entner-Doudoroff pathway and downregulating tricarboxylic acid cycle enzymes. En route towards sustainable bioproduction processes, our work lays the foundation for the efficient utilization of methanol as the dominant carbon and energy resource.

Automated identification of cell populations in flow cytometry data with transformers.

Acute Lymphoblastic Leukemia (ALL) is the most frequent hematologic malignancy in children and adolescents. A strong prognostic factor in ALL is given by the Minimal Residual Disease (MRD), which is a measure for the number of leukemic cells persistent in a patient. Manual MRD assessment from Multiparameter Flow Cytometry (FCM) data after treatment is time-consuming and subjective. In this work, we present an automated method to compute the MRD value directly from FCM data. We present a novel neural network approach based on the transformer architecture that learns to directly identify blast cells in a sample. We train our method in a supervised manner and evaluate it on publicly available ALL FCM data from three different clinical centers. Our method reaches a median F1 score of ≈0.94 when evaluated on 519 B-ALL samples and shows better results than existing methods on 4 different datasets.

UMAP Based Anomaly Detection for Minimal Residual Disease Quantification within Acute Myeloid Leukemia.

Leukemia is the most frequent malignancy in children and adolescents, with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) as the most common subtypes. Minimal residual disease (MRD) measured by flow cytometry (FCM) has proven to be a strong prognostic factor in ALL as well as in AML. Machine learning techniques have been emerging in the field of automated MRD quantification with the objective of superseding subjective and time-consuming manual analysis of FCM-MRD data. In contrast to ALL, where supervised multi-class classification methods have been successfully deployed for MRD detection, AML poses new challenges: AML is rarer (with fewer available training data) than ALL and much more heterogeneous in its immunophenotypic appearance, where one-class classification (anomaly detection) methods seem more suitable. In this work, a new semi-supervised approach based on the UMAP algorithm for MRD detection utilizing only labels of blast free FCM samples is presented. The method is tested on a newly gathered set of AML FCM samples and results are compared to state-of-the-art methods. We reach a median F1-score of 0.794, while providing a transparent classification pipeline with explainable results that facilitates inter-disciplinary work between medical and technical experts. This work shows that despite several issues yet to overcome, the merits of automated MRD quantification can be fully exploited also in AML.

Persistent Anti-Borrelia IgM Antibodies without Lyme Borreliosis in the Clinical and Immunological Context.

The aim of the study was to investigate the etiology of persistent IgM antibodies against Borrelia burgdorferi sensu lato (sl) and to analyze their association with nonspecific symptoms. The study group comprised individuals with persistent IgM antibodies in the absence of IgG. The relation between ELISA values and time elapsed since past erythema migrans (EM) was analyzed. Previous antibiotic treatments were assessed. The association between persistent IgM and nonspecific symptoms was evaluated statistically. Specificity of IgM antibodies for outer surface protein C (OspC) of B. burgdorferi sl was examined by immunoblotting. Further, we investigated the cross-reactivity with Borrelia-unrelated proteins. Fifty-nine patients (46 women; 78%) were included in the study group. The mean IgM-ELISA values did not change significantly during follow-up (median 6.2 months). The mean ELISA value in the study group was dependent on time elapsed since past EM. Nonspecific symptoms improved significantly more often in patients with lower IgM ELISA results. Persistent IgM antibodies were specific for the C-terminal PKKP motif of OspC. Cross-reacting C-terminal PKKP antigens from both human and prokaryotic origins were identified. We demonstrate that the C-terminal PKKP motif plays a main role for the reactivity of persistent Borrelia IgM toward OspC. However, cross-reactivity to other eukaryotic and/or prokaryotic antigens may hamper the specificity of OspC in the serological diagnosis of Lyme borreliosis. Lack of improvement of nonspecific symptoms was associated with higher IgM ELISA values. IMPORTANCE The reactivity of human IgM with the outer surface protein C (OspC) of Borrelia burgdorferi sensu lato is frequently used to detect Borrelia specific IgM in commercial immunoassays, and such antibodies usually occur in the early phase of the infection. We identified a group of individuals with persistent Borrelia IgM without symptoms of Lyme borreliosis. We used their sera to demonstrate that the C-terminal epitope of OspC binds the IgM. Strikingly, we found that the same epitope occurs also in certain proteins of human and environmental origin; the latter include other bacteria and food plants. Our experimental data show that these Borrelia-unrelated proteins cross-react with the OpsC-specific IgM. This knowledge is important for the development of serologic assays for Lyme borreliosis and provides a cross-reactive explanation for the persistence of Borrelia-IgM.

A Novel Flow Cytometric Approach for the Quantification and Quality Control of Chlamydia trachomatis Preparations.

Chlamydia trachomatis is an obligate intracellular pathogenic bacterium with a biphasic developmental cycle manifesting two distinct morphological forms: infectious elementary bodies (EBs) and replicative intracellular reticulate bodies (RBs). Current standard protocols for quantification of the isolates assess infectious particles by titering inclusion-forming units, using permissive cell lines, and analyzing via immunofluorescence. Enumeration of total particle counts is achieved by counting labeled EBs/RBs using a fluorescence microscope. Both methods are time-consuming with a high risk of observer bias. For a better assessment of C. trachomatis preparations, we developed a simple and time-saving flow cytometry-based workflow for quantifying small particles, such as EBs with a size of 300 nm. This included optimization of gain and threshold settings with the addition of a neutral density filter for small-particle discrimination. The nucleic acid dye SYBR® Green I (SGI) was used together with propidium iodide and 5(6)-carboxyfluorescein diacetate to enumerate and discriminate between live and dead bacteria. We found no significant differences between the direct particle count of SGI-stained C. trachomatis preparations measured by microscopy or flow cytometry (p > 0.05). Furthermore, we completed our results by introducing a cell culture-independent viability assay. Our measurements showed very good reproducibility and comparability to the existing state-of-the-art methods, indicating that the evaluation of C. trachomatis preparations by flow cytometry is a fast and reliable method. Thus, our method facilitates an improved assessment of the quality of C. trachomatis preparations for downstream applications.

The Reflection Map and Infinitesimal Deformations of Sphere Mappings.

The reflection map introduced by D'Angelo is applied to deduce simpler descriptions of nondegeneracy conditions for sphere maps and to the study of infinitesimal deformations of sphere maps. It is shown that the dimension of the space of infinitesimal deformations of a nondegenerate sphere map is bounded from above by the explicitly computed dimension of the space of infinitesimal deformations of the homogeneous sphere map. Moreover a characterization of the homogeneous sphere map in terms of infinitesimal deformations is provided.

Glycerophosphodiester Phosphodiesterase Identified as Non-Reliable Serological Marker for Borrelia miyamotoi Disease.

The relapsing fever group Borrelia miyamotoi is an emerging tick-borne pathogen. Diagnosis of infection is currently mainly based on serological methods detecting antibodies against B. miyamotoi glycerophosphodiester phosphodiesterase (GlpQ). Here, we scrutinized the reliability of GlpQ as a diagnostic marker and compared the seroprevalence in different study populations and by applying various immunoblotting methods. Antibodies were detected in the sera of 7/53 hunters and in 1/11 sera of Lyme neuroborreliosis patients. Furthermore, 17/74 sera of persons with high concentrations of anti-Borrelia burgdorferi sensu lato (α-Bbsl) antibodies reacted strongly with B. miyamotoi GlpQ in immunoblots. The B. miyamotoi GlpQ seroprevalence was 7/50 in α-Bbsl negative persons. In healthy blood donors from commercial suppliers and from the Austrian Red Cross, seroprevalences were 5/14 and 10/35, respectively. Strikingly, two B. miyamotoi PCR-positive cases from Austria had negative GlpQ serology, indicating poor sensitivity. Finally, when we analyzed sera of dogs, we found α-B. miyamotoi GlpQ antibody seroprevalence in tick-free dogs (n = 10) and in tick-exposed dogs (n = 19) with 2/10 and 8/19, respectively. Thus, our results indicate that GlpQ-based B. miyamotoi serology holds neither specificity nor sensitivity.

Methanol-dependent Escherichia coli strains with a complete ribulose monophosphate cycle.

Methanol is a biotechnologically promising substitute for food and feed substrates since it can be produced renewably from electricity, water and CO2. Although progress has been made towards establishing Escherichia coli as a platform organism for methanol conversion via the energy efficient ribulose monophosphate (RuMP) cycle, engineering strains that rely solely on methanol as a carbon source remains challenging. Here, we apply flux balance analysis to comprehensively identify methanol-dependent strains with high potential for adaptive laboratory evolution. We further investigate two out of 1200 candidate strains, one with a deletion of fructose-1,6-bisphosphatase (fbp) and another with triosephosphate isomerase (tpiA) deleted. In contrast to previous reported methanol-dependent strains, both feature a complete RuMP cycle and incorporate methanol to a high degree, with up to 31 and 99% fractional incorporation into RuMP cycle metabolites. These strains represent ideal starting points for evolution towards a fully methylotrophic lifestyle.

Association of Pain Location With Computed Tomography Abnormalities in Emergeny Department Patients With Abdominal Pain.

BACKGROUND: Over the last decade the usage of computed tomography (CT) imaging has risen dramatically in emergency department (ED) patients with abdominal pain. Recognizing the potential disadvantages of overuse of CT imaging, efforts are being made to reduce imaging. OBJECTIVE: We determined the operating characteristics for location of abdominal pain for the entities of acute appendicitis, diverticulitis, and intestinal obstruction. We hypothesized that patients with pain localized to the upper abdomen would be less likely to have CT abnormalities than those with lower abdominal pain. METHODS: This is a prospective, observational registry of ED patients with abdominal pain, performed in an academic, suburban ED with an annual census of 110,000. Presence of clinically significant CT abnormalities (e.g., appendicitis, diverticulitis, bowel obstruction) were recorded along with clinical variables including laboratory values, vital signs, reported location of pain, location of tenderness on examination, and physician pretest probability. RESULTS: A convenience sample of 1154 patients was enrolled. Of all patients, 273 cases (24%) had abnormal CT results, including appendicitis (n = 95), diverticulitis (n = 133), and bowel obstruction (n = 49). Right upper quadrant pain was negatively associated with abnormal CT (p = 0.02). Clinician gestalt was highly specific, but lacked sensitivity for the diagnosis of appendicitis, diverticulitis, and obstruction. Twenty-four percent of patients diagnosed with appendicitis had no right lower quadrant pain or tenderness, and 7% of patients with diverticulitis had no left lower quadrant pain or tenderness. CONCLUSIONS: Localization of abdominal pain by history or physical examination is not sufficient to accurately diagnose intra-abdominal pathology, especially cases of acute appendicitis, diverticulitis, or intestinal obstruction.

Author Correction: Complete biosynthesis of cannabinoids and their unnatural analogues in yeast.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.