Pathoadaptive mutations of Escherichia coli K1 in experimental neonatal systemic infection

Although Escherichia coli K1 strains are benign commensals in adults, their acquisition at birth by the newborn may result in life-threatening systemic infections, most commonly sepsis and meningitis. Key features of these infections, including stable gastrointestinal (GI) colonization and age-dependent invasion of the bloodstream, can be replicated in the neonatal rat. We previously increased the capacity of a septicemia isolate of E. coli K1 to elicit systemic infection following colonization of the small intestine by serial passage through two-day-old (P2) rat pups. The passaged strain, A192PP (belonging to sequence type 95), induces lethal infection in all pups fed 2–6 x 106 CFU. Here we use whole-genome sequencing to identify mutations responsible for the threefold increase in lethality between the initial clinical isolate and the passaged derivative. Only four single nucleotide polymorphisms (SNPs), in genes (gloB, yjgV, tdcE) or promoters (thrA) involved in metabolic functions, were found: no changes were detected in genes encoding virulence determinants associated with the invasive potential of E. coli K1. The passaged strain differed in carbon source utilization in comparison to the clinical isolate, most notably its inability to metabolize glucose for growth. Deletion of each of the four genes from the E. coli A192PP chromosome altered the proteome, reduced the number of colonizing bacteria in the small intestine and increased the number of P2 survivors. This work indicates that changes in metabolic potential lead to increased colonization of the neonatal GI tract, increasing the potential for translocation across the GI epithelium into the systemic circulation

Twist-2 Controls Myeloid Lineage Development and Function

Basic helix-loop-helix (bHLH) transcription factors play critical roles in lymphoid and erythroid development; however, little is known about their role in myeloid lineage development. In this study, we identify the bHLH transcription factor Twist-2 as a key negative regulator of myeloid lineage development, as manifested by marked increases in mature myeloid populations of macrophages, neutrophils, and basophils in Twist-2–deficient mice. Mechanistic studies demonstrate that Twist-2 inhibits the proliferation as well as differentiation of granulocyte macrophage progenitors (GMP) by interacting with and inhibiting the transcription factors Runx1 and C/EBPα. Moreover, Twist-2 was found to have a contrasting effect on cytokine production: inhibiting the production of proinflammatory cytokines such as interleukin-12 (IL-12) and interferon-γ (IFNγ) while promoting the regulatory cytokine IL-10 by myeloid cells. The data from further analyses suggest that Twist-2 activates the transcription factor c-Maf, leading to IL-10 expression. In addition, Twist-2 was found to be essential for endotoxin tolerance. Thus, this study reveals the critical role of Twist-2 in regulating the development of myeloid lineages, as well as the function and inflammatory responses of mature myeloid cells

Variation in Tropical Reef Symbiont Metagenomes Defined by Secondary Metabolism

The complex evolution of secondary metabolism is important in biology, drug development, and synthetic biology. To examine this problem at a fine scale, we compared the genomes and chemistry of 24 strains of uncultivated cyanobacteria, Prochloron didemni, that live symbiotically with tropical ascidians and that produce natural products isolated from the animals. Although several animal species were obtained along a >5500 km transect of the Pacific Ocean, P. didemni strains are >97% identical across much of their genomes, with only a few exceptions concentrated in secondary metabolism. Secondary metabolic gene clusters were sporadically present or absent in identical genomic locations with no consistent pattern of co-occurrence. Discrete mutations were observed, leading to new chemicals that we isolated from animals. Functional cassettes encoding diverse chemicals are exchanged among a single population of symbiotic P. didemni that spans the tropical Pacific, providing the host animals with a varying arsenal of secondary metabolites

Photochemically-produced SO2_2 in the atmosphere of WASP-39b

Photochemistry is a fundamental process of planetary atmospheres that regulates the atmospheric composition and stability. However, no unambiguous photochemical products have been detected in exoplanet atmospheres to date. Recent observations from the JWST Transiting Exoplanet Early Release Science Program found a spectral absorption feature at 4.05 $\mu$m arising from SO$_2$ in the atmosphere of WASP-39b. WASP-39b is a 1.27-Jupiter-radii, Saturn-mass (0.28 M$_J$) gas giant exoplanet orbiting a Sun-like star with an equilibrium temperature of $\sim$1100 K. The most plausible way of generating SO$_2$ in such an atmosphere is through photochemical processes. Here we show that the SO$_2$ distribution computed by a suite of photochemical models robustly explains the 4.05 $\mu$m spectral feature identified by JWST transmission observations with NIRSpec PRISM (2.7$\sigma$) and G395H (4.5$\sigma$). SO$_2$ is produced by successive oxidation of sulphur radicals freed when hydrogen sulphide (H$_2$S) is destroyed. The sensitivity of the SO$_2$ feature to the enrichment of the atmosphere by heavy elements (metallicity) suggests that it can be used as a tracer of atmospheric properties, with WASP-39b exhibiting an inferred metallicity of $\sim$10$\times$ solar. We further point out that SO$_2$ also shows observable features at ultraviolet and thermal infrared wavelengths not available from the existing observations.Comment: 39 pages, 14 figures, accepted to be published in Natur

Conflicted Emotions Following Trust-based Interaction

We investigated whether 20 emotional states, reported by 170 participants after participating in a Trust game, were experienced in a patterned way predicted by the “Recalibrational Model” or Valence Models. According to the Recalibrational Model, new information about trust-based interaction outcomes triggers specific sets of emotions. Unlike Valence Models that predict reports of large sets of either positive or negative emotional states, the Recalibrational Model predicts the possibility of conflicted (concurrent positive and negative) emotional states. Consistent with the Recalibrational Model, we observed reports of conflicted emotional states activated after interactions where trust was demonstrated but trustworthiness was not. We discuss the implications of having conflicted goals and conflicted emotional states for both scientific and well-being pursuits

Photochemically produced SO2 in the atmosphere of WASP-39b

Photochemistry is a fundamental process of planetary atmospheres that regulates the atmospheric composition and stability1. However, no unambiguous photochemical products have been detected in exoplanet atmospheres so far. Recent observations from the JWST Transiting Exoplanet Community Early Release Science Program2,3 found a spectral absorption feature at 4.05 μm arising from sulfur dioxide (SO2) in the atmosphere of WASP-39b. WASP-39b is a 1.27-Jupiter-radii, Saturn-mass (0.28 MJ) gas giant exoplanet orbiting a Sun-like star with an equilibrium temperature of around 1,100 K (ref. 4). The most plausible way of generating SO2 in such an atmosphere is through photochemical processes5,6. Here we show that the SO2 distribution computed by a suite of photochemical models robustly explains the 4.05-μm spectral feature identified by JWST transmission observations7 with NIRSpec PRISM (2.7σ)8 and G395H (4.5σ)9. SO2 is produced by successive oxidation of sulfur radicals freed when hydrogen sulfide (H2S) is destroyed. The sensitivity of the SO2 feature to the enrichment of the atmosphere by heavy elements (metallicity) suggests that it can be used as a tracer of atmospheric properties, with WASP-39b exhibiting an inferred metallicity of about 10× solar. We further point out that SO2 also shows observable features at ultraviolet and thermal infrared wavelengths not available from the existing observations

Phylogeographical analysis of the dominant multidrug-resistant H58 clade of Salmonella Typhi identifies inter- and intracontinental transmission events.

The emergence of multidrug-resistant (MDR) typhoid is a major global health threat affecting many countries where the disease is endemic. Here whole-genome sequence analysis of 1,832 Salmonella enterica serovar Typhi (S. Typhi) identifies a single dominant MDR lineage, H58, that has emerged and spread throughout Asia and Africa over the last 30 years. Our analysis identifies numerous transmissions of H58, including multiple transfers from Asia to Africa and an ongoing, unrecognized MDR epidemic within Africa itself. Notably, our analysis indicates that H58 lineages are displacing antibiotic-sensitive isolates, transforming the global population structure of this pathogen. H58 isolates can harbor a complex MDR element residing either on transmissible IncHI1 plasmids or within multiple chromosomal integration sites. We also identify new mutations that define the H58 lineage. This phylogeographical analysis provides a framework to facilitate global management of MDR typhoid and is applicable to similar MDR lineages emerging in other bacterial species

The Generation R Study: design and cohort update 2010

The Generation R Study is a population-based prospective cohort study from fetal life until young adulthood. The study is designed to identify early environmental and genetic causes of normal and abnormal growth, development and health during fetal life, childhood and adulthood. The study focuses on four primary areas of research: (1) growth and physical development; (2) behavioural and cognitive development; (3) diseases in childhood; and (4) health and healthcare for pregnant women and children. In total, 9,778 mothers with a delivery date from April 2002 until January 2006 were enrolled in the study. General follow-up rates until the age of 4 years exceed 75%. Data collection in mothers, fathers and preschool children included questionnaires, detailed physical and ultrasound examinations, behavioural observations, and biological samples. A genome wide association screen is available in the participating children. Regular detailed hands on assessment are performed from the age of 5 years onwards. Eventually, results forthcoming from the Generation R Study have to contribute to the development of strategies for optimizing health and healthcare for pregnant women and children

Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking

The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry techniques are well-suited to high-throughput characterization of natural products, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social molecular networking (GNPS, http://gnps.ucsd.edu), an open-access knowledge base for community wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of ‘living data’ through continuous reanalysis of deposited data

A deep dive into understanding tumor foci classification using multiparametric MRI based on convolutional neural network

PURPOSE: Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying deep learning models directly on prostate multiparametric MRI (mpMRI). Although model interpretation has been heavily studied for natural images for the past few years, there has been a lack of interpretation of deep learning models trained on medical images. In this paper, an efficient convolutional neural network (CNN) was developed and the model interpretation at various convolutional layers was systematically analyzed to improve the understanding of how CNN interprets multimodality medical images and the predictive powers of features at each layer. The problem of small sample size was addressed by feeding the intermediate features into a traditional classification algorithm known as weighted extreme learning machine (wELM), with imbalanced distribution among output categories taken into consideration. METHODS: The training data collection used a retrospective set of prostate MR studies, from SPIE-AAPM-NCI PROSTATEx Challenges held in 2017. Three hundred twenty biopsy samples of lesions from 201 prostate cancer patients were diagnosed and identified as clinically significant (malignant) or not significant (benign). All studies included T2-weighted (T2W), proton density-weighted (PD-W), dynamic contrast enhanced (DCE) and diffusion-weighted (DW) imaging. After registration and lesion-based normalization, a CNN with four convolutional layers were developed and trained on tenfold cross validation. The features from intermediate layers were then extracted as input to wELM to test the discriminative power of each individual layer. The best performing model from the tenfolds was chosen to be tested on the holdout cohort from two sources. Feature maps after each convolutional layer were then visualized to monitor the trend, as the layer propagated. Scatter plotting was used to visualize the transformation of data distribution. Finally, a class activation map was generated to highlight the region of interest based on the model perspective. RESULTS: Experimental trials indicated that the best input for CNN was a modality combination of T2W, apparent diffusion coefficient (ADC) and DWIb50. The convolutional features from CNN paired with a weighted extreme learning classifier showed substantial performance compared to a CNN end-to-end training model. The feature map visualization reveals similar findings on natural images where lower layers tend to learn lower level features such as edges, intensity changes, etc, while higher layers learn more abstract and task-related concept such as the lesion region. The generated saliency map revealed that the model was able to focus on the region of interest where the lesion resided and filter out background information, including prostate boundary, rectum, etc. CONCLUSIONS: This work designs a customized workflow for the small and imbalanced data set of prostate mpMRI where features were extracted from a deep learning model and then analyzed by a traditional machine learning classifier. In addition, this work contributes to revealing how deep learning models interpret mpMRI for prostate cancer patients stratification