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Chemoprotection of haematopoietic stem cells (HSCs) by gene therapeutic transfer of drug-resistance genes represents the encouraging approach to prevent myelosuppression, which is one of the most severe side effects in tumor therapy. Thus, we cloned and evaluated six different bicistronic lentiviral SIN vectors encoding two transgenes, MGMTP140K (an O6-benzylguanine-resistant mutant of methylguanine-DNA methyltransferase) and MDR1 (multidrug resistance 1), using various linker sequences (IRESEMCV, IRESFMDV and 2A-element of FMDV (F2A)). Expression of both transgenes in HL-60 and in K562 cells was assayed by quantitative real-time PCR. Combination therapy with ACNU plus paclitaxel in HL-60 cells and with carmustin (BCNU) plus doxorubicin in K562 cells resulted in the most significant survival advantage of cells transduced with the lentiviral vector HR'SIN-MGMTP140K-F2A-MDR1 compared with untransduced cells. In human HSCs, overexpression of both transgenes by this vector also caused significantly increased survival and enrichment of transduced cells after treatment with BCNU plus doxorubicin or temozolomide plus paclitaxel. In summary, we could show significant chemoprotection by overexpression of MDR1 and MGMTP140K with a lentiviral vector using the F2A linker element in two different haematopoietic cell lines and in human primary HSCs with various combination regimens. Consequently, we are convinced that these in vitro investigations will help to improve combination chemotherapy regimens by reducing myelotoxic side effects and increasing the therapeutic efficiency. [ABSTRACT FROM AUTHOR]

Effects on the growth and reproduction of birds are important endpoints in the environmental risk assessment (ERA) of pesticides. Toxicokinetic-toxicodynamic models based on dynamic energy budget theory (DEB) are promising tools to predict these effects mechanistically and make extrapolations relevant to ERA. However, before DEB-TKTD models are accepted as part of ERA for birds, ecotoxicological case studies are required so that stakeholders can assess their capabilities. We present such a case-study, modelling the effects of the fluopyram metabolite benzamide on the northern bobwhite quail (Colinus virginianus). We parametrised a DEB-TKTD model for the embryo stage on the basis of an egg injection study, designed to provide data for model development. We found that information on various endpoints, such as survival, growth, and yolk utilisation were needed to clearly distinguish between the performance of model variants with different TKTD assumptions. The calibration data were best explained when it was assumed that chemical uptake occurs via the yolk and that benzamide places stress on energy assimilation and mobilisation. To be able to bridge from the in vitro tests to real-life exposure, we developed a physiologically-based toxicokinetic (PBK) model for the quail and used it to predict benzamide exposure inside the eggs based on dietary exposure in a standard reproductive toxicity study. We then combined the standard DEB model with the TKTD module calibrated to the egg injection studies and used it to predict effects on hatchling and 14-day chick weight based on the exposure predicted by the PBK model. Observed weight reductions, relative to controls, were accurately predicted. Thus, we demonstrate that DEB-TKTD models, in combination with suitable experimental data and, if necessary, with an exposure model, can be used in bird ERA to predict chemical effects on reproduction., Competing Interests: Declaration of competing interest A. Gergs, M. Ebeling and D. Heckmann are employees of Bayer AG. The work of the other co-authors on the project was funded by Bayer AG. Bayer AG is a manufacturer of the active substance fluopyram investigated in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Introduction: Although the improving effect of nitric oxide (NO) donors has experimentally been demonstrated in shock, there are still no NO donor medications clinically available. Thiol-nitrosothiol-hydroxyethyl starch (S-NO-HES) is a novel molecule consisting of NO coupled to a thiolated derivative of hydroxyethyl starch (HES). It was aimed to assess the ability of S-NO-HES to serve as an NO donor under a variety of in vitro simulated physiologic conditions, which might be the first step to qualify this molecule as a novel type of NO donor-fluid., Methods: We studied the effect of temperature on NO-releasing properties of S-NO-HES in blood, at 34°C, 37°C, and 41°C. Ascorbic acid (Asc) and amylase were also tested in a medium environment. In addition, we evaluated the activity of S-NO-HES in the isolated aortic ring and Langendorff-perfused heart setup., Results: The NO release property of S-NO-HES was found at any temperature. Asc led to a significant increase in the production of NO compared to S-NO-HES incubation (P < 0.05). The addition of amylase together with Asc to the medium further increased the release of NO (P < 0.05). S-NO-HES exerted significant vasodilatory effects on phenylephrine precontracted aortic rings that were dose-dependent (P < 0.01). Furthermore, S-NO-HES significantly increased the heart rate and additionally reduced the duration of the cardiac action potential, as indicated by a reduction of QTc-B values (P < 0.01)., Conclusions: We demonstrated for the first time that the S-NO-HES molecule exhibited its NO-releasing effects. The effectiveness of this new NO donor to substitute NO deficiency under septic conditions or in other indications needs to be studied., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Physiologically-based kinetic (PBK) models are effective tools for designing toxicological studies and conducting extrapolations to inform hazard characterization in risk assessment by filling data gaps and defining safe levels of chemicals. In the present work, a generic avian PBK model for male and female birds was developed using PK-Sim and MoBi from the Open Systems Pharmacology Suite (OSPS). The PBK model includes an ovulation model (egg development) to predict concentrations of chemicals in eggs from dietary exposure. The model was parametrized for chicken (Gallus gallus), bobwhite quail (Colinus virginianus) and mallard duck (Anas platyrhynchos) and was tested with nine chemicals for which in vivo studies were available. Time-concentration profiles of chemicals reaching tissues and egg compartment were simulated and compared to in vivo data. The overall accuracy of the PBK model predictions across the analyzed chemicals was good. Model simulations were found to be in the range of 22-79% within a 3-fold and 41-89% were within 10- fold deviation of the in vivo observed data. However, for some compounds scarcity of in-vivo data and inconsistencies between published studies allowed only a limited goodness of fit evaluation. The generic avian PBK model was developed following a "best practice" workflow describing how to build a PBK model for novel species. The credibility and reproducibility of the avian PBK models were scored by evaluation according to the available guidance documents from WHO (2010), and OECD (2021), to increase applicability, confidence and acceptance of these in silico models in chemical risk assessment., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:The project was conducted by Bayer Crop Science division. Some of the authors are employed at Bayer or were paid for their work by Bayer. Bayer produce and sell pharmaceuticals and agrochemicals. All the authors have the interest to get PBPK/PBK model accepted for regulatory purposes., (Copyright © 2022. Published by Elsevier Ltd.)

Birds build up their reproductive system and undergo major tissue remodeling for each reproductive season. Energetic specifics of this process are still not completely clear, despite the increasing interest. We focused on the bobwhite quail - one of the most intensely studied species due to commercial and conservation interest - to elucidate the energy fluxes associated with reproduction, including the fate of the extra assimilates ingested prior to and during reproduction. We used the standard Dynamic Energy Budget model, which is a mechanistic process-based model capable of fully specifying and predicting the life cycle of the bobwhite quail: its growth, maturation and reproduction. We expanded the standard model with an explicit egg-laying module and formulated and tested two hypotheses for energy allocation of extra assimilates associated with reproduction: Hypothesis 1, that the energy and nutrients are used directly for egg production; and Hypothesis 2, that the energy is mostly spent fueling the increased metabolic costs incurred by building up and maintaining the reproductive system and, subsequently, by egg-laying itself. Our results suggest that Hypothesis 2 is the more likely energy pathway. Model predictions capture well the whole ontogeny of a generalized northern bobwhite quail and are able to reproduce most of the data variability via variability in (i) egg size, (ii) egg-laying rate and (iii) inter-individual physiological variability modeled via the zoom factor, i.e. assimilation potential. Reliable models with a capacity to predict physiological responses of individuals are relevant not only for experimental setups studying effects of various natural and anthropogenic pressures on the quail as a bird model organism, but also for wild quail management and conservation. The model is, with minor modifications, applicable to other species of interest, making it a most valuable tool in the emerging field of conservation physiology., (© The Author(s) 2022. Published by Oxford University Press and the Society for Experimental Biology.)

Background: The COVID-19 pandemic is a threat to global health and requires collaborative health research efforts across organizations and countries to address it. Although routinely collected digital health data are a valuable source of information for researchers, benefiting from these data requires accessing and sharing the data. Health care organizations focusing on individual risk minimization threaten to undermine COVID-19 research efforts, and it has been argued that there is an ethical obligation to use the European Union's General Data Protection Regulation (GDPR) scientific research exemption during the COVID-19 pandemic to support collaborative health research., Objective: This study aims to explore the practices and attitudes of stakeholders in the German federal state of Bavaria regarding the secondary use of health data for research purposes during the COVID-19 pandemic, with a specific focus on the GDPR scientific research exemption., Methods: Individual semistructured qualitative interviews were conducted between December 2020 and January 2021 with a purposive sample of 17 stakeholders from 3 different groups in Bavaria: researchers involved in COVID-19 research (n=5, 29%), data protection officers (n=6, 35%), and research ethics committee representatives (n=6, 35%). The transcripts were analyzed using conventional content analysis., Results: Participants identified systemic challenges in conducting collaborative secondary-use health data research in Bavaria; secondary health data research generally only happens when patient consent has been obtained, or the data have been fully anonymized. The GDPR research exemption has not played a significant role during the pandemic and is currently seldom and restrictively used. Participants identified 3 key groups of barriers that led to difficulties: the wider ecosystem at many Bavarian health care organizations, legal uncertainty that leads to risk-adverse approaches, and ethical positions that patient consent ought to be obtained whenever possible to respect patient autonomy. To improve health data research in Bavaria and across Germany, participants wanted greater legal certainty regarding the use of pseudonymized data for research purposes without the patient's consent., Conclusions: The current balance between enabling the positive goals of health data research and avoiding associated data protection risks is heavily skewed toward avoiding risks; so much so that it makes reaching the goals of health data research extremely difficult. This is important, as it is widely recognized that there is an ethical imperative to use health data to improve care. The current approach also creates a problematic conflict with the ambitions of Germany, and the federal state of Bavaria, to be a leader in artificial intelligence. A recent development in the field of German public administration known as norm screening (Normenscreening) could potentially provide a systematic approach to minimize legal barriers. This approach would likely be beneficial to other countries., (©Stuart McLennan, Sarah Rachut, Johannes Lange, Amelia Fiske, Dirk Heckmann, Alena Buyx. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.06.2022.)

Physiologically based kinetic (PBK) models are a promising tool for xenobiotic environmental risk assessment that could reduce animal testing by predicting in vivo exposure. PBK models for birds could further our understanding of species-specific sensitivities to xenobiotics, but would require species-specific parameterization. To this end, we summarize multiple major morphometric and physiological characteristics in chickens, particularly laying hens ( Gallus gallus ) and mallards ( Anas platyrhynchos ) in a meta-analysis of published data. Where such data did not exist, data are substituted from domesticated ducks ( Anas platyrhynchos ) and, in their absence, from chickens. The distribution of water between intracellular, extracellular, and plasma is similar in laying hens and mallards. Similarly, the lengths of the components of the small intestine (duodenum, jejunum, and ileum) are similar in chickens and mallards. Moreover, not only are the gastrointestinal absorptive areas similar in mallard and chickens but also they are similar to those in mammals when expressed on a log basis and compared to log body weight. In contrast, the following are much lower in laying hens than mallards: cardiac output (CO), hematocrit (Hct), and blood hemoglobin. There are shifts in ovary weight (increased), oviduct weight (increased), and plasma/serum concentrations of vitellogenin and triglyceride between laying hens and sexually immature females. In contrast, reproductive state does not affect the relative weights of the liver, kidneys, spleen, and gizzard., Competing Interests: JW, ME, AB, TP, and DH were employed by the company Bayer AG. SS and VB were employed by the company esqLABS GmbH. CS, his company Scanes Technology and Research and esqLABS GmbH received funding from Bayer AG., (Copyright © 2022 Scanes, Witt, Ebeling, Schaller, Baier, Bone, Preuss and Heckmann.)

Physiologically based kinetic (PBK) models facilitate chemical risk assessment by predicting in vivo exposure while reducing the need for animal testing. PBK models for mammals have seen significant progress, which has yet to be achieved for avian systems. Here, we quantitatively compare physiological, metabolic and anatomical characteristics between birds and mammals, with the aim of facilitating bird PBK model development. For some characteristics, there is considerable complementarity between avian and mammalian species with identical values for the following: blood hemoglobin and hemoglobin concentrations per unit erythrocyte volume together with relative weights of the liver, heart, and lungs. There are also systematic differences for some major characteristics between avian and mammalian species including erythrocyte volume, plasma concentrations of albumin, total protein and triglyceride together with liver cell size and relative weights of the kidney, spleen, and ovary. There are also major differences between characteristics between sexually mature and sexually immature female birds. For example, the relative weights of the ovary and oviduct are greater in sexually mature females compared to immature birds as are the plasma concentrations of triglyceride and vitellogenin. Both these sets of differences reflect the genetic "blue print" inherited from ancestral archosaurs such as the production of large eggs with yolk filled oocytes surrounded by egg white proteins, membranes and a calciferous shell together with adaptions for flight in birds or ancestrally in flightless birds., Competing Interests: JW, ME, AB, TP, and DH were employed by the company Bayer AG. SS and VB were employed by the company esqLABS GmbH. CS declares that his company Scanes Technology and Research and esqLABS GmbH received funding from Bayer AG. The funder (in the person of the Bayer authors) had the following involvement with the study: contributed to conceiving the study, revising the manuscript, and critiquing the analysis., (Copyright © 2022 Scanes, Witt, Ebeling, Schaller, Baier, Bone, Preuss and Heckmann.)

The Michaelis constant KM describes the affinity of an enzyme for a specific substrate and is a central parameter in studies of enzyme kinetics and cellular physiology. As measurements of KM are often difficult and time-consuming, experimental estimates exist for only a minority of enzyme-substrate combinations even in model organisms. Here, we build and train an organism-independent model that successfully predicts KM values for natural enzyme-substrate combinations using machine and deep learning methods. Predictions are based on a task-specific molecular fingerprint of the substrate, generated using a graph neural network, and on a deep numerical representation of the enzyme's amino acid sequence. We provide genome-scale KM predictions for 47 model organisms, which can be used to approximately relate metabolite concentrations to cellular physiology and to aid in the parameterization of kinetic models of cellular metabolism., Competing Interests: The authors have declared that no competing interests exist.

The regulation of resource allocation in biological systems observed today is the cumulative result of natural selection in ancestral and recent environments. To what extent are observed resource allocation patterns in different photosynthetic types optimally adapted to current conditions, and to what extent do they reflect ancestral environments? Here, we explore these questions for C 3 , C 4 , and C 3 -C 4 intermediate plants of the model genus Flaveria. We developed a detailed mathematical model of carbon fixation, which accounts for various environmental parameters and for energy and nitrogen partitioning across photosynthetic components. This allows us to assess environment-dependent plant physiology and performance as a function of resource allocation patterns. Models of C 4 plants optimized for conditions experienced by evolutionary ancestors perform better than models accounting for experimental growth conditions, indicating low phenotypic plasticity. Supporting this interpretation, the model predicts that C 4 species need to re-allocate more nitrogen between photosynthetic components than C 3 species to adapt to new environments. We thus hypothesize that observed resource distribution patterns in C 4 plants still reflect optimality in ancestral environments, allowing the quantitative inference of these environments from today's plants. Our work allows us to quantify environmental effects on photosynthetic resource allocation and performance in the light of evolutionary history., (© 2021. The Author(s).)

The availability of bacterial transcriptomes has dramatically increased in recent years. This data deluge could result in detailed inference of underlying regulatory networks, but the diversity of experimental platforms and protocols introduces critical biases that could hinder scalable analysis of existing data. Here, we show that the underlying structure of the E. coli transcriptome, as determined by Independent Component Analysis (ICA), is conserved across multiple independent datasets, including both RNA-seq and microarray datasets. We subsequently combined five transcriptomics datasets into a large compendium containing over 800 expression profiles and discovered that its underlying ICA-based structure was still comparable to that of the individual datasets. With this understanding, we expanded our analysis to over 3,000 E. coli expression profiles and predicted three high-impact regulons that respond to oxidative stress, anaerobiosis, and antibiotic treatment. ICA thus enables deep analysis of disparate data to uncover new insights that were not visible in the individual datasets., Competing Interests: The authors have declared that no competing interests exist.

Numerous noncoding transcripts have been reported to correlate with cancer development and progression. Nevertheless, there remains a paucity of long noncoding RNAs (lncRNA) with well-elucidated functional roles. Here, we leverage the International Cancer Genome Consortium-Early Onset Prostate Cancer transcriptome and identify the previously uncharacterized lncRNA LINC00920 to be upregulated in prostate tumors. Phenotypic characterization of LINC00920 revealed its positive impact on cellular proliferation, colony formation, and migration. We demonstrate that LINC00920 transcription is directly activated by ERG, an oncogenic transcription factor overexpressed in 50% of prostate cancers. Chromatin isolation by RNA purification-mass spectrometry revealed the interaction of LINC00920 with the 14-3-3ε protein, leading to enhanced sequestration of tumor suppressive FOXO1. Altogether, our results provide a rationale on how ERG overexpression, partly by driving LINC00920 transcription, could confer survival advantage to prostate cancer cells and potentially prime PTEN-intact prostate cells for cellular transformation through FOXO inactivation. IMPLICATIONS: The study describes a novel lncRNA-mediated mechanism of regulating the FOXO signaling pathway and provides additional insight into the role of ERG in prostate cancer cells., (©2020 American Association for Cancer Research.)

Enzyme turnover numbers ( k cat s) are essential for a quantitative understanding of cells. Because k cat s are traditionally measured in low-throughput assays, they can be inconsistent, labor-intensive to obtain, and can miss in vivo effects. We use a data-driven approach to estimate in vivo k cat s using metabolic specialist Escherichia coli strains that resulted from gene knockouts in central metabolism followed by metabolic optimization via laboratory evolution. By combining absolute proteomics with fluxomics data, we find that in vivo k cat s are robust against genetic perturbations, suggesting that metabolic adaptation to gene loss is mostly achieved through other mechanisms, like gene-regulatory changes. Combining machine learning and genome-scale metabolic models, we show that the obtained in vivo k cat s predict unseen proteomics data with much higher precision than in vitro k cat s. The results demonstrate that in vivo k cat s can solve the problem of inconsistent and low-coverage parameterizations of genome-scale cellular models., Competing Interests: The authors declare no competing interest., (Copyright © 2020 the Author(s). Published by PNAS.)

Background: Adaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover mutations that confer phenotypic functions of interest. However, the task of finding and understanding all beneficial mutations of an ALE experiment remains an open challenge for the field. To provide for better results than traditional methods of ALE mutation analysis, this work applied enrichment methods to mutations described by a multiscale annotation framework and a consolidated set of ALE experiment conditions. A total of 25,321 unique genome annotations from various sources were leveraged to describe multiple scales of mutated features in a set of 35 Escherichia coli based ALE experiments. These experiments totalled 208 independent evolutions and 2641 mutations. Additionally, mutated features were statistically associated across a total of 43 unique experimental conditions to aid in deconvoluting mutation selection pressures., Results: Identifying potentially beneficial, or key, mutations was enhanced by seeking coding and non-coding genome features significantly enriched by mutations across multiple ALE replicates and scales of genome annotations. The median proportion of ALE experiment key mutations increased from 62%, with only small coding and non-coding features, to 71% with larger aggregate features. Understanding key mutations was enhanced by considering the functions of broader annotation types and the significantly associated conditions for key mutated features. The approaches developed here were used to find and characterize novel key mutations in two ALE experiments: one previously unpublished with Escherichia coli grown on glycerol as a carbon source and one previously published with Escherichia coli tolerized to high concentrations of L-serine., Conclusions: The emergent adaptive strategies represented by sets of ALE mutations became more clear upon observing the aggregation of mutated features across small to large scale genome annotations. The clarification of mutation selection pressures among the many experimental conditions also helped bring these strategies to light. This work demonstrates how multiscale genome annotation frameworks and data-driven methods can help better characterize ALE mutations, and thus help elucidate the genotype-to-phenotype relationship of the studied organism.

Current machine learning classifiers have successfully been applied to whole-genome sequencing data to identify genetic determinants of antimicrobial resistance (AMR), but they lack causal interpretation. Here we present a metabolic model-based machine learning classifier, named Metabolic Allele Classifier (MAC), that uses flux balance analysis to estimate the biochemical effects of alleles. We apply the MAC to a dataset of 1595 drug-tested Mycobacterium tuberculosis strains and show that MACs predict AMR phenotypes with accuracy on par with mechanism-agnostic machine learning models (isoniazid AUC = 0.93) while enabling a biochemical interpretation of the genotype-phenotype map. Interpretation of MACs for three antibiotics (pyrazinamide, para-aminosalicylic acid, and isoniazid) recapitulates known AMR mechanisms and suggest a biochemical basis for how the identified alleles cause AMR. Extending flux balance analysis to identify accurate sequence classifiers thus contributes mechanistic insights to GWAS, a field thus far dominated by mechanism-agnostic results.

Background: The reconstruction of metabolic networks and the three-dimensional coverage of protein structures have reached the genome-scale in the widely studied Escherichia coli K-12 MG1655 strain. The combination of the two leads to the formation of a structural systems biology framework, which we have used to analyze differences between the reactive oxygen species (ROS) sensitivity of the proteomes of sequenced strains of E. coli. As proteins are one of the main targets of oxidative damage, understanding how the genetic changes of different strains of a species relates to its oxidative environment can reveal hypotheses as to why these variations arise and suggest directions of future experimental work., Results: Creating a reference structural proteome for E. coli allows us to comprehensively map genetic changes in 1764 different strains to their locations on 4118 3D protein structures. We use metabolic modeling to predict basal ROS production levels (ROStype) for 695 of these strains, finding that strains with both higher and lower basal levels tend to enrich their proteomes with antioxidative properties, and speculate as to why that is. We computationally assess a strain's sensitivity to an oxidative environment, based on known chemical mechanisms of oxidative damage to protein groups, defined by their localization and functionality. Two general groups - metalloproteins and periplasmic proteins - show enrichment of their antioxidative properties between the 695 strains with a predicted ROStype as well as 116 strains with an assigned pathotype. Specifically, proteins that a) utilize a molybdenum ion as a cofactor and b) are involved in the biogenesis of fimbriae show intriguing protective properties to resist oxidative damage. Overall, these findings indicate that a strain's sensitivity to oxidative damage can be elucidated from the structural proteome, though future experimental work is needed to validate our model assumptions and findings., Conclusion: We thus demonstrate that structural systems biology enables a proteome-wide, computational assessment of changes to atomic-level physicochemical properties and of oxidative damage mechanisms for multiple strains in a species. This integrative approach opens new avenues to study adaptation to a particular environment based on physiological properties predicted from sequence alone.

Cryptococcosis is a fungal infection of the central nervous system predominantly caused by Cryptococcus neoformans in immunocompromised patients. In several countries worldwide, up to 50% of isolates show in vitro resistance to clinically used antifungals including fluconazole. No prospective data on susceptibility to antifungal drugs are available for Germany. In this study, we characterised all C. neoformans isolates collected from individual patients' samples at the German reference laboratory for cryptococcosis 2011 and 2017 (n = 133) by multi-locus sequence typing and phenotypic drug susceptibility testing. We identified serotype A/genotype VNI isolates belonging to clonal complexes previously described from Europe, Africa, Asia and South America as the most prevalent agents of cryptococcosis in Germany. Overall, we observed minimal inhibitory concentrations (MICs) above the epidemiological cut-offs (ECVs) in 1.6% of isolates regarding fluconazole and 2.3% of isolates regarding 5-flucytosine. Here, two C. neoformans var. grubii isolates displayed decreased drug susceptibility to fluconazole, one of them additionally to 5-flucytosine. We also found 5-flucytosine MICs above the ECV for two C. neoformans var. neoformans isolates. We identified a novel mutation in the ERG11 gene which might be associated with the elevated fluconazole MIC in one of the isolates. The clinical importance of the detected in vitro resistance is documented by patient histories showing relapsed infection or primary fatal disease. Of note, sertraline demonstrated antifungal activity comparable to previous reports. Systematic collection of susceptibility data in combination with molecular typing of C. neoformans is important to comprehensively assess the spread of isolates and to understand their drug resistance patterns., (Copyright © 2019 Elsevier GmbH. All rights reserved.)

Catalysis using iron-sulfur clusters and transition metals can be traced back to the last universal common ancestor. The damage to metalloproteins caused by reactive oxygen species (ROS) can prevent cell growth and survival when unmanaged, thus eliciting an essential stress response that is universal and fundamental in biology. Here we develop a computable multiscale description of the ROS stress response in Escherichia coli , called OxidizeME. We use OxidizeME to explain four key responses to oxidative stress: 1) ROS-induced auxotrophy for branched-chain, aromatic, and sulfurous amino acids; 2) nutrient-dependent sensitivity of growth rate to ROS; 3) ROS-specific differential gene expression separate from global growth-associated differential expression; and 4) coordinated expression of iron-sulfur cluster (ISC) and sulfur assimilation (SUF) systems for iron-sulfur cluster biosynthesis. These results show that we can now develop fundamental and quantitative genotype-phenotype relationships for stress responses on a genome-wide basis., Competing Interests: The authors declare no conflict of interest.

Plasmodium falciparum causes the severe form of malaria that has high levels of mortality in humans. Blood-stage merozoites of P. falciparum invade erythrocytes, and this requires interactions between multiple ligands from the parasite and receptors in hosts. These interactions include the binding of the Rh5-CyRPA-Ripr complex with the erythrocyte receptor basigin 1,2 , which is an essential step for entry into human erythrocytes. Here we show that the Rh5-CyRPA-Ripr complex binds the erythrocyte cell line JK-1 significantly better than does Rh5 alone, and that this binding occurs through the insertion of Rh5 and Ripr into host membranes as a complex with high molecular weight. We report a cryo-electron microscopy structure of the Rh5-CyRPA-Ripr complex at subnanometre resolution, which reveals the organization of this essential invasion complex and the mode of interactions between members of the complex, and shows that CyRPA is a critical mediator of complex assembly. Our structure identifies blades 4-6 of the β-propeller of CyRPA as contact sites for Rh5 and Ripr. The limited contacts between Rh5-CyRPA and CyRPA-Ripr are consistent with the dissociation of Rh5 and Ripr from CyRPA for membrane insertion. A comparision of the crystal structure of Rh5-basigin with the cryo-electron microscopy structure of Rh5-CyRPA-Ripr suggests that Rh5 and Ripr are positioned parallel to the erythrocyte membrane before membrane insertion. This provides information on the function of this complex, and thereby provides insights into invasion by P. falciparum.

Identifying the earliest somatic changes in prostate cancer can give important insights into tumor evolution and aids in stratifying high- from low-risk disease. We integrated whole genome, transcriptome and methylome analysis of early-onset prostate cancers (diagnosis ≤55 years). Characterization across 292 prostate cancer genomes revealed age-related genomic alterations and a clock-like enzymatic-driven mutational process contributing to the earliest mutations in prostate cancer patients. Our integrative analysis identified four molecular subgroups, including a particularly aggressive subgroup with recurrent duplications associated with increased expression of ESRP1, which we validate in 12,000 tissue microarray tumors. Finally, we combined the patterns of molecular co-occurrence and risk-based subgroup information to deconvolve the molecular and clinical trajectories of prostate cancer from single patient samples., (Copyright © 2018 Elsevier Inc. All rights reserved.)