Something Old, Something New, Something Borrowed; How the Thermoacidophilic Archaeon Sulfolobus solfataricus Responds to Oxidative Stress

To avoid molecular damage of biomolecules due to oxidation, all cells have evolved constitutive and responsive systems to mitigate and repair chemical modifications. Archaea have adapted to some of the most extreme environments known to support life, including highly oxidizing conditions. However, in comparison to bacteria and eukaryotes, relatively little is known about the biology and biochemistry of archaea in response to changing conditions and repair of oxidative damage. In this study transcriptome, proteome, and chemical reactivity analyses of hydrogen peroxide (H2O2) induced oxidative stress in Sulfolobus solfataricus (P2) were conducted. Microarray analysis of mRNA expression showed that 102 transcripts were regulated by at least 1.5 fold, 30 minutes after exposure to 30 µM H2O2. Parallel proteomic analyses using two-dimensional differential gel electrophoresis (2D-DIGE), monitored more than 800 proteins 30 and 105 minutes after exposure and found that 18 had significant changes in abundance. A recently characterized ferritin-like antioxidant protein, DPSL, was the most highly regulated species of mRNA and protein, in addition to being post-translationally modified. As expected, a number of antioxidant related mRNAs and proteins were differentially regulated. Three of these, DPSL, superoxide dismutase, and peroxiredoxin were shown to interact and likely form a novel supramolecular complex for mitigating oxidative damage. A scheme for the ability of this complex to perform multi-step reactions is presented. Despite the central role played by DPSL, cells maintained a lower level of protection after disruption of the dpsl gene, indicating a level of redundancy in the oxidative stress pathways of S. solfataricus. This work provides the first “omics” scale assessment of the oxidative stress response for an archeal organism and together with a network analysis using data from previous studies on bacteria and eukaryotes reveals evolutionarily conserved pathways where complex and overlapping defense mechanisms protect against oxygen toxicity

Predicting acute ovarian failure in female survivors of childhood cancer: a cohort study in the Childhood Cancer Survivor Study (CCSS) and the St Jude Lifetime Cohort (SJLIFE).

BACKGROUND: Cancer treatment can cause gonadal impairment. Acute ovarian failure is defined as the permanent loss of ovarian function within 5 years of cancer diagnosis. We aimed to develop and validate risk prediction tools to provide accurate clinical guidance for paediatric patients with cancer. METHODS: In this cohort study, prediction models of acute ovarian failure risk were developed using eligible female US and Canadian participants in the Childhood Cancer Survivor Study (CCSS) cohort and validated in the St Jude Lifetime Cohort (SJLIFE) Study. 5-year survivors from the CCSS cohort were included if they were at least 18 years old at their most recent follow-up and had complete treatment exposure and adequate menstrual history (including age at menarche, current menstrual status, age at last menstruation, and menopausal aetiology) information available. Participants in the SJLIFE cohort were at least 10-year survivors. Participants were excluded from the prediction analysis if they had an ovarian hormone deficiency, had missing exposure information, or had indeterminate ovarian status. The outcome of acute ovarian failure was defined as permanent loss of ovarian function within 5 years of cancer diagnosis or no menarche after cancer treatment by the age of 18 years. Logistic regression, random forest, and support vector machines were used as candidate methods to develop the risk prediction models in the CCSS cohort. Prediction performance was evaluated internally (in the CCSS cohort) and externally (in the SJLIFE cohort) using the areas under the receiver operating characteristic curve (AUC) and the precision-recall curve (average precision [AP; average positive predictive value]). FINDINGS: Data from the CCSS cohort were collected for participants followed up between Nov 3, 1992, and Nov 25, 2016, and from the SJLIFE cohort for participants followed up between Oct 17, 2007, and April 16, 2012. Of 11 336 female CCSS participants, 5886 (51·9%) met all inclusion criteria for analysis. 1644 participants were identified from the SJLIFE cohort, of whom 875 (53·2%) were eligible for analysis. 353 (6·0%) of analysed CCSS participants and 50 (5·7%) of analysed SJLIFE participants had acute ovarian failure. The overall median follow-up for the CCSS cohort was 23·9 years (IQR 20·4-27·9), and for SJLIFE it was 23·9 years (19·0-30·0). The three candidate methods (logistic regression, random forest, and support vector machines) yielded similar results, and a prescribed dose model with abdominal and pelvic radiation doses and an ovarian dose model with ovarian radiation dosimetry using logistic regression were selected. Common predictors in both models were history of haematopoietic stem-cell transplantation, cumulative alkylating drug dose, and an interaction between age at cancer diagnosis and haematopoietic stem-cell transplant. External validation of the model in the SJLIFE cohort produced an estimated AUC of 0·94 (95% CI 0·90-0·98) and AP of 0·68 (95% CI 0·53-0·81) for the ovarian dose model, and AUC of 0·96 (0·94-0·97) and AP of 0·46 (0·34-0·61) for the prescribed dose model. Based on these models, an online risk calculator has been developed for clinical use. INTERPRETATION: Both acute ovarian failure risk prediction models performed well. The ovarian dose model is preferred if ovarian radiation dosimetry is available. The models, along with the online risk calculator, could help clinical discussions regarding the need for fertility preservation interventions in girls and young women newly diagnosed with cancer

Lineage Abundance Estimation for SARS-CoV-2 in Wastewater Using Transcriptome Quantification Techniques

Effectively monitoring the spread of SARS-CoV-2 mutants is essential to efforts to counter the ongoing pandemic. Predicting lineage abundance from wastewater, however, is technically challenging. We show that by sequencing SARS-CoV-2 RNA in wastewater and applying algorithms initially used for transcriptome quantification, we can estimate lineage abundance in wastewater samples. We find high variability in signal among individual samples, but the overall trends match those observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in mutant prevalence in situations where clinical sequencing is unavailable

The historical origins of corruption in the developing world: a comparative analysis of East Asia

A new approach has emerged in the literature on corruption in the developing world that breaks with the assumption that corruption is driven by individualistic self-interest and, instead, conceptualizes corruption as an informal system of norms and practices. While this emerging neo-institutionalist approach has done much to further our understanding of corruption in the developing world, one key question has received relatively little attention: how do we explain differences in the institutionalization of corruption between developing countries? The paper here addresses this question through a systematic comparison of seven developing and newly industrialized countries in East Asia. The argument that emerges through this analysis is that historical sequencing mattered: countries in which the "political marketplace" had gone through a process of concentration before universal suffrage was introduced are now marked by less harmful types of corruption than countries where mass voting rights where rolled out in a context of fragmented political marketplaces. The paper concludes by demonstrating that this argument can be generalized to the developing world as a whole