The RavA-ViaA Chaperone-Like System Interacts with and Modulates the Activity of the Fumarate Reductase Respiratory Complex

Regulatory ATPase variant A (RavA) is a MoxR AAA + protein that functions together with a partner protein that we termed VWA interacting with AAA + ATPase (ViaA) containing a von Willebrand Factor A domain. However, the functional role of RavA-ViaA in the cell is not yet well established. Here, we show that RavA-ViaA are functionally associated with anaerobic respiration in Escherichia coli through interactions with the fumarate reductase (Frd) electron transport complex. Expression analysis of ravA and viaA genes showed that both proteins are co-expressed with multiple anaerobic respiratory genes, many of which are regulated by the anaerobic transcriptional regulator Fnr. Consistently, the expression of both ravA and viaA was found to be dependent on Fnr in cells grown under oxygen-limiting condition. ViaA was found to physically interact with FrdA, the flavin-containing subunit of the Frd complex. Both RavA and the Fe–S-containing subunit of the Frd complex, FrdB, regulate this interaction. Importantly, Frd activity was observed to increase in the absence of RavA and ViaA. This indicates that RavA and ViaA modulate the activity of the Frd complex, signifying a potential regulatory chaperone-like function for RavA-ViaA during bacterial anaerobic respiration with fumarate as the terminal electron acceptor

Splicing factor ESRP1 controls ER-positive breast cancer by altering metabolic pathways

The epithelial splicing regulatory proteins 1 and 2 (ESRP1 and ESRP2) control the epithelial-to-mesenchymal transition (EMT) splicing program in cancer. However, their role in breast cancer recurrence is unclear. In this study, we report that high levels of ESRP1, but not ESRP2, are associated with poor prognosis in estrogen receptor positive (ER+) breast tumors. Knockdown of ESRP1 in endocrine-resistant breast cancer models decreases growth significantly and alters the EMT splicing signature, which we confirm using TCGA SpliceSeq data of ER+ BRCA tumors. However, these changes are not accompanied by the development of a mesenchymal phenotype or a change in key EMT-transcription factors. In tamoxifen-resistant cells, knockdown of ESRP1 affects lipid metabolism and oxidoreductase processes, resulting in the decreased expression of fatty acid synthase (FASN), stearoyl-CoA desaturase 1 (SCD1), and phosphoglycerate dehydrogenase (PHGDH) at both the mRNA and protein levels. Furthermore, ESRP1 knockdown increases the basal respiration and spare respiration capacity. This study reports a novel role for ESRP1 that could form the basis for the prevention of tamoxifen resistance in ER+ breast cancer

Molecular Typing of Canine Parvovirus from Sulaimani, Iraq and Phylogenetic Analysis Using Partial Vp2 Gene

Canine parvovirus (CPV) remains the most significant viral cause of haemorrhagic enteritis and bloody diarrhoea in puppies over the age of 12 weeks. The objective of the present study was to detect and genotype CPV-2 by polymerase chain reaction (PCR) and to perform phylogenetic analysis using partial VP2 gene sequences. We analysed eight faecal samples of unvaccinated dogs with signs of vomiting and bloody diarrhoea during the period from December 2013 to May 2014 in different locations in Sulaimani, Kurdistan, Iraq. After PCR detection, we found that all viral sequences in our study were CPV-2b variants, which differed genetically by 0.8% to 3.6% from five commercially available vaccines. Alignment between eight nucleotides of field virus sequences showed 95% to 99.5% similarity. The phylogenetic analysis for the 8 field sequences formed two distinct clusters with two sequences belonging to strains from China and Thailand and the other six - with a strain from Egypt. Molecular characterisation and CPV typing are crucial in epidemiological studies for future prevention and control of the disease

Comparison of diagnostic accuracy of early screening for pre-eclampsia by NICE guidelines and a method combining maternal factors and biomarkers: results of SPREE

Objective To test the hypothesis that the performance of first-trimester screening for pre-eclampsia (PE) by a method that uses Bayes’ theorem to combine maternal factors with biomarkers is superior to that defined by current National Institute for Health and Care Excellence (NICE) guidelines. Methods This was a prospective multicenter study (screening program for pre-eclampsia (SPREE)) in seven National Health Service maternity hospitals in England, of women recruited between April and December 2016. Singleton pregnancies at 11–13weeks’ gestation had recording of maternal characteristics and medical history and measurements of mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI), serum placental growth factor (PlGF) and serum pregnancy-associated plasma protein-A (PAPP-A). The performance of screening for PE by the Bayes’ theorem-based method was compared with that of the NICE method. Primary comparison was detection rate (DR) using NICE method vs mini-combined test (maternal factors, MAP and PAPP-A) in the prediction of PE at any gestational age (all-PE) for the same screen-positive rate determined by the NICE method. Key secondary comparisons were DR of screening recommended by the NICE guidelines vs three Bayes’ theorem-based methods (maternal factors, MAP and PAPP-A; maternal factors, MAP and PlGF; and maternal factors, MAP, UtA-PI and PlGF) in the prediction of preterm PE, defined as that requiring delivery <37 weeks. Results All-PE developed in 473 (2.8%) of the 16 747 pregnancies and preterm PE developed in 142 (0.8%). The screen-positive rate by the NICE method was 10.3% and the DR for all-PE was 30.4% and for preterm PE it was 40.8%. Compliance with the NICE recommendation that women at high risk for PE should be treated with aspirin from the first trimester to the end of pregnancy was only 23%. The DR of the mini-combined test for all-PE was 42.5%, which was superior to that of the NICE method by 12.1% (95% CI, 7.9–16.2%). In screening for preterm PE by a combination of maternal factors, MAP and PlGF, the DR was 69.0%, which was superior to that of the NICE method by 28.2% (95% CI, 19.4–37.0%) and with the addition of UtA-PI the DR was 82.4%, which was higher than that of the NICE method by 41.6% (95% CI, 33.2–49.9%). Conclusions The performance of screening for PE as currently recommended by NICE guidelines is poor and compliance with these guidelines is low. The performance of screening is substantially improved by a method combining maternal factors with biomarkers

Single-cell RNA sequencing uncovers the nuclear decoy lincRNA PIRAT as a regulator of systemic monocyte immunity during COVID-19

The systemic immune response to viral infection is shaped by master transcription fac-tors, such as NF-κB, STAT1, or PU.1. Although long noncoding RNAs (lncRNAs)have been suggested as important regulators of transcription factor activity, their contri-butions to the systemic immunopathologies observed during SARS-CoV-2 infectionhave remained unknown. Here, we employed a targeted single-cell RNA sequencingapproach to reveal lncRNAs differentially expressed in blood leukocytes during severeCOVID-19. Our results uncover the lncRNA PIRAT (PU.1-induced regulator of alar-min transcription) as a major PU.1 feedback-regulator in monocytes, governing the pro-duction of the alarmins S100A8/A9, key drivers of COVID-19 pathogenesis. Knockoutand transgene expression, combined with chromatin-occupancy profiling, characterizedPIRATasanucleardecoyRNA,keepingPU.1frombindingtoalarminpromotersandpromoting its binding to pseudogenes in naïve monocytes. NF-κB–dependent PIRATdown-regulation during COVID-19 consequently releases a transcriptional brake, fuelingalarmin production. Alarmin expression is additionally enhanced by the up-regulation ofthe lncRNA LUCAT1, which promotes NF-κB–dependentgeneexpressionattheexpenseof targets of the JAK-STAT pathway. Our results suggest a major role of nuclear noncod-ing RNA networks in systemic antiviral responses to SARS-CoV-2 in humans

The DESiGN trial (DEtection of Small for Gestational age Neonate), evaluating the effect of the Growth Assessment Protocol (GAP): study protocol for a randomised controlled trial.

BACKGROUND: Stillbirth rates in the United Kingdom (UK) are amongst the highest of all developed nations. The association between small-for-gestational-age (SGA) foetuses and stillbirth is well established, and observational studies suggest that improved antenatal detection of SGA babies may halve the stillbirth rate. The Growth Assessment Protocol (GAP) describes a complex intervention that includes risk assessment for SGA and screening using customised fundal-height growth charts. Increased detection of SGA from the use of GAP has been implicated in the reduction of stillbirth rates by 22%, in observational studies of UK regions where GAP uptake was high. This study will be the first randomised controlled trial examining the clinical efficacy, health economics and implementation of the GAP programme in the antenatal detection of SGA. METHODS/DESIGN: In this randomised controlled trial, clusters comprising a maternity unit (or National Health Service Trust) were randomised to either implementation of the GAP programme, or standard care. The primary outcome is the rate of antenatal ultrasound detection of SGA in infants found to be SGA at birth by both population and customised standards, as this is recognised as being the group with highest risk for perinatal morbidity and mortality. Secondary outcomes include antenatal detection of SGA by population centiles, antenatal detection of SGA by customised centiles, short-term maternal and neonatal outcomes, resource use and economic consequences, and a process evaluation of GAP implementation. Qualitative interviews will be performed to assess facilitators and barriers to implementation of GAP. DISCUSSION: This study will be the first to provide data and outcomes from a randomised controlled trial investigating the potential difference between the GAP programme compared to standard care for antenatal ultrasound detection of SGA infants. Accurate information on the performance and service provision requirements of the GAP protocol has the potential to inform national policy decisions on methods to reduce the rate of stillbirth. TRIAL REGISTRATION: Primary registry and trial identifying number: ISRCTN 67698474 . Registered on 2 November 2016

Structural correlations in bacterial metabolic networks

<p>Abstract</p> <p>Background</p> <p>Evolution of metabolism occurs through the acquisition and loss of genes whose products acts as enzymes in metabolic reactions, and from a presumably simple primordial metabolism the organisms living today have evolved complex and highly variable metabolisms. We have studied this phenomenon by comparing the metabolic networks of 134 bacterial species with known phylogenetic relationships, and by studying a neutral model of metabolic network evolution.</p> <p>Results</p> <p>We consider the 'union-network' of 134 bacterial metabolisms, and also the union of two smaller subsets of closely related species. Each reaction-node is tagged with the number of organisms it belongs to, which we denote organism degree (OD), a key concept in our study. Network analysis shows that common reactions are found at the centre of the network and that the average OD decreases as we move to the periphery. Nodes of the same OD are also more likely to be connected to each other compared to a random OD relabelling based on their occurrence in the real data. This trend persists up to a distance of around five reactions. A simple growth model of metabolic networks is used to investigate the biochemical constraints put on metabolic-network evolution. Despite this seemingly drastic simplification, a 'union-network' of a collection of unrelated model networks, free of any selective pressure, still exhibit similar structural features as their bacterial counterpart.</p> <p>Conclusions</p> <p>The OD distribution quantifies topological properties of the evolutionary history of bacterial metabolic networks, and lends additional support to the importance of horizontal gene transfer during bacterial metabolic evolution where new reactions are attached at the periphery of the network. The neutral model of metabolic network growth can reproduce the main features of real networks, but we observe that the real networks contain a smaller common core, while they are more similar at the periphery of the network. This suggests that natural selection and biochemical correlations can act both to diversify and to narrow down metabolic evolution.</p

A Screen for RNA-Binding Proteins in Yeast Indicates Dual Functions for Many Enzymes

Hundreds of RNA-binding proteins (RBPs) control diverse aspects of post-transcriptional gene regulation. To identify novel and unconventional RBPs, we probed high-density protein microarrays with fluorescently labeled RNA and selected 200 proteins that reproducibly interacted with different types of RNA from budding yeast Saccharomyces cerevisiae. Surprisingly, more than half of these proteins represent previously known enzymes, many of them acting in metabolism, providing opportunities to directly connect intermediary metabolism with posttranscriptional gene regulation. We mapped the RNA targets for 13 proteins identified in this screen and found that they were associated with distinct groups of mRNAs, some of them coding for functionally related proteins. We also found that overexpression of the enzyme Map1 negatively affects the expression of experimentally defined mRNA targets. Our results suggest that many proteins may associate with mRNAs and possibly control their fates, providing dense connections between different layers of cellular regulation

Predicting protein linkages in bacteria: Which method is best depends on task

<p>Abstract</p> <p>Background</p> <p>Applications of computational methods for predicting protein functional linkages are increasing. In recent years, several bacteria-specific methods for predicting linkages have been developed. The four major genomic context methods are: Gene cluster, Gene neighbor, Rosetta Stone, and Phylogenetic profiles. These methods have been shown to be powerful tools and this paper provides guidelines for when each method is appropriate by exploring different features of each method and potential improvements offered by their combination. We also review many previous treatments of these prediction methods, use the latest available annotations, and offer a number of new observations.</p> <p>Results</p> <p>Using <it>Escherichia coli </it>K12 and <it>Bacillus subtilis</it>, linkage predictions made by each of these methods were evaluated against three benchmarks: functional categories defined by COG and KEGG, known pathways listed in EcoCyc, and known operons listed in RegulonDB. Each evaluated method had strengths and weaknesses, with no one method dominating all aspects of predictive ability studied. For functional categories, as previous studies have shown, the Rosetta Stone method was individually best at detecting linkages and predicting functions among proteins with shared KEGG categories while the Phylogenetic profile method was best for linkage detection and function prediction among proteins with common COG functions. Differences in performance under COG versus KEGG may be attributable to the presence of paralogs. Better function prediction was observed when using a weighted combination of linkages based on reliability versus using a simple unweighted union of the linkage sets. For pathway reconstruction, 99 complete metabolic pathways in <it>E. coli </it>K12 (out of the 209 known, non-trivial pathways) and 193 pathways with 50% of their proteins were covered by linkages from at least one method. Gene neighbor was most effective individually on pathway reconstruction, with 48 complete pathways reconstructed. For operon prediction, Gene cluster predicted completely 59% of the known operons in <it>E. coli </it>K12 and 88% (333/418)in <it>B. subtilis</it>. Comparing two versions of the <it>E. coli </it>K12 operon database, many of the unannotated predictions in the earlier version were updated to true predictions in the later version. Using only linkages found by both Gene Cluster and Gene Neighbor improved the precision of operon predictions. Additionally, as previous studies have shown, combining features based on intergenic region and protein function improved the specificity of operon prediction.</p> <p>Conclusion</p> <p>A common problem for computational methods is the generation of a large number of false positives that might be caused by an incomplete source of validation. By comparing two versions of a database, we demonstrated the dramatic differences on reported results. We used several benchmarks on which we have shown the comparative effectiveness of each prediction method, as well as provided guidelines as to which method is most appropriate for a given prediction task.</p

The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package

The Astropy project supports and fosters the development of open-source and openly-developed Python packages that provide commonly-needed functionality to the astronomical community. A key element of the Astropy project is the core package Astropy, which serves as the foundation for more specialized projects and packages. In this article, we provide an overview of the organization of the Astropy project and summarize key features in the core package as of the recent major release, version 2.0. We then describe the project infrastructure designed to facilitate and support development for a broader ecosystem of inter-operable packages. We conclude with a future outlook of planned new features and directions for the broader Astropy project