Identification of the regions involved in DNA binding by the mouse PEBP2α protein

AbstractThe polyomavirus enhancer binding protein 2α (PEBP2α) is a DNA binding transcriptional regulatory protein that binds conserved sites in the polyomavirus enhancer, mammalian type C retroviral enhancers and T-cell receptor gene enhancers. Binding of PEBP2α and homologous proteins to the consensus DNA sequence TGPyGGTPy is mediated through a protein domain known as the runt domain. Although recent NMR studies of DNA-bound forms of the runt domain have shown an immunoglobulin-like (Ig) fold, the identification of residues of the protein that are involved in DNA binding has been obscured by the low solubility of the runt domain. Constructs of the mouse PEBP2αA1 gene were generated with N- and C-terminal extensions beyond the runt homology region. The construct containing residues Asp90 to Lys225 of the sequence (PEBP2α90–225) yielded soluble protein. The residues that participate in DNA binding were determined by comparing the NMR spectra of free and DNA-bound PEBP2α90–225. Analysis of the changes in the NMR spectra of the two forms of the protein by chemical shift deviation mapping allowed the unambiguous determination of the regions that are responsible for specific DNA recognition by PEBP2α. Five regions in PEBP2α90–225 that are localized at one end of the β-barrel were found to interact with DNA, similar to the DNA binding interactions of other Ig fold proteins

Identification of Regulatory Elements in the Untranslated Regions of Streptolysin S Associated Gene A Messenger RNA from Group A Streptococcus

Streptococcus pyogenes, also known as group A Streptococcus (GAS), is a human pathogen associated with a variety of diseases such as strep throat, scarlet fever, toxic shock syndrome, and necrotizing fasciitis. One of the virulence factors released by GAS during an invasive infection is a cytotoxic peptide, streptolysin S (SLS), which inhibits the immune response to necrotizing fasciitis. The streptolysin S associated gene A product, SagA, is modified to produce SLS. Thesag operon includes sagA and the genes required for enzyme-mediated post-translational modifications of SagA and the export of SLS. The sagA gene is contained within the pleiotropic effect locus (pel), which produces a small RNA (sRNA) that regulates the expression of other virulence factors. Potential mRNA interactions with the Pel sRNA have been mapped to the 5\u27 and 3\u27 untranslated regions (UTRs) of sagA. Our studies aim to identify and characterize RNA structural motifs in Pel/sagA that regulate the expression of sagA and other virulence factors. Several RNA constructs of Pel/sagA were designed to include regions predicted to contain secondary structure. The corresponding sequences were isolated by PCR from genomic DNA to create templates for in vitro transcription. After purification, the RNA constructs were analyzed by gel electrophoresis to verify size, and by RNase T1 digestion to assay for secondary structure. Three-dimensional models were generated using the FARFAR algorithm in Rosetta in order to identify regions of Pel/sagA that may be involved in regulatory interactions. Differential scanning fluorimetry provided evidence that the 5\u27 and 3\u27 UTRs of Pel/sagA contain stable structural regions. It is expected that the identification of structural motifs necessary for the regulation of gene expression will aid in the design of therapeutic strategies to inhibit the production of streptolysin S and other virulence factors

Evaluation of factors leading to poor outcomes for pediatric acute lymphoblastic leukemia in Mexico: a multi-institutional report of 2,116 patients

Background and aimsPediatric acute lymphoblastic leukemia (ALL) survival rates in low- and middle-income countries are lower due to deficiencies in multilevel factors, including access to timely diagnosis, risk-stratified therapy, and comprehensive supportive care. This retrospective study aimed to analyze outcomes for pediatric ALL at 16 centers in Mexico.MethodsPatients <18 years of age with newly diagnosed B- and T-cell ALL treated between January 2011 and December 2019 were included. Clinical and biological characteristics and their association with outcomes were examined.ResultsOverall, 2,116 patients with a median age of 6.3 years were included. B-cell immunophenotype was identified in 1,889 (89.3%) patients. The median white blood cells at diagnosis were 11.2.5 × 103/mm3. CNS-1 status was reported in 1,810 (85.5%), CNS-2 in 67 (3.2%), and CNS-3 in 61 (2.9%). A total of 1,488 patients (70.4%) were classified as high-risk at diagnosis. However, in 52.5% (991/1,889) of patients with B-cell ALL, the reported risk group did not match the calculated risk group allocation based on National Cancer Institute (NCI) criteria. Fluorescence in situ hybridization (FISH) and PCR tests were performed for 407 (19.2%) and 736 (34.8%) patients, respectively. Minimal residual disease (MRD) during induction was performed in 1,158 patients (54.7%). The median follow-up was 3.7 years. During induction, 191 patients died (9.1%), and 45 patients (2.1%) experienced induction failure. A total of 365 deaths (17.3%) occurred, including 174 deaths after remission. Six percent (176) of patients abandoned treatment. The 5-year event-free survival (EFS) was 58.9% ± 1.7% for B-cell ALL and 47.4% ± 5.9% for T-cell ALL, while the 5-year overall survival (OS) was 67.5% ± 1.6% for B-cell ALL and 54.3% ± 0.6% for T-cell ALL. The 5-year cumulative incidence of central nervous system (CNS) relapse was 5.5% ± 0.6%. For the whole cohort, significantly higher outcomes were seen for patients aged 1–10 years, with DNA index >0.9, with hyperdiploid ALL, and without substantial treatment modifications. In multivariable analyses, age and Day 15 MRD continued to have a significant effect on EFS.ConclusionOutcomes in this multi-institutional cohort describe poor outcomes, influenced by incomplete and inconsistent risk stratification, early toxic death, high on-treatment mortality, and high CNS relapse rate. Adopting comprehensive risk-stratification strategies, evidence-informed de-intensification for favorable-risk patients and optimized supportive care could improve outcomes

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Multiple Sequence Alignment of the SARS-CoV −1 PRF with Nine Homologous Signals Found in Other Coronavirus Genomes

<p>AIBV, avian infectious bronchitis virus; BCoV, bovine coronavirus; HCoV-229E, human coronavirus 229E; HCoV-HKU1; HCoV-NL63, human coronavirus NL63; HCoV-OC43, human coronavirus OC43; MHV, murine hepatitis virus; PEDV, porcine epidemic diarrhea virus; SARS, SARS coronavirus; TGV, transmissible gastroenteritis virus. Heptameric slippery sites are indicated in brown; dashes indicate gaps in the sequence alignments; basepairing positions involved in the consensus first, second, and third helices are denoted by blue, red, and green nucleotides, respectively. Downstream regions homologous to the kissing loop known to promote frameshifting in HCoV-229E [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0030172#pbio-0030172-b16" target="_blank">16</a>,<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0030172#pbio-0030172-b17" target="_blank">17</a>]. HCoV-229R, HCoV-NL, PEDV, and TGV are also highlighted in red with the flanking stem-forming sequences underlined. Asterisks indicate perfectly conserved positions in primary sequence.</p

Molecular Genetic Analyses of Stems 1 and 3

<p>Constructs used to examine the contributions of stem structures and bulged adenosine residues to programmed −1 ribosomal frameshifting are depicted. Shading is used to indicate mutagenized bases. Programmed −1 ribosomal frameshifting promoted by the wild-type SARS-CoV −1 PRF signal was monitored in Vero, as described in the <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0030172#s4" target="_blank">Materials and Methods</a>. Standard deviations (S.D.) are indicated for each sample, as previously described [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0030172#pbio-0030172-b25" target="_blank">25</a>]. The S2 series (above) examines the roles of structures and bases in stem 2. The S3 series (below) examines the roles of structures and bases in stem 3.</p