ZDHHC11 and ZDHHC11B are critical novel components of the oncogenic MYC-miR-150-MYB network in Burkitt lymphoma

Leukemia accepted article preview online, 23 March 2017. doi:10.1038/leu.2017.9

Generating and repairing genetically programmed DNA breaks during immunoglobulin class switch recombination

Adaptive immune responses require the generation of a diverse repertoire of immunoglobulins (Igs) that can recognize and neutralize a seemingly infinite number of antigens. V(D)J recombination creates the primary Ig repertoire, which subsequently is modified by somatic hypermutation (SHM) and class switch recombination (CSR). SHM promotes Ig affinity maturation whereas CSR alters the effector function of the Ig. Both SHM and CSR require activation-induced cytidine deaminase (AID) to produce dU:dG mismatches in the Ig locus that are transformed into untemplated mutations in variable coding segments during SHM or DNA double-strand breaks (DSBs) in switch regions during CSR. Within the Ig locus, DNA repair pathways are diverted from their canonical role in maintaining genomic integrity to permit AID-directed mutation and deletion of gene coding segments. Recently identified proteins, genes, and regulatory networks have provided new insights into the temporally and spatially coordinated molecular interactions that control the formation and repair of DSBs within the Ig locus. Unravelling the genetic program that allows B cells to selectively alter the Ig coding regions while protecting non-Ig genes from DNA damage advances our understanding of the molecular processes that maintain genomic integrity as well as humoral immunity

Alteration of Proteins and Pigments Influence the Function of Photosystem I under Iron Deficiency from Chlamydomonas reinhardtii

BACKGROUND: Iron is an essential micronutrient for all organisms because it is a component of enzyme cofactors that catalyze redox reactions in fundamental metabolic processes. Even though iron is abundant on earth, it is often present in the insoluble ferric [Fe (III)] state, leaving many surface environments Fe-limited. The haploid green alga Chlamydomonas reinhardtii is used as a model organism for studying eukaryotic photosynthesis. This study explores structural and functional changes in PSI-LHCI supercomplexes under Fe deficiency as the eukaryotic photosynthetic apparatus adapts to Fe deficiency. RESULTS: 77K emission spectra and sucrose density gradient data show that PSI and LHCI subunits are affected under iron deficiency conditions. The visible circular dichroism (CD) spectra associated with strongly-coupled chlorophyll dimers increases in intensity. The change in CD signals of pigments originates from the modification of interactions between pigment molecules. Evidence from sucrose gradients and non-denaturing (green) gels indicates that PSI-LHCI levels were reduced after cells were grown for 72 h in Fe-deficient medium. Ultrafast fluorescence spectroscopy suggests that red-shifted pigments in the PSI-LHCI antenna were lost during Fe stress. Further, denaturing gel electrophoresis and immunoblot analysis reveals that levels of the PSI subunits PsaC and PsaD decreased, while PsaE was completely absent after Fe stress. The light harvesting complexes were also susceptible to iron deficiency, with Lhca1 and Lhca9 showing the most dramatic decreases. These changes in the number and composition of PSI-LHCI supercomplexes may be caused by reactive oxygen species, which increase under Fe deficiency conditions. CONCLUSIONS: Fe deficiency induces rapid reduction of the levels of photosynthetic pigments due to a decrease in chlorophyll synthesis. Chlorophyll is important not only as a light-harvesting pigment, but also has a structural role, particularly in the pigment-rich LHCI subunits. The reduced level of chlorophyll molecules inhibits the formation of large PSI-LHCI supercomplexes, further decreasing the photosynthetic efficiency

A Regulatory Role for NBS1 in Strand-Specific Mutagenesis during Somatic Hypermutation

Activation-induced cytidine deaminase (AID) is believed to initiate somatic hypermutation (SHM) by deamination of deoxycytidines to deoxyuridines within the immunoglobulin variable regions genes. The deaminated bases can subsequently be replicated over, processed by base excision repair or mismatch repair, leading to introduction of different types of point mutations (G/C transitions, G/C transversions and A/T mutations). It is evident that the base excision repair pathway is largely dependent on uracil-DNA glycosylase (UNG) through its uracil excision activity. It is not known, however, which endonuclease acts in the step immediately downstream of UNG, i.e. that cleaves at the abasic sites generated by the latter. Two candidates have been proposed, an apurinic/apyrimidinic endonuclease (APE) and the Mre11-Rad50-NBS1 complex. The latter is intriguing as this might explain how the mutagenic pathway is primed during SHM. We have investigated the latter possibility by studying the in vivo SHM pattern in B cells from ataxia-telangiectasia-like disorder (Mre11 deficient) and Nijmegen breakage syndrome (NBS1 deficient) patients. Our results show that, although the pattern of mutations in the variable heavy chain (VH) genes was altered in NBS1 deficient patients, with a significantly increased number of G (but not C) transversions occurring in the SHM and/or AID targeting hotspots, the general pattern of mutations in the VH genes in Mre11 deficient patients was only slightly altered, with an increased frequency of A to C transversions. The Mre11-Rad50-NBS1 complex is thus unlikely to be the major nuclease involved in cleavage of the abasic sites during SHM, whereas NBS1 might have a specific role in regulating the strand-biased repair during phase Ib mutagenesis

MEKK1-MKK4-JNK-AP1 Pathway Negatively Regulates Rgs4 Expression in Colonic Smooth Muscle Cells

Background: Regulator of G-protein Signaling 4 (RGS4) plays an important role in regulating smooth muscle contraction, cardiac development, neural plasticity and psychiatric disorder. However, the underlying regulatory mechanisms remain elusive. Our recent studies have shown that upregulation of Rgs4 by interleukin (IL)-1b is mediated by the activation of NFkB signaling and modulated by extracellular signal-regulated kinases, p38 mitogen-activated protein kinase, and phosphoinositide-3 kinase. Here we investigate the effect of the c-Jun N-terminal kinase (JNK) pathway on Rgs4 expression in rabbit colonic smooth muscle cells. Methodology/Principal Findings: Cultured cells at first passage were treated with or without IL-1b (10 ng/ml) in the presence or absence of the selective JNK inhibitor (SP600125) or JNK small hairpin RNA (shRNA). The expression levels of Rgs4 mRNA and protein were determined by real-time RT-PCR and Western blot respectively. SP600125 or JNK shRNA increased Rgs4 expression in the absence or presence of IL-1b stimulation. Overexpression of MEKK1, the key upstream kinase of JNK, inhibited Rgs4 expression, which was reversed by co-expression of JNK shRNA or dominant-negative mutants for MKK4 or JNK. Both constitutive and inducible upregulation of Rgs4 expression by SP600125 was significantly inhibited by pretreatment with the transcription inhibitor, actinomycin D. Dual reporter assay showed that pretreatment with SP600125 sensitized the promoter activity of Rgs4 in response to IL-1b. Mutation of the AP1-binding site within Rgs

Genetic polymorphisms associated with the inflammatory response in bacterial meningitis

BACKGROUND Bacterial meningitis (BM) is an infectious disease that results in high mortality and morbidity. Despite efficacious antibiotic therapy, neurological sequelae are often observed in patients after disease. Currently, the main challenge in BM treatment is to develop adjuvant therapies that reduce the occurrence of sequelae. In recent papers published by our group, we described the associations between the single nucleotide polymorphisms (SNPs) AADAT +401C > T, APEX1 Asn148Glu, OGG1 Ser326Cys and PARP1 Val762Ala and BM. In this study, we analyzed the associations between the SNPs TNF -308G > A, TNF -857C > T, IL-8 -251A > T and BM and investigated gene-gene interactions, including the SNPs that we published previously. METHODS The study was conducted with 54 BM patients and 110 healthy volunteers (as the control group). The genotypes were investigated via primer-introduced restriction analysis-polymerase chain reaction (PIRA-PCR) or polymerase chain reaction-based restriction fragment length polymorphism (PCR-RFLP) analysis. Allelic and genotypic frequencies were also associated with cytokine and chemokine levels, as measured with the x-MAP method, and cell counts. We analyzed gene-gene interactions among SNPs using the generalized multifactor dimensionality reduction (GMDR) method. RESULTS We did not find significant association between the SNPs TNF -857C > T and IL-8 -251A > T and the disease. However, a higher frequency of the variant allele TNF -308A was observed in the control group, associated with changes in cytokine levels compared to individuals with wild type genotypes, suggesting a possible protective role. In addition, combined inter-gene interaction analysis indicated a significant association between certain genotypes and BM, mainly involving the alleles APEX1 148Glu, IL8 -251 T and AADAT +401 T. These genotypic combinations were shown to affect cyto/chemokine levels and cell counts in CSF samples from BM patients. CONCLUSIONS In conclusion, this study revealed a significant association between genetic variability and altered inflammatory responses, involving important pathways that are activated during BM. This knowledge may be useful for a better understanding of BM pathogenesis and the development of new therapeutic approaches

Emergency logistics for wildfire suppression based on forecasted disaster evolution

This paper aims to develop a two-layer emergency logistics system with a single depot and multiple demand sites for wildfire suppression and disaster relief. For the first layer, a fire propagation model is first built using both the flame-igniting attributes of wildfires and the factors affecting wildfire propagation and patterns. Second, based on the forecasted propagation behavior, the emergency levels of fire sites in terms of demand on suppression resources are evaluated and prioritized. For the second layer, considering the prioritized fire sites, the corresponding resource allocation problem and vehicle routing problem (VRP) are investigated and addressed. The former is approached using a model that can minimize the total forest loss (from multiple sites) and suppression costs incurred accordingly. This model is constructed and solved using principles of calculus. To address the latter, a multi-objective VRP model is developed to minimize both the travel time and cost of the resource delivery vehicles. A heuristic algorithm is designed to provide the associated solutions of the VRP model. As a result, this paper provides useful insights into effective wildfire suppression by rationalizing resources regarding different fire propagation rates. The supporting models can also be generalized and tailored to tackle logistics resource optimization issues in dynamic operational environments, particularly those sharing the same feature of single supply and multiple demands in logistics planning and operations (e.g., allocation of ambulances and police forces). © 2017 The Author(s

Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds

In the past decade, machine learning methods for empirical rainfall–runoff modeling have seen extensive development and been proposed as a useful complement to physical hydrologic models, particularly in basins where data to support process-based models are limited. However, the majority of research has focused on a small number of methods, such as artificial neural networks, despite the development of multiple other approaches for non-parametric regression in recent years. Furthermore, this work has often evaluated model performance based on predictive accuracy alone, while not considering broader objectives, such as model interpretability and uncertainty, that are important if such methods are to be used for planning and management decisions. In this paper, we use multiple regression and machine learning approaches (including generalized additive models, multivariate adaptive regression splines, artificial neural networks, random forests, and M5 cubist models) to simulate monthly streamflow in five highly seasonal rivers in the highlands of Ethiopia and compare their performance in terms of predictive accuracy, error structure and bias, model interpretability, and uncertainty when faced with extreme climate conditions. While the relative predictive performance of models differed across basins, data-driven approaches were able to achieve reduced errors when compared to physical models developed for the region. Methods such as random forests and generalized additive models may have advantages in terms of visualization and interpretation of model structure, which can be useful in providing insights into physical watershed function. However, the uncertainty associated with model predictions under extreme climate conditions should be carefully evaluated, since certain models (especially generalized additive models and multivariate adaptive regression splines) become highly variable when faced with high temperatures

Evaluating urban accessibility: leveraging open-source data and analytics to overcome existing limitations

We revisit the standard methodology for evaluating proximity to urban services and recommend enhancements to address existing limitations. Existing approaches often simplify their measure of proximity by using large areal units and by imposing arbitrary distance thresholds. By doing so, these approaches risk overlooking vulnerable, access-poor populations - the very populations that such studies are often trying to identify. These limitations are primarily motivated by computational constraints. However, recent advances in computational power, open data, and open-source analytics permit high-resolution proximity analyses on large scales. Given the impetus for equitable accessibility in our communities, this is of fundamental importance for researchers and practitioners. In this paper, we present an approach that leverages these open source advances to (a) measure proximity using network distance at the building level, (b) estimate population at that level, and (c) present the resulting distributions so vulnerable populations can be identified. Using three cities and modes of transport, we demonstrate how the approach enhances existing measures and identifies service-poor populations where the previous methods fall short. The proximity results could be used alone, or as inputs to access metrics. Our collating of these components into an open source code provides opportunities for researchers and practitioners to explore fine-resolution, city-wide accessibility across multiple cities and the host of questions that follow