Mice lacking the mitochondrial exonuclease MGME1 develop inflammatory kidney disease with glomerular dysfunction

Mitochondrial DNA (mtDNA) maintenance disorders are caused by mutations in ubiquitously expressed nuclear genes and lead to syndromes with variable disease severity and tissue-specific phenotypes. Loss of function mutations in the gene encoding the mitochondrial genome and maintenance exonuclease 1 (MGME1) result in deletions and depletion of mtDNA leading to adult-onset multisystem mitochondrial disease in humans. To better understand the in vivo function of MGME1 and the associated disease pathophysiology, we characterized a Mgme1 mouse knockout model by extensive phenotyping of ageing knockout animals. We show that loss of MGME1 leads to de novo formation of linear deleted mtDNA fragments that are constantly made and degraded. These findings contradict previous proposal that MGME1 is essential for degradation of linear mtDNA fragments and instead support a model where MGME1 has a critical role in completion of mtDNA replication. We report that Mgme1 knockout mice develop a dramatic phenotype as they age and display progressive weight loss, cataract and retinopathy. Surprisingly, aged animals also develop kidney inflammation, glomerular changes and severe chronic progressive nephropathy, consistent with nephrotic syndrome. These findings link the faulty mtDNA synthesis to severe inflammatory disease and thus show that defective mtDNA replication can trigger an immune response that causes age-associated progressive pathology in the kidney.     

The Rab11-family interacting proteins reveal selective interaction of mammalian recycling endosomes with the Toxoplasma parasitophorous vacuole in a Rab11- and Arf6-dependent manner

After mammalian cell invasion, the parasite Toxoplasma multiplies in a self-made membrane-bound compartment, the parasitophorous vacuole (PV). We previously showed that Toxoplasma interacts with many host cell organelles, especially from recycling pathways, and sequestrates Rab11A and Rab11B vesicles into the PV. Here, we examine the specificity of host Rab11 vesicle interaction with the PV by focusing on the recruitment of subpopulations of Rab11 vesicles characterized by different effectors, for example, Rab11-family interacting roteins (FIPs) or Arf6. Our quantitative microscopic analysis illustrates the presence of intra-PV vesicles with FIPs from class I (FIP1C, FIP2, FIP5) and class II (FIP3, FIP4) but to various degrees. The intra-PV delivery of vesicles with class I, but not class II, FIPs is dependent on Rab11 binding. Cell depletion of Rab11A results in a significant decrease in intra-PV FIP5, but not FIP3 vesicles. Class II FIPs also bind to Arf6, and we observe vesicles associated with FIP3-Rab11A or FIP3-Arf6 complexes concomitantly within the PV. Abolishing FIP3 binding to both Rab11 and Arf6 reduces the number of intra-PV FIP3 vesicles. These data point to a selective process of mammalian Rab11 vesicle recognition and scavenging mediated by Toxoplasma, suggesting that specific parasite PV proteins may be involved in these processes.     

Effect of clinician information sessions on diagnostic testing for Chagas disease

BackgroundChagas disease is a potentially life-threatening neglected disease of poverty that is endemic in continental Latin America. Caused by Trypanosoma cruzi (T. cruzi), it is one of six parasitic diseases in the United States targeted by the Centers for Disease Control as a public health problem in need of action. An estimated 300,000 people are infected with T. cruzi in the United States (US). Although its morbidity, mortality and economic burden are high, awareness of Chagas disease is lacking among many healthcare providers in the US. The purpose of this analysis is to determine if the number of diagnostic tests performed at a community health center serving an at-risk population for Chagas disease increased after information sessions. A secondary aim was to determine if there was a difference by provider type, i.e., nurse practitioner vs. physician, or by specialty in the number of patients screened.Methodology/Principal findingsWe conducted a retrospective data analysis of the number of Chagas serology tests performed at a community health center before and after information sessions for clinicians. A time series analysis was conducted focusing on the Adult and Family Medicine Departments at East Boston Neighborhood Health Center (EBNHC). Across all departments there were 1,957 T. cruzi tests performed before the sessions vs. 2,623 after the sessions. Interrupted time series analysis across departments indicated that testing volume was stable over time prior to the sessions (pre-period slope = +4.1 per month; p = 0.12), followed by an immediate shift after the session (+51.6; p = 0.03), while testing volume remained stable over time after the session (post-period slope = -6.0 per month; p = 0.11).Conclusion/SignificanceIn this study, Chagas testing increased after information sessions. Clinicians who began testing their patients for Chagas disease after learning of the importance of this intervention added an extra, potentially time-consuming task to their already busy workdays without external incentives or recognition.     

Characterizing the complexity of enzymes on the basis of their mechanisms and structures with a bio-computational analysis

Enzymes are basically composed of 20 naturally occurring amino acids, yet they catalyse a dizzying array of chemical reactions, with regiospecificity and stereospecificity and under physiological conditions. In this review, we attempt to gain some understanding of these complex proteins, from the chemical versatility of the catalytic toolkit, including the use of cofactors (both metal ions and organic molecules), to the complex mapping of reactions to proteins (which is rarely one-to-one), and finally the structural complexity of enzymes and their active sites, often involving multidomain or multisubunit assemblies. This work highlights how the enzymes that we see today reflect millions of years of evolution, involving de novo design followed by exquisite regulation and modulation to create optimal fitness for life.     

Mutant p53: it’s not all one and the same

Mutation of the TP53 tumor suppressor gene is the most common genetic alteration in cancer, and almost 1000 alleles have been identified in human tumors. While virtually all TP53 mutations are thought to compromise wild type p53 activity, the prevalence and recurrence of missense TP53 alleles has motivated countless research studies aimed at understanding the function of the resulting mutant p53 protein. The data from these studies support three distinct, but perhaps not necessarily mutually exclusive, mechanisms for how different p53 mutants impact cancer: first, they lose the ability to execute wild type p53 functions to varying degrees; second, they act as a dominant negative (DN) inhibitor of wild type p53 tumor-suppressive programs; and third, they may gain oncogenic functions that go beyond mere p53 inactivation. Of these possibilities, the gain of function (GOF) hypothesis is the most controversial, in part due to the dizzying array of biological functions that have been attributed to different mutant p53 proteins. Herein we discuss the current state of understanding of TP53 allele variation in cancer and recent reports that both support and challenge the p53 GOF model. In these studies and others, researchers are turning to more systematic approaches to profile TP53 mutations, which may ultimately determine once and for all how different TP53 mutations act as cancer drivers and whether tumors harboring distinct mutations are phenotypically unique. From a clinical perspective, such information could lead to new therapeutic approaches targeting the effects of different TP53 alleles and/or better sub-stratification of patients harboring TP53 mutant cancers.Subject terms: Cancer genetics, Tumour-suppressor proteins     

The Activity of Neutral α-Glucosidase and Selected Biochemical Parameters in the Annual Cycle of Breeding Carp (Cyprinus carpio L.)

The aim of the study was to demonstrate seasonal changes in the hydrolytic and transferase activity of neutral α-glucosidase, the level of glucose, cholesterol, triglycerides and total protein in the annual breeding cycle of the carp. The study was conducted on fish from a fish farm in Lower Silesia (Poland). Blood serum was collected from the heart in: June, September and December of two consecutive years. The results of the study show that the hydrolytic and transferase activity of neutral α-glucosidase, as well as the results of basic biochemical parameters are highest in summer, when the fish seek and intake food intensively. The lowest values were observed in spring, when carp have the lowest metabolism after the wintering period.     

Macro-Climatic Distribution Limits Show Both Niche Expansion and Niche Specialization among C4 Panicoids

Grasses are ancestrally tropical understory species whose current dominance in warm open habitats is linked to the evolution of C₄ photosynthesis. C₄ grasses maintain high rates of photosynthesis in warm and water stressed environments, and the syndrome is considered to induce niche shifts into these habitats while adaptation to cold ones may be compromised. Global biogeographic analyses of C₄ grasses have, however, concentrated on diversity patterns, while paying little attention to distributional limits. Using phylogenetic contrast analyses, we compared macro-climatic distribution limits among ~1300 grasses from the subfamily Panicoideae, which includes 4/5 of the known photosynthetic transitions in grasses. We explored whether evolution of C₄ photosynthesis correlates with niche expansions, niche changes, or stasis at subfamily level and within the two tribes Paniceae and Paspaleae. We compared the climatic extremes of growing season temperatures, aridity, and mean temperatures of the coldest months. We found support for all the known biogeographic distribution patterns of C₄ species, these patterns were, however, formed both by niche expansion and niche changes. The only ubiquitous response to a change in the photosynthetic pathway within Panicoideae was a niche expansion of the C₄ species into regions with higher growing season temperatures, but without a withdrawal from the inherited climate niche. Other patterns varied among the tribes, as macro-climatic niche evolution in the American tribe Paspaleae differed from the pattern supported in the globally distributed tribe Paniceae and at family level.     

The alternative aerobic ribonucleotide reductase of Escherichia coli, NrdEF, is a manganese-dependent enzyme that enables cell replication during periods of iron starvation

The genome of Escherichia coli encodes two class I ribonucleotide reductases. The first, NrdAB, is a well-studied iron-dependent enzyme that is essential for aerobic growth. The second, NrdEF, is not functional under routine conditions, and its role is obscure. Recent studies demonstrated that NrdEF can be activated in vitro by manganese as well as iron. Since iron enzymes are potential targets for hydrogen peroxide, and since the nrdHIEF operon is induced during H(2) O(2) stress, we hypothesized that H(2) O(2) might inactivate NrdAB and that NrdEF might be induced to compensate. This idea was tested using E. coli mutants that are chronically stressed by H(2) O(2) . Contrary to expectation, NrdAB remained active. Its resistance to H(2) O(2) depended upon YfaE, which helps to activate NrdB. The induction of NrdEF during H(2) O(2) stress was mediated by the inactivation of Fur, an iron-dependent repressor. This regulatory arrangement implied that NrdEF has a physiological role during periods of iron starvation. Indeed, NrdEF supported cell replication in iron-depleted cells. Iron bound to NrdF when it was expressed in iron-rich cells, but NrdEF was functional only in cells that were both iron-depleted and manganese-rich. Thus NrdEF supports DNA replication when iron is unavailable to activate the housekeeping NrdAB enzyme.     

Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand?

Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model‐data intercomparison project, where we tested the ability of 10 terrestrial biosphere models to reproduce the observed sensitivity of ecosystem productivity to rainfall changes at 10 sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are as follows: (a) Inter‐model variation is generally large and model agreement varies with timescales. In severely water‐limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent on gross primary productivity. In more mesic sites, model agreement for both water and carbon fluxes is typically higher on fine (daily–monthly) timescales and reduces on longer (seasonal–annual) scales. (b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter‐model variability. (c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition. (d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. The relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models.