A Cellular Perspective on Brain Energy Metabolism and Functional Imaging.

The energy demands of the brain are high: they account for at least 20% of the body's energy consumption. Evolutionary studies indicate that the emergence of higher cognitive functions in humans is associated with an increased glucose utilization and expression of energy metabolism genes. Functional brain imaging techniques such as fMRI and PET, which are widely used in human neuroscience studies, detect signals that monitor energy delivery and use in register with neuronal activity. Recent technological advances in metabolic studies with cellular resolution have afforded decisive insights into the understanding of the cellular and molecular bases of the coupling between neuronal activity and energy metabolism and point at a key role of neuron-astrocyte metabolic interactions. This article reviews some of the most salient features emerging from recent studies and aims at providing an integration of brain energy metabolism across resolution scales.

Expression and functional role of the prorenin receptor in the human adrenocortical zona glomerulosa and in primary aldosteronism.

OBJECTIVES: Prorenin can be detected in plasma of hypertensive patients. If detected in patients with primary aldosteronism could implicate prorenin in the development of primary aldosteronism. To address this issue, we measured the plasma prorenin levels in primary aldosteronism patients, the expression of the prorenin receptor (PRR) in the normal human adrenocortical zona glomerulosa and aldosterone-producing adenoma (APA), and we investigated the functional effects of PRR activation in human adrenocortical cells. METHOD: Plasma renin activity, aldosterone, and active and total trypsin-activated renin were measured in primary aldosteronism patients, essential hypertensive patients, and healthy individuals, and then prorenin levels were calculated. Localization and functional role of PRR were investigated in human and rat tissues, and aldosterone-producing cells. RESULTS: Primary aldosteronism patients had detectable plasma levels of prorenin. Using digital-droplet real-time PCR, we found a high PRR-to-porphobilinogen deaminase ratio in both the normal adrenal cortex and APAs. Marked expression of the PRR gene and protein was also found in HAC15 cells. Immunoblotting, confocal, and immunogold electron microscopy demonstrated PRR at the cell membrane and intracellularly. Renin and prorenin significantly triggered both CYP11B2 expression (aldosterone synthase) and ERK1/2 phosphorylation, but only CYP11B2 transcription was prevented by aliskiren. CONCLUSION: The presence of detectable plasma prorenin in primary aldosteronism patients, and the high expression of PRR in the normal human adrenal cortex, APA tissue, CD56+ aldosterone-producing cells, along with activation of CYP11B2 synthesis and ERK1/2 phosphorylation, suggest that the circulating and locally produced prorenin may contribute to the development or maintenance of human primary aldosteronism.

Prospective motion correction of 3D echo-planar imaging data for functional MRI using optical tracking.

We evaluated the performance of an optical camera based prospective motion correction (PMC) system in improving the quality of 3D echo-planar imaging functional MRI data. An optical camera and external marker were used to dynamically track the head movement of subjects during fMRI scanning. PMC was performed by using the motion information to dynamically update the sequence's RF excitation and gradient waveforms such that the field-of-view was realigned to match the subject's head movement. Task-free fMRI experiments on five healthy volunteers followed a 2×2×3 factorial design with the following factors: PMC on or off; 3.0mm or 1.5mm isotropic resolution; and no, slow, or fast head movements. Visual and motor fMRI experiments were additionally performed on one of the volunteers at 1.5mm resolution comparing PMC on vs PMC off for no and slow head movements. Metrics were developed to quantify the amount of motion as it occurred relative to k-space data acquisition. The motion quantification metric collapsed the very rich camera tracking data into one scalar value for each image volume that was strongly predictive of motion-induced artifacts. The PMC system did not introduce extraneous artifacts for the no motion conditions and improved the time series temporal signal-to-noise by 30% to 40% for all combinations of low/high resolution and slow/fast head movement relative to the standard acquisition with no prospective correction. The numbers of activated voxels (p<0.001, uncorrected) in both task-based experiments were comparable for the no motion cases and increased by 78% and 330%, respectively, for PMC on versus PMC off in the slow motion cases. The PMC system is a robust solution to decrease the motion sensitivity of multi-shot 3D EPI sequences and thereby overcome one of the main roadblocks to their widespread use in fMRI studies.

Functional error modeling for uncertainty quantification in hydrogeology

Approximate models (proxies) can be employed to reduce the computational costs of estimating uncertainty. The price to pay is that the approximations introduced by the proxy model can lead to a biased estimation. To avoid this problem and ensure a reliable uncertainty quantification, we propose to combine functional data analysis and machine learning to build error models that allow us to obtain an accurate prediction of the exact response without solving the exact model for all realizations. We build the relationship between proxy and exact model on a learning set of geostatistical realizations for which both exact and approximate solvers are run. Functional principal components analysis (FPCA) is used to investigate the variability in the two sets of curves and reduce the dimensionality of the problem while maximizing the retained information. Once obtained, the error model can be used to predict the exact response of any realization on the basis of the sole proxy response. This methodology is purpose-oriented as the error model is constructed directly for the quantity of interest, rather than for the state of the system. Also, the dimensionality reduction performed by FPCA allows a diagnostic of the quality of the error model to assess the informativeness of the learning set and the fidelity of the proxy to the exact model. The possibility of obtaining a prediction of the exact response for any newly generated realization suggests that the methodology can be effectively used beyond the context of uncertainty quantification, in particular for Bayesian inference and optimization.

Functional characterization of the Pneumocystis jirovecii potential drug targets dhfs and abz2 involved in folate biosynthesis.

Pneumocystis species are fungal parasites colonizing mammal lungs with strict host specificity. Pneumocystis jirovecii is the human-specific species and can turn into an opportunistic pathogen causing severe pneumonia in immunocompromised individuals. This disease is currently the second most frequent life-threatening invasive fungal infection worldwide. The most efficient drug, cotrimoxazole, presents serious side effects, and resistance to this drug is emerging. The search for new targets for the development of new drugs is thus of utmost importance. The recent release of the P. jirovecii genome sequence opens a new era for this task. It can now be carried out on the actual targets to be inhibited instead of on those of the relatively distant model Pneumocystis carinii, the species infecting rats. We focused on the folic acid biosynthesis pathway because (i) it is widely used for efficient therapeutic intervention, and (ii) it involves several enzymes that are essential for the pathogen and have no human counterparts. In this study, we report the identification of two such potential targets within the genome of P. jirovecii, the dihydrofolate synthase (dhfs) and the aminodeoxychorismate lyase (abz2). The function of these enzymes was demonstrated by the rescue of the null allele of the orthologous gene of Saccharomyces cerevisiae.

Refractory intraretinal or subretinal fluid in neovascular age-related macular degeneration treated with intravitreal ranizubimab: Functional and Structural Outcome.

PURPOSE: To investigate the visual acuity results of eyes with neovascular age-related macular degeneration and refractory fluid despite monthly treatment with ranibizumab, and to investigate differences between refractory subretinal fluid and intraretinal cystic changes. METHODS: Retrospective chart review of consecutive treatment-refractory neovascular age-related macular degeneration, defined as persistent intraretinal or subretinal fluid despite monthly ranibizumab injections during 12 months or more. Data were evaluated for baseline characteristics, type and location of the refractory fluid, mean visual acuity change, number of injections, and the time point of first complete disappearance of all fluid on spectral domain optical coherence tomography. RESULTS: Seventy-six eyes (74 patients, mean age, 76.8 years) were identified. The mean follow-up was 33.6 months (range, 12-73 months). The mean number of injections was 11.4 in the first year and 27.7 over follow-up. The refractory fluid was located subfoveally in 61.8%. In 27 eyes (35.5%), the fluid resolved after a mean of 21.8 months (range, 13-49 months). Mean visual acuity increased by 9.0, 7.9, and 7.9 letters by Month 12, Month 24, and Month 36, respectively. Subgroup analysis revealed a higher risk for fibrosis (odds ratio, 3.30) or atrophy (odds ratio, 3.34) in patients with refractory cysts as compared with refractory subretinal fluid. Furthermore, refractory cysts showed a higher risk for a 10-letter visual acuity loss (P = 0.018). CONCLUSION: Fluid refractory to monthly treatment with ranibizumab for neovascular age-related macular degeneration still allowed for well-maintained visual improvement, even in subfoveal location. Late fluid resolution may occur. However, refractory cysts were associated with poorer anatomical and functional outcome than subretinal fluid.

Identification of potential HIV restriction factors by combining evolutionary genomic signatures with functional analyses.

BACKGROUND: Known antiretroviral restriction factors are encoded by genes that are under positive selection pressure, induced during HIV-1 infection, up-regulated by interferons, and/or interact with viral proteins. To identify potential novel restriction factors, we performed genome-wide scans for human genes sharing molecular and evolutionary signatures of known restriction factors and tested the anti-HIV-1 activity of the most promising candidates. RESULTS: Our analyses identified 30 human genes that share characteristics of known restriction factors. Functional analyses of 27 of these candidates showed that over-expression of a strikingly high proportion of them significantly inhibited HIV-1 without causing cytotoxic effects. Five factors (APOL1, APOL6, CD164, TNFRSF10A, TNFRSF10D) suppressed infectious HIV-1 production in transfected 293T cells by >90% and six additional candidates (FCGR3A, CD3E, OAS1, GBP5, SPN, IFI16) achieved this when the virus was lacking intact accessory vpr, vpu and nef genes. Unexpectedly, over-expression of two factors (IL1A, SP110) significantly increased infectious HIV-1 production. Mechanistic studies suggest that the newly identified potential restriction factors act at different steps of the viral replication cycle, including proviral transcription and production of viral proteins. Finally, we confirmed that mRNA expression of most of these candidate restriction factors in primary CD4+ T cells is significantly increased by type I interferons. CONCLUSIONS: A limited number of human genes share multiple characteristics of genes encoding for known restriction factors. Most of them display anti-retroviral activity in transient transfection assays and are expressed in primary CD4+ T cells.

Using species richness and functional traits predictions to constrain assemblage predictions from stacked species distribution models

Aim: Modelling species at the assemblage level is required to make effective forecast of global change impacts on diversity and ecosystem functioning. Community predictions may be achieved using macroecological properties of communities (MEM), or by stacking of individual species distribution models (S-SDMs). To obtain more realistic predictions of species assemblages, the SESAM framework suggests applying successive filters to the initial species source pool, by combining different modelling approaches and rules. Here we provide a first test of this framework in mountain grassland communities. Location: The western Swiss Alps. Methods: Two implementations of the SESAM framework were tested: a "Probability ranking" rule based on species richness predictions and rough probabilities from SDMs, and a "Trait range" rule that uses the predicted upper and lower bound of community-level distribution of three different functional traits (vegetative height, specific leaf area and seed mass) to constraint a pool of environmentally filtered species from binary SDMs predictions. Results: We showed that all independent constraints expectedly contributed to reduce species richness overprediction. Only the "Probability ranking" rule allowed slightly but significantly improving predictions of community composition. Main conclusion: We tested various ways to implement the SESAM framework by integrating macroecological constraints into S-SDM predictions, and report one that is able to improve compositional predictions. We discuss possible improvements, such as further improving the causality and precision of environmental predictors, using other assembly rules and testing other types of ecological or functional constraints.

NLRP3 Inflammasome Is Expressed and Functional in Mouse Brain Microglia but Not in Astrocytes.

Neuroinflammation is the local reaction of the brain to infection, trauma, toxic molecules or protein aggregates. The brain resident macrophages, microglia, are able to trigger an appropriate response involving secretion of cytokines and chemokines, resulting in the activation of astrocytes and recruitment of peripheral immune cells. IL-1β plays an important role in this response; yet its production and mode of action in the brain are not fully understood and its precise implication in neurodegenerative diseases needs further characterization. Our results indicate that the capacity to form a functional NLRP3 inflammasome and secretion of IL-1β is limited to the microglial compartment in the mouse brain. We were not able to observe IL-1β secretion from astrocytes, nor do they express all NLRP3 inflammasome components. Microglia were able to produce IL-1β in response to different classical inflammasome activators, such as ATP, Nigericin or Alum. Similarly, microglia secreted IL-18 and IL-1α, two other inflammasome-linked pro-inflammatory factors. Cell stimulation with α-synuclein, a neurodegenerative disease-related peptide, did not result in the release of active IL-1β by microglia, despite a weak pro-inflammatory effect. Amyloid-β peptides were able to activate the NLRP3 inflammasome in microglia and IL-1β secretion occurred in a P2X7 receptor-independent manner. Thus microglia-dependent inflammasome activation can play an important role in the brain and especially in neuroinflammatory conditions.

Functional importance of cardiac enhancer-associated noncoding RNAs in heart development and disease.

The key information processing units within gene regulatory networks are enhancers. Enhancer activity is associated with the production of tissue-specific noncoding RNAs, yet the existence of such transcripts during cardiac development has not been established. Using an integrated genomic approach, we demonstrate that fetal cardiac enhancers generate long noncoding RNAs (lncRNAs) during cardiac differentiation and morphogenesis. Enhancer expression correlates with the emergence of active enhancer chromatin states, the initiation of RNA polymerase II at enhancer loci and expression of target genes. Orthologous human sequences are also transcribed in fetal human hearts and cardiac progenitor cells. Through a systematic bioinformatic analysis, we identified and characterized, for the first time, a catalog of lncRNAs that are expressed during embryonic stem cell differentiation into cardiomyocytes and associated with active cardiac enhancer sequences. RNA-sequencing demonstrates that many of these transcripts are polyadenylated, multi-exonic long noncoding RNAs. Moreover, knockdown of two enhancer-associated lncRNAs resulted in the specific downregulation of their predicted target genes. Interestingly, the reactivation of the fetal gene program, a hallmark of the stress response in the adult heart, is accompanied by increased expression of fetal cardiac enhancer transcripts. Altogether, these findings demonstrate that the activity of cardiac enhancers and expression of their target genes are associated with the production of enhancer-derived lncRNAs.