Translocation and de novo synthesis of eicosapentaenoic acid (EPA) during nitrogen starvation in Nannochloropsis gaditana

The microalga Nannochloropsis gaditana is known for accumulating fatty acids, including the commercially interesting eicosapentaenoic acid (EPA) within the polar lipids (PL) and neutral lipids (NL). During microalgal growth EPA is mainly present in the PL. Upon nitrogen starvation N. gaditana accumulates large amounts of TAG in lipid bodies. The neutral lipid fraction will mainly consist of triacylglycerol (TAG). When expressed per total cell dry weight, the NL-localized EPA increased while the PL-localized EPA decreased, suggesting that EPA is translocated from the PL into the NL lipids during nitrogen starvation. Here, we elucidated the origin of EPA in NL of N. gaditana by firstly growing this microalga under optimal growth conditions with 13CO2 as the sole carbon source followed by nitrogen starvation with 12CO2 as the sole carbon source. By measuring both 12C and 13C fatty acid isotope species in time, the de novo synthesized fatty acids and the already present fatty acids can be distinguished. For the first time, we proved that actual translocation of EPA from the PL into the NL occurs during nitrogen starvation of N. gaditana. Next to being translocated, EPA was synthesized de novo in both PL and NL during nitrogen starvation. EPA was made by carbon reshuffling within the cell as well. EPA was the main fatty acid translocated, suggesting that the enzyme responsible for fatty acid translocation has a high specificity for EPA.publishedVersio

Comparative Methods for Association Studies: A Case Study on Metabolite Variation in a Brassica rapa Core Collection

Background Association mapping is a statistical approach combining phenotypic traits and genetic diversity in natural populations with the goal of correlating the variation present at phenotypic and allelic levels. It is essential to separate the true effect of genetic variation from other confounding factors, such as adaptation to different uses and geographical locations. The rapid availability of large datasets makes it necessary to explore statistical methods that can be computationally less intensive and more flexible for data exploration. Methodology/Principal Findings A core collection of 168 Brassica rapa accessions of different morphotypes and origins was explored to find genetic association between markers and metabolites: tocopherols, carotenoids, chlorophylls and folate. A widely used linear model with modifications to account for population structure and kinship was followed for association mapping. In addition, a machine learning algorithm called Random Forest (RF) was used as a comparison. Comparison of results across methods resulted in the selection of a set of significant markers as promising candidates for further work. This set of markers associated to the metabolites can potentially be applied for the selection of genotypes with elevated levels of these metabolites. Conclusions/Significance The incorporation of the kinship correction into the association model did not reduce the number of significantly associated markers. However incorporation of the STRUCTURE correction (Q matrix) in the linear regression model greatly reduced the number of significantly associated markers. Additionally, our results demonstrate that RF is an interesting complementary method with added value in association studies in plants, which is illustrated by the overlap in markers identified using RF and a linear mixed model with correction for kinship and population structure. Several markers that were selected in RF and in the models with correction for kinship, but not for population structure, were also identified as QTLs in two bi-parental DH populations

Disorganization of language and working memory systems in frontal versus temporal lobe epilepsy

Cognitive impairment is a common comorbidity of epilepsy, and adversely impacts people with both frontal lobe epilepsy (FLE) and temporal lobe epilepsy (TLE). While its neural substrates have been extensively investigated in TLE, functional imaging studies in FLE are scarce. In this study, we profiled the neural processes underlying cognitive impairment in FLE, and directly compared FLE and TLE to establish commonalities and differences. We investigated 172 adult participants (56 with FLE, 64 with TLE, and 52 controls) using neuropsychological tests and four functional MRI tasks probing expressive language (verbal fluency, verb generation) and working memory (verbal and visuo-spatial). Patient groups were comparable in disease duration and anti-seizure medication load. We devise a multiscale approach to map brain activation and deactivation during cognition, and track reorganization in FLE and TLE. Voxel-based analyses were complemented with profiling of task effects across established motifs of functional brain organization: (i) canonical resting-state functional systems, and (ii) the principal functional connectivity gradient, which encodes a continuous transition of regional connectivity profiles, anchoring lower-level sensory and transmodal brain areas at the opposite ends of a spectrum. We show that cognitive impairment in FLE is associated with reduced activation across attentional and executive systems, and reduced deactivation of the default mode system, indicative of a large-scale disorganization of task-related recruitment. The imaging signatures of dysfunction in FLE were broadly similar to those in TLE, but some patterns were syndrome-specific: altered default-mode deactivation was more prominent in FLE, while impaired recruitment of posterior language areas during a task with semantic demands was more marked in TLE. Functional abnormalities in FLE and TLE appeared overall modulated by disease load. On balance, our study elucidates neural processes underlying language and working memory impairment in FLE, identifies shared and syndrome-specific alterations in the two most common focal epilepsies, and sheds light on system behavior that may be amenable to future remediation strategies

Identification of different MRI atrophy progression trajectories in epilepsy by subtype and stage inference

Artificial intelligence (AI)-based tools are widely employed, but their use for diagnosis and prognosis of neurological disorders is still evolving. Here we analyse a cross-sectional multicentre structural MRI dataset of 696 people with epilepsy and 118 control subjects. We use an innovative machine-learning algorithm, Subtype and Stage Inference, to develop a novel data-driven disease taxonomy, whereby epilepsy subtypes correspond to distinct patterns of spatiotemporal progression of brain atrophy.In a discovery cohort of 814 individuals, we identify two subtypes common to focal and idiopathic generalized epilepsies, characterized by progression of grey matter atrophy driven by the cortex or the basal ganglia. A third subtype, only detected in focal epilepsies, was characterized by hippocampal atrophy. We corroborate external validity via an independent cohort of 254 people and confirm that the basal ganglia subtype is associated with the most severe epilepsy.Our findings suggest fundamental processes underlying the progression of epilepsy-related brain atrophy. We deliver a novel MRI- and AI-guided epilepsy taxonomy, which could be used for individualized prognostics and targeted therapeutics

Identification of different MRI atrophy progression trajectories in epilepsy by subtype and stage inference

Artificial intelligence (AI)-based tools are widely employed, but their use for diagnosis and prognosis of neurological disorders is still evolving. Here we analyse a cross-sectional multicentre structural MRI dataset of 696 people with epilepsy and 118 control subjects. We use an innovative machine-learning algorithm, Subtype and Stage Inference, to develop a novel data-driven disease taxonomy, whereby epilepsy subtypes correspond to distinct patterns of spatiotemporal progression of brain atrophy.In a discovery cohort of 814 individuals, we identify two subtypes common to focal and idiopathic generalized epilepsies, characterized by progression of grey matter atrophy driven by the cortex or the basal ganglia. A third subtype, only detected in focal epilepsies, was characterized by hippocampal atrophy. We corroborate external validity via an independent cohort of 254 people and confirm that the basal ganglia subtype is associated with the most severe epilepsy.Our findings suggest fundamental processes underlying the progression of epilepsy-related brain atrophy. We deliver a novel MRI- and AI-guided epilepsy taxonomy, which could be used for individualized prognostics and targeted therapeutics

Local density of optical states in the three-dimensional band gap of a finite photonic crystal

A three-dimensional (3D) photonic band gap crystal is an ideal tool to completely inhibit the local density of optical states (LDOS) at every position in the crystal throughout the band gap. This notion, however, pertains to ideal infinite crystals, whereas any real crystal device is necessarily finite. This raises the question as to how the LDOS in the gap depends on the position and orientation inside a finite-size crystal. Therefore, we employ rigorous numerical calculations using finite-difference time domain simulations of 3D silicon inverse woodpile crystals filled with air or with toluene, as previously studied in experiments. We find that the LDOS versus position decreases exponentially into the bulk of the crystal. From the dependence on dipole orientation, we infer that the characteristic LDOS decay length ℓρ is mostly related to far-field dipolar radiation effects, whereas the prefactor is mostly related to near-field dipolar effects. The LDOS decay length has a remarkably similar magnitude to the Bragg length for directional transport, which suggests that the LDOS in the crystal is dominated by vacuum states that tunnel from the closest interface toward the position of interest. Our work leads to design rules for applications of 3D photonic band gaps in emission control and lighting, quantum information processing, and in photovoltaics

Clostridia Initiate Heavy Metal Bioremoval in Mixed Sulfidogenic Cultures

Sulfate reducing bacteria (SRB) are widely used for attenuating heavy metal pollution by means of sulfide generation. Due to their low metal tolerance, several SRB species depend on associated bacteria in mixed cultures to cope with metal-induced stress. Yet the identity of the SRB protecting bacteria is largely unknown. We aimed to identify these associated bacteria and their potential role in two highly metal-resistant mixed SRB cultures by comparing bacterial community composition and SRB activity between these cultures and two sensitive ones. The SRB composition in the resistant and sensitive consortia was similar. However, whereas the SRB in the sensitive cultures were strongly inhibited by a mixture of copper, zinc, and iron, no influence of these metals was detected on SRB growth and activity in the resistant cultures. In the latter, a Gram-positive population mostly assigned to Clostridium spp.initiated heavy metal bioremoval based on sulfide generation from components of the medium (mainly sulfite) but not from sulfate. After metal levels were lowered by the Clostridium spp. populations, SRB started sulfate reduction and raised the pH of the medium. The combination of sulfite reducing Clostridium spp. with SRB may improve green technologies for removal of heavy metals

Porphyromonas Gingivalis and E-coli induce different cytokine production patterns in pregnant women

OBJECTIVE: Pregnant individuals of many species, including humans, are more sensitive to various bacteria or their products as compared with non-pregnant individuals. Pregnant individuals also respond differently to different bacteria or their products. Therefore, in the present study, we evaluated whether the increased sensitivity of pregnant women to bacterial products and their heterogeneous response to different bacteria was associated with differences in whole blood cytokine production upon stimulation with bacteria or their products. METHODS: Blood samples were taken from healthy pregnant and age-matched non-pregnant women and ex vivo stimulated with bacteria or LPS from Porphyromonas Gingivalis (Pg) or E-coli for 24 hrs. TNFα, IL-1ß, IL-6, IL-12 and IL-10 were measured using a multiplex Luminex system. RESULTS: We observed a generally lower cytokine production after stimulation with Pg bacteria or it's LPS as compared with E-coli bacteria. However, there was also an effect of pregnancy upon cytokine production: in pregnant women the production of IL-6 upon Pg stimulation was decreased as compared with non-pregnant women. After stimulation with E-coli, the production of IL-12 and TNFα was decreased in pregnant women as compared with non-pregnant women. CONCLUSION: Our results showed that cytokine production upon bacterial stimulation of whole blood differed between pregnant and non-pregnant women, showing that the increased sensitivity of pregnant women may be due to differences in cytokine production. Moreover, pregnancy also affected whole blood cytokine production upon Pg or E-coli stimulation differently. Thus, the different responses of pregnant women to different bacteria or their products may result from variations in cytokine production