Differences in metal sequestration between zebra mussels from clean and polluted field locations

Organisms are able to detoxify accumulated metals by, e.g. binding them to metallothionein (MT) and/or sequestering them in metal-rich granules (MRG). The different factors involved in determining the capacity or efficiency with which metals are detoxified are not yet known.In this work we studied how the sub-cellular distribution pattern of cadmium, copper and zinc in whole tissue of zebra mussels from clean and polluted surface waters is influenced by the total accumulated metal concentration and by its physiological condition. Additionally we measured the metallothionein concentration in the mussel tissue. Metal concentration increased gradually in the metal-sensitive and detoxified sub-cellular fractions with increasing whole tissue concentrations. However, metal concentrations in the sensitive fractions did not increase to the same extent as metal concentrations in whole tissues. In more polluted mussels the contribution of MRG and MT became more important. Nevertheless, metal detoxification was not sufficient to prevent metal binding to heat-sensitive low molecular weight proteins (HDP fraction). Finally we found an indication that metal detoxification was influenced by the condition of the zebra mussels. MT content could be explained for up to 83% by variations in Zn concentration and physiological condition of the mussels

The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy.

ObjectiveThe distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment.MethodsT1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results.ResultsThe SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p < .001; accuracy: 70%, p < .001) and the CV-model (AUC: .59, p < .001; accuracy: 64%, p < .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance.ConclusionThe SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance

Ultra-high-field fMRI reveals a role for the subiculum in scene perceptual discrimination

Recent “representational” accounts suggest a key role for the hippocampus in complex scene perception. Due to limitations in scanner field strength, however, the functional neuroanatomy of hippocampal-dependent scene perception is unknown. Here, we applied 7 T high-resolution functional magnetic resonance imaging (fMRI) alongside a perceptual oddity task, modified from nonhuman primate studies. This task requires subjects to discriminate highly similar scenes, faces, or objects from multiple viewpoints, and has revealed selective impairments during scene discrimination following hippocampal lesions. Region-of-interest analyses identified a preferential response in the subiculum subfield of the hippocampus during scene, but not face or object, discriminations. Notably, this effect was in the anteromedial subiculum and was not modulated by whether scenes were subsequently remembered or forgotten. These results highlight the value of ultra-high-field fMRI in generating more refined, anatomically informed, functional accounts of hippocampal contributions to cognition, and a unique role for the human subiculum in discrimination of complex scenes from different viewpoints

Revealing natural relationships among arbuscular mycorrhizal fungi: culture line BEG47 represents Diversispora epigaea, not Glomus versiforme

Background: Understanding the mechanisms underlying biological phenomena, such as evolutionarily conservative trait inheritance, is predicated on knowledge of the natural relationships among organisms. However, despite their enormous ecological significance, many of the ubiquitous soil inhabiting and plant symbiotic arbuscular mycorrhizal fungi (AMF, phylum Glomeromycota) are incorrectly classified. Methodology/Principal Findings: Here, we focused on a frequently used model AMF registered as culture BEG47. This fungus is a descendent of the ex-type culture-lineage of Glomus epigaeum, which in 1983 was synonymised with Glomus versiforme. It has since then been used as ‘G. versiforme BEG47’. We show by morphological comparisons, based on type material, collected 1860–61, of G. versiforme and on type material and living ex-type cultures of G. epigaeum, that these two AMF species cannot be conspecific, and by molecular phylogenetics that BEG47 is a member of the genus Diversispora. Conclusions: This study highlights that experimental works published during the last >25 years on an AMF named ‘G. versiforme’ or ‘BEG47’ refer to D. epigaea, a species that is actually evolutionarily separated by hundreds of millions of years from all members of the genera in the Glomerales and thus from most other commonly used AMF ‘laboratory strains’. Detailed redescriptions substantiate the renaming of G. epigaeum (BEG47) as D. epigaea, positioning it systematically in the order Diversisporales, thus enabling an evolutionary understanding of genetical, physiological, and ecological traits, relative to those of other AMF. Diversispora epigaea is widely cultured as a laboratory strain of AMF, whereas G. versiforme appears not to have been cultured nor found in the field since its original description

A note on decoding elicited and self-generated inner speech

A recent result shows that inner speech can, with proper care, be decoded to the same high-level of accuracy as articulated speech. This relies, however, on neural data obtained while subjects perform elicited tasks, such as covert reading and repeating, whereas a neural speech prosthetic will require the decoding of inner speech that is self-generated. Prior work has, moreover, emphasised differences between these two kinds of inner speech, raising the question of how well a decoder optimised for one will generalise to the other. In this study, we trained phoneme-level decoders on an atypically large, elicited inner speech dataset, previously acquired using 7T fMRI in a single subject. We then acquired a second self-generated inner speech dataset in the same subject. Although the decoders were trained exclusively on neural recordings obtained during elicited inner speech, they predicted unseen phonemes accurately in both elicited and selfgenerated test conditions, illustrating the viability of zero-shot task transfer. This has significant practical importance for the development of a neural speech prosthetic, as labelled data is far easier to acquire at scale for elicited than for self-generated inner speech. Indeed, elicited tasks may be the only option for acquiring labelled data in critical patient populations who cannot control their vocal articulators

How do changes in the mtDNA and mitochondrial dysfunction influence cancer and cancer therapy? Challenges, opportunities and models

Several mutations in nuclear genes encoding for mitochondrial components have been associated with an increased cancer risk or are even causative, e.g. succinate dehydrogenase (SDHB, SDHC and SDHD genes) and iso-citrate dehydrogenase (IDH1 and IDH2 genes). Recently, studies have suggested an eminent role for mitochondrial DNA (mtDNA) mutations in the development of a wide variety of cancers. Various studies associated mtDNA abnormalities, including mutations, deletions, inversions and copy number alterations, with mitochondrial dysfunction. This might, explain the hampered cellular bioenergetics in many cancer cell types. Germline (e.g. m.10398A>G; m.6253T>C) and somatic mtDNA mutations as well as differences in mtDNA copy number seem to be associated with cancer risk. It seems that mtDNA can contribute as driver or as complementary gene mutation according to the multiple-hit model. This can enhance the mutagenic/clonogenic potential of the cell as observed for m.8993T>G or influences the metastatic potential in later stages of cancer progression. Alternatively, other mtDNA variations will be innocent passenger mutations in a tumor and therefore do not contribute to the tumorigenic or metastatic potential. In this review, we discuss how reported mtDNA variations interfere with cancer treatment and what implications this has on current successful pharmaceutical interventions. Mutations in MT-ND4 and mtDNA depletion have been reported to be involved in cisplatin resistance. Pharmaceutical impairment of OXPHOS by metformin can increase the efficiency of radiotherapy. To study mitochondrial dysfunction in cancer, different cellular models (like ρ0 cells or cybrids), in vivo murine models (xenografts and specific mtDNA mouse models in combination with a spontaneous cancer mouse model) and small animal models (e.g. Danio rerio) could be potentially interesting to use. For future research, we foresee that unraveling mtDNA variations can contribute to personalized therapy for specific cancer types and improve the outcome of the disease

Scene-selectivity in CA1/subicular complex:Multivoxel pattern analysis at 7T

Prior univariate functional magnetic resonance imaging (fMRI) studies in humans suggest that the anteromedial subicular complex of the hippocampus is a hub for scene-based cognition. However, it is possible that univariate approaches were not sufficiently sensitive to detect scene-related activity in other subfields that have been implicated in spatial processing (e.g., CA1). Further, as connectivity-based functional gradients in the hippocampus do not respect classical subfield boundary definitions, category sensitivity may be distributed across anatomical subfields. Region-of-interest approaches, therefore, may limit our ability to observe category selectivity across discrete subfield boundaries. To address these issues, we applied searchlight multivariate pattern analysis to 7T fMRI data of healthy adults who undertook a simultaneous visual odd-one-out discrimination task for scene and non-scene (including face) visual stimuli, hypothesising that scene classification would be possible in multiple hippocampal regions within, but not constrained to, anteromedial subicular complex and CA1. Indeed, we found that the scene-selective searchlight map overlapped not only with anteromedial subicular complex (distal subiculum, pre/para subiculum), but also inferior CA1, alongside retrosplenial and parahippocampal cortices. Probabilistic overlap maps revealed gradients of scene category selectivity, with the strongest overlap located in the medial hippocampus, converging with searchlight findings. This was contrasted with gradients of face category selectivity, which had stronger overlap in more lateral hippocampus, supporting ideas of parallel processing streams for these two categories. Our work helps to map the scene, in contrast to, face processing networks within, and connected to, the human hippocampus