Biology subjects

Schmidt, K.

Publications and source records attributed to Schmidt, K..

44 records · Page 3Linked to original sources

bioRxiv2021

Restriction of Wolbachia bacteria in early embryogenesis of neotropical Drosophila species via ER-mediated autophagy

Wolbachia bacteria are maternally transmitted intracellular microbes that are not only restricted to the reproductive organs but also found in various somatic tissues of their native hosts. The abundance of the endosymbiont in somatic tissues, usually a dead end for vertically transmitted bacteria, causes a multitude of effects on life history traits of their hosts, which are still not well understood. Thus, deciphering the host-symbiont interactions on a cellular level throughout a hosts lifecycle is of great importance to understand their homeostatic nature, persistence and spreading success. Using fluorescent and transmission electron microscopy, we conducted a comprehensive analysis of Wolbachia tropism in somatic and reproductive tissues of six Drosophila species at the intracellular level during host development. Our data uncovered diagnostic patterns of infections to embryonic primordial germ cells and to particular cells of somatic tissues in three different neotropical Drosophila species of the willistoni and saltans groups that have apparently evolved in both independently. We further found that restricted patterns of Wolbachia tropism are already determined in early fly embryogenesis. This is achieved via selective autophagy, and the restriction of infection is preserved through larval hatching and metamorphosis. We further uncovered tight interactions of Wolbachia with membranes of the endoplasmic reticulum, which might play a scaffolding role for autophagosome formation and subsequent elimination of the endosymbiont. Finally, by analyzing D. simulans lines transinfected with non-native Wolbachia, we uncovered that the host genetic background regulates tissue tropism of infection. Our data demonstrate a peculiar and novel mechanism to limit and spatially restrict bacterial infection in somatic tissues during a very early stage of host development.

developmental biology↗

bioRxiv2020

Metagenome-assembled genomes of phytoplankton communities across the Arctic Circle

Phytoplankton communities significantly contribute to global biogeochemical cycles of elements and underpin marine food webs. Although their uncultured genetic diversity has been estimated by planetary-scale metagenome sequencing and subsequent reconstruction of metagenome-assembled genomes (MAGs), this approach has yet to be applied for eukaryote-enriched polar and non-polar phytoplankton communities. Here, we have assembled draft prokaryotic and eukaryotic MAGs from environmental DNA extracted from chlorophyll a maximum layers in the surface ocean across the Arctic Circle in the Atlantic. From 679 Gbp and estimated 50 million genes in total, we recovered 140 MAGs of medium to high quality. Although there was a strict demarcation between polar and non-polar MAGs, adjacent sampling stations in each environment on either side of the Arctic Circle had MAGs in common. Furthermore, phylogenetic placement revealed eukaryotic MAGs to be more diverse in the Arctic whereas prokaryotic MAGs were more diverse in the Atlantic south of the Arctic Circle. Approximately 60% of protein families were shared between polar and non-polar MAGs for both prokaryotes and eukaryotes. However, eukaryotic MAGs had more protein families unique to the Arctic whereas prokaryotic MAGs had more families unique to south of the Arctic circle. Thus, our study enabled us to place differences in functional plankton diversity in a genomic context to reveal that the evolution of these MAGs likely was driven by significant differences in the seascape on either side of an ecosystem boundary that separates polar from non-polar surface ocean waters in the North Atlantic.

microbiology↗

bioRxiv2020

An endocrine-hepato-muscular metabolic cycle links skeletal muscle atrophy and hyperglycemia in type 2 diabetes

Both obesity and sarcopenia are frequently associated in ageing, and together may promote the progression of related conditions such as diabetes and frailty. However, little is known about the pathophysiological mechanisms underpinning this association. Here we uncover dysregulated systemic alanine metabolism and hyper-expression of the alanine transaminases (ALT) in the liver of obese/diabetic mice and humans. Hepatocyte-selective silencing of both ALT enzymes revealed a clear role in systemic alanine clearance which related to glycemic control. In obese/diabetic mice, not only did silencing both ALT enzymes retard hyperglycemia, but also reversed skeletal muscle atrophy. This was due to a rescue of depressed skeletal muscle protein synthesis, with a liver-skeletal muscle amino acid metabolic crosstalk exemplified by ex vivo experiments. Mechanistically, chronic liver glucocorticoid and glucagon signaling driven liver alanine catabolism promoted hyperglycemia and skeletal muscle wasting. Taken together, here we reveal an endocrine-hepato-muscular metabolic cycle linking hyperglycemia and skeletal muscle atrophy in type 2 diabetes.

physiology↗

bioRxiv2020

A Cytoskeletal Protein Complex is Essential for Division of Intracellular Amastigotes of Leishmania mexicana

Previous studies in Leishmania mexicana have identified the cytoskeletal protein KHARON as being important for both flagellar trafficking of the glucose transporter GT1 and for successful cytokinesis and survival of infectious amastigote forms inside mammalian macrophages. KHARON is located in three distinct regions of the cytoskeleton: the base of the flagellum, the subpellicular microtubules, and the mitotic spindle. To deconvolve the different functions for KHARON, we have identified two partner proteins, KHAP1 and KHAP2, that associate with KHARON. KHAP1 is located only in the subpellicular microtubules, while KHAP2 is located at the subpellicular microtubules and the base of the flagellum. Both the KHAP1 and KHAP2 null mutants are unable to execute cytokinesis but are able to traffic GT1 to the flagellum. These results confirm that KHARON assembles into distinct functional complexes and that the subpellicular complex is essential for cytokinesis and viability of disease-causing amastigotes but not for flagellar membrane trafficking.

microbiology↗

bioRxiv2018

Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?

The internal representations of early deep artificial neural networks (ANNs) were found to be remarkably similar to the internal neural representations measured experimentally in the primate brain. Here we ask, as deep ANNs have continued to evolve, are they becoming more or less brain-like? ANNs that are most functionally similar to the brain will contain mechanisms that are most like those used by the brain. We therefore developed Brain-Score - a composite of multiple neural and behavioral benchmarks that score any ANN on how similar it is to the brains mechanisms for core object recognition - and we deployed it to evaluate a wide range of state-of-the-art deep ANNs. Using this scoring system, we here report that: (1) DenseNet-169, CORnet-S and ResNet-101 are the most brain-like ANNs. (2) There remains considerable variability in neural and behavioral responses that is not predicted by any ANN, suggesting that no ANN model has yet captured all the relevant mechanisms. (3) Extending prior work, we found that gains in ANN ImageNet performance led to gains on Brain-Score. However, correlation weakened at [≥] 70% top-1 ImageNet performance, suggesting that additional guidance from neuroscience is needed to make further advances in capturing brain mechanisms. (4) We uncovered smaller (i.e. less complex) ANNs that are more brain-like than many of the best-performing ImageNet models, which suggests the opportunity to simplify ANNs to better understand the ventral stream. The scoring system used here is far from complete. However, we propose that evaluating and tracking model-benchmark correspondences through a Brain-Score that is regularly updated with new brain data is an exciting opportunity: experimental benchmarks can be used to guide machine network evolution, and machine networks are mechanistic hypotheses of the brains network and thus drive next experiments. To facilitate both of these, we release Brain-Score.org: a platform that hosts the neural and behavioral benchmarks, where ANNs for visual processing can be submitted to receive a Brain-Score and their rank relative to other models, and where new experimental data can be naturally incorporated.

neuroscience↗

bioRxiv2018

Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks

Primates--including humans--can typically recognize objects in visual images at a glance even in the face of naturally occurring identity-preserving image transformations (e.g. changes in viewpoint). A primary neuroscience goal is to uncover neuron-level mechanistic models that quantitatively explain this behavior by predicting primate performance for each and every image. Here, we applied this stringent behavioral prediction test to the leading mechanistic models of primate vision (specifically, deep, convolutional, artificial neural networks; ANNs) by directly comparing their behavioral signatures against those of humans and rhesus macaque monkeys. Using high-throughput data collection systems for human and monkey psychophysics, we collected over one million behavioral trials for 2400 images over 276 binary object discrimination tasks. Consistent with previous work, we observed that state-of-the-art deep, feed-forward convolutional ANNs trained for visual categorization (termed DCNNIC models) accurately predicted primate patterns of object-level confusion. However, when we examined behavioral performance for individual images within each object discrimination task, we found that all tested DCNNIC models were significantly non-predictive of primate performance, and that this prediction failure was not accounted for by simple image attributes, nor rescued by simple model modifications. These results show that current DCNNIC models cannot account for the image-level behavioral patterns of primates, and that new ANN models are needed to more precisely capture the neural mechanisms underlying primate object vision. To this end, large-scale, high-resolution primate behavioral benchmarks--such as those obtained here--could serve as direct guides for discovering such models.\n\nSIGNIFICANCE STATEMENTRecently, specific feed-forward deep convolutional artificial neural networks (ANNs) models have dramatically advanced our quantitative understanding of the neural mechanisms underlying primate core object recognition. In this work, we tested the limits of those ANNs by systematically comparing the behavioral responses of these models with the behavioral responses of humans and monkeys, at the resolution of individual images. Using these high-resolution metrics, we found that all tested ANN models significantly diverged from primate behavior. Going forward, these high-resolution, large-scale primate behavioral benchmarks could serve as direct guides for discovering better ANN models of the primate visual system.

neuroscience↗

bioRxiv2017

Strong positive biodiversity-productivity relationships in a subtropical forest experiment

Forest ecosystems contribute substantially to global terrestrial primary productivity and climate regulation, but, in contrast to grasslands, experimental evidence for a positive biodiversity-productivity relationship in highly diverse forests is still lacking1. Here, we provide such evidence from a large forest biodiversity experiment with a novel design2 in subtropical China. Productivity (stand-level tree basal area, aboveground volume and carbon and their annual increment) increased linearly with the logarithm of tree species richness. Additive partitioning3 showed that increasing positive complementarity effects combined with weakening negative selection effects caused a strengthening of the relationship over time. In 2-species mixed stands, complementary effects increased with functional distance and selection effects with vertical crown dissimilarity between species. Understorey shrubs reduced stand-level tree productivity, but this effect of competition was attenuated by shrub species richness, indicating that a diverse understorey may facilitate overall ecosystem functioning. Identical biodiversity-productivity relationships were found in plots of different size, suggesting that extrapolation to larger scales is possible. Our results highlight the potential of multi-species afforestation strategies to simultaneously contribute to mitigation of climate change and biodiversity restoration.

ecology↗

bioRxiv2017

Targeted re-sequencing of coding DNA sequences for SNP discovery in non-model species

Hybridization capture coupled with high-throughput sequencing can be used to gain information about nuclear sequence variation at hundreds to thousands of loci. A cross-species approach makes use of molecular data of one species to enrich target loci in other (related) species. This is particularly valuable for non-model organisms, for which often no a priori knowledge exists regarding these loci. Here, we have adopted cross-species capture to obtain data for 809 nuclear coding DNA sequences (CDS) in a non-model organism, the Eurasian lynx Lynx lynx, using baits designed with the help of the published genome of a related model organism (the domestic cat Felis catus). In this manner, we were able to survey intraspecific variation at hundreds of nuclear loci across the European range of L. lynx. A large set of bi-allelic candidate SNPs was then tested in a high throughput SNP-genotyping platform (Fluidigm), which we reduced to a final 96 SNP-panel based on assay performance and reliability; validation was carried out with additional samples not included in the SNP discovery phase. The 96 SNP-panel developed from CDS performed very successfully in the identification of individuals and in population genetic structure inference (incl. the assignment of individuals to their source population). In keeping with recent studies, our results show that genic SNPs can be valuable for genetic monitoring of wildlife species.

genetics↗