Preprint
Refine
Document Type
- Preprint (59) (remove)
Language
- English (59)
Has Fulltext
- yes (59)
Is part of the Bibliography
- no (59)
Keywords
- Computational model (1)
- Cortical column (1)
- HCV (1)
- Hypercolumn (1)
- NS3 (1)
- NS5A (1)
- Neural map (1)
- Optimal wiring (1)
- Orientation preference (1)
- Pinwheel (1)
Institute
- Biowissenschaften (59) (remove)
The establishment and maintenance of protected areas (PAs) is viewed as a key action in delivering post-2020 biodiversity targets. PAs often need to meet multiple objectives, ranging from biodiversity protection to ecosystem service provision and climate change mitigation, but available land and conservation funding is limited. Therefore, optimizing resources by selecting the most beneficial PAs is vital. Here, we advocate for a flexible and transparent approach to selecting protected areas based on multiple objectives, and illustrate this with a decision support tool on a global scale. The tool allows weighting and prioritization of different conservation objectives according to user-specified preferences, as well as real-time comparison of the selected areas that result from such different priorities. We apply the tool across 1347 terrestrial PAs and highlight frequent trade-offs among different objectives, e.g., between species protection and ecosystem integrity. Outputs indicate that decision makers frequently face trade-offs among conflicting objectives. Nevertheless, we show that transparent decision-support tools can reveal synergies and trade-offs associated with PA selection, thereby helping to illuminate and resolve land-use conflicts embedded in divergent societal and political demands and values.
Tracking influenza a virus infection in the lung from hematological data with machine learning
(2022)
The tracking of pathogen burden and host responses with minimal-invasive methods during respiratory infections is central for monitoring disease development and guiding treatment decisions. Utilizing a standardized murine model of respiratory Influenza A virus (IAV) infection, we developed and tested different supervised machine learning models to predict viral burden and immune response markers, i.e. cytokines and leukocytes in the lung, from hematological data. We performed independently in vivo infection experiments to acquire extensive data for training and testing purposes of the models. We show here that lung viral load, neutrophil counts, cytokines like IFN-γ and IL-6, and other lung infection markers can be predicted from hematological data. Furthermore, feature analysis of the models shows that blood granulocytes and platelets play a crucial role in prediction and are highly involved in the immune response against IAV. The proposed in silico tools pave the path towards improved tracking and monitoring of influenza infections and possibly other respiratory infections based on minimal-invasively obtained hematological parameters.
Fungi play pivotal roles in ecosystem functioning, but little is known about their global patterns of diversity, endemicity, vulnerability to global change drivers and conservation priority areas. We applied the high-resolution PacBio sequencing technique to identify fungi based on a long DNA marker that revealed a high proportion of hitherto unknown fungal taxa. We used a Global Soil Mycobiome consortium dataset to test relative performance of various sequencing depth standardization methods (calculation of residuals, exclusion of singletons, traditional and SRS rarefaction, use of Shannon index of diversity) to find optimal protocols for statistical analyses. Altogether, we used six global surveys to infer these patterns for soil-inhabiting fungi and their functional groups. We found that residuals of log-transformed richness (including singletons) against log-transformed sequencing depth yields significantly better model estimates compared with most other standardization methods. With respect to global patterns, fungal functional groups differed in the patterns of diversity, endemicity and vulnerability to main global change predictors. Unlike α-diversity, endemicity and global-change vulnerability of fungi and most functional groups were greatest in the tropics. Fungi are vulnerable mostly to drought, heat, and land cover change. Fungal conservation areas of highest priority include wetlands and moist tropical ecosystems.
Each lifecycle of the Hepatitis C virus (HCV) produces structural and non-structural (NS) proteins in equimolar. Structural proteins were either assembled or degraded by host proteolysis systems, while NS proteins remain inside the host cells and don’t accumulate. Therefore, they must be degraded. Here, NS3 and NS5A half-lives were quantified in the presence of autolysosome and proteasome different modulators. Inhibitors of both systems increased the half-life, while inducers decreased the half-life. Furthermore, polyubiquitination of NS3 and NS5A was observed. Additionally, their intracellular co-localization with autolysosome (LAMP2) and proteasome (PSMB5) was observed, and inhibitors of both systems increased the degree of co-localization. A better understanding of NS protein degradation might help to improve medical interventions during HCV infections in the future.
Orthologs document the evolution of genes and metabolic capacities encoded in extant and ancient genomes. Orthologous genes that are detected across the full diversity of contemporary life allow reconstructing the gene set of LUCA, the last universal common ancestor. These genes presumably represent the functional repertoire common to – and necessary for – all living organisms. Design of artificial life has the potential to test this. Recently, a minimal gene (MG) set for a self-replicating cell was determined experimentally, and a surprisingly high number of genes have unknown functions and are not represented in LUCA. However, as similarity between orthologs decays with time, it becomes insufficient to infer common ancestry, leaving ancient gene set reconstructions incomplete and distorted to an unknown extent. Here we introduce the evolutionary traceability, together with the software protTrace, that quantifies, for each protein, the evolutionary distance beyond which the sensitivity of the ortholog search becomes limiting. We show that the LUCA set comprises only high-traceable proteins most of which have catalytic functions. We further show that proteins in the MG set lacking orthologs outside bacteria mostly have low traceability, leaving open whether their eukaryotic orthologs have just been overlooked. On the example of REC8, a protein essential for chromosome cohesion, we demonstrate how a traceability-informed adjustment of the search sensitivity identifies hitherto missed orthologs in the fast-evolving microsporidia. Taken together, the evolutionary traceability helps to differentiate between true absence and non-detection of orthologs, and thus improves our understanding about the evolutionary conservation of functional protein networks.
The brains of black 6 mice (Mus musculus) and Seba’s short-tailed bats (Carollia perspicillata) weigh roughly the same and share the mammalian neocortical laminar architecture. Bats have highly developed sonar calls and social communication and are an excellent neuroethological animal model for auditory research. Mice are olfactory and somatosensory specialists and are used frequently in auditory neuroscience, particularly for their advantage of standardization and genetic tools. Investigating their potentially different general auditory processing principles would advance our understanding of how the ecological needs of a species shape the development and function of the mammalian nervous system. We compared two existing datasets, recorded with linear multichannel electrodes down the depth of the primary auditory cortex (A1) while awake, across both species while presenting repetitive stimulus trains with different frequencies (∼5 and ∼40 Hz). We found that while there are similarities between cortical response profiles in bats and mice, there was a better signal to noise ratio in bats under these conditions, which allowed for a clearer following response to stimuli trains. This was most evident at higher frequency trains, where bats had stronger response amplitude suppression to consecutive stimuli. Phase coherence was far stronger in bats during stimulus response, indicating less phase variability in bats across individual trials. These results show that although both species share cortical laminar organization, there are structural differences in relative depth of layers. Better signal to noise ratio in bats could represent specialization for faster temporal processing shaped by their individual ecological niches.
Non-ribosomal peptide synthetases (NRPSs) are the origin of a wide range of natural products, including many clinically used drugs. Engineering of these often giant biosynthetic machineries to produce novel non-ribosomal peptides (NRPs) at high titre is an ongoing challenge. Here we describe a strategy to functionally combine NRPS fragments of Gram-negative and -positive origin, synthesising novel peptides at titres up to 290 mg l-1. Extending from the recently introduced definition of eXchange Units (XUs), we inserted synthetic zippers (SZs) to split single protein NRPSs into up to three independently expressed and translated polypeptide chains. These synthetic type of NRPS (type S) enables easier access to engineering, overcomes cloning limitations, and provides a simple and rapid approach to building peptide libraries via the combination of different NRPS subunits.
Communication sounds are ubiquitous in the animal kingdom, where they play a role in advertising physiological states and/or socio-contextual scenarios. Distress sounds, for example, are typically uttered in distressful scenarios such as agonistic interactions. Here, we report on the occurrence of superfast temporal periodicities in distress calls emitted by bats (species Carollia perspicillata). Distress vocalizations uttered by this bat species are temporally modulated at frequencies close to 1.7 kHz, that is, ∼17 times faster than modulation rates observed in human screams. Fast temporal periodicities are represented in the bats’ brain by means of frequency following responses, and temporally periodic sounds are more effective in boosting the heart rate of awake bats than their demodulated versions. Altogether, our data suggest that bats, an animal group classically regarded as ultrasonic, can exploit the low frequency portion of the soundscape during distress calling to create spectro-temporally complex, arousing sounds.
Zinc finger (ZnF) domains appear in a pool of structural contexts and despite their small size achieve varying target specificities, covering single-stranded and double-stranded DNA and RNA as well as proteins. Combined with other RNA-binding domains, ZnFs enhance affinity and specificity of RNA-binding proteins (RBPs). The ZnF-containing immunoregulatory RBP Roquin initiates mRNA decay, thereby controlling the adaptive immune system. Its unique ROQ domain shape-specifically recognizes stem-looped cis-elements in mRNA 3’-untranslated regions (UTR). The N-terminus of Roquin contains a RING domain for protein-protein interactions and a ZnF, which was suggested to play an essential role in RNA decay by Roquin. The ZnF domain boundaries, its RNA motif preference and its interplay with the ROQ domain have remained elusive, also driven by the lack of high-resolution data of the challenging protein. We provide the solution structure of the Roquin-1 ZnF and use an RBNS-NMR pipeline to show that the ZnF recognizes AU-rich elements (ARE). We systematically refine the contributions of adenines in a poly(U)-background to specific complex formation. With the simultaneous binding of ROQ and ZnF to a natural target transcript of Roquin, our study for the first time suggests how Roquin integrates RNA shape and sequence specificity through the ROQ-ZnF tandem.
The epitranscriptome embodies many new and largely unexplored functions of RNA. A major roadblock in the epitranscriptomics field is the lack of transcriptome-wide methods to detect more than a single RNA modification type at a time, identify RNA modifications in individual molecules, and estimate modification stoichiometry accurately. We address these issues with CHEUI (CH3 (methylation) Estimation Using Ionic current), a new method that concurrently detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual RNA molecules from the same sample, as well as differential methylation between any two conditions, using signals from nanopore direct RNA sequencing. CHEUI processes observed and expected signals with convolutional neural networks to achieve high single-molecule accuracy and outperform other methods in detecting m6A and m5C sites and quantifying their stoichiometry. CHEUI’s unique capability to identify two modification types in the same sample reveals a non-random co-occurrence of m6A and m5C in mRNA transcripts in cell lines and tissues. CHEUI unlocks an unprecedented potential to study RNA modification configurations and discover new epitranscriptome functions.