Biology subjects

Braun, W.

Publications and source records attributed to Braun, W..

10 recordsLinked to original sources

bioRxiv2024

AllergenAI: a deep learning model predicting allergenicity based on protein sequence

Innovations in protein engineering can help redesign allergenic proteins to reduce adverse reactions in sensitive individuals. To accomplish this aim, a better knowledge of the molecular properties of allergenic proteins and the molecular features that make a protein allergenic is needed. We present a novel AI-based tool, AllergenAI, to quantify the allergenic potential of a given protein. Our approach is solely based on protein sequences, differentiating it from previous tools that use some knowledge of the allergens physicochemical and other properties in addition to sequence homology. We used the collected data on protein sequences of allergenic proteins as archived in the three well-established databases, SDAP 2.0, COMPARE, and AlgPred 2, to train a convolutional neural network and assessed its prediction performance by cross-validation. We then used Allergen AI to find novel potential proteins of the cupin family in date palm, spinach, maize, and red clover plants with a high allergenicity score that might have an adverse allergenic effect on sensitive individuals. By analyzing the feature importance scores (FIS) of vicilins, we identified a proline-alanine-rich (P-A) motif in the top 50% of FIS regions that overlapped with known IgE epitope regions of vicilin allergens. Furthermore, using[~] 1600 allergen structures in our SDAP database, we showed the potential to incorporate 3D information in a CNN model. Future, incorporating 3D information in training data should enhance the accuracy. AllergenAI is a novel foundation for identifying the critical features that distinguish allergenic proteins.

bioinformatics↗

bioRxiv2024

A functional network model for body column neural connectivity in Hydra

Hydra is a non-senescent animal with a relatively small number of cell types and overall low structural complexity, but a surprisingly rich behavioral repertoire. The main drivers of Hydra s behavior are neurons that are arranged in two nerve nets comprising several distinct neuronal populations. Among these populations is the ectodermal nerve net N3 which is located throughout the animal. It has been shown that N3 is necessary and sufficient for the complex behavior of somersaulting and is also involved in Hydra feeding behavior. Despite being a behavioral jack-of-all-trades, there is insufficient knowledge on the coupling structure of neurons in N3, its connectome, and its role in activity propagation and function. We construct a model connectome for the part of N3 located on the body column. Using experimental data on the placement of neuronal somata and the spatial dimensions of the body column, we show that a generative network model combining non-random placement of neuronal somata and the preferred orientation of primary neurites yields good agreement with experimentally observed distributions of connection distances, connection angles, and the number of primary neurites per neuron. Having validated the N3 connectome model in this fashion, we place a simple excitable dynamical model on each node of the body column network and show that it generates directed, short-lived, fast propagating patterns of activity. In addition, by slightly changing the parameters of the dynamical model, the same structural network can also generate persistent activity. Finally, we use a neuromorphic circuit based on the Morris-Lecar model to show that the same structural connectome can, in addition to through-conductance with biologically plausible time scales, also host a dynamical pattern related to the complex behavioral pattern of somersaulting. We speculate that such different dynamical regimes act as dynamical substrates for the different functional roles of N3, allowing Hydra to exhibit behavioral complexity with a relatively simple nervous system that does not possess modules or hubs.

neuroscience↗

bioRxiv2024

A new approach for estimating effective connectivity from activity in neural networks

Inferring and understanding the underlying connectivity structure of a system solely from the observed activity of its constituent components is a challenge in many areas of science. In neuroscience, techniques for estimating connectivity are paramount when attempting to understand the network structure of neural systems from their recorded activity patterns. To date, no universally accepted method exists for the inference of effective connectivity, which describes how the activity of a neural node mechanistically affects the activity of other nodes. Here, focussing on purely excitatory networks of small to intermediate size and continuous node dynamics, we provide a systematic comparison of different approaches for estimating effective connectivity. Starting with the Hopf neuron model in conjunction with known ground truth structural connectivity, we reconstruct the systems connectivity matrix using a variety of algorithms. We show that, in sparse non-linear networks with delays, combining a lagged-cross-correlation (LCC) approach with a recently published derivative-based covariance analysis method provides the most reliable estimation of the known ground truth connectivity matrix. We outline how the parameters of the Hopf model, including those controlling the bifurcation, noise, and delay distribution, affect this result. We also show that in linear networks, LCC has comparable performance to a method based on transfer entropy, at a drastically lower computational cost. We highlight that LCC works best for small sparse networks, and show how performance decreases in larger and less sparse networks. Applying the method to linear dynamics without time delays, we find that it does not outperform derivative-based methods. We comment on this finding in light of recent theoretical results for such systems. Employing the Hopf model, we then use the estimated structural connectivity matrix as the basis for a forward simulation of the system dynamics, in order to recreate the observed node activity patterns. We show that, under certain conditions, the best method, LCC, results in higher trace-to-trace correlations than derivative-based methods for sparse noise-driven systems. Finally, we apply the LCC method to empirical biological data. Choosing a suitable threshold for binarization, we reconstruct the structural connectivity of a subset of the nervous system of the nematode C. Elegans. We show that the computationally simple LCC method performs better than another recently published, computationally more expensive reservoir computing-based method. We apply different methods to this dataset and find that they all lead to similar performances. Our results show that a comparatively simple method can be used to reliably estimate directed effective connectivity in sparse neural systems in the presence of spatio-temporal delays and noise. We provide concrete suggestions for the estimation of effective connectivity in a scenario common in biological research, where only neuronal activity of a small set of neurons, but not connectivity or single-neuron and synapse dynamics, are known.

neuroscience↗

bioRxiv2022

Peptide and protein alphavirus antigens for broad spectrum vaccine design

Vaccines based on proteins and peptides may be safer and more broad-spectrum than other approaches Physicochemical property consensus (PCPcon) alphavirus antigens from the B-domain of the E2 envelope protein were designed and synthesized recombinantly. Those based on individual species (eastern or Venezuelan equine encephalitis (EEEVcon, VEEVcon), or chikungunya (CHIKVcon) viruses generated species-specific antibodies. Peptides designed to surface exposed areas of the E2-A-domain were added to the inocula to provide neutralizing antibodies against CHIKV. EVCcon, based on the three different alphavirus species, combined with E2-A-domain peptides from AllAV, a PCPcon of 24 diverse alphavirus, generated broad spectrum antibodies. The abs in the sera bound and neutralized diverse alphaviruses with less than 35% amino acid identity to each other. These included VEEV and its relative Mucambo virus, EEEV and the related Madariaga virus, and CHIKV strain 181/25. Further understanding of the role of coordinated mutations in the envelope proteins may yield a single, protein and peptide vaccine against all alphaviruses.

bioengineering↗

bioRxiv2022

Radioactive Contamination in Feral Dogs in the Chernobyl Exclusion Zone: Population Body-Burden Survey and Implications for Human Radiation Exposure

This report describes a two-year effort to survey the internal 137Cs and external {beta}-emitter contamination present in the feral dog population near the Chernobyl nuclear power plant (ChNPP) site, and to quantify the potential for human radiation exposure from this contamination. This work was performed as an integral part of the radiation safety and control procedures of an animal welfare oriented trap-neuter-release (TNR) program. The measurement program employed handheld {beta}-sensitive probes, and a simple whole-body counter to measure internal 137Cs burden during post-surgical observation and recovery. External {beta} contamination surveys performed during intake showed that 21/288 animals had significant, removable external contamination. Measurements with the whole-body counter indicated internal 137Cs body burdens ranging from undetectable (minimum detection level [~]100 Bq/kg in 2017, [~] 30 Bq/kg in 2018) to approximately 30,000 Bq/kg. A total of 33 animals had 137Cs body-burdens above 1 kBq/kg. We observe that internal contamination levels are positively correlated with capture locations within ChNPP boundaries. The large variation in the 137Cs concentration in these animals is not well-understood, could be due to prey selection, access to human food scraps, or extended residence in highly contaminated areas. These internally-contaminated animals are unlikely to pose an exposure hazard despite their large body-burdens due to their limited exposure to humans. However, the small minority of animals with external contamination may pose a contamination hazard to workers, tourists, and others interacting with the dogs, as evidenced by total quantity of removable activity and incidents of transfer to materials used in animal capture.

zoology↗

bioRxiv2021

High-frequency oscillations and replay in a two-population model of hippocampal region CA1

Hippocampal sharp wave/ripple oscillations are a prominent pattern of collective activity, which consists of a strong overall increase of activity with onmodulated (140 - 200 Hz) ripple oscillations. Despite its prominence and its experimentally demonstrated importance for memory consolidation, the mechanisms underlying its generation are to date not understood. Several models assume that recurrent networks of inhibitory cells alone can explain the generation and main characteristics of the ripple oscillations. Recent experiments, however, indicate that in addition to inhibitory basket cells, the pattern requires in vivo the activity of the local population of excitatory pyramidal cells. Here we study a model for networks in the hippocampal region CA1 incorporating such a local excitatory population of pyramidal neurons and investigate its ability to generate ripple oscillations using extensive simulations. We find that with biologically plausible values for single neuron, synapse and connectivity parameters, random connectivity and absent strong feedforward drive to the inhibitory population, oscillation patterns similar to in vivo sharp wave/ripples can only be generated if excitatory cell spiking is triggered by short pulses of external excitation. Specifically, whereas temporally broad excitation can lead to high-frequency oscillations in the ripple range, sparse pyramidal cell activity is only obtained with pulse-like external CA3 excitation. Further simulations indicate that such short pulses could originate from dendritic spikes in the apical or basal dendrites of CA1 pyramidal cells, which are triggered by coincident spike arrivals from hippocampal region CA3. Finally we show that replay of sequences by pyramidal neurons and ripple oscillations can arise intrinsically in CA1 due to structured connectivity that gives rise to alternating excitatory pulse and inhibitory gap coding; the latter implies phases of silence in specific basket cell groups and selective disinhibition of groups of pyramidal neurons. This general mechanism for sequence generation leads to sparse pyramidal cell and dense basket cell spiking, does not rely on synfire chain-like feedforward excitation and may be relevant for other brain regions as well. Author summaryDuring certain phases of sleep, rest and consummatory behavior the hippocampus brain area of many species, including humans, is known to intermittently generate strong high frequency oscillations. These oscillations are important for memory formation and consolidation. To date, the mechanisms underlying their generation remain incompletely understood. We find that in unstructured networks carefully designing how excitation is transmitted in the hippocampus is required for the generation of robust fast oscillations in its main output region. Broad, temporally extended excitation of cells results in unrealistic single cell activity, whereas temporally narrow input that differs from cell to cell gives rise to oscillations with realistic single cell and network activity. We show that the biophysical mechanism to generate the required temporally narrow excitation may be related to spiking events in the dendrites, which are triggered by coincident input. Our results in structured networks suggest that the interplay of hippocampal excitation and inhibition can serve as a means to generate robust sequential activity, which is thought to be crucial for memory formation and recall. The sequence generation mechanism also leads to strong high frequency oscillations with sparse excitatory cell and frequent inhibitory cell spiking, as observed in the hippocampus.

neuroscience↗

bioRxiv2021

Laser facilitated epicutaneous peptide immunization using dry patch technology

The skin has been intensely investigated as a target tissue for immunization because it is populated by multiple types of antigen presenting cells. Directly addressing dendritic cells or Langerhans cells in vivo represents an attractive strategy for inducing T cell responses in cancer immunotherapy. We and others have studied fractional laser ablation as a novel method combining efficient delivery of macromolecules to the skin with an inherent adjuvant effect of laser illumination. In this proof of concept study, we demonstrate the feasibility of peptide delivery to the skin using the P.L.E.A.S.E. professional Erb:YAG fractional infrared laser together with EPIMMUN patches. In an ovalbumin mouse model we demonstrate that a dry patch formulation of SIINFEKL peptide in combination with CpG-ODN1826, but not imiquimod or polyI:C, induces potent cytotoxic T cell responses, which can be further boosted by co-delivery of the pan-helper T cell epitope PADRE.

immunology↗

bioRxiv2021

Laser-facilitated epicutaneous immunization with SARS-CoV-2 spike protein induces ACE2 blocking antibodies in mice

The skin represents an attractive target tissue for vaccination against respiratory viruses such as SARS-CoV-2. Laser-facilitated epicutaneous immunization (EPI) has been established as a novel technology to overcome the skin barrier, which combines efficient delivery via micropores with an inherent adjuvant effect due to the release of danger-associated molecular patterns. Here we delivered the S1 subunit of the Spike protein of SARS-CoV-2 to the skin of BALB/c mice via laser-generated micropores with or without CpG-ODN1826 or the B subunit of heat-labile enterotoxin of E.coli (LT-B). EPI induced serum IgG titers of 1:3200 that could be boosted 5 to 10-fold by co-administration of LT-B and CpG, respectively. Sera were able to inhibit binding of the spike protein to its receptor ACE2. Our data indicate that delivery of recombinant spike protein via the skin may represent an alternative route for vaccines against Covid-19.

immunology↗

bioRxiv2020

D-graph clusters flaviviruses and β-coronaviruses according to their hosts, disease type and human cell receptors

MotivationThere is a need for rapid and easy to use, alignment free methods to cluster large groups of protein sequence data. Commonly used phylogenetic trees based on alignments can be used to visualize only a limited number of protein sequences. DGraph, introduced here, is a dynamic programming application developed to generate 2D-maps based on similarity scores for sequences. The program automatically calculates and graphically displays property distance (PD) scores based on physico-chemical property (PCP) similarities from an unaligned list of FASTA files. Such "PD-graphs" show the interrelatedness of the sequences, whereby clusters can reveal deeper connectivities. ResultsPD-Graphs generated for flavivirus (FV), enterovirus (EV), and coronavirus (CoV) sequences from complete polyproteins or individual proteins are consistent with biological data on vector types, hosts, cellular receptors and disease phenotypes. PD-graphs separate the tick- from the mosquito-borne FV, clusters viruses that infect bats, camels, seabirds and humans separately and the clusters correlate with disease phenotype. The PD method segregates the {beta}-CoV spike proteins of SARS, SARS-CoV-2, and MERS sequences from other human pathogenic CoV, with clustering consistent with cellular receptor usage. The graphs also suggest evolutionary relationships that may be difficult to determine with conventional bootstrapping methods that require postulating an ancestral sequence. Availability and implementationDGraph is written in Java, compatible with the Java 5 runtime or newer. Source code and executable is available from the GitHub website (https://github.com/bjmnbraun/DGraph/releases). Documentation for installation and use of the software is available from the Readme.md file at (https://github.com/bjmnbraun/DGraph). Contactbjmnbraun@gmail.com or webraun@utmb.edu Supplementary informationSupplementary information Table S1 and Fig. S1 are online available.

bioinformatics↗

bioRxiv2019

From Single Neurons to Behavior in the Jellyfish Aurelia aurita

Jellyfish nerve nets provide insight into the origins of nervous systems, as both their taxonomic position and their evolutionary age imply that jellyfish resemble some of the earliest neuron-bearing, actively-swimming animals. Here we develop the first neuronal network model for the nerve nets of jellyfish. Specifically, we focus on the moon jelly Aurelia aurita and the control of its energy-eZcient swimming motion. The proposed single neuron model disentangles the contributions of different currents to a spike. The network model identifies factors ensuring non-pathological activity and suggests an optimization for the transmission of signals. After modeling the jellyfishs muscle system and its bell in a hydrodynamic environment, we explore the swimming elicited by neural activity. We find that different delays between nerve net activations lead to well-controlled, differently directed movements. Our model bridges the scales from single neurons to behavior, allowing for a comprehensive understanding of jellyfish neural control.

neuroscience↗