Biology subjects

Hess, M.

Publications and source records attributed to Hess, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

Sex- and GABAergic-modulated neuronal subnetwork assemblies in the developing somatosensory cortex

Neuronal subnetworks, also known as ensembles, are functional units formed by interconnected neurons for information processing and encoding in the adult brain. Our study investigates the establishment of neuronal subnetworks in the mouse primary somatosensory (S1) cortex from postnatal days (P)11 to P21 using in vivo two-photon calcium imaging. We found that at P11, neuronal activity was highly synchronized but became sparser by P21. Clustering analyses revealed that while the number of subnetworks remained constant, their activity patterns became more distinct, with increased coherence, independent of cortical layer or sex. Furthermore, the coherence of neuronal activity within individual subnetworks significantly increased when synchrony frequencies were reduced by augmenting GABAergic activity at P15/16, a period when the neuronal subnetworks were still maturing. Together, these findings indicate the early formation of subnetworks and underscore the pivotal roles of GABAergic inputs in modulating S1 neuronal subnetworks.

neuroscience↗

bioRxiv2022

Mechanotransduction coordinates extracellular matrix protein homeostasis promoting longevity in C. elegans

Although it is postulated that dysfunctional extracellular matrices (ECM) drive aging and disease, how ECM integrity assures longevity is unknown. Here, using proteomics and in-vivo monitoring of fluorescently tagged ECM proteins, we systematically examined the ECM composition during Caenorhabditis elegans aging revealing three distinct collagen dynamics. We show that age-dependent stiffening of inert collagen was slowed by longevity interventions through prolonged replenishing of collagens. In genetic and automated lifespan screens for the regulators that drive this remodeling, we identify hemidesmosome-containing structures that span from the exoskeletal ECM through the hypodermis, basement membrane ECM, to the muscles, coupling mechanical forces to adjust ECM gene expression across tissues. The hemidesmosome tension-induced adaptation is mediated via transcriptional co-activator YAP. Our data reveal a novel mechanism of mechano-coupling and synchronizing of two functionally distinct and spatially distant ECMs that is indispensable for longevity. Thus, besides signaling molecules, mechanotransduction-coordinated ECM remodeling systemically promotes healthy aging. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/505802v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@1b45b6dorg.highwire.dtl.DTLVardef@1de8e80org.highwire.dtl.DTLVardef@5da7b2org.highwire.dtl.DTLVardef@acc577_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIProteomics, genetics screen, and automated lifespan assays of >55000 animals all point to hemidesmosome-containing structures for the mechano-regulation of ECM homeostasis and longevity C_LIO_LICoupling of biomechanical properties of two ECMs with underlying cellular signaling C_LIO_LITranscriptional co-activator YAP-1 is required for longevity and pressure-induced collagen homeostasis C_LI

molecular biology↗

bioRxiv2022

Deep haplotype analyses of target-site resistance locus ACCase in blackgrass enabled by pool-based amplicon sequencing

Rapid adaptation of weeds to herbicide applications in agriculture through resistance development is a widespread phenomenon. In particular, the grass Alopecurus myosuroides is an extremely problematic weed in cereal crops with the potential to manifest resistance in the course of only a few generations. Target-site resistances (TSRs), with their strong phenotypic response, play an important role in this rapid adaptive response. Recently, using PacBios long-read amplicon sequencing technology in hundreds of individuals, we were able to decipher the genomic context in which TSR mutations occur. However, sequencing individual amplicons is both costly and time consuming, thus impractical to implement for other resistance loci or applications. Alternatively, pool-based approaches overcome these limitations and provide reliable allele frequencies, albeit at the expense of not preserving haplotype information. In this proof-of-concept study, we sequenced with PacBio High Fidelity (HiFi) reads long-range amplicons (13.2 kb) encompassing the entire ACCase gene in pools of over hundred individuals, and resolved them into haplotypes using the clustering algorithm PacBio amplicon analysis (pbaa), a new application for pools and for plants. From these amplicon pools, we were able to recover most haplotypes from previously sequenced individuals of the same population. In addition, we analyzed new pools from a Germany-wide collection of A. myosuroides populations and found that TSR mutations originating from soft sweeps of independent origin were common. Forward-in-time simulations indicate that TSR haplotypes will persist for decades even at relatively low frequencies and without selection, pointing to the importance of accurate measurement of TSR haplotype prevalence for weed management.

genomics↗

bioRxiv2022

Multimodal spatiotemporal phenotyping of human organoid development

Organoids generated from human pluripotent stem cells (PSCs) provide experimental systems to study development and disease. However, we lack quantitative spatiotemporal descriptions of organoid development that incorporate measurements across different molecular modalities. Here we focus on the retina and use a single-cell multimodal approach to reconstruct human retinal organoid development. We establish an experimental and computational pipeline to generate multiplexed spatial protein maps over a retinal organoid time course and primary adult human retina, registering protein expression features at the population, cellular, and subcellular levels. We develop an analytical toolkit to segment nuclei, identify local and global tissue units, infer morphology trajectories, and analyze cell neighborhoods from multiplexed imaging data. We use this toolkit to visualize progenitor and neuron location, the spatial arrangements of extracellular and subcellular components, and global patterning in each organoid and primary tissue. In addition, we generate a single-cell transcriptome and chromatin accessibility time course dataset and infer a gene regulatory network underlying organoid development. We then integrate genomic data with spatially segmented nuclei into a multi-modal atlas enabling virtual exploration of retinal organoid development. We visualize molecular, cellular, and regulatory dynamics during organoid lamination, and identify regulons associated with neuronal differentiation and maintenance. We use the integrated atlas to explore retinal ganglion cell (RGC) spatial neighborhoods, highlighting pathways involved in RGC cell death. Finally, we show that mosaic CRISPR/Cas genetic perturbations in retinal organoids provide insight into cell fate regulation. Altogether, our work is a major advance toward a virtual human retinal organoid, and provides new directions for how to approach disorders of the visual system. More broadly, our approaches can be adapted to many organoid systems.

systems biology↗

bioRxiv2022

CipA mediates complement resistance of Acinetobacter baumannii by formation of a Factor I-dependent quadripartite assemblage

Multidrug-resistant Acinetobacter baumannii is known to be one of the leading pathogens that cause severe nosocomial infections. To overcome eradication by the innate immune system during infection, A. baumannii developed a number of immune evasion strategies. Previously, we identified CipA as a plasminogen-binding and complement-inhibitory protein. Here we show that CipA strongly inhibits all three complement activation pathways and interacts with key complement components C3, C3b, C4b, C5, Factor B, Factor D, and in particular Factor I. CipA also targets function of the C5 convertase as cleavage of C5 was impaired. Systematic screening of CipA variants identified two separate binding sites for C3b and a Factor I-interacting domain located at the C-terminus. Structure predictions using AlphaFold2 and binding analyses employing CipA variants lacking Factor I-binding capability confirmed that the orientation of the C-terminal domain is essential for the interaction with Factor I. Hence, our analyses point to a novel, Factor I-dependent mechanisms of complement inactivation mediated by CipA of A. baumannii. Recrutiment of Factor I by CipA initiates the assembly of a quadripartite complex following binding of either Factor H or C4b-binding protein to degrade C3b and C4b, respectively. Loss of Factor I binding in a CipA-deficient strain, or a strain producing a CipA variant lacking Factor I-binding capability, correlated with a higher susceptibility to human serum, indicating that recruitment of Factor I enables A. baumannii to resist complement-mediated killing. Significance StatementThe Gram-negative bacterium Acinetobacter baumannii causes severe nosocomial infections and has developed various immune evasion strategies to overcome complement. The mechanisms how Acinetobacter baumannii successfully circumvents complement-mediated bacteriolysis are still poorly understood. Here, we show that the plasminogen-binding and complement inhibitory protein CipA terminates all three complement activation pathways. We describe a novel mechanism by which CipA cleaves the key complement components C3b and C4b in the presence of Factor H and C4b-binding protein by formation of a novel Factor I-dependent quadripartite complex. CipA, which has recently been successfully used for vaccination approaches, might represent an attractive target for the development of novel therapeutic interventions to block disorders with excessive hyperinflammatory complement activation.

microbiology↗

bioRxiv2021

Stratified neural networks in a time-to-event setting

Deep neural networks are now frequently employed to predict survival conditional on omics-type biomarkers, e.g. by employing the partial likelihood of Cox proportional hazards model as loss function. Due to the generally limited number of observations in clinical studies, combining different data-sets has been proposed to improve learning of network parameters. However, if baseline hazards differ between the studies, the assumptions of Cox proportional hazards model are violated. Based on high dimensional transcriptome profiles from different tumor entities, we demonstrate how using a stratified partial likelihood as loss function allows for accounting for the different baseline hazards in a deep learning framework. Additionally, we compare the partial likelihood with the ranking loss, which is frequently employed as loss function in machine learning approaches due to its seemingly simplicity. Using RNA-seq data from the Cancer Genome Atlas (TCGA) we show that use of stratified loss functions leads to an overall better discriminatory power and lower prediction error compared to their nonstratified counterparts. We investigate which genes are identified to have the greatest marginal impact on prediction of survival when using different loss functions. We find that while similar genes are identified, in particular known prognostic genes receive higher importance from stratified loss functions. Taken together, pooling data from different sources for improved parameter learning of deep neural networks benefits largely from employing stratified loss functions that consider potentially varying baseline hazards. For easy application, we provide PyTorch code for stratified loss functions and an explanatory Jupyter notebook in a GitHub repository.

bioinformatics↗

bioRxiv2020

Inherent population structure determines the importance of filtering parameters for reduced representation sequencing analyses

As technological advancements enhance our ability to study population genetics, we must understand how the intrinsic properties of our datasets influence the decisions we make when designing experiments. Filtering parameter thresholds, such as call rate and minimum minor allele frequency (MAF), are known to affect inferences of population structure in reduced representation sequencing (RRS) studies. However, it is unclear to what extent the impacts of these parameter choices vary across datasets. Here, we reviewed literature on filtering choices and levels of genetic differentiation across RRS studies on wild populations to highlight the diverse approaches that have been used. Next, we hypothesized that choices in filtering thresholds would have the greatest impact when analyzing datasets with low levels of genetic differentiation between populations. To test this hypothesis, we produced seven simulated RRS datasets with varying levels of population structure, and analyzed them using four different combinations of call rate and MAF. We performed the same analysis on two empirical RRS datasets (low or high population structure). Our simulated and empirical results suggest that the effects of filtering choices indeed vary based on inherent levels of differentiation: specifically, choosing stringent filtering choices was important to detect distinct populations that were slightly differentiated, but not those that were highly differentiated. As a result, experimental design and analysis choices need to consider attributes of each specific dataset. Based on our literature review and analyses, we recommend testing a range of filtering parameter choices, and presenting all results with clear justification for ultimate filtering decisions used in downstream analyses.

bioinformatics↗

bioRxiv2020

Ecology of inorganic sulfur auxiliary metabolism in widespread bacteriophages

Microbial sulfur metabolism contributes to biogeochemical cycling on global scales. Sulfur metabolizing microbes are infected by phages that can encode auxiliary metabolic genes (AMGs) to alter sulfur metabolism within host cells but remain poorly characterized. Here we identified 191 phages derived from twelve environments that encoded 227 AMGs for oxidation of sulfur and thiosulfate (dsrA, dsrC/tusE, soxC, soxD and soxYZ). Evidence for retention of AMGs during niche-differentiation of diverse phage populations provided evidence that auxiliary metabolism imparts measurable fitness benefits to phages with ramifications for ecosystem biogeochemistry. Gene abundance and expression profiles of AMGs suggested significant contributions by phages to sulfur and thiosulfate oxidation in freshwater lakes and oceans, and a sensitive response to changing sulfur concentrations in hydrothermal environments. Overall, our study provides novel insights on the distribution, diversity and ecology of phage auxiliary metabolism associated with sulfur and reinforces the necessity of incorporating viral contributions into biogeochemical configurations.

microbiology↗

bioRxiv2020

Synthetic observations from deep generative models and binary omics data with limited sample size

Deep generative models can be trained to represent the joint distribution of data, such as measurements of single nucleotide polymorphisms (SNPs) from several individuals. Subsequently, synthetic observations are obtained by drawing from this distribution. This has been shown to be useful for several tasks, such as removal of noise, imputation, for better understanding underlying patterns, or even exchanging data under privacy constraints. Yet, it is still unclear how well these approaches work with limited sample size. We investigate such settings specifically for binary data, e.g., as relevant when considering SNP measurements, and evaluate three frequently employed generative modeling approaches, variational autoencoders (VAEs), deep Boltzmann machines (DBMs) and generative adversarial networks (GANs). This includes conditional approaches, such as when considering gene expression conditional on SNPs. Recovery of pair-wise odds ratios is considered as a primary performance criterion. For simulated as well as real SNP data, we observe that DBMs generally can recover structure for up to 100 variables with as little as 500 observations, with a tendency of over-estimating odds ratios when not carefully tuned. VAEs generally get the direction and relative strength of pairwise relations right, yet with considerable under-estimation of odds ratios. GANs provide stable results only with larger sample sizes and strong pair-wise relations in the data. Taken together, DBMs and VAEs (in contrast to GANs) appear to be well suited for binary omics data, even at rather small sample sizes. This opens the way for many potential applications where synthetic observations from omics data might be useful.

bioinformatics↗

bioRxiv2020

Ramularia leaf spot disease of barley is highly host genotype-dependent and suppressed by continuous drought stress in the field

Since the 1980s, Ramularia leaf spot (RLS) is an emerging barley disease world-wide. The control of RLS is increasingly aggravated by a recent decline in fungicide efficacy and a lack of RLS-resistant cultivars. Furthermore, climate change increases drought periods in Europe, enhances variable weather conditions and thus will have an impact on severity of plant diseases. Hence, identification of RLS-resistant cultivars and understanding of disease progression under abiotic stress are important aims in integrated disease management under climate change. In the present study, we evaluated quantitative RLS resistance of 15 spring barley genotypes under drought, controlled irrigation and field conditions between 2016 and 2019 and monitored microclimatic conditions within the canopy. We identified genotypes that show robust quantitative resistance to RLS in different field environments. Our findings suggest that long-lasting drought periods create unfavourable conditions for the disease and supports that the extent and duration of leaf wetness is a key factor for RLS epidemics.

plant biology↗

bioRxiv2020

Generating Synthetic Single-Cell RNA-Sequencing Datafrom Small Pilot Studies using Deep Learning

Deep generative models, such as variational autoencoders (VAEs) or deep Boltzmann machines (DBM), can generate an arbitrary number of synthetic observations after being trained on an initial set of samples. This has mainly been investigated for imaging data but could also be useful for single-cell transcriptomics (scRNA-seq). A small pilot study could be used for planning a full-scale study by investigating planned analysis strategies on synthetic data with different sample sizes. It is unclear whether synthetic observations generated based on a small scRNA-seq dataset reflect the properties relevant for subsequent data analysis steps. We specifically investigated two deep generative modeling approaches, VAEs and DBMs. First, we considered single-cell variational inference (scVI) in two variants, generating samples from the posterior distribution, the standard approach, or the prior distribution. Second, we propose single-cell deep Boltzmann machines (scDBM). When considering the similarity of clustering results on synthetic data to ground-truth clustering, we find that the scVIposterior variant resulted in high variability, most likely due to amplifying artifacts of small data sets. All approaches showed mixed results for cell types with different abundance by overrepresenting highly abundant cell types and missing less abundant cell types. With increasing pilot dataset sizes, the proportions of the cells in each cluster became more similar to that of ground-truth data. We also showed that all approaches learn the univariate distribution of most genes, but problems occurred with bimodality. Overall, the results showed that generative deep learning approaches might be valuable for supporting the design of scRNA-seq experiments.

bioinformatics↗

bioRxiv2020

Ecology and molecular targets of hypermutation in the global microbiome.

Changes in the sequence of an organisms genome, i.e. mutations, are the raw material of evolution1. The frequency and location of mutations can be constrained by specific molecular mechanisms, such as Diversity-generating retroelements (DGRs)2-4. DGRs introduce mutations in specific target genes, and were characterized from several cultivated bacteria and bacteriophages2. Whilst a larger diversity of DGR loci has been identified in genomic data from environmental samples, i.e. metagenomes, the ecological role of these DGRs and their associated evolutionary drivers remain poorly understood5-7. Here we built and analyzed an extensive dataset of >30,000 metagenome-derived DGRs, and determine that DGRs have a single evolutionary origin and a universal bias towards adenine mutations. We further identified six major lineages of DGRs, each associated with a specific ecological niche defined as a genome type, i.e. whether the DGR is encoded on a viral or cellular genome, a limited set of taxa and environments, and a distinct type of target. Finally, we leverage read mapping and metagenomic time series to demonstrate that DGRs are consistently and broadly active, and responsible for >10% of all amino acid changes in some organisms at a conservative estimate. Overall, these results highlight the strong constraints under which DGRs diversify and expand, and elucidate several distinct roles these elements play in natural communities and in shaping microbial community structure and function in our environment.

microbiology↗

bioRxiv2020

Proteome specialization of anaerobic fungi during ruminal degradation of recalcitrant plant fiber

The rumen harbors a complex microbial mixture of archaea, bacteria, protozoa and fungi that efficiently breakdown plant biomass and its complex dietary carbohydrates into soluble sugars that can be fermented and subsequently converted into metabolites and nutrients utilized by the host animal. While rumen bacterial populations have been well documented, only a fraction of the rumen eukarya are taxonomically and functionally characterized, despite the recognition that they contribute to the cellulolytic phenotype of the rumen microbiota. To investigate how anaerobic fungi actively engage in digestion of recalcitrant fiber that is resistant to degradation, we resolved genome-centric metaproteome and metatranscriptome datasets generated from switchgrass samples incubated for 48 hours in nylon bags within the rumen of cannulated dairy cows. Across a gene catalogue covering anaerobic rumen bacteria, fungi and viruses, a significant portion of the detected proteins originated from fungal populations. Intriguingly, the carbohydrate-active enzyme (CAZyme) profile suggested a domain-specific functional specialization, with bacterial populations primarily engaged in the degradation of polysaccharides such as hemicellulose, whereas fungi were inferred to target recalcitrant cellulose structures via the detection of a number of endo- and exo-acting enzymes belonging to the glycoside hydrolase (GH) family 5, 6, 8 and 48. Notably, members of the GH48 family were amongst the highest abundant CAZymes and detected representatives from this family also included dockerin domains that are associated with fungal cellulosomes. A eukaryote-selected metatranscriptome further reinforced the contribution of uncultured fungi in the ruminal degradation of recalcitrant fibers. These findings elucidate the intricate networks of in situ recalcitrant fiber deconstruction, and importantly, suggests that the anaerobic rumen fungi contribute a specific set of CAZymes that complement the enzyme repertoire provided by the specialized plant cell wall degrading rumen bacteria.

microbiology↗

bioRxiv2019

TIS7 and SKMc15 Regulate Adipocyte Differentiation and Intestinal Lipid Absorption

Delta-like homolog 1 (DLK1), an inhibitor of adipogenesis, controls the cell fate of adipocyte progenitors. Here we identify two independent regulatory mechanisms, transcriptional and translational, by which TIS7 (IFRD1) and its orthologue SKMc15 (IFRD2) regulate DLK1 levels. Mice deficient in both TIS7 and SKMc15 (dKO) had severely reduced adipose tissue and were resistant to high fat diet-induced obesity. Wnt signaling, a negative regulator of adipocyte differentiation was significantly up regulated in dKO mice. Elevated levels of the Wnt/{beta}-catenin target protein Dlk-1 inhibited the expression of adipogenesis regulators PPAR{gamma} and C/EBP, and fatty acid transporter CD36. Although both, TIS7 and SKMc15, contributed to this phenotype, they utilized two different mechanisms. TIS7 acted by controlling Wnt signaling and thereby transcriptional regulation of Dlk-1. On the other hand, here we provide distinctive experimental evidence that SKMc15 acts as a general translational inhibitor significantly affecting DLK-1 protein levels. Our study provides data describing novel mechanisms of DLK1 regulation in adipocyte differentiation involving TIS7 and SKMc15. SYNOPSIS O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=184 SRC="FIGDIR/small/719922v3_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@77d89corg.highwire.dtl.DTLVardef@2e0ad4org.highwire.dtl.DTLVardef@c923fdorg.highwire.dtl.DTLVardef@159d117_HPS_FORMAT_FIGEXP M_FIG C_FIG This study uncovered that IFRD1 (TIS7) and its orthologue IFRD2 (SKMc15) are two essential regulators of adipogenesis. These proteins are highly similar on the sequence level, yet they regulate adipocyte differentiation using different but complementary mechanisms. Our main findings are: O_LIIFRD1 (TIS7) and IFRD2 (SKMc15) knockout mice are resistant against diet-induced obesity C_LIO_LIIFRD1 (TIS7) and IFRD2 (SKMc15) are critical for proper nutritional fat uptake and adipogenesis C_LIO_LIIFRD1 (TIS7) controls adipogenesis via Wnt/{beta}-catenin-dependent transcriptional regulation of adipocyte-specific genes C_LIO_LIIFRD2 (SKMc15) regulates adipocyte-specific genes acting as a novel general translational inhibitor C_LI

cell biology↗

bioRxiv2019

A restriction enzyme reduced representation sequencing approach for low-cost, high-throughput metagenome profiling

Microbial community profiles have been associated with a variety of traits, including methane emissions in livestock, however, these profiles can be difficult and expensive to obtain for thousands of samples. The objective of this work was to develop a low-cost, high-throughput approach to capture the diversity of the rumen microbiome. Restriction enzyme reduced representation sequencing (RE-RRS) using ApeKI or PstI, and two bioinformatic pipelines (reference-based and reference-free) were compared to 16S rRNA gene sequencing using repeated samples collected two weeks apart from 118 sheep that were phenotypically extreme (60 high and 58 low) for methane emitted per kg dry matter intake (n=236). DNA was extracted from freeze-dried rumen samples using a phenol chloroform and bead-beating protocol prior to sequencing. The resulting sequences were used to investigate the repeatability of the rumen microbial community profiles, the effect of host genetics, laboratory and analytical method, and the genetic and phenotypic correlations with methane production. The results suggested that the best method was PstI RE-RRS analyzed with the reference-free approach via a correspondence analysis, with estimates for repeatability of 0.62{+/-}0.06, heritability 0.31{+/-}0.29, and genetic and phenotypic correlation with methane emissions of 0.88{+/-}0.25 and 0.64{+/-}0.05 respectively for the first component of correspondence analysis. The reference-free approach assigned 62.0{+/-}5.7% of reads to common 65 bp tags, much higher than the reference-based approach of 6.8{+/-}1.8% of reads assigned. Sensitivity studies suggested approximately 2000 samples could be sequenced in a single lane on an Illumina HiSeq 2500, therefore the current work of 118 samples/lane and future proposed 384 samples/lane are well within that threshold. Our approach is now being used to investigate host factors affecting the rumen and its association with a variety of production and environmental traits. With minor adaptations, our approach could be used to obtain microbial profiles from other metagenomic samples.

microbiology↗

bioRxiv2019

Unsupervised deep learning on biomedical data with BoltzmannMachines.jl

Deep Boltzmann machines (DBMs) are models for unsupervised learning in the field of artificial intelligence, promising to be useful for dimensionality reduction and pattern detection in clinical and genomic data. Multimodal and partitioned DBMs alleviate the problem of small sample sizes and make it possible to combine different input data types in one DBM model. We present the package \"BoltzmannMachines\" for the Julia programming language, which makes this model class available for practical use in working with biomedical data.\n\nAvailabilityNotebook with example data: http://github.com/stefan-m-lenz/BMs4BInf2019 Julia package: http://github.com/stefan-m-lenz/BoltzmannMachines.jl

bioinformatics↗

bioRxiv2019

In silico identification of novel peptides with antibacterial activity against multidrug resistant Staphylococcus aureus

Herein we report the identification and characterisation of two linear antimicrobial peptides (AMPs), HG2 and HG4, with activity against a wide range of multidrug resistant (MDR) bacteria, especially methicillin resistant Staphylococcus aureus (MRSA) strains, a highly problematic group of Gram-positive bacteria in the hospital and community environment. To identify the novel AMPs presented here, we employed the classifier model design, a feature extraction method using molecular descriptors for amino acids for the analysis, visualization, and interpretation of AMP activities from a rumen metagenomic dataset. This allowed for the in silico discrimination of active and inactive peptides in order to define a small number of promising novel lead AMP test candidates for chemical synthesis and experimental evaluation. In vitro data suggest that the chosen AMPs are fast acting, show strong biofilm inhibition and dispersal activity and are efficacious in an in vivo model of MRSA USA300 infection, whilst showing little toxicity to human erythrocytes and human primary cell lines ex vivo. Observations from biophysical AMP-lipid-interactions and electron microscopy suggest that the newly identified peptides interact with the cell membrane and may be involved in the inhibition of other cellular processes. Amphiphilic conformations associated with membrane disruption are also observed in 3D molecular modelling of the peptides. HG2 and HG4 both preferentially bind to MRSA total lipids rather than with human cell lipids indicating that HG4 may form superior templates for safer therapeutic candidates for MDR bacterial infections.\n\nAuthor SummaryWe are losing our ability to treat multidrug resistant (MDR) bacteria, otherwise known as superbugs. This poses a serious global threat to human health as bacteria are increasingly acquiring resistance to antibiotics. There is therefore urgent need to intensify our efforts to develop new safer alternative drug candidates. We emphasise the usefulness of complementing wet-lab and in silico techniques for the rapid identification of new drug candidates from environmental samples, especially antimicrobial peptides (AMPs). HG2 and HG4, the AMPs identified in our study show promise as effective therapies for the treatment of methicillin resistant Staphylococcus aureus infections both in vitro and in vivo whilst having little cytotoxicity against human primary cells, a step forward in the fight against MDR infections.

microbiology↗

bioRxiv2019

A 2.08 A resolution structure of HLB5, a novel cellulase from the anaerobic gut bacterium Parabacteroides johnsonii DSM 18315.

Cellulases play a significant role in the degradation of complex carbohydrates. In the human gut anaerobic bacteria are essential to the well-being of the host by producing these essential enzymes that convert plant polymers into simple sugars that can then be further metabolized by the host. Here we report the 2.08 [A] resolution structure of HLB5, a chemically verified cellulase that was identified previously from an anaerobic gut bacterium and that has no structural cellulase homologues in PDB nor possesses any conserved region typical for enzymes that degrade carbohydrates. We anticipate that the information presented here will facilitate the identification of additional cellulases for which no homologues have been identified until to date and in enhancing our understanding how these novel cellulases bind and hydrolyze their substrates.

systems biology↗