Biology subjects

Mikut, R.

Publications and source records attributed to Mikut, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Entomoscope 2.0 and ENIMAS 2.0: An Open-Source, AI-Integrated Platform for Rapid and Affordable Insect Digitization

The rapid decline of global biodiversity necessitates scalable and accessible tools for monitoring insect populations, yet the high cost and slow pace of specimen digitization remain significant bottlenecks. To address this challenge, we present the Entomoscope 2.0, an open-source platform that integrates a low-cost photomicroscope with an AI-integrated software suite, ENIMAS 2.0. The system combines optimized hardware with an end-to-end software module for digital specimen curation. This module comprises automated specimen cropping, background standardization, morphometric analysis using an Oriented Bounding Box (OBB) with a human-in-the-loop (HITL) supervision, and a flexible interface for rapid taxonomic screening using custom AI identification models. With a material cost of only 400 {euro}, the system offers a cost-effective alternative to expensive commercial solutions. We compare results from the Entomoscope 2.0 with a high-end commercial system (Keyence) using 54 insect specimens and demonstrate the efficiency of the proposed AI workflow. Entomoscope 2.0 completed the whole digitization process in an average of 54.6 seconds per specimen, representing a 2.28-fold increase in speed over the Keyence systems multi-step workflow (124.3 seconds). Crucially, all hardware specifications and construction manuals are freely available to support widespread adoption. By lowering financial barriers and accelerating research workflows, the Entomoscope 2.0 platform offers a practical solution to enable high-throughput digitization for researchers, educators, and citizen scientists.

bioengineering↗

bioRxiv2025

A kinetics-based model of hematopoiesis reveals extrinsic regulation of skewed lineage output from stem cells

Residing at the top of the hematopoietic hierarchy, long-term hematopoietic stem cells (HSCs) are capable of self-renewal and sustained blood cell regeneration. Over the past decades, single-cell and clonal analyses have revealed substantial functional and molecular heterogeneity within this compartment, challenging the notion that self-renewal is inherently tied to balanced, multi-lineage blood production. However, a cohesive model that explains the relationships among these diverse HSC states remains elusive. Here, we combined single-cell transplantations of over 1,000 highly purified murine long-term HSCs with in-depth phenotyping of their clonal progeny to achieve a detailed, time-resolved understanding of heterogeneous reconstitution outcomes. We identified reconstitution kinetics as an overall unifying metric of HSC functional potency, with the most potent HSCs displaying the greatest delay in hematopoietic reconstitution. Importantly, a progressive acceleration in reconstitution kinetics was also associated with a gradual shift in mature cell production from platelet and erythro-myeloid bias to balanced, and eventually lymphoid bias. Serial single-cell transplantations of HSCs revealed a unidirectional acceleration in reconstitution kinetics accompanied by a gradual decline in functional potency of daughter HSCs, aligning diverse phenotypes along a linear hierarchical trajectory. Mathematical modeling, together with experimental modulation of lineage-biased blood production, demonstrated that apparent lineage biases actually arise from cell-extrinsic feedback regulation and clonal competition between slow- and fast-engrafting clones to occupy the limited compartment sizes of mature lineages. Our study reconciles multiple layers of HSC heterogeneity into a unifying framework, prompting a reevaluation of the meaning of lineage biases in both normal and diseased hematopoiesis, with broad implications for other regenerating tissues during development, homeostasis, and repair.

cell biology↗

bioRxiv2023

Can BioSAXS Detect Ultrastructural Changes Of Antifungal Compounds In Candida Albicans? - An Exploratory Study

The opportunistic yeast Candida albicans is the most common cause of candidiasis. With only four classes of antifungal drugs on the market, resistance is becoming a problem in the treatment of fungal infections, especially in immunocompromised patients. The development of novel antifungal drugs with different modes of action is urgent. In 2016, we developed a groundbreaking new medium-throughput method to distinguish the effects of antibacterial agents. Using small-angle X-ray scattering for biological samples (BioSAXS), it is now possible to screen hundreds of new antibacterial compounds and select those with the highest probability for a novel mode of action. However, yeast (eukaryotic) cells are highly structured compared to bacteria and the action of an antifungal drug might leave most structures unchanged. In the pioneering work described here, we explored the possibility if BioSAXS can be used to measure the ultrastructural changes of Candida albicans directly or indirectly induced by antifungal compounds. For this exploratory study, we used the well-characterized antifungal drug flucytosine. BioSAXS measurements were performed on the synchrotron P12 BioSAXS beamline, EMBL (DESY, Hamburg) on treated and untreated yeast C. albicans. BioSAXS curves were analysed using principal component analysis (PCA). The PCA showed that flucytosine-treated and untreated yeast were separated. Based on that success further measurements were performed on five antifungal peptides (1. Cecropin A-melittin hybrid [CA(1-7)M(2-9)], KWKLFKKIGAVLKVL; 2. Lasioglossin LL-III, VNWKKILGKIIKVVK; 3. Mastoparan M, INLKAIAALAKKLL; 4. Bmkn2, FIGAIARLLSKIFGKR; and 5. optP7, KRRVRWIIW). The ultrastructural changes of C. albicans indicate that the peptides may have different modes of action compared to Flucytosine as well as to each other, except for the Cecropin A-melittin hybrid [CA(1-7)M(2-9)] and optP7, showing very similar effects on Candida albicans. This very first study demonstrates that BioSAXS shows great promise to be used for antifungal drug development. The use of such a tool like BioSAXS could accelerate and de-risk antifungal drug development, however, further experiments are necessary to establish this application.

microbiology↗

bioRxiv2023

An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids

BackgroundCerebral organoids simulate the structure and function of the developing human brain in vitro, offering a large potential for personalized therapeutic strategies. The enormous growth of this research area over the past decade with its capability for clinical translation makes a non-invasive, automated analysis pipeline of organoids highly desirable. PurposeThis work presents the first application of magnetic resonance imaging (MRI) for the non-invasive quantification and quality assessment of cerebral organoids using an automated analysis tool. Three specific objectives are addressed, namely organoid segmentation to investigate organoid development over time, global cysticity classification, and local cyst segmentation. MethodsNine wildtype cerebral organoids were imaged over nine weeks using high-field 9.4T MRI including a 3D T2*-w and 2D diffusion tensor imaging (DTI) sequence. This dataset was used to train a deep learning-based 3D U-Net for organoid and local cyst segmentation. For global cysticity classification, we developed a new metric, compactness, to separate low- and high-quality organoids. ResultsThe 3D U-Net achieved a Dice score of 0.92{+/-}0.06 (mean {+/-} SD) for organoid segmentation in the T2*-w sequence. For global cysticity classification, compactness separated low- and high-quality organoids with high accuracy (ROC AUC 0.98). DTI showed that low-quality organoids have a significantly higher diffusion than high-quality organoids (p < .001). For local cyst segmentation in T2*-w, the 3D U-Net achieved a Dice score of 0.63{+/-}0.15 (mean {+/-} SD). ConclusionWe present a novel non-invasive approach to monitor and analyze cerebral organoids over time using high-field MRI and state-of-the-art tools for automated image analysis, offering a comparative pipeline for personalized medicine. We show that organoid growth can be monitored reliably over time and low- and high-quality organoids can be separated with high accuracy. Local cyst segmentation is feasible but could be further improved in the future.

developmental biology↗

bioRxiv2022

Synchronization of oscillatory growth prepares fungal hyphae for fusion

Communication is crucial for organismic interactions, from bacteria, to fungi, to humans. Humans may use the visual sense to monitor the environment before starting acoustic interactions. In comparison, fungi lack a visual system, instead, hyphae use a cell-to-cell dialogue based on secreted signaling molecules to orchestrate cell fusion and establish hyphal networks. Hyphae alternate roles as signal-sender and signal-receiver, as can be visualized via the putative signaling protein, Soft, which is recruited in an oscillatory manner to the respective cytoplasmic membrane of interacting hyphae. Here, we show that signal oscillations already occur in single hyphae of Arthrobotrys flagrans in the absence of a potential fusion partner. They occurred in the same phase as growth oscillations. Once two fusion partners came into each others vicinity, their oscillation frequencies slowed down (entrainment phase) and transit into anti-phasic synchronization of the two cells oscillations with frequencies of 130 +/-20 sec. Single-cell oscillations, transient entrainment, and anti-phasic oscillations were reproduced by a mathematical model where nearby hyphae can absorb and secrete a limited molecular signaling component into a shared extra-cellular space. We show that intracellular Ca2+ concentrations oscillate in two approaching hyphae, and depletion of Ca2+ in the surrounding affected vesicle-driven extension of the hyphal tip, abolished single-cell molecular oscillations and the anti-phasic synchronization of two hyphae. Our results suggest that single hyphae engage in a "monologue" that may be used for exploration of the environment and can dynamically shift their extra-cellular signaling systems into a "dialogue" to initiate hyphal fusion. Significance statementCommunication at the cellular level often relies on chemical signal exchange. One prominent example is the fusion of fungal hyphae to form complex hyphal networks. As opposed to mating-type dependent cell fusion, cell-fusion events described here occur in genetically identical cells. Relying only on one chemical signaling channel raises the question of how communication is initiated. We discovered that individual hyphae constantly perform signal oscillations, comparable to a cellular "monologue" until they meet another hypha with which they then coordinate signal oscillations in a cell-to-cell dialogue. We also show that signal oscillations are mechanistically interlinked with calcium-dependent growth oscillations. Although the signaling molecule(s) has not been identified yet, it is highly likely linked to the hyphal growth machinery.

cell biology↗

bioRxiv2022

ObiWan-Microbi: OMERO-based integrated workflow for annotating microbes in the cloud

SummaryReliable deep learning segmentation for microfluidic live-cell imaging requires comprehensive ground truth data. ObiWan-Microbi is a microservice platform combining the strength of state-of-the-art technologies into a unique integrated workflow for data management and efficient ground truth generation for instance segmentation, empowering collaborative semi-automated image annotation in the cloud. Availability and ImplementationObiWan-Microbi is open-source and available under the MIT license at https://github.com/hip-satomi/ObiWan-Microbi, along documentation and usage examples. Contactk.noeh@fz-juelich.de Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2022

Characterization of the Pace-and-Drive Capacity of the Human Sinoatrial Node: a 3D in silico Study

The sinoatrial node (SAN) is a complex structure that spontaneously depolarizes rhythmically ("pacing") and excites the surrounding non-automatic cardiac cells ("drive") to initiate each heart beat. However, the mechanisms by which the SAN cells can activate the large and hyperpolarized surrounding cardiac tissue are incompletely understood. Experimental studies demonstrated the presence of an insulating border that separates the SAN from the hyperpolarizing influence of the surrounding myocardium, except at a discrete number of sinoatrial exit pathways (SEP). We propose a highly detailed 3D model of the human SAN, including 3D SEPs to study the requirements for successful electrical activation of the primary pacemaking structure of the human heart. A total of 788 simulations investigate the ability of the SAN to pace and drive with different heterogeneous characteristics of the nodal tissue (gradient and mosaic models) and myocyte orientation. A sigmoidal distribution of the tissue conductivity combined with a mosaic model of SAN and atrial cells in the SEP was able to drive the right atrium (RA). Additionally, we investigated the influence of the SEPs by varying their number, length and width. SEPs created a transition zone of transmembrane voltage (TMV) and ionic currents to enable successful pace and drive. Unsuccessful simulations showed a hyperpolarized TMV (-66 mV), which blocked the L-type channels and attenuated the sodium-calcium exchanger. The fiber direction influenced the SEPs that preferentially activated the crista terminalis (CT). The location of the leading pacemaker site (LPS) shifted towards the SEP-free areas. LPSs were located closer to the SEP-free areas (3.46{+/-}1.42 mm), where the hyperpolarizing influence of the CT was reduced, compared to a larger distance from the LPS to the areas where SEPs were located (7.17{+/-}0.98 mm). This study identified the geometrical and electrophysiological aspects of the 3D SAN-SEP-CT structure required for successful pace-and-drive in silico. SIGNIFICANCEThe human sinoatrial node (SAN) is the intrinsic natural pacemaker of the heart. Despite its remarkable robustness to failure, the electrophysiological properties, and mechanisms by which the SAN overcomes the source-sink mismatch towards the hyperpolarized surrounding cardiac tissue remains a mystery. The SAN is electrically isolated from the hyperpolarized cardiac tissue, except at a discrete number of sinoatrial exit pathways (SEP). Using in silico experiments, we explore the influence of the fiber orientation, the SEPs number, geometry and location on the activation of the SAN and the surrounding atrial tissue. We provide the mechanisms in a first 3D model of the human SAN-SEP structure that can successfully drive the working myocardium.

physiology↗

bioRxiv2022

microbeSEG: Accurate Cell Segmentation with OMERO Data Management

In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell segmentation at the single-cell level. Therefore, we present microbeSEG, a user-friendly Python-based cell segmentation tool with a graphical user interface and OMERO data management. microbeSEG utilizes a state-of-the-art deep learning-based segmentation method and can be used for instance segmentation of a wide range of cell morphologies and imaging techniques, e.g., phase contrast or fluorescence microscopy. The main focus of microbeSEG is a comprehensible, easy, efficient, and complete workflow from the creation of training data to the final application of the trained segmentation model. We demonstrate that accurate cell segmentation results can be obtained within 45 minutes of user time. Utilizing public segmentation datasets or pre-labeling further accelerates the microbeSEG workflow. This opens the door for accurate and efficient data analysis of microbial cultures.

bioinformatics↗

bioRxiv2022

Noise facilitates entrainment of a population of uncoupled limit cycle oscillators

Many biological oscillators share two properties: they are subject to stochastic fluctuations (noise) and they must reliably adjust their period to changing environmental conditions (entrainment). While noise seems to distort the ability of single oscillators to entrain, in populations of oscillators noise allows entrainment for a wider range of input amplitudes and periods. Here, we investigate, how this effect depends on the noise intensity and the number of oscillators in the population. We have found that, if a population consists of a sufficient number of oscillators, increasing noise intensity leads to faster entrainment after a phase change of the input signal (jet lag) and increases sensitivity to low-amplitude input signals. SIGNIFICANCELive is characterized by rhythms, such as daily changes in activity or the heartbeat. These rhythms are reflected in molecular oscillations generated at the level of individual cells. These oscillations are inherently noisy, but still cells reliably synchronize to external signals and provide reliable timing for other biological processes. Here, we show how noise can be beneficial to cell populations in terms of synchronization to external signals. Specifically, noise can increase the sensitivity to weak external signals and speed up adjustment to jet-lag-like perturbations.

systems biology↗

bioRxiv2021

Rational Designed Hybrid Peptides Show up to a 6-fold Increase in Antimicrobial Activity and Demonstrate Different Ultrastructural Changes as the Parental Peptides measured by BioSAXS

Antimicrobial peptides (AMPs) are a promising class of compounds being developed against multi-drug resistant bacteria. Hybridization has been reported to increase antimicrobial activity. Here, two proline-rich peptides (consP1: VRKPPYLPRPRPRPL-CONH2 and Bac5-v291: RWRRPIRRRPIRPPFWR-CONH2) were combined with two arginine-isoleucine-rich peptides (optP1: KIILRIRWR-CONH2 and optP7: KRRVRWIIW-CONH2). Proline-rich antimicrobial peptides (PrAMPs) are known to inhibit the bacterial ribosome, shown also for Bac5-v291, whereas it is hypothesized a "dirty drug" model for the arginine-isoleucine-rich peptides. That hypothesis was underpinned by transmission electron microscopy and biological small-angle X-ray scattering (BioSAXS). The hybrid peptides showed a stronger antimicrobial activity compared to the proline-rich peptides, except when compared to Bac5-v291 against E. coli. The increase in activity compared to the arginine-isoleucine-rich peptides was up to 6-fold, however, it was not a general increase but was dependent on the combination of peptides and bacteria. BioSAXS experiments revealed that proline-rich peptides and arginine-isoleucine-rich peptides induce very different ultrastructural changes in E. coli, whereas a hybrid peptide (hyP7B5GK) shows changes, different to both parental peptides and the untreated control. These different ultrastructural changes indicated that the mode of action of the parental peptides is different from each other as well as from the hybrid peptide hyP7B5GK. All peptides showed very low haemolytic activity, some of them showed a 100-fold or larger therapeutic window, demonstrating the potential for further drug development.

microbiology↗

bioRxiv2021

On Improving an Already Competitive Segmentation Algorithm for the Cell Tracking Challenge - Lessons Learned

The virtually error-free segmentation and tracking of densely packed cells and cell nuclei is still a challenging task. Especially in low-resolution and low signal-to-noise-ratio microscopy images erroneously merged and missing cells are common segmentation errors making the subsequent cell tracking even more difficult. In 2020, we successfully participated as team KIT-Sch-GE (1) in the 5th edition of the ISBI Cell Tracking Challenge. With our deep learning-based distance map regression segmentation and our graph-based cell tracking, we achieved multiple top 3 rankings on the diverse data sets. In this manuscript, we show how our approach can be further improved by using another optimizer and by fine-tuning training data augmentation parameters, learning rate schedules, and the training data representation. The fine-tuned segmentation in combination with an improved tracking enabled to further improve our performance in the 6th edition of the Cell Tracking Challenge 2021 as team KIT-Sch-GE (2).

bioinformatics↗

bioRxiv2021

A stochastic oscillator model simulates the entrainment of vertebrate cellular clocks by light

The circadian clock is a cellular mechanism that synchronizes various biological processes with respect to the time of the day. While much progress has been made characterizing the molecular mechanisms underlying this clock, it is less clear how external light cues influence the dynamics of the core clock mechanism and thereby entrain it with the light-dark cycle. Zebrafish-derived cell cultures possess clocks that are directly light-entrainable, thus providing an attractive laboratory model for circadian entrainment. Here, we have developed a stochastic oscillator model of the zebrafish circadian clock, which accounts for the core clock negative feedback loop, light input, and the proliferation of single-cell oscillator noise into population-level luminescence recordings. The model accurately predicts the entrainment dynamics observed in bioluminescent clock reporter assays upon exposure to a wide range of lighting conditions. Furthermore, we have applied the model to obtain refitted parameter sets for cell cultures exposed to a variety of pharmacological treatments and predict changes in single-cell oscillator parameters. Our work paves the way for model-based, large-scale screens for genetic or pharmacologically-induced modifications to the entrainment of circadian clock function. Author summaryThe circadian clock is a key, cell-autonomous timing mechanism that is encountered in most organisms. It is entrained by environmental lighting conditions and in turn temporally coordinates most aspects of physiology according to the time of day. Cell lines derived from zebrafish are attractive experimental models for studying how clocks are entrained by light since they possess clocks that respond directly to light stimuli. Here we describe a mathematical model for the behavior of the circadian clock in zebrafish cell lines during exposure to a range of lighting conditions. Using this model, we can determine how different pharmacological treatments may affect the entrainment dynamics of the clock and the degree of synchronization of individual cells circadian clocks in bioluminescent clock reporter assays. Our current model is mathematically simple and thus easy to apply and extend in future studies.

systems biology↗

bioRxiv2021

A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction

Automatic cell segmentation and tracking enables to gain quantitative insights into the processes driving cell migration. To investigate new data with minimal manual effort, cell tracking algorithms should be easy to apply and reduce manual curation time by providing automatic correction of segmentation errors. Current cell tracking algorithms, however, are either easy to apply to new data sets but lack automatic segmentation error correction, or have a vast set of parameters that needs either manual tuning or annotated data for parameter tuning. In this work, we propose a tracking algorithm with only few manually tunable parameters and automatic segmentation error correction. Moreover, no training data is needed. We compare the performance of our approach to three well-performing tracking algorithms from the Cell Tracking Challenge on data sets with simulated, degraded segmentation - including false negatives, over- and under-segmentation errors. Our tracking algorithm can correct false negatives, over- and under-segmentation errors as well as a mixture of the aforementioned segmentation errors. On data sets with under-segmentation errors or a mixture of segmentation errors our approach performs best. Moreover, without requiring additional manual tuning, our approach ranks several times in the top 3 on the 6th edition of the Cell Tracking Challenge.

bioinformatics↗

bioRxiv2019

Synergy pattern of short cationic antimicrobial peptides against multidrug-resistant Pseudomonas aeruginosa

With the rise of various multi-drug resistance pathogenic bacteria, worldwide health care is under pressure to respond. Conventional antibiotics are failing and the development of novel classes or alternative strategies is a major priority. Antimicrobial peptides (AMPs) can not only kill multi-drug resistant bacteria, but also can be used synergistically with conventional antibiotics. We selected 30 short AMPs from different origins and measured their synergy in combination with Polymyxin B, Piperacillin, Ceftazidime, Cefepime, Meropenem, Imipenem, Tetracycline, Erythromycin, Kanamycin, Tobramycin, Amikacin, Gentamycin, and Ciprofloxacin. In total 403 unique combinations were tested against a multi-drug resistant Pseudomonas aeruginosa isolate (PA910). As a measure of the synergistic effects, fractional inhibitory concentrations (FICs) were determined using microdilution assays with FICs ranges between 0.25 and 2. A high number of combinations between peptides and Polymyxin B, Erythromycin and Tetracycline were found to be synergistic. Novel variants of Indolicidin also showed a high frequency in synergist interaction.

microbiology↗

bioRxiv2019

Tissue Mechanics Determines Cell Fate in the Axial Stem Zones

Most embryos and regenerating tissues grow by the action of stem zones. Two epithelial stem zones drive axial elongation in amniotes: the mature organizer generates mesoderm, the neuralised ectoderm around it extends the neuraxis. Bipotential progenitors were also shown to exist. How are these stem cell populations organised and what controls the cell fate of bipotential progenitors? We use direct, in vivo imaging of these stem cells in the chick. We find that progenitors of single and dual fates are mingled in a small region between the specialised stem zones. Divergent tissue movements surround this region. When transplanted downstream of these flows, cells from the region of mixed fates adopt the molecular identity and behaviour of the target stem zone, irrespective of their normal fate. Thus, multipotent cells serve to separate the specialized stem zones, instead of a classical boundary. We propose their fate is determined extrinsically by morphogenetic shearing.

developmental biology↗

bioRxiv2019

BioSAXS measurements reveal that two antimicrobial peptides induce similar molecular changes in Gram-negative and Gram-positive bacteria

Two highly active short broad-spectrum AMPs (14D and 69D) with unknown mode of action have been investigated in regards to their effect against the Gram-negative bacteria E. coli and the Gram-positive bacteria methicillin-resistant Staphylococcus aureus (MRSA). Minimal inhibitory concentration (MIC) measurements using a cell density of 108 cfu/ml resulted in values between 16 and 32 g/ml. Time kill experiments using 108 cfu/ml revealed complete killing, except for 69D in combination with MRSA, where bacterial load was reduced a million times. Small angle X-ray scattering of biological samples (BioSAXS) at 108 cfu/ml was applied to investigate the ultrastructural changes in E. coli and MRSA in response to these two broad-spectrum AMPs. In addition, electron microscopy (EM) was performed to visualize the treated and non-treated bacteria. As expected, the scattering curves generated using BioSAXS show the ultrastructure of the Gram-positive and Gram-negative bacteria to be very different (BioSAXS is not susceptible to the outer shape). After treatment with either peptide, the scattering curves of E. coli and MRSA cells are much more alike. This data in conjunction with the EM indicates that ribosomes might be effected by the treatment as well as changes in the nucleoid occurs. Whereas in EM it is notoriously difficult to observe changes for spherical Gram-positives, the BioSAXS results are superior and reveal strongly similar effects for both peptides induced in Gram-positive as well as Gram-negative bacteria. Given the high-throughput possibility and robust statistics BioSAXS can support and speed up mode of action research in AMPs and other antimicrobial compounds, making a contribution towards the development of urgently needed drugs against resistant bacteria.

biophysics↗

bioRxiv2019

Motion prediction enables simulated MR-imaging of freely moving model organisms

AbstractMagnetic resonance tomography typically applies the Fourier transform to k-space signals repeatedly acquired from a frequency encoded spatial region of interest, therefore requiring a stationary object during scanning. Any movement of the object results in phase errors in the recorded signal, leading to deformed images, phantoms, and artifacts, since the encoded information does not originate from the intended region of the object. However, if the type and magnitude of movement is known instantaneously, the scanner or the reconstruction algorithm could be adjusted to compensate for the movement, directly allowing high quality imaging with non-stationary objects. This would be an enormous boon to studies that tie cell metabolomics to spontaneous organism behaviour, eliminating the stress otherwise necessitated by restraining measures such as anesthesia or clamping.\n\nIn the present theoretical study, we use a phantom of the animal model C. elegans to examine the feasibility to automatically predict its movement and position, and to evaluate the impact of movement prediction, within a sufficiently long time horizon, on image reconstruction. For this purpose, we use automated image processing to annotate body parts in freely moving C. elegans, and predict their path of movement. We further introduce an MRI simulation platform based on brightfield-videos of the moving worm, combined with a stack of high resolution transmission electron microscope (TEM) slice images as virtual high resolution phantoms. A phantom provides an indication of the spatial distribution of signal-generating nuclei on a particular imaging slice. We show that adjustment of the scanning to the predicted movements strongly reduces distortions in the resulting image, opening the door for implementation in a high-resolution NMR scanner.

systems biology↗

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↗