Biology subjects

Herman, S.

Publications and source records attributed to Herman, S..

6 recordsLinked to original sources

bioRxiv2025

A Biosynthetic Thumb Prosthesis

In cases of severe damage to the extremities, the function and structure of compromised biological tissues must be replaced. If biological reconstruction using autologous tissue is not feasible, amputation and replacement with a synthetic prosthesis is often the next best option. Currently, the synthetic materials available for prostheses are limited, especially in their ability to restore skin sensation. Here we show a biosynthetic prosthesis that combines the versatility of titanium with the rich sensory capabilities of biological skin, to both increase movement and restore sensation to an amputated thumb. The prosthesis recreates opposition pinch by linking motion of the prosthetic joint to that of the residual biological joint. The device is enclosed in neurotized skin from the patients own body, providing natural sensation. We validated the biosynthetic thumbs ability to reproduce opposition pinch on the benchtop and in a cadaver, and showed in vivo viability of the skin interface in an animal model. These results provide a framework for functional reconstruction of amputated digits using a combination of synthetic materials and biological tissues. One Sentence SummaryA prosthetic thumb made up of a titanium mechanism and living skin can restore opposition pinch and touch sensation following thumb amputation.

bioengineering↗

bioRxiv2023

Computationally Engineered CRISPR-SpyCas9 High-Fidelity Variants with Improved Specificity and Reduced Non-specific DNA Damage

The CRISPR-Cas system holds great promise in the treatment of diseases caused by genetic variations. As wildtype SpyCas9 is known to generate many off-target effects, its use in the clinic remains controversial due to safety concerns. Several high-fidelity Cas9 variants with greater specificity have been developed using rational design and directed evolution. However, the enhancement of specificity by these methods is limited by factors like selection pressure and library diversity. Thus, in-silico protein engineering may provide a more efficient route for enhancing specificity, although computationally testing these proteins remains challenging. We recently demonstrated the advantage of normal mode analysis to simulate and predict the enzymatic function of SpyCas9 in the presence of mismatches. Here, we report several mathematical models describing the entropy and functionality relationships in the CRISPR-Cas9 system. We demonstrate the invariant characteristics of these models across different conformational structures. Based on these invariant models, we developed ComPE, a novel computational protein engineering method to modify the protein and measure the vibrational entropy of wildtype or variant SpyCas9 in complex with its sgRNA and target DNA. Using this platform, we discovered novel high-fidelity Cas9 variants with improved specificity. We functionally validated the improved specificity of four variants, and the intact on-target activity in one of them. Lastly, we demonstrate their reduced off-target editing and non-specific gRNA-independent DNA damage, highlighting their advantages for clinical applications. The described method could be applied to a wide range of proteins, from CRISPR-Cas orthologs to distinct proteins in any field where engineered proteins can improve biological processes.

bioengineering↗

bioRxiv2022

Impaired iron recycling from erythrocytes is an early iron-dependent hallmark of aging

Aging affects iron homeostasis, as evidenced by tissue iron loading and toxicity and common anemia in the elderly. Iron needs in mammals are met primarily by iron-recycling from senescent red blood cells (RBCs), a task chiefly accomplished by splenic red pulp macrophages (RPMs) via erythrophagocytosis. Given that RPMs continuously process iron, their cellular functions might be susceptible to age-dependent decline, a condition that has been largely unexplored to date. Here, we found that 10-11-months-old female mice exhibit iron loading, diminished lysosomal activity, and decreased erythrophagocytosis rate in RPMs. These impairments lead to the retention of senescent hemolytic RBCs in the spleen, and the formation of undegradable iron- and heme-rich extracellular protein aggregates, likely derived from ferroptotic RPMs. We further found that feeding mice an iron-reduced diet alleviates iron accumulation in RPMs, enhances their ability to clear erythrocytes, and limits ferroptosis. Consequently, this diet ameliorates hemolysis of splenic RBCs and the formation of iron-rich aggregates, increasing serum iron availability in aging mice. Using RPM-like cells, we show that the diminished iron-recycling capacity of RPMs is underlain by iron accumulation and reduced expression of heme-catabolizing enzyme heme oxygenase 1 (HO-1). Taken together, we identified RPM collapse as an early hallmark of aging and demonstrated that dietary iron reduction improves iron turnover efficacy.

physiology↗

bioRxiv2018

PhenoMeNal: Processing and analysis of Metabolomics data in the Cloud

BackgroundMetabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organisms metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological and many other applied biological domains. Its computationally-intensive nature has driven requirements for open data formats, data repositories and data analysis tools. However, the rapid progress has resulted in a mosaic of independent - and sometimes incompatible - analysis methods that are difficult to connect into a useful and complete data analysis solution.\n\nFindingsThe PhenoMeNal (Phenome and Metabolome aNalysis) e-infrastructure provides a complete, workflow-oriented, interoperable metabolomics data analysis solution for a modern infrastructure-as-a-service (IaaS) cloud platform. PhenoMeNal seamlessly integrates a wide array of existing open source tools which are tested and packaged as Docker containers through the projects continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi and Pachyderm.\n\nConclusionsPhenoMeNal constitutes a keystone solution in cloud infrastructures available for metabolomics. It provides scientists with a ready-to-use, workflow-driven, reproducible and shareable data analysis platform harmonizing the software installation and configuration through user-friendly web interfaces. The deployed cloud environments can be dynamically scaled to enable large-scale analyses which are interfaced through standard data formats, versioned, and have been tested for reproducibility and interoperability. The flexible implementation of PhenoMeNal allows easy adaptation of the infrastructure to other application areas and omics research domains.

bioinformatics↗

bioRxiv2018

Container-based bioinformatics with Pachyderm

MotivationComputational biologists face many challenges related to data size, and they need to manage complicated analyses often including multiple stages and multiple tools, all of which must be deployed to modern infrastructures. To address these challenges and maintain reproducibility of results, researchers need (i) a reliable way to run processing stages in any computational environment, (ii) a well-defined way to orchestrate those processing stages, and (iii) a data management layer that tracks data as it moves through the processing pipeline.\n\nResultsPachyderm is an open-source workflow system and data management framework that fulfills these needs by creating a data pipelining and data versioning layer on top of projects from the container ecosystem, having Kubernetes as the backbone for container orchestration. We adapted Pachyderm and demonstrated its attractive properties in bioinformatics. A Helm Chart was created so that researchers can use Pachyderm in multiple scenarios. The Pachyderm File System was extended to support block storage. A wrapper for initiating Pachyderm on cloud-agnostic virtual infrastructures was created. The benefits of Pachyderm are illustrated via a large metabolomics workflow, demonstrating that Pachyderm enables efficient and sustainable data science workflows while maintaining reproducibility and scalability.\n\nAvailabilityPachyderm is available from https://github.com/pachyderm/pachyderm. The Pachyderm Helm Chart is available from https://github.com/kubernetes/charts/tree/master/stable/pachyderm. Pachyderm is available out-of-the-box from the PhenoMeNal VRE (https://github.com/phnmnl/KubeNow-plugin) and general Kubernetes environments instantiated via KubeNow. The code of the workflow used for the analysis is available on GitHub (https://github.com/pharmbio/LC-MS-Pachyderm).\n\nContactjon.novella@farmbio.uu.se

bioinformatics↗

bioRxiv2017

Interoperable and scalable metabolomics data analysis with microservices

Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed in parallel using the Kubernetes container orchestrator. The access point is a virtual research environment which can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and established workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry studies, one nuclear magnetic resonance spectroscopy study and one fluxomics study, showing that the method scales dynamically with increasing availability of computational resources. We achieved a complete integration of the major software suites resulting in the first turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, multivariate statistics, and metabolite identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science.

bioinformatics↗