The role of microtubules in initial neuronal polarization

Neurons are highly polarized cells with two structurally and functionally distinct compartments, axons and dendrites. This dichotomy is the basis for unidirectional signal propagation, the quintessential function of neurons. During neuronal development, the formation of the axon is the initial step in breaking cellular symmetry and the establishment of neuronal polarity. Although a number of polarity regulators involved in this process have been identified, our understanding of the intracellular mechanisms underlying neuronal polarization still remains fragmentary. In my studies, I addressed the role of microtubule dynamics in initial neuronal polarization. To this end I aimed to investigate the following issues: 1) How do microtubule dynamics and stability change during initial neuronal development? 2) Do microtubules play an instructive role in axon formation? 3) What are possible regulators mediating changes in microtubule dynamics during axon formation? Using hippocampal neurons in culture as a model system for neuronal polarization I first addressed the dynamics of microtubules in early developmental stages of neurons. Assessing posttranslational modifications of tubulin which serve as markers of microtubule turnover I found that microtubule stability is increased in a single neurite already before axon formation and in the axon of morphologically polarized cells. This polarized distribution of microtubule stability was confirmed by testing the resistance of neuronal microtubules to pharmacologically induced depolymerization. The axon of polarized neurons and a single neurite in morphologically unpolarized cells showed increased microtubule stability. Thus, I established a correlation between the identity of a process and its microtubule stability. By manipulating specific regulators of neuronal polarity, SAD kinases and GSK-3beta, I analyzed a possible relation between a polarization of microtubule stability and neuronal polarity. I found that a loss of polarity correlated with a loss of polarized microtubule stability in neurons defective for SAD A and SAD B kinases. In marked contrast, the formation of multiple axons, induced by the inhibition of GSK-3beta, was associated with increased microtubule stability in these supernumerary axons. These results suggested that SAD kinases and GSK-3beta regulate neuronal polarization –at least in part– by modulating microtubule dynamics. To establish a possible causal relation between microtubule dynamics and axon formation I assessed the effects of specific pharmacological alterations of microtubule dynamics on neuronal polarization. I found that application of low doses of the microtubule destabilizing drug nocodazole selectively reduced the formation of future dendrites. Conversely, low doses of the microtubule stabilizing drug taxol led to the formation of multiple axons. I also studied microtubule dynamics in living neurons transfected with GFP-tagged EB3, a protein binding specifically to polymerizing microtubule plus ends. In line with my previous observations I found that microtubules are stabilized along the shaft of the growing axons while dynamic microtubules enrich at the tip of the growing process, suggesting that a well- balanced shift of microtubule dynamics towards more stable microtubules is necessary to induce axon formation. By uncaging a photoactivatable analog of taxol I induced a local stabilization of microtubules at the neurite tip of an unpolarized neuron which was sufficient to favor the site of axon formation. This indicates that a transient stabilization of microtubules is sufficient to trigger axon formation. In summary, my data allow the following conclusions: 1) Microtubule stability correlates with the identity of a neuronal process. 2) Microtubule stabilization causes axon formation. 3) Microtubule stabilization precedes axon formation. I therefore deduce that microtubules are actively involved in the process of axon formation and that local microtubule stabilization in one neuronal process is a physiological signal specifying neuronal polarization

Microtubule stabilization specifies initial neuronal polarization

Axon formation is the initial step in establishing neuronal polarity. We examine here the role of microtubule dynamics in neuronal polarization using hippocampal neurons in culture. We see increased microtubule stability along the shaft in a single neurite before axon formation and in the axon of morphologically polarized cells. Loss of polarity or formation of multiple axons after manipulation of neuronal polarity regulators, synapses of amphids defective (SAD) kinases, and glycogen synthase kinase-3β correlates with characteristic changes in microtubule turnover. Consistently, changing the microtubule dynamics is sufficient to alter neuronal polarization. Application of low doses of the microtubule-destabilizing drug nocodazole selectively reduces the formation of future dendrites. Conversely, low doses of the microtubule-stabilizing drug taxol shift polymerizing microtubules from neurite shafts to process tips and lead to the formation of multiple axons. Finally, local stabilization of microtubules using a photoactivatable analogue of taxol induces axon formation from the activated area. Thus, local microtubule stabilization in one neurite is a physiological signal specifying neuronal polarization

Big Data in Laboratory Medicine—FAIR Quality for AI?

Laboratory medicine is a digital science. Every large hospital produces a wealth of data each day—from simple numerical results from, e.g., sodium measurements to highly complex output of “-omics” analyses, as well as quality control results and metadata. Processing, connecting, storing, and ordering extensive parts of these individual data requires Big Data techniques. Whereas novel technologies such as artificial intelligence and machine learning have exciting application for the augmentation of laboratory medicine, the Big Data concept remains fundamental for any sophisticated data analysis in large databases. To make laboratory medicine data optimally usable for clinical and research purposes, they need to be FAIR: findable, accessible, interoperable, and reusable. This can be achieved, for example, by automated recording, connection of devices, efficient ETL (Extract, Transform, Load) processes, careful data governance, and modern data security solutions. Enriched with clinical data, laboratory medicine data allow a gain in pathophysiological insights, can improve patient care, or can be used to develop reference intervals for diagnostic purposes. Nevertheless, Big Data in laboratory medicine do not come without challenges: the growing number of analyses and data derived from them is a demanding task to be taken care of. Laboratory medicine experts are and will be needed to drive this development, take an active role in the ongoing digitalization, and provide guidance for their clinical colleagues engaging with the laboratory data in research

The BioRef Infrastructure, a Framework for Real-Time, Federated, Privacy-Preserving, and Personalized Reference Intervals: Design, Development, and Application.

BACKGROUND Reference intervals (RIs) for patient test results are in standard use across many medical disciplines, allowing physicians to identify measurements indicating potentially pathological states with relative ease. The process of inferring cohort-specific RIs is, however, often ignored because of the high costs and cumbersome efforts associated with it. Sophisticated analysis tools are required to automatically infer relevant and locally specific RIs directly from routine laboratory data. These tools would effectively connect clinical laboratory databases to physicians and provide personalized target ranges for the respective cohort population. OBJECTIVE This study aims to describe the BioRef infrastructure, a multicentric governance and IT framework for the estimation and assessment of patient group-specific RIs from routine clinical laboratory data using an innovative decentralized data-sharing approach and a sophisticated, clinically oriented graphical user interface for data analysis. METHODS A common governance agreement and interoperability standards have been established, allowing the harmonization of multidimensional laboratory measurements from multiple clinical databases into a unified "big data" resource. International coding systems, such as the International Classification of Diseases, Tenth Revision (ICD-10); unique identifiers for medical devices from the Global Unique Device Identification Database; type identifiers from the Global Medical Device Nomenclature; and a universal transfer logic, such as the Resource Description Framework (RDF), are used to align the routine laboratory data of each data provider for use within the BioRef framework. With a decentralized data-sharing approach, the BioRef data can be evaluated by end users from each cohort site following a strict "no copy, no move" principle, that is, only data aggregates for the intercohort analysis of target ranges are exchanged. RESULTS The TI4Health distributed and secure analytics system was used to implement the proposed federated and privacy-preserving approach and comply with the limitations applied to sensitive patient data. Under the BioRef interoperability consensus, clinical partners enable the computation of RIs via the TI4Health graphical user interface for query without exposing the underlying raw data. The interface was developed for use by physicians and clinical laboratory specialists and allows intuitive and interactive data stratification by patient factors (age, sex, and personal medical history) as well as laboratory analysis determinants (device, analyzer, and test kit identifier). This consolidated effort enables the creation of extremely detailed and patient group-specific queries, allowing the generation of individualized, covariate-adjusted RIs on the fly. CONCLUSIONS With the BioRef-TI4Health infrastructure, a framework for clinical physicians and researchers to define precise RIs immediately in a convenient, privacy-preserving, and reproducible manner has been implemented, promoting a vital part of practicing precision medicine while streamlining compliance and avoiding transfers of raw patient data. This new approach can provide a crucial update on RIs and improve patient care for personalized medicine

Direct Detection and Genotyping of Klebsiella pneumoniae Carbapenemases from Urine by Use of a New DNA Microarray Test

Klebsiella pneumoniae carbapenemases (KPC) are considered a serious threat to antibiotic therapy as they confer resistance to carbapenems, which are used to treat Extended-spectrum beta-lactamase (ESBL) producing bacteria. Here, we describe the development and evaluation of a DNA microarray for detection and genotyping of KPC genes (blaKPC) within 5 hours. To test the whole assay procedure (DNA extraction + DNA microarray assay) directly from clinical specimen, we compared two commercial DNA extraction kits (QIAprep Spin Miniprep Kit (Qiagen), Urine Bacterial DNA Isolation Kit (Norgen)) for the direct DNA extraction from urine samples (dilution series spiked in human urine). A reliable SNP typing from 1×105 CFU/mL urine was demonstrated for Escherichia coli (Qiagen and Norgen) and 80 CFU/mL urine on average for K. pneumoniae (Norgen). The study presents for the first time the combination of a new KPC-microarray with commercial sample preparation for the detection and genotyping of microbial pathogens directly from clinical specimen which paves the way towards tests providing epidemiological and diagnostic data enabling better antimicrobial stewardship

Statistical learning and big data applications

The amount of data generated in the field of laboratory medicine has grown to an extent that conventional laboratory information systems (LISs) are struggling to manage and analyze this complex, entangled information (“Big Data”). Statistical learning, a generalized framework from machine learning (ML) and artificial intelligence (AI) is predestined for processing “Big Data” and holds the potential to revolutionize the field of laboratory medicine. Personalized medicine may in particular benefit from AI-based systems, especially when coupled with readily available wearables and smartphones which can collect health data from individual patients and offer new, cost-effective access routes to healthcare for patients worldwide. The amount of personal data collected, however, also raises concerns about patient-privacy and calls for clear ethical guidelines for “Big Data” research, including rigorous quality checks of data and algorithms to eliminate underlying bias and enable transparency. Likewise, novel federated privacy-preserving data processing approaches may reduce the need for centralized data storage. Generative AI-systems including large language models such as ChatGPT currently enter the stage to reshape clinical research, clinical decision-support systems, and healthcare delivery. In our opinion, AI-based systems have a tremendous potential to transform laboratory medicine, however, their opportunities should be weighed against the risks carefully. Despite all enthusiasm, we advocate for stringent added-value assessments, just as for any new drug or treatment. Human experts should carefully validate AI-based systems, including patient-privacy protection, to ensure quality, transparency, and public acceptance. In this opinion paper, data prerequisites, recent developments, chances, and limitations of statistical learning approaches are highlighted

Techniques for Galactic Dust Measurements in the Heliosphere

Galactic interstellar dust (ISD) is the major ingredient in planetary formation. However, information on this important material has been extremely limited. Recently the Ulysses dust detector has identified and measured interstellar dust outside 1.8~AU from the Sun at ecliptic latitudes above $50^{\circ}$. Inside this distance it could not reliably distinguish interstellar from interplanetary dust. Modeling the Ulysses data suggests that up to 30 % of dust flux with masses above $10^{-16}\rm kg$ at 1~AU is of interstellar origin. From the Hiten satellite in high eccentric orbit about the Earth there are indications that ISD indeed reaches the Earth's orbit. Two new missions carrying dust detectors, Cassini and Stardust, will greatly increase our observational knowledge. In this paper we briefly review instruments used on these missions and compare their capabilities. The Stardust mission [{\em Brownlee et al.}, 1996] will analyze the local interstellar dust population by an in-situ chemical analyzer and collect ISD between 2 and 3~AU from the Sun. The dust analyzer on the Cassini mission will determine the interstellar dust flux outside Venus' orbit and will provide also some compositional information. Techniques to identify the ISD flux levels at 1~AU are described that can quantify the interstellar dust flux in high-Earth orbit (outside the debris belts) and provide chemical composition information of galactic dust.Comment: Accepted for Journal of Geophysical Research, 6 figures, Late