Bayesian inference of sample-specific coexpression networks.

Gene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of biological systems. Inferring coexpression networks is a critical element of GRN inference, as the correlation between expression patterns may indicate that genes are coregulated by common factors. However, methods that estimate coexpression networks generally derive an aggregate network representing the mean regulatory properties of the population and so fail to fully capture population heterogeneity. BONOBO (Bayesian Optimized Networks Obtained By assimilating Omics data) is a scalable Bayesian model for deriving individual sample-specific coexpression matrices that recognizes variations in molecular interactions across individuals. For each sample, BONOBO assumes a Gaussian distribution on the log-transformed centered gene expression and a conjugate prior distribution on the sample-specific coexpression matrix constructed from all other samples in the data. Combining the sample-specific gene coexpression with the prior distribution, BONOBO yields a closed-form solution for the posterior distribution of the sample-specific coexpression matrices, thus allowing the analysis of large datasets. We demonstrate BONOBO's utility in several contexts, including analyzing gene regulation in yeast transcription factor knockout studies, the prognostic significance of miRNA-mRNA interaction in human breast cancer subtypes, and sex differences in gene regulation within human thyroid tissue. We find that BONOBO outperforms other methods that have been used for sample-specific coexpression network inference and provides insight into individual differences in the drivers of biological processes.

Gene regulatory networks reveal sex difference in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) has been observed to have significant sex differences in incidence, prognosis, and response to therapy. However, the molecular mechanisms responsible for these disparities have not been investigated extensively. METHODS: Sample-specific gene regulatory network methods were used to analyze RNA sequencing data from non-cancerous human lung samples from The Genotype Tissue Expression Project (GTEx) and lung adenocarcinoma primary tumor samples from The Cancer Genome Atlas (TCGA); results were validated on independent data. RESULTS: We found that genes associated with key biological pathways including cell proliferation, immune response and drug metabolism are differentially regulated between males and females in both healthy lung tissue and tumor, and that these regulatory differences are further perturbed by tobacco smoking. We also discovered significant sex bias in transcription factor targeting patterns of clinically actionable oncogenes and tumor suppressor genes, including AKT2 and KRAS. Using differentially regulated genes between healthy and tumor samples in conjunction with a drug repurposing tool, we identified several small-molecule drugs that might have sex-biased efficacy as cancer therapeutics and further validated this observation using an independent cell line database. CONCLUSIONS: These findings underscore the importance of including sex as a biological variable and considering gene regulatory processes in developing strategies for disease prevention and management.

Lung adenocarcinoma (LUAD) is a disease that affects males and females differently. Biological sex not only influences chances of developing the disease, but also how the disease progresses and how effective various therapies may be. We analyzed sex-specific gene regulatory networks consisting of transcription factors and the genes they regulate in both healthy lung tissue and in LUAD and identified sex-biased differences. We found that genes associated with cell proliferation, immune response, and drug metabolism are differentially targeted by transcription factors between males and females. We also found that several genes that are drug targets in LUAD, are also regulated differently between males and females. Importantly, these differences are also influenced by an individual's smoking history. Extending our analysis using a drug repurposing tool, we found candidate drugs with evidence that they might work better for one sex or the other. These results demonstrate that considering the differences in gene regulation between males and females will be essential if we are to develop precision medicine strategies for preventing and treating LUAD.

Disease-specific T cell receptors maintain pathogenic T helper cell responses in postinfectious Lyme arthritis.

BACKGROUND: Antibiotic-Refractory Lyme Arthritis (ARLA) involves a complex interplay of T cell responses targeting Borrelia burgdorferi antigens succeeding towards autoantigens by epitope spreading. However, the precise molecular mechanisms driving the pathogenic T cell response in ARLA remain unclear. Our aim was to elucidate the molecular program of disease-specific Th cells. METHODS: Using flow cytometry, high-throughput T cell receptor (TCR) sequencing and scRNA-seq of CD4+ Th cells isolated from the joints of European ARLA patients, we aimed at inferring antigen specificity through unbiased analysis of TCR repertoire patterns, identifying surrogate markers for disease-specific TCRs and connecting TCR specificity to transcriptional patterns. RESULTS: PD-1hiHLA-DR+CD4+ effector T cells were clonally expanded within the inflamed joints and persisted throughout disease course. Among these cells, we identified a distinct TCRß motif restricted to HLA-DRB1*11 or *13 alleles. These alleles, being underrepresented in North American ARLA patients, were unexpectedly prevalent in our European cohort. The identified TCRß motif served as surrogate marker for a convergent TCR response specific to ARLA, distinguishing it from other rheumatic diseases. In the scRNA-seq dataset, the TCRß motif particularly mapped to peripheral T helper (TPH) cells displaying signs of sustained proliferation, continuous TCR signaling, and expressing CXCL13 and IFN-γ. CONCLUSION: By inferring disease-specific TCRs from synovial T cells we identified a convergent TCR response in the joints of ARLA patients that continuously fueled the expansion of TPH cells expressing a pathogenic cytokine effector program. The identified TCRs will aid in uncovering the major antigen targets of the maladaptive immune response. FUNDING: Supported by the German Research Foundation (DFG) MO 2160/4-1; the Federal Ministry of Education and Research (BMBF; Advanced Clinician Scientist-Program INTERACT; 01EO2108) embedded in the Interdisciplinary Center for Clinical Research (IZKF) of the University Hospital Würzburg; the German Center for Infection Research (DZIF; Clinical Leave Program; TI07.001_007) and the Interdisciplinary Center for Clinical Research (IZKF) Würzburg (Clinician Scientist Program, Z-2/CSP-30).

node2vec2rank: Large Scale and Stable Graph Differential Analysis via Multi-Layer Node Embeddings and Ranking.

Computational methods in biology can infer large molecular interaction networks from multiple data sources and at different resolutions, creating unprecedented opportunities to explore the mechanisms driving complex biological phenomena. Networks can be built to represent distinct conditions and compared to uncover graph-level differences-such as when comparing patterns of gene-gene interactions that change between biological states. Given the importance of the graph comparison problem, there is a clear and growing need for robust and scalable methods that can identify meaningful differences. We introduce node2vec2rank (n2v2r), a method for graph differential analysis that ranks nodes according to the disparities of their representations in joint latent embedding spaces. Improving upon previous bag-of-features approaches, we take advantage of recent advances in machine learning and statistics to compare graphs in higher-order structures and in a data-driven manner. Formulated as a multi-layer spectral embedding algorithm, n2v2r is computationally efficient, incorporates stability as a key feature, and can provably identify the correct ranking of differences between graphs in an overall procedure that adheres to veridical data science principles. By better adapting to the data, node2vec2rank clearly outperformed the commonly used node degree in finding complex differences in simulated data. In the real-world applications of breast cancer subtype characterization, analysis of cell cycle in single-cell data, and searching for sex differences in lung adenocarcinoma, node2vec2rank found meaningful biological differences enabling the hypothesis generation for therapeutic candidates. Software and analysis pipelines implementing n2v2r and used for the analyses presented here are publicly available.

Biologically informed NeuralODEs for genome-wide regulatory dynamics.

BACKGROUND: Gene regulatory network (GRN) models that are formulated as ordinary differential equations (ODEs) can accurately explain temporal gene expression patterns and promise to yield new insights into important cellular processes, disease progression, and intervention design. Learning such gene regulatory ODEs is challenging, since we want to predict the evolution of gene expression in a way that accurately encodes the underlying GRN governing the dynamics and the nonlinear functional relationships between genes. Most widely used ODE estimation methods either impose too many parametric restrictions or are not guided by meaningful biological insights, both of which impede either scalability, explainability, or both. RESULTS: We developed PHOENIX, a modeling framework based on neural ordinary differential equations (NeuralODEs) and Hill-Langmuir kinetics, that overcomes limitations of other methods by flexibly incorporating prior domain knowledge and biological constraints to promote sparse, biologically interpretable representations of GRN ODEs. We tested the accuracy of PHOENIX in a series of in silico experiments, benchmarking it against several currently used tools. We demonstrated PHOENIX's flexibility by modeling regulation of oscillating expression profiles obtained from synchronized yeast cells. We also assessed the scalability of PHOENIX by modeling genome-scale GRNs for breast cancer samples ordered in pseudotime and for B cells treated with Rituximab. CONCLUSIONS: PHOENIX uses a combination of user-defined prior knowledge and functional forms from systems biology to encode biological "first principles" as soft constraints on the GRN allowing us to predict subsequent gene expression patterns in a biologically explainable manner.

Biologically informed NeuralODEs for genome-wide regulatory dynamics.

Modeling dynamics of gene regulatory networks using ordinary differential equations (ODEs) allow a deeper understanding of disease progression and response to therapy, thus aiding in intervention optimization. Although there exist methods to infer regulatory ODEs, these are generally limited to small networks, rely on dimensional reduction, or impose non-biological parametric restrictions - all impeding scalability and explainability. PHOENIX is a neural ODE framework incorporating prior domain knowledge as soft constraints to infer sparse, biologically interpretable dynamics. Extensive experiments - on simulated and real data - demonstrate PHOENIX's unique ability to learn key regulatory dynamics while scaling to the whole genome.

Erratum: Transcranial optical monitoring for detecting intracranial pressure alterations in children with benign external hydrocephalus: a proof-of-concept study.

[This corrects the article DOI: 10.1117/1.NPh.9.4.045005.].

Bayesian Optimized sample-specific Networks Obtained By Omics data (BONOBO).

Gene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of biological systems. Inferring co-expression networks is a critical element of GRN inference as the correlation between expression patterns may indicate that genes are coregulated by common factors. However, methods that estimate co-expression networks generally derive an aggregate network representing the mean regulatory properties of the population and so fail to fully capture population heterogeneity. To address these concerns, we introduce BONOBO (Bayesian Optimized Networks Obtained By assimilating Omics data), a scalable Bayesian model for deriving individual sample-specific co-expression networks by recognizing variations in molecular interactions across individuals. For every sample, BONOBO assumes a Gaussian distribution on the log-transformed centered gene expression and a conjugate prior distribution on the sample-specific co-expression matrix constructed from all other samples in the data. Combining the sample-specific gene expression with the prior distribution, BONOBO yields a closed-form solution for the posterior distribution of the sample-specific co-expression matrices, thus making the method extremely scalable. We demonstrate the utility of BONOBO in several contexts, including analyzing gene regulation in yeast transcription factor knockout studies, prognostic significance of miRNA-mRNA interaction in human breast cancer subtypes, and sex differences in gene regulation within human thyroid tissue. We find that BONOBO outperforms other sample-specific co-expression network inference methods and provides insight into individual differences in the drivers of biological processes.

Gene regulatory Networks Reveal Sex Difference in Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) has been observed to have significant sex differences in incidence, prognosis, and response to therapy. However, the molecular mechanisms responsible for these disparities have not been investigated extensively. Sample-specific gene regulatory network methods were used to analyze RNA sequencing data from non-cancerous human lung samples from The Genotype Tissue Expression Project (GTEx) and lung adenocarcinoma primary tumor samples from The Cancer Genome Atlas (TCGA); results were validated on independent data. We observe that genes associated with key biological pathways including cell proliferation, immune response and drug metabolism are differentially regulated between males and females in both healthy lung tissue, as well as in tumor, and that these regulatory differences are further perturbed by tobacco smoking. We also uncovered significant sex bias in transcription factor targeting patterns of clinically actionable oncogenes and tumor suppressor genes, including AKT2 and KRAS. Using differentially regulated genes between healthy and tumor samples in conjunction with a drug repurposing tool, we identified several small-molecule drugs that might have sex-biased efficacy as cancer therapeutics and further validated this observation using an independent cell line database. These findings underscore the importance of including sex as a biological variable and considering gene regulatory processes in developing strategies for disease prevention and management.

Recognition and reconstruction of cell differentiation patterns with deep learning.

Cell lineage decisions occur in three-dimensional spatial patterns that are difficult to identify by eye. There is an ongoing effort to replicate such patterns using mathematical modeling. One approach uses long ranging cell-cell communication to replicate common spatial arrangements like checkerboard and engulfing patterns. In this model, the cell-cell communication has been implemented as a signal that disperses throughout the tissue. On the other hand, machine learning models have been developed for pattern recognition and pattern reconstruction tasks. We combined synthetic data generated by the mathematical model with spatial summary statistics and deep learning algorithms to recognize and reconstruct cell fate patterns in organoids of mouse embryonic stem cells. Application of Moran's index and pair correlation functions for in vitro and synthetic data from the model showed local clustering and radial segregation. To assess the patterns as a whole, a graph neural network was developed and trained on synthetic data from the model. Application to in vitro data predicted a low signal dispersion value. To test this result, we implemented a multilayer perceptron for the prediction of a given cell fate based on the fates of the neighboring cells. The results show a 70% accuracy of cell fate imputation based on the nine nearest neighbors of a cell. Overall, our approach combines deep learning with mathematical modeling to link cell fate patterns with potential underlying mechanisms.

Effect of high-dose glucocorticoid treatment on human brown adipose tissue activity: a randomised, double-blinded, placebo-controlled cross-over trial in healthy men.

BACKGROUND: Glucocorticoids (GCs) are widely applied anti-inflammatory drugs that are associated with adverse metabolic effects including insulin resistance and weight gain. Previous research indicates that GCs may negatively impact brown adipose tissue (BAT) activity in rodents and humans. METHODS: We performed a randomised, double-blinded cross-over trial in 16 healthy men (clinicaltrials.govNCT03269747). Participants received 40 mg of prednisone per day for one week or placebo. After a washout period of four weeks, participants crossed-over to the other treatment arm. Primary endpoint was the increase in resting energy expenditure (EE) in response to a mild-cold stimulus (cold-induced thermogenesis, CIT). Secondary outcomes comprised mean 18F-FDG uptake into supraclavicular BAT (SUVmean) as determined by FDG-PET/CT, volume of the BAT depot as well as fat content determined by MRI. The plasma metabolome and the transcriptome of supraclavicular BAT and of skeletal muscle biopsies after each treatment period were analysed. FINDINGS: Sixteen participants were recruited to the trial and completed it successfully per protocol. After prednisone treatment resting EE was higher both during warm and cold conditions. However, CIT was similar, 153 kcal/24 h (95% CI 40-266 kcal/24 h) after placebo and 186 kcal/24 h (95% CI 94-277 kcal/24 h, p = 0.38) after prednisone. SUVmean of BAT after cold exposure was not significantly affected by prednisone (3.36 g/ml, 95% CI 2.69-4.02 g/ml, vs 3.07 g/ml, 95% CI 2.52-3.62 g/ml, p = 0.28). Results of plasma metabolomics and BAT transcriptomics corroborated these findings. RNA sequencing of muscle biopsies revealed higher expression of genes involved in calcium cycling. No serious adverse events were reported and adverse events were evenly distributed between the two treatments. INTERPRETATION: Prednisone increased EE in healthy men possibly by altering skeletal muscle calcium cycling. Cold-induced BAT activity was not affected by GC treatment, which indicates that the unfavourable metabolic effects of GCs are independent from thermogenic adipocytes. FUNDING: Grants from Swiss National Science Foundation (PZ00P3_167823), Bangerter-Rhyner Foundation and from Nora van der Meeuwen-Häfliger Foundation to MJB. A fellowship-grant from the Swiss National Science Foundation (SNF211053) to WS. Grants from German Research Foundation (project number: 314061271-TRR 205) and Else Kröner-Fresenius (grant support 2012_A103 and 2015_A228) to MR.

Biotic and Abiotic Interactions in Freshwater Mesocosms Determine Fate and Toxicity of CuO Nanoparticles.

Transformation, dissolution, and sorption of copper oxide nanoparticles (CuO-NP) play an important role in freshwater ecosystems. We present the first mesocosm experiment on the fate of CuO-NP and the dynamics of the zooplankton community over a period of 12 months. Increasingly low (0.08-0.28 mg Cu L-1) and high (0.99-2.99 mg Cu L-1) concentrations of CuO-NP and CuSO4 (0.10-0.34 mg Cu L-1) were tested in a multiple dosing scenario. At the high applied concentration (CuO-NP_H) CuO-NP aggregated and sank onto the sediment layer, where we recovered 63% of Cu applied. For the low concentration (CuO-NP_L) only 41% of applied copper could be recovered in the sediment. In the water column, the percentage of initially applied Cu recovered was on average 3-fold higher for CuO-NP_L than for CuO-NP_H. Zooplankton abundance was substantially compromised in the treatments CuSO4 (p < 0.001) and CuO-NP_L (p < 0.001). Community analysis indicated that Cladocera were most affected (bk = -0.49), followed by Nematocera (bk = -0.32). The abundance of Cladocera over time and of Dixidae in summer was significantly reduced in the treatment CuO-NP_L (p < 0.001; p < 0.05) compared to the Control. Our results indicate a higher potential for negative impacts on the freshwater community when lower concentrations of CuO-NP (<0.1 mg Cu L-1) enter the ecosystem.

Efficiently quantifying DNA methylation for bulk- and single-cell bisulfite data.

MOTIVATION: DNA CpG methylation (CpGm) has proven to be a crucial epigenetic factor in the mammalian gene regulatory system. Assessment of DNA CpG methylation values via whole-genome bisulfite sequencing (WGBS) is, however, computationally extremely demanding. RESULTS: We present FAst MEthylation calling (FAME), the first approach to quantify CpGm values directly from bulk or single-cell WGBS reads without intermediate output files. FAME is very fast but as accurate as standard methods, which first produce BS alignment files before computing CpGm values. We present experiments on bulk and single-cell bisulfite datasets in which we show that data analysis can be significantly sped-up and help addressing the current WGBS analysis bottleneck for large-scale datasets without compromising accuracy. AVAILABILITY AND IMPLEMENTATION: An implementation of FAME is open source and licensed under GPL-3.0 at https://github.com/FischerJo/FAME.

Transcranial, Non-Invasive Evaluation of Potential Misery Perfusion During Hyperventilation Therapy of Traumatic Brain Injury Patients.

Hyperventilation (HV) therapy uses vasoconstriction to reduce intracranial pressure (ICP) by reducing cerebral blood volume. However, as HV also lowers cerebral blood flow (CBF), it may provoke misery perfusion (MP), in which the decrease in CBF is coupled with increased oxygen extraction fraction (OEF). MP may rapidly lead to the exhaustion of brain energy metabolites, making the brain vulnerable to ischemia. MP is difficult to detect at the bedside, which is where transcranial hybrid, near-infrared spectroscopies are promising because they non-invasively measure OEF and CBF. We have tested this technology during HV (∼30 min) with bilateral, frontal lobe monitoring to assess MP in 27 sessions in 18 patients with traumatic brain injury. In this study, HV did not lead to MP at a group level (p > 0.05). However, a statistical approach yielded 89 events with a high probability of MP in 19 sessions. We have characterized each statistically significant event in detail and its possible relationship to clinical and radiological status (decompressive craniectomy and presence of a cerebral lesion), without detecting any statistically significant difference (p > 0.05). However, MP detection stresses the need for personalized, real-time assessment in future clinical trials with HV, in order to provide an optimal evaluation of the risk-benefit balance of HV. Our study provides pilot data demonstrating that bedside transcranial hybrid near-infrared spectroscopies could be utilized to assess potential MP.

Transcranial optical monitoring for detecting intracranial pressure alterations in children with benign external hydrocephalus: a proof-of-concept study.

Significance: Benign external hydrocephalus (BEH) is considered a self-limiting pathology with a good prognosis. However, some children present a pathological intracranial pressure (ICP) characterized by quantitative and qualitative alterations (the so-called B-waves) that can lead to neurological sequelae. Aim: Our purpose was to evaluate whether there were cerebral hemodynamic changes associated with ICP B-waves that could be evaluated with noninvasive neuromonitoring. Approach: We recruited eleven patients (median age 16 months, range 7 to 55 months) with BEH and an unfavorable evolution requiring ICP monitoring. Bedside, nocturnal monitoring using near-infrared time-resolved and diffuse correlation spectroscopies synchronized to the clinical monitoring was performed. Results: By focusing on the timing of different ICP patterns that were identified manually by clinicians, we detected significant tissue oxygen saturation ( StO 2 ) changes ( p = 0.002 ) and blood flow index (BFI) variability ( p = 0.005 ) between regular and high-amplitude B-wave patterns. A blinded analysis looking for analogs of ICP patterns in BFI time traces achieved 90% sensitivity in identifying B-waves and 76% specificity in detecting the regular patterns. Conclusions: We revealed the presence of StO 2 and BFI variations-detectable with optical techniques-during ICP B-waves in BEH children. Finally, the feasibility of detecting ICP B-waves in hemodynamic time traces obtained noninvasively was shown.

Optical imaging and spectroscopy for the study of the human brain: status report.

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.

An amphipathic helix in Brl1 is required for nuclear pore complex biogenesis in S. cerevisiae.

The nuclear pore complex (NPC) is the central portal for macromolecular exchange between the nucleus and cytoplasm. In all eukaryotes, NPCs assemble into an intact nuclear envelope (NE) during interphase, but the process of NPC biogenesis remains poorly characterized. Furthermore, little is known about how NPC assembly leads to the fusion of the outer and inner NE, and no factors have been identified that could trigger this event. Here, we characterize the transmembrane protein Brl1 as an NPC assembly factor required for NE fusion in budding yeast. Brl1 preferentially associates with NPC assembly intermediates and its depletion halts NPC biogenesis, leading to NE herniations that contain inner and outer ring nucleoporins but lack the cytoplasmic export platform. Furthermore, we identify an essential amphipathic helix in the luminal domain of Brl1 that mediates interactions with lipid bilayers. Mutations in this amphipathic helix lead to NPC assembly defects, and cryo-electron tomography analyses reveal multilayered herniations of the inner nuclear membrane with NPC-like structures at the neck, indicating a failure in NE fusion. Taken together, our results identify a role for Brl1 in NPC assembly and suggest a function of its amphipathic helix in mediating the fusion of the inner and outer nuclear membranes.

Clay Types Modulate the Toxicity of Low Concentrated Copper Oxide Nanoparticles Toward Springtails in Artificial Test Soils.

Copper oxide nanoparticles (CuO-NPs) can be applied as an efficient alternative to conventional Cu in agriculture. Negative effects of CuO-NPs on soil organisms were found, but only in clay-rich loamy soils. It is hypothesized that clay-NP interactions are the origin of the observed toxic effects. In the present study, artificial Organisation for Economic Co-operation and Development soils containing 30% of kaolin or montmorillonite as clay type were spiked with 1-32 mg Cu/kg of uncoated CuO-NPs or CuCl2 . We performed 28-day reproduction tests with springtails of the species Folsomia candida and recorded the survival, reproduction, dry weight, and Cu content of adults. In a second experiment, molting frequency and the Cu content of exuviae, as well as the biochemical endpoints metallothionein and catalase (CAT) in springtails, were investigated. In the reproduction assay, negative effects on all endpoints were observed, but only in soils containing montmorillonite and mostly for CuO-NPs. For the biochemical endpoints and Cu content of exuviae, effects were clearly distinct between Cu forms in montmorillonite soil, but a significant reduction compared to the control was only found for CAT activity. Therefore, the reduced CAT activity in CuO-NP-montmorillonite soil might be responsible for the observed toxicity, potentially resulting from reactive oxygen species formation overloading the antioxidant system. This process seems to be highly concentration-dependent, because all endpoints investigated in reproduction and biochemical assays of CuO-NP-montmorillonite treatments showed a nonlinear dose-response relationship and were constantly reduced by approximately 40% at a field-realistic concentration of 3 mg/kg, but not at 32 mg/kg. The results underline that clay-CuO-NP interactions are crucial for their toxic behavior, especially at low, field-realistic concentrations, which should be considered for risk assessment of CuO-NPs. Environ Toxicol Chem 2022;41:2454-2465. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Arrested in Glass: Actin within Sophisticated Architectures of Biosilica in Sponges.

Actin is a fundamental member of an ancient superfamily of structural intracellular proteins and plays a crucial role in cytoskeleton dynamics, ciliogenesis, phagocytosis, and force generation in both prokaryotes and eukaryotes. It is shown that actin has another function in metazoans: patterning biosilica deposition, a role that has spanned over 500 million years. Species of glass sponges (Hexactinellida) and demosponges (Demospongiae), representatives of the first metazoans, with a broad diversity of skeletal structures with hierarchical architecture unchanged since the late Precambrian, are studied. By etching their skeletons, organic templates dominated by individual F-actin filaments, including branched fibers and the longest, thickest actin fiber bundles ever reported, are isolated. It is proposed that these actin-rich filaments are not the primary site of biosilicification, but this highly sophisticated and multi-scale form of biomineralization in metazoans is ptterned.

Acute effect of propranolol on resting energy expenditure in hyperthyroid patients.

Objective: Hyperthyroidism is a common endocrine disorder which leads to higher resting energy expenditure (REE). Increased activity of brown adipose tissue (BAT) contributes to elevated REE in hyperthyroid patients. For rapid control of hyperthyroid symptoms, the non-selective ß-blocker propranolol is widely used. While, long-term treatment with propranolol reduces REE it is currently unclear whether it can also acutely diminish REE. Design: In the present prospective interventional trial we investigated the effect of propranolol on REE in hyperthyroid patients. Methods: Nineteen patients with overt primary hyperthyroidism were recruited from the endocrine outpatient clinic. REE was measured by indirect calorimetry before and after an acute dose of 80mg propranolol and during a control period, respectively. Additionally, skin temperature was recorded at eleven predefined locations during each study visit, vital signes and heart rate (HR) were measured before and after administration of propranolol. Results: Mean REE decreased slightly after acute administration of 80mg propranolol (p= 0.03) from 1639 ± 307 kcal/24h to 1594 ± 283 kcal/24h. During the control visit REE did not change significantly. HR correlated significantly with the level of free T3 (R2 = 0.38, p=0.029) free T4 (R2 = 0.39, p=0.026). HR decreased 81 ± 12 bpm to 67 ± 7.6 bpm 90 minutes after oral administration of propranolol (p<0.0001). Skin temperature did not change after propranolol intake. Conclusions: In hyperthyroid patients a single dose of propranolol reduced heart rate substantially but REE diminished only marginally probably due to reduced myocardial energy consumption. Our data speak against a relevant contribution of BAT to the higher REE in hyperthyroidism. Clinical trial registration: ClinicalTrials.gov, identifier (NCT03379181).