Maturation Kinetics of a Multiprotein Complex Revealed by Metabolic Labeling.

All proteins interact with other cellular components to fulfill their function. While tremendous progress has been made in the identification of protein complexes, their assembly and dynamics remain difficult to characterize. Here, we present a high-throughput strategy to analyze the native assembly kinetics of protein complexes. We apply our approach to characterize the co-assembly for 320 pairs of nucleoporins (NUPs) constituting the ≈50 MDa nuclear pore complex (NPC) in yeast. Some NUPs co-assemble fast via rapid exchange whereas others require lengthy maturation steps. This reveals a hierarchical principle of NPC biogenesis where individual subcomplexes form on a minute timescale and then co-assemble from center to periphery in a ∼1 h-long maturation process. Intriguingly, the NUP Mlp1 stands out as joining very late and associating preferentially with aged NPCs. Our approach is readily applicable beyond the NPC, making it possible to analyze the intracellular dynamics of a variety of multiprotein assemblies.

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.

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.

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.

Cerebral and systemic physiological effects of wearing face masks in young adults.

The COVID-19 pandemic led to widespread mandates requiring the wearing of face masks, which led to debates on their benefits and possible adverse effects. To that end, the physiological effects at the systemic and at the brain level are of interest. We have investigated the effect of commonly available face masks (FFP2 and surgical) on cerebral hemodynamics and oxygenation, particularly microvascular cerebral blood flow (CBF) and blood/tissue oxygen saturation (StO2), measured by transcranial hybrid near-infrared spectroscopies and on systemic physiology in 13 healthy adults (ages: 23 to 33 y). The results indicate small but significant changes in cerebral hemodynamics while wearing a mask. However, these changes are comparable to those of daily life activities. This platform and the protocol provides the basis for large or targeted studies of the effects of mask wearing in different populations and while performing critical tasks.

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.

IL-21+ CD4+ T helper cells co-expressing IFN-γ and TNF-α accumulate in the joints of antinuclear antibody positive patients with juvenile idiopathic arthritis.

Juvenile Idiopathic Arthritis (JIA) is currently classified into seven subgroups. Recently, antinuclear antibody (ANA) positive JIA patients were suggested to encompass a clinically homogenous new subgroup. CD4+ T helper (Th) cells play an essential role in JIA pathogenesis. Herein, we analyzed cytokine expression in synovial fluid (SF) CD4+ Th cells of JIA patients by using flow cytometry and compared cytokine patterns between JIA subgroups. We could show increased frequencies of IL-21 expressing CD4+ Th cells in the joints of ANA+ Oligo-/Poly-JIA patients, which co-expressed the Th-1 cytokines IFN-γ/TNF-α. In contrast, frequencies of IL-17 expressing cells were lowest in the joints of ANA+ Oligo-/Poly-JIA but enriched in that of ERA-JIA patients. This is the first description of a diverse SF Th cell cytokine pattern in different JIA subgroups. Additionally, we could define IL-21 as an effector cytokine expressed in SF Th cell in a significant proportion of ANA+ JIA patients.

Evidence for Electronically Driven Ferroelectricity in a Strongly Correlated Dimerized BEDT-TTF Molecular Conductor.

By applying measurements of the dielectric constants and relative length changes to the dimerized molecular conductor κ-(BEDT-TTF)_{2}Hg(SCN)_{2}Cl, we provide evidence for order-disorder type electronic ferroelectricity that is driven by the charge order within the (BEDT-TTF)_{2} dimers and stabilized by a coupling to the anions. According to our density functional theory calculations, this material is characterized by a moderate strength of dimerization. This system thus bridges the gap between strongly dimerized materials, often approximated as dimer-Mott systems at 1/2 filling, and nondimerized or weakly dimerized systems at 1/4 filling, exhibiting a charge order. Our results indicate that intradimer charge degrees of freedom are of particular importance in correlated κ-(BEDT-TTF)_{2}X salts and can create novel states, such as electronically driven multiferroicity or charge-order-induced quasi-one-dimensional spin liquids.

Single-Crystal to Single-Crystal Transformation of a Nonporous Fe(II) Metal-Organic Framework into a Porous Metal-Organic Framework via a Solid-State Reaction.

We report the synthesis of an air-stable nonporous coordination compound based on iron(II) centers, formate anions, and a 4,4'-bipyrazole (H2BPZ) ligand. Upon thermal treatment, a porous metal-organic framework (MOF) formed due to decomposition of the incorporated formate anions. This decomposition step and the following structural changes constituted a single-crystal to single-crystal transformation. The resulting [Fe(BPZ)] framework contained tetrahedrally coordinated iron(II) metal centers. The framework was sensitive toward oxidation by molecular oxygen even at temperatures of 183 K, as followed by oxygen sorption measurements and a color change from colorless to metallic black. The semiconductor properties of the oxidized material were studied by diffuse reflectance UV/vis/NIR spectroscopy and dielectric spectroscopy.

Forearm pressure distribution during ambulation with elbow crutches: a cross-sectional study.

BACKGROUND: Increasing numbers of patients require permanent walking aids to maintain mobility. Current elbow crutches are not designed for long-term use, and overuse is often associated with hematoma formation and pain along the forearm. We therefore hypothesized that the highest pressures between the forearm and crutch cuff during walking and stance are located in the ulnar region and that the level of weight-bearing, forearm circumference and kinematic parameters influence peak pressure values and pressure distribution. METHODS: Ten healthy adults participated in a cross-sectional study. A pressure sensor array was attached to the forearm of each participant separating the forearm into four quadrants (lateral, ulnar, intermediate and medial). Measurements were taken during crutch gait and during partial and full weight-bearing stance. A three-dimensional motion analysis system with reflective markers attached to the subject's body and to the crutches was used to obtain kinematic data. RESULTS: The mean pressure on the forearm during crutch gait was 37.5 kPa (SD 8.8 kPa). Highest mean pressure values were measured in the ulnar (41.0 kPa, SD 9.6 kPa) and intermediate (38.0 kPa, SD 9.0 kPa) quadrants. The center of pressure was mainly located in an oblique lamellar area in these two quadrants. With increasing weight-bearing on the crutches during stance, we observed a shift of the peak pressures towards the ulnar quadrant. The circumference of the forearm correlated with the peak pressure in the medial and intermediate quadrants during crutch gait (P < 0.05). Peak pressures on the forearm showed a trend towards correlation with crutch abduction, but no association with other kinematic parameters was detected. CONCLUSION: The pressure load on the forearm during crutch-assisted gait is located predominantly over the ulna and may be linked to a range of secondary conditions caused by crutch use including hematoma formation and pain.

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.

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.

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.

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

BACKGROUNDAntibiotic-Refractory Lyme Arthritis (ARLA) involves a complex interplay of T cell responses targeting Borrelia burgdorferi antigens progressing toward 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.METHODSUsing flow cytometry, high-throughput T cell receptor (TCR) sequencing, and scRNA-Seq of CD4+ Th cells isolated from the joints of patients with ARLA living in Europe, we aimed to infer antigen specificity through unbiased analysis of TCR repertoire patterns, identifying surrogate markers for disease-specific TCRs, and connecting TCR specificity to transcriptional patterns.RESULTSPD-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 TCR-ß motif restricted to HLA-DRB1*11 or *13 alleles. These alleles, being underrepresented in patients with ARLA living in North America, were unexpectedly prevalent in our European cohort. The identified TCR-ß motif served as surrogate marker for a convergent TCR response specific to ARLA, distinguishing it from other rheumatic diseases. In the scRNA-Seq data set, the TCR-ß motif particularly mapped to peripheral T helper (TPH) cells displaying signs of sustained proliferation, continuous TCR signaling, and expressing CXCL13 and IFN-γ.CONCLUSIONBy inferring disease-specific TCRs from synovial T cells we identified a convergent TCR response in the joints of patients with ARLA 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.FUNDINGSupported 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).

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.

Educational quality of YouTube videos on knee arthrocentesis.

BACKGROUND: Knee arthrocentesis is a commonly performed diagnostic and therapeutic procedure in rheumatology and orthopedic surgery. Classic teaching of arthrocentesis skills relies on hands-on practice under supervision. Video-based online teaching is an increasingly utilized educational tool in higher and clinical education. YouTube is a popular video-sharing Web site that can be accessed as a teaching source. OBJECTIVE: The objective of this study was to assess the educational value of YouTube videos on knee arthrocentesis posted by health professionals and institutions during the period from 2008 to 2012. METHODS: The YouTube video database was systematically searched using 5 search terms related to knee arthrocentesis. Two independent clinical reviewers assessed videos for procedural technique and educational value using a 5-point global score, ranging from 1 = poor quality to 5 = excellent educational quality. As validated international guidelines are lacking, we used the guidelines of the Swiss Society of Rheumatology as criterion standard for the procedure. RESULTS: Of more than thousand findings, 13 videos met the inclusion criteria. Of those, 2 contained additional animated video material: one was purely animated, and one was a check list. The average length was 3.31 ± 2.28 minutes. The most popular video had 1388 hits per month. Our mean global score for educational value was 3.1 ± 1.0. Eight videos (62 %) were considered useful for teaching purposes. Use of a "no-touch" procedure, meaning that once disinfected the skin remains untouched before needle penetration, was present in all videos. Six videos (46%) demonstrated full sterile conditions. There was no clear preference of a medial (n = 8) versus lateral (n = 5) approach. CONCLUSIONS: A discreet number of YouTube videos on knee arthrocentesis appeared to be suitable for application in a Web-based format for medical students, fellows, and residents. The low-average mean global score for overall educational value suggests an improvement of future video-based instructional materials on YouTube would be necessary before regular use for teaching could be recommended.

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.

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.