Coccydynia: A Review of Anatomy, Causes, Diagnosis, and Treatment.

¼ Coccydynia is a painful condition affecting many patients at the terminal caudal end of the spine.¼ An understanding of coccyx anatomy and variations of morphology is necessary for proper diagnosis. A multifactorial etiology for pain may be responsible for this clinical entity.¼ Several treatment options exist. Successful outcomes for patients depend on individual patient characteristics and the etiology of pain.

Expression and Characterization of Intein-Cyclized Trimer of Staphylococcus aureus Protein A Domain Z.

Staphylococcus aureus protein A (SpA) is an IgG Fc-binding virulence factor that is widely used in antibody purification and as a scaffold to develop affinity molecules. A cyclized SpA Z domain could offer exopeptidase resistance, reduced chromatographic ligand leaching after single-site endopeptidase cleavage, and enhanced IgG binding properties by preorganization, potentially reducing conformational entropy loss upon binding. In this work, a Z domain trimer (Z3) was cyclized using protein intein splicing. Interactions of cyclic and linear Z3 with human IgG1 were characterized by differential scanning fluorimetry (DSF), surface plasmon resonance (SPR), and isothermal titration calorimetry (ITC). DSF showed a 5 ℃ increase in IgG1 melting temperature when bound by each Z3 variant. SPR showed the dissociation constants of linear and cyclized Z3 with IgG1 to be 2.9 nM and 3.3 nM, respectively. ITC gave association enthalpies for linear and cyclic Z3 with IgG1 of -33.0 kcal/mol and -32.7 kcal/mol, and -T∆S of association 21.2 kcal/mol and 21.6 kcal/mol, respectively. The compact cyclic Z3 protein contains 2 functional binding sites and exhibits carboxypeptidase Y-resistance. The results suggest cyclization as a potential approach toward more stable SpA-based affinity ligands, and this analysis may advance our understanding of protein engineering for ligand and drug development.

Protein A-Nanoluciferase fusion protein for generalized, sensitive detection of immunoglobulin G.

Detection and quantification of antibodies, especially immunoglobulin G (IgG), is a cornerstone of ELISAs, many diagnostics, and the development of antibody-based drugs. Current state-of-the-art immunoassay techniques for antibody detection require species-specific secondary antibodies and carefully-controlled bioconjugations. Poor conjugation efficiency degrades assay performance and increases the risk of clinical false positives due to non-specific binding. We developed a generic, highly-sensitive platform for IgG quantification by fusing the IgG-Fc binding Z domain of Staphylococcal Protein A with the ultrabright bioluminescence reporter Nanoluc-luciferase (Nluc). We demonstrated the application of this fusion protein in a sandwich IgG detection immunoassay using surface-bound antigens to capture target IgG and protein A-Nanoluc fusion as the detector. We optimized the platform's sensitivity by incorporating multiple repeats of the Z domain into the fusion protein constructs. Using rabbit and mouse anti-SARS-CoV-2 Nucleoprotein IgGs as model analytes, we performed ELISAs in two different formats, either with SARS-CoV-2 Nucleoprotein as the capture antigen or with polyclonal chicken IgY as the capture antibody. Using standard laboratory equipment, the platform enabled the quantitation of antibody analytes at concentrations as low as 10 pg/mL (67 fM).

Integrating transcriptomics and bulk time course data into a mathematical framework to describe and predict therapeutic resistance in cancer.

A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other types of longitudinal data. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal bulk cell population (bulk time course) data. We demonstrate that the explicit inclusion of the phenotypic composition estimate, derived from single cell RNA-sequencing data (scRNA-seq), improves accuracy in the prediction of new treatments with a concordance correlation coefficient (CCC) of 0.92 compared to a prediction accuracy of CCC = 0.64 when fitting on longitudinal bulk cell population data alone. To our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with bulk time-course data to jointly calibrate a mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multiple data types into mathematical models to develop optimized treatment regimens from data.

Tet inactivation disrupts YY1 binding and long-range chromatin interactions during embryonic heart development.

Tet-mediated DNA demethylation plays an important role in shaping the epigenetic landscape and chromatin accessibility to control gene expression. While several studies demonstrated pivotal roles of Tet in regulating embryonic development, little is known about their functions in heart development. Here we analyze DNA methylation and hydroxymethylation dynamics during early cardiac development in both human and mice. We find that cardiac-specific deletion of Tet2 and Tet3 in mice (Tet2/3-DKO) leads to ventricular non-compaction cardiomyopathy (NCC) with embryonic lethality. Single-cell RNA-seq analyses reveal a reduction in cardiomyocyte numbers and transcriptional reprogramming in cardiac tissues upon Tet2/3 depletion. Impaired DNA demethylation and reduced chromatin accessibility in Tet2/3-DKO mice further compromised Ying-yang1 (YY1) binding to its genomic targets, and perturbed high-order chromatin organization at key genes involved in heart development. Our studies provide evidence of the physiological role of Tet in regulating DNA methylation dynamics and chromatin organization during early heart development.

Cancer cell population growth kinetics at low densities deviate from the exponential growth model and suggest an Allee effect.

Most models of cancer cell population expansion assume exponential growth kinetics at low cell densities, with deviations to account for observed slowing of growth rate only at higher densities due to limited resources such as space and nutrients. However, recent preclinical and clinical observations of tumor initiation or recurrence indicate the presence of tumor growth kinetics in which growth rates scale positively with cell numbers. These observations are analogous to the cooperative behavior of species in an ecosystem described by the ecological principle of the Allee effect. In preclinical and clinical models, however, tumor growth data are limited by the lower limit of detection (i.e., a measurable lesion) and confounding variables, such as tumor microenvironment, and immune responses may cause and mask deviations from exponential growth models. In this work, we present alternative growth models to investigate the presence of an Allee effect in cancer cells seeded at low cell densities in a controlled in vitro setting. We propose a stochastic modeling framework to disentangle expected deviations due to small population size stochastic effects from cooperative growth and use the moment approach for stochastic parameter estimation to calibrate the observed growth trajectories. We validate the framework on simulated data and apply this approach to longitudinal cell proliferation data of BT-474 luminal B breast cancer cells. We find that cell population growth kinetics are best described by a model structure that considers the Allee effect, in that the birth rate of tumor cells increases with cell number in the regime of small population size. This indicates a potentially critical role of cooperative behavior among tumor cells at low cell densities with relevance to early stage growth patterns of emerging and relapsed tumors.

Decoding the dynamic DNA methylation and hydroxymethylation landscapes in endodermal lineage intermediates during pancreatic differentiation of hESC.

Dynamic changes in DNA methylation and demethylation reprogram transcriptional outputs to instruct lineage specification during development. Here, we applied an integrative epigenomic approach to unveil DNA (hydroxy)methylation dynamics representing major endodermal lineage intermediates during pancreatic differentiation of human embryonic stem cells (hESCs). We found that 5-hydroxymethylcytosine (5hmC) marks genomic regions to be demethylated in the descendent lineage, thus reshaping the DNA methylation landscapes during pancreatic lineage progression. DNA hydroxymethylation is positively correlated with enhancer activities and chromatin accessibility, as well as the selective binding of lineage-specific pioneer transcription factors, during pancreatic differentiation. We further discovered enrichment of hydroxymethylated regions (termed '5hmC-rim') at the boundaries of large hypomethylated functional genomic regions, including super-enhancer, DNA methylation canyon and broad-H3K4me3 peaks. We speculate that '5hmC-rim' might safeguard low levels of cytosine methylation at these regions. Our comprehensive analysis highlights the importance of dynamic changes of epigenetic landscapes in driving pancreatic differentiation of hESC.