An ultrastructural study of the clitellar epithelium of the earthworm Metaphire posthuma (vail.)

The ultrastructure of clitellar epithelium of Metuphire posthuma revealed mainly three types of secretory cells. Most prominent among these are the large slender granular cells which contain a large number of secretory granules filling in the entire columncr region of the cell. The secretory granules are 2-4mu in diameter with a limiting membrane and containing numerous tiny vesicles in a matrix of varying electron density. Basolateral rough endoplasmic reticulum and extensive Golgi cisternae were seen interspersed with the secretory granules. The Golgi cisternae in these cells were quite prominent extending all around the secretory granules. The secretory granules of type 2 cells are spheroid bodies with motley appearance due to varying electron density of the matrix. The immature granules contain fibrillar material. Type 3 cells contained electron lucent membrane-bound mucous like secretory granules which are reticulated with filamentous materials. All the three cell types open to the exterior at the cuticular region which is characterised by the presence of numerous microvilli.     

An ultrastructural study of the prostate gland of megascolicid earthworm Lampito mauritii (Kinberg)

The ultrastructure of prostate gland of Lampito mauritii revealed two types of secretory cells. Type 1 cells with a broad basal region and a long apical region contain electron dense oval secretory granules with an increased density at the core region. Numerous electron lucent granules with fine filamentous and electron dense amorphous materials also occur at the basal region of these cells. Type 2 cells contain electron lucent mucous-like secretory granules. This cell type contains exceptionally large Golgi complexes having 20-23 stacked cisternae. Both cell types open into a common lumen and numerous microtubules are visible at the apical end. Junctional complexes, such as desmosomes and septate junctions, are observed in this glandular tissue.     

Cryo-electron tomography reveals the comparative three-dimensional architecture of Prochlorococcus, a globally important marine cyanobacterium

In an age of comparative microbial genomics, knowledge of the near-native architecture of microorganisms is essential for achieving an integrative understanding of physiology and function. We characterized and compared the three-dimensional architecture of the ecologically important cyanobacterium Prochlorococcus in a near-native state using cryo-electron tomography and found that closely related strains have diverged substantially in cellular organization and structure. By visualizing native, hydrated structures within cells, we discovered that the MED4 strain, which possesses one of the smallest genomes (1.66 Mbp) of any known photosynthetic organism, has evolved a comparatively streamlined cellular architecture. This strain possesses a smaller cell volume, an attenuated cell wall, and less extensive intracytoplasmic (photosynthetic) membrane system compared to the more deeply branched MIT9313 strain. Comparative genomic analyses indicate that differences have evolved in key structural genes, including those encoding enzymes involved in cell wall peptidoglycan biosynthesis. Although both strains possess carboxysomes that are polygonal and cluster in the central cytoplasm, the carboxysomes of MED4 are smaller. A streamlined cellular structure could be advantageous to microorganisms thriving in the low-nutrient conditions characteristic of large regions of the open ocean and thus have consequences for ecological niche differentiation. Through cryo-electron tomography we visualized, for the first time, the three-dimensional structure of the extensive network of photosynthetic lamellae within Prochlorococcus and the potential pathways for intracellular and intermembrane movement of molecules. Comparative information on the near-native structure of microorganisms is an important and necessary component of exploring microbial diversity and understanding its consequences for function and ecology.     

Severe hypertriglyceridemia in human APOC1 transgenic mice is caused by apoC-I-induced inhibition of LPL

Studies in humans and mice have shown that increased expression of apolipoprotein C-I (apoC-I) results in combined hyperlipidemia with a more pronounced effect on triglycerides (TGs) compared with total cholesterol (TC). The aim of this study was to elucidate the main reason for this effect using human apoC-I-expressing (APOC1) mice. Moderate plasma human apoC-I levels (i.e., 4-fold higher than human levels) caused a 12-fold increase in TG, along with a 2-fold increase in TC, mainly confined to VLDL. Cross-breeding of APOC1 mice on an apoE-deficient background resulted in a marked 55-fold increase in TG, confirming that the apoC-I-induced hyperlipidemia cannot merely be attributed to blockade of apoE-recognizing hepatic lipoprotein receptors. The plasma half-life of [3H]TG-VLDL-mimicking particles was 2-fold increased in APOC1 mice, suggesting that apoC-I reduces the lipolytic conversion of VLDL. Although total postheparin plasma LPL activity was not lower in APOC1 mice compared with controls, apoC-I was able to dose-dependently inhibit the LPL-mediated lipolysis of [3H]TG-VLDL-mimicking particles in vitro with a 60% efficiency compared with the main endogenous LPL inhibitor apoC-III. Finally, purified apoC-I impaired the clearance of [3H]TG-VLDL-mimicking particles independent of apoE-mediated hepatic uptake in lactoferrin-treated mice. Therefore, we conclude that apoC-I is a potent inhibitor of LPL-mediated TG-lipolysis.     

Differential Regulation of Cellular Senescence and Differentiation by Prolyl Isomerase Pin1 in Cardiac Progenitor Cells

Autologous c-kit⁺ cardiac progenitor cells (CPCs) are currently used in the clinic to treat heart disease. CPC-based regeneration may be further augmented by better understanding molecular mechanisms of endogenous cardiac repair and enhancement of pro-survival signaling pathways that antagonize senescence while also increasing differentiation. The prolyl isomerase Pin1 regulates multiple signaling cascades by modulating protein folding and thereby activity and stability of phosphoproteins. In this study, we examine the heretofore unexplored role of Pin1 in CPCs. Pin1 is expressed in CPCs in vitro and in vivo and is associated with increased proliferation. Pin1 is required for cell cycle progression and loss of Pin1 causes cell cycle arrest in the G₁ phase in CPCs, concomitantly associated with decreased expression of Cyclins D and B and increased expression of cell cycle inhibitors p53 and retinoblastoma (Rb). Pin1 deletion increases cellular senescence but not differentiation or cell death of CPCs. Pin1 is required for endogenous CPC response as Pin1 knock-out mice have a reduced number of proliferating CPCs after ischemic challenge. Pin1 overexpression also impairs proliferation and causes G₂/M phase cell cycle arrest with concurrent down-regulation of Cyclin B, p53, and Rb. Additionally, Pin1 overexpression inhibits replicative senescence, increases differentiation, and inhibits cell death of CPCs, indicating that cell cycle arrest caused by Pin1 overexpression is a consequence of differentiation and not senescence or cell death. In conclusion, Pin1 has pleiotropic roles in CPCs and may be a molecular target to promote survival, enhance repair, improve differentiation, and antagonize senescence.     

Proteome-wide Structural Analysis of PTM Hotspots Reveals Regulatory Elements Predicted to Impact Biological Function and Disease

Post-translational modifications (PTMs) regulate protein behavior through modulation of protein-protein interactions, enzymatic activity, and protein stability essential in the translation of genotype to phenotype in eukaryotes. Currently, less than 4% of all eukaryotic PTMs are reported to have biological function - a statistic that continues to decrease with an increasing rate of PTM detection. Previously, we developed SAPH-ire (Structural Analysis of PTM Hotspots) - a method for the prioritization of PTM function potential that has been used effectively to reveal novel PTM regulatory elements in discrete protein families (Dewhurst et al., 2015). Here, we apply SAPH-ire to the set of eukaryotic protein families containing experimental PTM and 3D structure data - capturing 1,325 protein families with 50,839 unique PTM sites organized into 31,747 modified alignment positions (MAPs), of which 2010 (∼6%) possess known biological function. Here, we show that using an artificial neural network model (SAPH-ire NN) trained to identify MAP hotspots with biological function results in prediction outcomes that far surpass the use of single hotspot features, including nearest neighbor PTM clustering methods. We find the greatest enhancement in prediction for positions with PTM counts of five or less, which represent 98% of all MAPs in the eukaryotic proteome and 90% of all MAPs found to have biological function. Analysis of the top 1092 MAP hotspots revealed 267 of truly unknown function (containing 5443 distinct PTMs). Of these, 165 hotspots could be mapped to human KEGG pathways for normal and/or disease physiology. Many high-ranking hotspots were also found to be disease-associated pathogenic sites of amino acid substitution despite the lack of observable PTM in the human protein family member. Taken together, these experiments demonstrate that the functional relevance of a PTM can be predicted very effectively by neural network models, revealing a large but testable body of potential regulatory elements that impact hundreds of different biological processes important in eukaryotic biology and human health.Since the discovery of phosphorylation in 1954 (1), post-translational modifications (PTMs)¹ have emerged as a broad class of protein feature that expand the functional proteome in eukaryotes. Improvements in the detection of PTMs by mass spectrometry have resulted in an exponential increase in our knowledge of the number and type of PTMs that make up the landscape of a modified eukaryotic proteome. As a result, the rate at which PTMs are discovered now far surpasses the rate at which they can be experimentally tested for biological function - a characteristic that is specific for each PTM and likely not equivalent between all PTMs that have been observed (2–4). Thus, effective methods of prioritization are essential for quantifying the likelihood of a site to be regulatory and/or impactful on biological function, which we refer to as the function potential of a PTM.Several unique features have been identified as predictors of biological impact for any given PTM - the determination of which relies on placing each PTM in the context of a multiple sequence alignment for a discrete protein or domain family, which we refer to as a Modified Alignment Position (MAP). For example, MAPs that are evolutionarily well conserved are more likely to exhibit biological function (3, 4). Similarly, functional PTMs are more commonly found within MAPs that exhibit a higher PTM observation frequency, are dynamic with respect to biological condition, located at protein interaction interfaces, and more solvent-accessible within a folded protein structure (5–7). Although efforts to elucidate the features associated with functional PTMs are relatively longstanding, few if any have established an integrative approach to quantitatively prioritize the function potential of PTMs beyond the use of single features.Previous evidence from our lab first demonstrated that multiple feature integration can improve functional prioritization. To accomplish this, we built Structural Analysis of PTM Hotspots (SAPH-ire)—an algorithm through which multiple predictors of PTM function are integrated to produce a single, quantitative function potential (FP) score that rank orders each hotspot within or between protein families (6) (Fig. 1). Previously, we used SAPH-ire to predict novel PTM regulatory elements in G protein families—including heterotrimeric G proteins—for which we discovered and experimentally confirmed a novel PTM regulatory element that is critical for cell signaling (6, 8). We propose that similar analysis of PTMs across the entire eukaryotic proteome is likely to result in the discovery of several novel regulatory elements that have yet to be realized.Open in a separate windowFig. 1.Schematic diagram of SAPH-ire. A, A theoretical segment of the multiple sequence alignment for a protein family (IPR000276; G protein-coupled receptor, rhodopsin-like) used here for illustrating the concept of SAPH-ire. Circled amino acid residues represent PTM sites experimentally observed on respective protein family members. Circle and arrow color represents the PTM observation frequency at each aligned position, called a MAP (modified alignment position), where green indicates 1 observation, blue for 2, orange for 3, and red for 5 or more. B, Cartoon rendering of bovine rhodopsin ({"type":"entrez-protein","attrs":{"text":"P02699","term_id":"129204","term_text":"P02699"}}P02699, RHO; PDB 2PED, chain A) showing side chains with projected PTM hotspots colored according to the number of observations within the family at each position aligned with the structural sequence. PDB coordinate data from the structurally projected PTM hotspots is used for calculation of solvent accessible surface area (SASA) and determination of protein interface residence (PPI). C, Hotspot features derived from the sequence and structural data are extracted for each protein family, where each hotspot corresponds to a precise family alignment position containing at least one PTM observation. D, Comparison of the comprehensive and SAPH-ire datasets representing all known experimental PTM data versus PTM data included in this study, respectively. E, Values calculated and derived from extracted hotspot features are analyzed by logistic regression or neural network models to produce probability scores for each hotspot.Here we apply SAPH-ire to protein families for which PTMs and protein structure are currently available, resulting in function potential prediction for 50,839 experimental PTM sites distributed across 31,747 MAPs. Using a neural network model (SAPH-ire NN) trained to predict the identity of embedded known-function MAPs, we derived a probability score that allows rank ordering for the likelihood of function for all MAPs including those with unknown function. We show that the SAPH-ire NN model significantly outperforms all other single or multi-feature predictive models and exhibits a proportional increase in predictive power for known function hotspots that have been more frequently studied (and therefore published). Using a strictly conservative probability threshold, we characterized the top-ranked 1092 MAPs corresponding to “function potential hotspots,” revealing 267 with truly unknown function - a striking fraction of which are also found mutated in human disease irrespective of whether the human protein, specifically, contains an observed PTM.