Comparative genomic analysis reveals evolutionary characteristics and patterns of microRNA clusters in vertebrates

MicroRNAs (miRNAs) are a class of small non-coding RNAs that can play important regulatory roles in many important biological processes. Although clustering patterns of miRNA clusters have been uncovered in animals, the origin and evolution of miRNA clusters in vertebrates are still poorly understood. Here, we performed comparative genomic analyses to construct 51 sets of orthologous miRNA clusters (SOMCs) across seven test vertebrate species, a collection of miRNA clusters from two or more species that are likely to have evolved from a common ancestral miRNA cluster, and used these to systematically examine the evolutionary characteristics and patterns of miRNA clusters in vertebrates. We found that miRNA clusters are continuously generated, and most of them tend to be conserved and maintained in vertebrate genomes, although some adaptive gains and losses of miRNA cluster have occurred during evolution. Furthermore, miRNA clusters appeared relatively early in the evolutionary history might suffer from more complicated adaptive gain-and-loss than those young miRNA clusters. Detailed analysis showed that genomic duplication events of ancestral miRNAs or miRNA clusters are likely to be major driving force and apparently contribute to origin and evolution of miRNA clusters. Comparison of conserved with lineage-specific miRNA clusters revealed that the contribution of duplication events for the formation of miRNA cluster appears to be more important for conserved miRNA clusters than lineage-specific. Our study provides novel insights for further exploring the origins and evolution of miRNA clusters in vertebrates at a genome scale.     

Prediction of human disease-related gene clusters by clustering analysis

Since genes associated with similar diseases/disorders show an increased tendency for their protein products to interact with each other through protein-protein interactions (PPI), clustering analysis obviously as an efficient technique can be easily used to predict human disease-related gene clusters/subnetworks. Firstly, we used clustering algorithms, Markov cluster algorithm (MCL), Molecular complex detection (MCODE) and Clique percolation method (CPM) to decompose human PPI network into dense clusters as the candidates of disease-related clusters, and then a log likelihood model that integrates multiple biological evidences was proposed to score these dense clusters. Finally, we identified disease-related clusters using these dense clusters if they had higher scores. The efficiency was evaluated by a leave-one-out cross validation procedure. Our method achieved a success rate with 98.59% and recovered the hidden disease-related clusters in 34.04% cases when removed one known disease gene and all its gene-disease associations. We found that the clusters decomposed by CPM outperformed MCL and MCODE as the candidates of disease-related clusters with well-supported biological significance in biological process, molecular function and cellular component of Gene Ontology (GO) and expression of human tissues. We also found that most of the disease-related clusters consisted of tissue-specific genes that were highly expressed only in one or several tissues, and a few of those were composed of housekeeping genes (maintenance genes) that were ubiquitously expressed in most of all the tissues.     

Circulating cell clusters aggravate the hemorheological abnormalities in COVID-19

Microthrombi and circulating cell clusters are common microscopic findings in patients with coronavirus disease 2019 (COVID-19) at different stages in the disease course, implying that they may function as the primary drivers in disease progression. Inspired by a recent flow imaging cytometry study of the blood samples from patients with COVID-19, we perform computational simulations to investigate the dynamics of different types of circulating cell clusters, namely white blood cell (WBC) clusters, platelet clusters, and red blood cell clusters, over a range of shear flows and quantify their impact on the viscosity of the blood. Our simulation results indicate that the increased level of fibrinogen in patients with COVID-19 can promote the formation of red blood cell clusters at relatively low shear rates, thereby elevating the blood viscosity, a mechanism that also leads to an increase in viscosity in other blood diseases, such as sickle cell disease and type 2 diabetes mellitus. We further discover that the presence of WBC clusters could also aggravate the abnormalities of local blood rheology. In particular, the extent of elevation of the local blood viscosity is enlarged as the size of the WBC clusters grows. On the other hand, the impact of platelet clusters on the local rheology is found to be negligible, which is likely due to the smaller size of the platelets. The difference in the impact of WBC and platelet clusters on local hemorheology provides a compelling explanation for the clinical finding that the number of WBC clusters is significantly correlated with thrombotic events in COVID-19 whereas platelet clusters are not. Overall, our study demonstrates that our computational models based on dissipative particle dynamics can serve as a powerful tool to conduct quantitative investigation of the mechanism causing the pathological alterations of hemorheology and explore their connections to the clinical manifestations in COVID-19.     

Automatic recognition of hydrophobic clusters and their correlation with protein folding units.

A method is described to objectively identify hydrophobic clusters in proteins of known structure. Clusters are found by examining a protein for compact groupings of side chains. Compact clusters contain seven or more residues, have an average of 65% hydrophobic residues, and usually occur in protein interiors. Although smaller clusters contain only side-chain moieties, larger clusters enclose significant portions of the peptide backbone in regular secondary structure. These clusters agree well with hydrophobic regions assigned by more intuitive methods and many larger clusters correlate with protein domains. These results are in striking contrast with the clustering algorithm of J. Heringa and P. Argos (1991, J Mol Biol 220:151-171). That method finds that clusters located on a protein's surface are not especially hydrophobic and average only 3-4 residues in size. Hydrophobic clusters can be correlated with experimental evidence on early folding intermediates. This correlation is optimized when clusters with less than nine hydrophobic residues are removed from the data set. This suggests that hydrophobic clusters are important in the folding process only if they have enough hydrophobic residues.     

Prediction of operon-like gene clusters in the Arabidopsis thaliana genome based on co-expression analysis of neighboring genes

Operon-like arrangements of genes occur in eukaryotes ranging from yeasts and filamentous fungi to nematodes, plants, and mammals. In plants, several examples of operon-like gene clusters involved in metabolic pathways have recently been characterized, e.g. the cyclic hydroxamic acid pathways in maize, the avenacin biosynthesis gene clusters in oat, the thalianol pathway in Arabidopsis thaliana, and the diterpenoid momilactone cluster in rice. Such operon-like gene clusters are defined by their co-regulation or neighboring positions within immediate vicinity of chromosomal regions. A comprehensive analysis of the expression of neighboring genes therefore accounts a crucial step to reveal the complete set of operon-like gene clusters within a genome. Genome-wide prediction of operon-like gene clusters should contribute to functional annotation efforts and provide novel insight into evolutionary aspects acquiring certain biological functions as well. We predicted co-expressed gene clusters by comparing the Pearson correlation coefficient of neighboring genes and randomly selected gene pairs, based on a statistical method that takes false discovery rate (FDR) into consideration for 1469 microarray gene expression datasets of A. thaliana. We estimated that A. thaliana contains 100 operon-like gene clusters in total. We predicted 34 statistically significant gene clusters consisting of 3 to 22 genes each, based on a stringent FDR threshold of 0.1. Functional relationships among genes in individual clusters were estimated by sequence similarity and functional annotation of genes. Duplicated gene pairs (determined based on BLAST with a cutoff of E<10(-5)) are included in 27 clusters. Five clusters are associated with metabolism, containing P450 genes restricted to the Brassica family and predicted to be involved in secondary metabolism. Operon-like clusters tend to include genes encoding bio-machinery associated with ribosomes, the ubiquitin/proteasome system, secondary metabolic pathways, lipid and fatty-acid metabolism, and the lipid transfer system.     

Stochastic assembly of chemoreceptor clusters in Escherichia coli

Chemoreceptors and cytoplasmic chemotaxis proteins in Escherichia coli form clusters that play a key role in signal processing. These clusters localize at cell poles and at specific positions along the cell body which correspond to future division sites, but the details of cluster formation and the mechanism of cluster distribution remain unclear. Here, we used fluorescence microscopy to investigate how the numbers and sizes of receptor clusters depend on the expression level of chemotaxis proteins and on the cell length. We show that the average cluster number saturates at high levels of protein expression at approximately 3.7 clusters per cell, well below the number of available positioning sites. Correspondingly, distances between clusters in filamentous cells saturate at an average of 1 mum but, even at saturating expression levels, individual cluster numbers and distances show a broad distribution around the mean. Our data imply a stochastic mode of cluster assembly, where a defined average interval between clusters along the cell body arises from competition between nucleation of new clusters and growth of existing clusters. Upon subsequent anchorage to defined lateral sites, clusters grow with rates that inversely depend on their size, and become polar upon several rounds of cell division.     

Simple atomistic modeling of dominant B m I n clusters in boron diffusion

In this paper, we present a simple atomistic model for describing the evolution of interstitial clusters during boron diffusion in kinetic Monte-Carlo (KMC) calculation. It has been known that clusters generated after ion implantation play a decisive role in the enhanced boron diffusion at the tail region while being immobile at the peak region. Our model, which is based on the simple continuum model, takes the intermediate clusters into account as well as dominant clusters for describing the evolutionary behavior of interstitial clusters during boron diffusion. We found that the intermediate clusters such as B₃I₃ and B₂I₃ play a significant role during the evolution of clusters despite the fact that the lifetimes of the corresponding intermediate clusters are relatively short due to low binding energies. Further, our investigation revealed that B₃I is the most dominant cluster after annealing. We applied our simple atomistic model to the study of boron retardation in arsenic pre-doped substrate. KMC simulation results were compared with experimental SIMS data, which supports our theoretical model.     

An approach for obtaining perfect hybridization probes for unknown polyketide synthase genes: a search for the epothilone gene cluster

An approach is described for obtaining 'perfect probes' for type I modular polyketide synthase (PKS) gene clusters that in turn enables the identification of all such gene clusters in a genome. The approach involves sequencing small fragments of a random genomic DNA library containing one or more modular PKS gene clusters, and identifying which fragments emanate from PKS genes. Knowing the approximate sizes of the genome and the target gene cluster, one can predict the the frequency that a PKS gene fragment will be present in the library sequenced. Computer simulations of the approach were applied to the known PKS and non-ribosomal peptide synthetase (NRPS) gene clusters in the Bacillus subtilus genome. The approach was then used to identify PKS gene fragments in a strain of Sorangium cellulosum that produces epothilone. In addition to identifying fragments of the epothilone gene cluster, we obtained 11 unique fragments from other PKS gene clusters; the results suggest that there may be six to eight PKS gene clusters in this organism. In addition, we identified four unique fragments of NRPS genes, demonstrating that the approach is also applicable for identification of these modular gene clusters.     

Observation of Small Cluster Formation in Concentrated Monoclonal Antibody Solutions and Its Implications to Solution Viscosity

Monoclonal antibodies (mAbs) are a major class of biopharmaceuticals. It is hypothesized that some concentrated mAb solutions exhibit formation of a solution phase consisting of reversibly self-associated aggregates (or reversible clusters), which is speculated to be responsible for their distinct solution properties. Here, we report direct observation of reversible clusters in concentrated solutions of mAbs using neutron spin echo. Specifically, a stable mAb solution is studied across a transition from dispersed monomers in dilute solution to clustered states at more concentrated conditions, where clusters of a preferred size are observed. Once mAb clusters have formed, their size, in contrast to that observed in typical globular protein solutions, is observed to remain nearly constant over a wide range of concentrations. Our results not only conclusively establish a clear relationship between the undesirable high viscosity of some mAb solutions and the formation of reversible clusters with extended open structures, but also directly observe self-assembled mAb protein clusters of preferred small finite size similar to that in micelle formation that dominate the properties of concentrated mAb solutions.     

Receptor complexes cotransported via polarized endocytic pathways form clusters with distinct organizations

Previously, FRET confocal microscopy has shown that polymeric IgA-receptor (pIgA-R) is distributed in a clustered manner in apical endosomes. To test whether different membrane-bound components form clusters during membrane trafficking, live-cell quantitative FRET was used to characterize the organization of pIgA-R and transferrin receptor (TFR) in endocytic membranes of polarized MDCK cells upon internalization of donor- and acceptor-labeled ligands. We show that pIgA-R and TFR complexes form increasingly organized clusters during cotransport from basolateral to perinuclear endosomes. The organization of these receptor clusters in basolateral versus perinuclear/apical endosomes is significantly different; the former showing a mixed random/clustered distribution while the latter highly organized clusters. Our results indicate that although both perinuclear and apical endosomes comprise pIgA-R and TFR clusters, their E% levels are significantly different suggesting that these receptors are packed into clusters in a distinct manner. The quantitative FRET-based assay presented here suggests that different receptor complexes form clusters, with diverse levels of organization, while being cotransported via the polarized endocytic pathways.     

Polar Chemoreceptor Clustering by Coupled Trimers of Dimers

Receptors of bacterial chemotaxis form clusters at the cell poles, where clusters act as “antennas” to amplify small changes in ligand concentration. It is worthy of note that chemoreceptors cluster at multiple length scales. At the smallest scale, receptors form dimers, which assemble into stable timers of dimers. At a large scale, trimers form large polar clusters composed of thousands of receptors. Although much is known about the signaling properties emerging from receptor clusters, it is unknown how receptors localize at the cell poles and what the determining factors are for cluster size. Here, we present a model of polar receptor clustering based on coupled trimers of dimers, where cluster size is determined as a minimum of the cluster-membrane free energy. This energy has contributions from the cluster-membrane elastic energy, penalizing large clusters due to their high intrinsic curvature, and receptor-receptor coupling that favors large clusters. We find that the reduced cluster-membrane curvature mismatch at the curved cell poles leads to large and robust polar clusters, in line with experimental observation, whereas lateral clusters are efficiently suppressed.     

Denervation disperses acetylcholine receptor clusters at the neuromuscular junction in Xenopus cultures

The effect of denervation on acetylcholine receptor (AChR) cluster distribution on cultured Xenopus muscle cells has been examined in order to study the role of intact nerve in the maintenance of clusters at the nerve-muscle junction during development. AChRs on the muscle cell were labeled with tetramethyl rhodamine-conjugated alpha-bungarotoxin and sequential changes in AChR cluster distribution were examined with a fluorescence microscope using an image intensifier. Denervation was carried out by exposing the nerve cell body to a focused laser light of a high intensity. After this procedure the neurites originating from the cell quickly disintegrated and large AChR clusters associated with nerve divided into smaller clusters. Individual clusters subsequently decreased in size and finally disappeared. In about 30% of the cases new AChR clusters appeared at the extrajunctional region after denervation. These observations indicate that intact nerves are necessary for the maintenance of receptor localization at the nerve-muscle junction and that nerve-induced accumulation is seemingly reversible during the early period of synapse formation. We tested the idea that receptor clusters were lost due to diffusion of receptors in the muscle membrane after denervation. However, the rate of receptor cluster dispersal after denervation was much slower than that predicted by the diffusion model, suggesting that diffusion of receptors is not a rate-limiting step. Furthermore, we found that receptor clusters at the junction stabilize during days in culture. Thus, 80-90% of receptor clusters at the nerve-muscle junction disappeared at 7 hr after denervation in 1-day cocultures, while about 50% of receptor clusters remained after denervation in 3-day cocultures.     

Super-resolution Imaging Reveals the Internal Architecture of Nano-sized Syntaxin Clusters

Key synaptic proteins from the soluble SNARE (N-ethylmaleimide-sensitive factor attachment protein receptor) family, among many others, are organized at the plasma membrane of cells as clusters containing dozens to hundreds of protein copies. However, the exact membranal distribution of proteins into clusters or as single molecules, the organization of molecules inside the clusters, and the clustering mechanisms are unclear due to limitations of the imaging and analytical tools. Focusing on syntaxin 1 and SNAP-25, we implemented direct stochastic optical reconstruction microscopy together with quantitative clustering algorithms to demonstrate a novel approach to explore the distribution of clustered and nonclustered molecules at the membrane of PC12 cells with single-molecule precision. Direct stochastic optical reconstruction microscopy images reveal, for the first time, solitary syntaxin/SNAP-25 molecules and small clusters as well as larger clusters. The nonclustered syntaxin or SNAP-25 molecules are mostly concentrated in areas adjacent to their own clusters. In the clusters, the density of the molecules gradually decreases from the dense cluster core to the periphery. We further detected large clusters that contain several density gradients. This suggests that some of the clusters are formed by unification of several clusters that preserve their original organization or reorganize into a single unit. Although syntaxin and SNAP-25 share some common distributional features, their clusters differ markedly from each other. SNAP-25 clusters are significantly larger, more elliptical, and less dense. Finally, this study establishes methodological tools for the analysis of single-molecule-based super-resolution imaging data and paves the way for revealing new levels of membranal protein organization.     

From hormones to secondary metabolism: the emergence of metabolic gene clusters in plants

Gene clusters for the synthesis of secondary metabolites are a common feature of microbial genomes. Well-known examples include clusters for the synthesis of antibiotics in actinomycetes, and also for the synthesis of antibiotics and toxins in filamentous fungi. Until recently it was thought that genes for plant metabolic pathways were not clustered, and this is certainly true in many cases; however, five plant secondary metabolic gene clusters have now been discovered, all of them implicated in synthesis of defence compounds. An obvious assumption might be that these eukaryotic gene clusters have arisen by horizontal gene transfer from microbes, but there is compelling evidence to indicate that this is not the case. This raises intriguing questions about how widespread such clusters are, what the significance of clustering is, why genes for some metabolic pathways are clustered and those for others are not, and how these clusters form. In answering these questions we may hope to learn more about mechanisms of genome plasticity and adaptive evolution in plants. It is noteworthy that for the five plant secondary metabolic gene clusters reported so far, the enzymes for the first committed steps all appear to have been recruited directly or indirectly from primary metabolic pathways involved in hormone synthesis. This may or may not turn out to be a common feature of plant secondary metabolic gene clusters as new clusters emerge.     

Origins and divergence times of mammalian class II MHC gene clusters

The class I and II major histocompatibility complex (MHC) genes are apparently subject to evolution by a birth-and-death process. The rate of gene turnover is much slower in the latter genes than in the former. In placental mammals, the class II region can be subdivided into different orthologous subregions or gene clusters (DR, DQ, DO, and DN), but the origins and evolutionary relationships of these gene clusters are not well established. Here we report the results of our study of the times of origin and evolutionary relationships of these gene clusters in mammals. Our analysis suggests that both class II alpha-chain and beta-chain gene clusters are shared by placental mammals and marsupials, but the gene clusters from nonmammalian species are paralogous to mammalian gene clusters. We estimated the times of divergence between gene clusters in placental mammals using the linearized tree and distance regression methods. Our results indicate that most gene clusters originated 170-200 million years (MY) ago, but that DO beta-chain genes diverged from the other beta-chain gene clusters approximately 210-260 MY ago. The phylogenetic trees for the alpha- and beta-chain genes were not congruent, suggesting that the evolutionary history of the class II gene clusters is more complex than previously thought.     

Observation of Small Cluster Formation in Concentrated Monoclonal Antibody Solutions and Its Implications to Solution Viscosity

Monoclonal antibodies (mAbs) are a major class of biopharmaceuticals. It is hypothesized that some concentrated mAb solutions exhibit formation of a solution phase consisting of reversibly self-associated aggregates (or reversible clusters), which is speculated to be responsible for their distinct solution properties. Here, we report direct observation of reversible clusters in concentrated solutions of mAbs using neutron spin echo. Specifically, a stable mAb solution is studied across a transition from dispersed monomers in dilute solution to clustered states at more concentrated conditions, where clusters of a preferred size are observed. Once mAb clusters have formed, their size, in contrast to that observed in typical globular protein solutions, is observed to remain nearly constant over a wide range of concentrations. Our results not only conclusively establish a clear relationship between the undesirable high viscosity of some mAb solutions and the formation of reversible clusters with extended open structures, but also directly observe self-assembled mAb protein clusters of preferred small finite size similar to that in micelle formation that dominate the properties of concentrated mAb solutions.     

<Emphasis Type="Italic">Escherichia coli</Emphasis> FtnA acts as an iron buffer for re-assembly of iron–sulfur clusters in response to hydrogen peroxide stress

Iron–sulfur clusters are one of the most ubiquitous redox centers in biology. Ironically, iron-sulfur clusters are highly sensitive to reactive oxygen species. Disruption of iron-sulfur clusters will not only change the activity of proteins that host iron–sulfur clusters, the iron released from the disrupted iron–sulfur clusters will further promote the production of deleterious hydroxyl free radicals via the Fenton reaction. Here, we report that ferritin A (FtnA), a major iron-storage protein in Escherichia coli, is able to scavenge the iron released from the disrupted iron–sulfur clusters and alleviates the production of hydroxyl free radicals. Furthermore, we find that the iron stored in FtnA can be retrieved by an iron chaperon IscA for the re-assembly of the iron–sulfur cluster in a proposed scaffold IscU in the presence of the thioredoxin reductase system which emulates normal intracellular redox potential. The results suggest that E. coli FtnA may act as an iron buffer to sequester the iron released from the disrupted iron–sulfur clusters under oxidative stress conditions and to facilitate the re-assembly of the disrupted iron–sulfur clusters under normal physiological conditions.     

BrdU Birth Dating Can Produce Errors in Cell Fate Specification in Chick Brain Development

Birth dating neurons with bromodeoxyuridine (BrdU) labeling is an established method widely employed by neurobiologists to study cell proliferation in embryonic, postnatal, and adult brain. Birth dating studies in the chick dorsal telencephalon and the mammalian striatum have suggested that these structures develop in a strikingly similar manner, in which neurons with the same birth date aggregate to form “isochronic clusters.” Here we show that isochronic cluster formation in the chick dorsal telencephalon is an artifact. In embryos given standardly employed doses of BrdU, we observed isochronic clusters but found that clusters were absent with BrdU doses close to the limits of detection. In addition, in situ hybridization experiments established that neurons in the clusters display errors in cell type specification: BrdU cell clusters in nidopallium adopted a mesopallial neuronal fate, mesopallial clusters were misspecified as nidopallial cells, and in some instances, the BrdU clusters failed to express neuronal differentiation markers characteristic of the dorsal telencephalon. These results demonstrate that the chick dorsal telencephalon does not develop by isochronic cluster formation and highlight the need to test the integrity of BrdU-treated tissue with gene expression markers of regional and cell type identity.