PRPs localized to the middle lamellae are required for cortical tissue integrity in Medicago truncatula roots.

KEY MESSAGE: A family of repetitive proline-rich proteins interact with acidic pectins and play distinct roles in legume root cell walls affecting cortical and vascular structure. A proline-rich protein (PRP) family, composed of tandemly repeated Pro-Hyp-Val-X-Lys pentapeptide motifs, is found primarily in the Leguminosae. Four distinct size classes within this family are encoded by seven tightly linked genes: MtPRP1, MtPRP2 and MtPRP3, and four nearly identical MtPRP4 genes. Promoter fusions to ß-glucuronidase showed strong expression in the stele of hairy roots for all 4 PRP genes tested, with additional expression in the cortex for PRP1, PRP2 and PRP4. All except MtPRP4 are strongly expressed in non-tumorous roots, and secreted and ionically bound to root cell walls. These PRPs are absent from root epidermal cell walls, and PRP accumulation is highly localized within the walls of root cortical and vascular tissues. Within xylem tissue, PRPs are deposited in secondary thickenings where it is spatially exclusive to lignin. In newly differentiating xylem, PRPs are deposited in the regularly spaced paired-pits and pit membranes that hydraulically connect neighboring xylem elements. Hairpin-RNA knock-down constructs reducing PRP expression in Medicago truncatula hairy root tumors disrupted cortical and vascular patterning. Immunoblots showed that the knockdown tumors had potentially compensating increases in the non-targeted PRPs, all of which cross-react with the anti-PRP antibodies. However, PRP3 knockdown differed from knockdown of PRP1 and PRP2 in that it greatly reduced viability of hairy root tumors. We hypothesize that repetitive PRPs interact with acidic pectins to form block-copolymer gels that can play distinct roles in legume root cell walls.

AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number.

BACKGROUND: Clustering the information content of large high-dimensional gene expression datasets has widespread application in "omics" biology. Unfortunately, the underlying structure of these natural datasets is often fuzzy, and the computational identification of data clusters generally requires knowledge about cluster number and geometry. RESULTS: We integrated strategies from machine learning, cartography, and graph theory into a new informatics method for automatically clustering self-organizing map ensembles of high-dimensional data. Our new method, called AutoSOME, readily identifies discrete and fuzzy data clusters without prior knowledge of cluster number or structure in diverse datasets including whole genome microarray data. Visualization of AutoSOME output using network diagrams and differential heat maps reveals unexpected variation among well-characterized cancer cell lines. Co-expression analysis of data from human embryonic and induced pluripotent stem cells using AutoSOME identifies >3400 up-regulated genes associated with pluripotency, and indicates that a recently identified protein-protein interaction network characterizing pluripotency was underestimated by a factor of four. CONCLUSIONS: By effectively extracting important information from high-dimensional microarray data without prior knowledge or the need for data filtration, AutoSOME can yield systems-level insights from whole genome microarray expression studies. Due to its generality, this new method should also have practical utility for a variety of data-intensive applications, including the results of deep sequencing experiments. AutoSOME is available for download at http://jimcooperlab.mcdb.ucsb.edu/autosome webcite.

XSTREAM: a practical algorithm for identification and architecture modeling of tandem repeats in protein sequences.

BACKGROUND: Biological sequence repeats arranged in tandem patterns are widespread in DNA and proteins. While many software tools have been designed to detect DNA tandem repeats (TRs), useful algorithms for identifying protein TRs with varied levels of degeneracy are still needed. RESULTS: To address limitations of current repeat identification methods, and to provide an efficient and flexible algorithm for the detection and analysis of TRs in protein sequences, we designed and implemented a new computational method called XSTREAM. Running time tests confirm the practicality of XSTREAM for analyses of multi-genome datasets. Each of the key capabilities of XSTREAM (e.g., merging, nesting, long-period detection, and TR architecture modeling) are demonstrated using anecdotal examples, and the utility of XSTREAM for identifying TR proteins was validated using data from a recently published paper. CONCLUSION: We show that XSTREAM is a practical and valuable tool for TR detection in protein and nucleotide sequences at the multi-genome scale, and an effective tool for modeling TR domains with diverse architectures and varied levels of degeneracy. Because of these useful features, XSTREAM has significant potential for the discovery of naturally-evolved modular proteins with applications for engineering novel biostructural and biomimetic materials, and identifying new vaccine and diagnostic targets.

Identifying stem cell gene expression patterns and phenotypic networks with AutoSOME.

Stem cells have the unique property of differentiation and self-renewal and play critical roles in normal development, tissue repair, and disease. To promote systems-wide analysis of cells and tissues, we developed AutoSOME, a machine-learning method for identifying coordinated gene expression patterns and correlated cellular phenotypes in whole-transcriptome data, without prior knowledge of cluster number or structure. Here, we present a facile primer demonstrating the use of AutoSOME for identification and characterization of stem cell gene expression signatures and for visualization of transcriptome networks using Cytoscape. This protocol should serve as a general foundation for gene expression cluster analysis of stem cells, with applications for studying pluripotency, multi-lineage potential, and neoplastic disease.

Efficient selection of biomineralizing DNA aptamers using deep sequencing and population clustering.

DNA-based information systems drive the combinatorial optimization processes of natural evolution, including the evolution of biominerals. Advances in high-throughput DNA sequencing expand the power of DNA as a potential information platform for combinatorial engineering, but many applications remain to be developed due in part to the challenge of handling large amounts of sequence data. Here we employ high-throughput sequencing and a recently developed clustering method (AutoSOME) to identify single-stranded DNA sequence families that bind specifically to ZnO semiconductor mineral surfaces. These sequences were enriched from a diverse DNA library after a single round of screening, whereas previous screening approaches typically require 5-15 rounds of enrichment for effective sequence identification. The consensus sequence of the largest cluster was poly d(T)30. This consensus sequence exhibited clear aptamer behavior and was shown to promote the synthesis of crystalline ZnO from aqueous solution at near-neutral pH. This activity is significant, as the crystalline form of this wide-bandgap semiconductor is not typically amenable to solution synthesis in this pH range. High-resolution TEM revealed that this DNA synthesis route yields ZnO nanoparticles with an amorphous-crystalline core-shell structure, suggesting that the mechanism of mineralization involves nanoscale coacervation around the DNA template. We thus demonstrate that our new method, termed Single round Enrichment of Ligands by deep Sequencing (SEL-Seq), can facilitate biomimetic synthesis of technological nanomaterials by accelerating combinatorial selection of biomolecular-mineral interactions. Moreover, by enabling direct characterization of sequence family demographics, we anticipate that SEL-Seq will enhance aptamer discovery in applications employing additional rounds of screening.

Systems-level analysis of age-related macular degeneration reveals global biomarkers and phenotype-specific functional networks.

UNLABELLED: Please see related commentary: http://www.biomedcentral.com/1741-7015/10/21/abstract BACKGROUND: Age-related macular degeneration (AMD) is a leading cause of blindness that affects the central region of the retinal pigmented epithelium (RPE), choroid, and neural retina. Initially characterized by an accumulation of sub-RPE deposits, AMD leads to progressive retinal degeneration, and in advanced cases, irreversible vision loss. Although genetic analysis, animal models, and cell culture systems have yielded important insights into AMD, the molecular pathways underlying AMD's onset and progression remain poorly delineated. We sought to better understand the molecular underpinnings of this devastating disease by performing the first comparative transcriptome analysis of AMD and normal human donor eyes. METHODS: RPE-choroid and retina tissue samples were obtained from a common cohort of 31 normal, 26 AMD, and 11 potential pre-AMD human donor eyes. Transcriptome profiles were generated for macular and extramacular regions, and statistical and bioinformatic methods were employed to identify disease-associated gene signatures and functionally enriched protein association networks. Selected genes of high significance were validated using an independent donor cohort. RESULTS: We identified over 50 annotated genes enriched in cell-mediated immune responses that are globally over-expressed in RPE-choroid AMD phenotypes. Using a machine learning model and a second donor cohort, we show that the top 20 global genes are predictive of AMD clinical diagnosis. We also discovered functionally enriched gene sets in the RPE-choroid that delineate the advanced AMD phenotypes, neovascular AMD and geographic atrophy. Moreover, we identified a graded increase of transcript levels in the retina related to wound response, complement cascade, and neurogenesis that strongly correlates with decreased levels of phototransduction transcripts and increased AMD severity. Based on our findings, we assembled protein-protein interactomes that highlight functional networks likely to be involved in AMD pathogenesis. CONCLUSIONS: We discovered new global biomarkers and gene expression signatures of AMD. These results are consistent with a model whereby cell-based inflammatory responses represent a central feature of AMD etiology, and depending on genetics, environment, or stochastic factors, may give rise to the advanced AMD phenotypes characterized by angiogenesis and/or cell death. Genes regulating these immunological activities, along with numerous other genes identified here, represent promising new targets for AMD-directed therapeutics and diagnostics.

Global analysis of proline-rich tandem repeat proteins reveals broad phylogenetic diversity in plant secretomes.

Cell walls, constructed by precisely choreographed changes in the plant secretome, play critical roles in plant cell physiology and development. Along with structural polysaccharides, secreted proline-rich Tandem Repeat Proteins (TRPs) are important for cell wall function, yet the evolutionary diversity of these structural TRPs remains virtually unexplored. Using a systems-level computational approach to analyze taxonomically diverse plant sequence data, we identified 31 distinct Pro-rich TRP classes targeted for secretion. This analysis expands upon the known phylogenetic diversity of extensins, the most widely studied class of wall structural proteins, and demonstrates that extensins evolved before plant vascularization. Our results also show that most Pro-rich TRP classes have unexpectedly restricted evolutionary distributions, revealing considerable differences in plant secretome signatures that define unexplored diversity.

Lab-specific gene expression signatures in pluripotent stem cells.

Pluripotent stem cells derived from both embryonic and reprogrammed somatic cells have significant potential for human regenerative medicine. Despite similarities in developmental potential, however, several groups have found fundamental differences between embryonic stem cell (ESC) and induced-pluripotent stem cell (iPSC) lines that may have important implications for iPSC-based medical therapies. Using an unsupervised clustering algorithm, we further studied the genetic homogeneity of iPSC and ESC lines by reanalyzing microarray gene expression data from seven different laboratories. Unexpectedly, this analysis revealed a strong correlation between gene expression signatures and specific laboratories in both ESC and iPSC lines. Nearly one-third of the genes with lab-specific expression signatures are also differentially expressed between ESCs and iPSCs. These data are consistent with the hypothesis that in vitro microenvironmental context differentially impacts the gene expression signatures of both iPSCs and ESCs.

NITZSCHIA OVALIS (BACILLARIOPHYCEAE) MONO LAKE STRAIN ACCUMULATES 1,4/2,5 CYCLOHEXANETETROL IN RESPONSE TO INCREASED SALINITY(1).

The growth of microalgae in hypersaline conditions requires that cells accumulate osmoprotectants. In many instances, these are polyols. We isolated the diatom Nitzschia ovalis H. J. Arn. from the saline and alkaline water body Mono Lake (CA, USA). This isolate can grow in salinities ranging from 5 to 120 parts per thousand (ppt) of salt but normally at 90 ppt salinity. In this report, we identified the major polyol osmoprotectant as 1,4/2,5 cyclohexanetetrol by electron ionization-mass spectrometry (EI-MS), (1) H, (13) C nuclear magnetic resonance spectroscopy (NMR), and infrared (IR) and showed an increase in cellular concentration in response to rising salinity. This increase in the cyclitol concentration was evaluated by gas chromatography of the derived tetraacetylated cyclohexanetetrol obtaining an average of 0.7 fmol · cell(-1) at 5 ppt and rising to 22.5 fmol · cell(-1) at 120 ppt. The 1,4/2,5 cyclohexanetetrol was also detected in the red alga Porphyridium purpureum. Analysis of the free amino acid content in N. ovalis cultures exposed to changes in salinity showed that proline and lysine also accumulate with increased salinity, but the cellular concentration of these amino acids is about 10-fold lower than the concentration of 1,4/2,5 cyclohexanetetrol. The comparison of amino acid concentration per cell with cyclitol suggests that this polyol is important in compensating the cellular osmotic pressure due to increased salinity, but other physiological functions could also be considered.