Phosphonylmethyl Analogs of Nucleotides and Their Derivatives: Chemistry and Biology

Abstract

Ribonucleoside O-phosphonylmethyl derivatives are novel isopolar analogs of nucleotides. This review summarizes data on their synthesis and properties, as well as data on novel type of open-chain nucleotide analogs.     

Spatial scale affects novel and disappeared climate change projections in Alaska

The formation of novel and disappeared climates between the last glacial maximum (LGM) and the present is important to consider to understand the expansion and contraction of species niches and distributions, as well as the formation and loss of communities and ecological interactions over time. Our choice in climate data resolution has the potential to complicate predictions of the ecological impacts of climate change, since climate varies from local to global scales and this spatial variation is reflected in climate data. To address this issue, we downscaled LGM and modern (1975–2005) 30‐year averaged climate data to 60‐m resolution for the entire state of Alaska for 10 different climate variables, and then upsampled each variable to coarser resolutions (60 m to 12 km). We modeled the distributions of novel and disappeared climates to evaluate the locations and fractional area of novel and disappeared climates for each of our climate variables and resolutions. Generally, novel and disappeared climates were located in southern Alaska, although there were cases where some disappeared climates existed within coastal and interior Alaska. Climate resolution affected the fractional area of novel and disappeared climates in three patterns: As the spatial resolution of climate became coarser, the fractional area of novel and disappeared climates (a) increased, (b) decreased, or (c) had no explainable relationship. Overall, we found the use of coarser climate data increased the fractional area of novel and disappeared climates due to decreased environmental variability and removal of climate extremes. Our results reinforce the importance of downscaling coarse climate data and suggest that studies analyzing the effects of climate change on ecosystems may overestimate or underestimate their conclusions when utilizing coarse climate data.     

Novel bacterial taxa in the human microbiome

The human gut harbors thousands of bacterial taxa. A profusion of metagenomic sequence data has been generated from human stool samples in the last few years, raising the question of whether more taxa remain to be identified. We assessed metagenomic data generated by the Human Microbiome Project Consortium to determine if novel taxa remain to be discovered in stool samples from healthy individuals. To do this, we established a rigorous bioinformatics pipeline that uses sequence data from multiple platforms (Illumina GAIIX and Roche 454 FLX Titanium) and approaches (whole-genome shotgun and 16S rDNA amplicons) to validate novel taxa. We applied this approach to stool samples from 11 healthy subjects collected as part of the Human Microbiome Project. We discovered several low-abundance, novel bacterial taxa, which span three major phyla in the bacterial tree of life. We determined that these taxa are present in a larger set of Human Microbiome Project subjects and are found in two sampling sites (Houston and St. Louis). We show that the number of false-positive novel sequences (primarily chimeric sequences) would have been two orders of magnitude higher than the true number of novel taxa without validation using multiple datasets, highlighting the importance of establishing rigorous standards for the identification of novel taxa in metagenomic data. The majority of novel sequences are related to the recently discovered genus Barnesiella, further encouraging efforts to characterize the members of this genus and to study their roles in the microbial communities of the gut. A better understanding of the effects of less-abundant bacteria is important as we seek to understand the complex gut microbiome in healthy individuals and link changes in the microbiome to disease.     

Exploring and exploiting disease interactions from multi-relational gene and phenotype networks

The availability of electronic health care records is unlocking the potential for novel studies on understanding and modeling disease co-morbidities based on both phenotypic and genetic data. Moreover, the insurgence of increasingly reliable phenotypic data can aid further studies on investigating the potential genetic links among diseases. The goal is to create a feedback loop where computational tools guide and facilitate research, leading to improved biological knowledge and clinical standards, which in turn should generate better data. We build and analyze disease interaction networks based on data collected from previous genetic association studies and patient medical histories, spanning over 12 years, acquired from a regional hospital. By exploring both individual and combined interactions among these two levels of disease data, we provide novel insight into the interplay between genetics and clinical realities. Our results show a marked difference between the well defined structure of genetic relationships and the chaotic co-morbidity network, but also highlight clear interdependencies. We demonstrate the power of these dependencies by proposing a novel multi-relational link prediction method, showing that disease co-morbidity can enhance our currently limited knowledge of genetic association. Furthermore, our methods for integrated networks of diverse data are widely applicable and can provide novel advances for many problems in systems biology and personalized medicine.     

Towards accurate imputation of quantitative genetic interactions

Recent technological breakthroughs have enabled high-throughput quantitative measurements of hundreds of thousands of genetic interactions among hundreds of genes in Saccharomyces cerevisiae. However, these assays often fail to measure the genetic interactions among up to 40% of the studied gene pairs. Here we present a novel method, which combines genetic interaction data together with diverse genomic data, to quantitatively impute these missing interactions. We also present data on almost 190,000 novel interactions.     

Stalking the fourth domain in metagenomic data: searching for, discovering, and interpreting novel, deep branches in marker gene phylogenetic trees

Background

Most of our knowledge about the ancient evolutionary history of organisms has been derived from data associated with specific known organisms (i.e., organisms that we can study directly such as plants, metazoans, and culturable microbes). Recently, however, a new source of data for such studies has arrived: DNA sequence data generated directly from environmental samples. Such metagenomic data has enormous potential in a variety of areas including, as we argue here, in studies of very early events in the evolution of gene families and of species.

Methodology/Principal Findings

We designed and implemented new methods for analyzing metagenomic data and used them to search the Global Ocean Sampling (GOS) Expedition data set for novel lineages in three gene families commonly used in phylogenetic studies of known and unknown organisms: small subunit rRNA and the recA and rpoB superfamilies. Though the methods available could not accurately identify very deeply branched ss-rRNAs (largely due to difficulties in making robust sequence alignments for novel rRNA fragments), our analysis revealed the existence of multiple novel branches in the recA and rpoB gene families. Analysis of available sequence data likely from the same genomes as these novel recA and rpoB homologs was then used to further characterize the possible organismal source of the novel sequences.

Conclusions/Significance

Of the novel recA and rpoB homologs identified in the metagenomic data, some likely come from uncharacterized viruses while others may represent ancient paralogs not yet seen in any cultured organism. A third possibility is that some come from novel cellular lineages that are only distantly related to any organisms for which sequence data is currently available. If there exist any major, but so-far-undiscovered, deeply branching lineages in the tree of life, we suggest that methods such as those described herein currently offer the best way to search for them.     

Doing more than just the structure-structural genomics in kinase drug discovery

Structural genomics (SG) has significantly increased the number of novel protein structures of targets with medical relevance. In the protein kinase area, SG has contributed >50% of all novel kinases structures during the past three years and determined more than 30 novel catalytic domain structures. Many of the released structures are inhibitor complexes and a number of them have identified new inhibitor binding modes and scaffolds. In addition, generated reagents, assays, and inhibitor screening data provide a diversity of chemogenomic data that can be utilized for early drug development. Here we discuss the currently available structural data for the kinase family considering novel structures as well as inhibitor complexes. Our analysis revealed that the structural coverage of many kinases families is still rather poor, and inhibitor complexes with diverse inhibitors are only available for a few kinases. However, we anticipate that with the current rate of structure determination and high throughput technologies developed by SG programs these gaps will be closed soon. In addition, the generated reagents will put SG initiatives in a unique position providing data beyond protein structure determination by identifying chemical probes, determining their binding modes and target specificity.     

Data integration in the era of omics: current and future challenges

To integrate heterogeneous and large omics data constitutes not only a conceptual challenge but a practical hurdle in the daily analysis of omics data. With the rise of novel omics technologies and through large-scale consortia projects, biological systems are being further investigated at an unprecedented scale generating heterogeneous and often large data sets. These data-sets encourage researchers to develop novel data integration methodologies. In this introduction we review the definition and characterize current efforts on data integration in the life sciences. We have used a web-survey to assess current research projects on data-integration to tap into the views, needs and challenges as currently perceived by parts of the research community.     

Challenges and solutions in proteomics

The accelerated growth of proteomics data presents both opportunities and challenges. Large-scale proteomic profiling of biological samples such as cells, organelles or biological fluids has led to discovery of numerous key and novel proteins involved in many biological/disease processes including cancers, as well as to the identification of novel disease biomarkers and potential therapeutic targets. While proteomic data analysis has been greatly assisted by the many bioinformatics tools developed in recent years, a careful analysis of the major steps and flow of data in a typical highthroughput analysis reveals a few gaps that still need to be filled to fully realize the value of the data. To facilitate functional and pathway discovery for large-scale proteomic data, we have developed an integrated proteomic expression analysis system, iProXpress, which facilitates protein identification using a comprehensive sequence library and functional interpretation using integrated data. With its modular design, iProXpress complements and can be integrated with other software in a proteomic data analysis pipeline. This novel approach to complex biological questions involves the interrogation of multiple data sources, thereby facilitating hypothesis generation and knowledge discovery from the genomic-scale studies and fostering disease diagnosis and drug development.     

Synthesis and biological evaluation of a novel series of bis-salicylaldehydes as mushroom tyrosinase inhibitors

A series of novel bis-salicylaldehydes were synthesised and evaluated as tyrosinase inhibitors using a tyrosinase-dependent l-DOPA oxidation assay. The bis-salicylaldehydes exhibited greater inhibitory activity than salicylaldehyde. Our data suggests that these novel compounds may serve as a structural template for the design and development of novel tyrosinase inhibitors.     

Novel high-rank phylogenetic lineages within a sulfur spring (Zodletone Spring, Oklahoma), revealed using a combined pyrosequencing-sanger approach

The utilization of high-throughput sequencing technologies in 16S rRNA gene-based diversity surveys has indicated that within most ecosystems, a significant fraction of the community could not be assigned to known microbial phyla. Accurate determination of the phylogenetic affiliation of such sequences is difficult due to the short-read-length output of currently available high-throughput technologies. This fraction could harbor multiple novel phylogenetic lineages that have so far escaped detection. Here we describe our efforts in accurate assessment of the novelty and phylogenetic affiliation of selected unclassified lineages within a pyrosequencing data set generated from source sediments of Zodletone Spring, a sulfide- and sulfur-rich spring in southwestern Oklahoma. Lineage-specific forward primers were designed for 78 putatively novel lineages identified within the pyrosequencing data set, and representative nearly full-length small-subunit (SSU) rRNA gene sequences were obtained by pairing those primers with reverse universal bacterial primers. Of the 78 lineages tested, amplifiable products were obtained for 52, 32 of which had at least one nearly full-length sequence that was representative of the lineage targeted. Analysis of phylogenetic affiliation of the obtained Sanger sequences identified 5 novel candidate phyla and 10 novel candidate classes (within Fibrobacteres, Planctomycetes, and candidate phyla BRC1, GN12, TM6, TM7, LD1, WS2, and GN06) in the data set, in addition to multiple novel orders and families. The discovery of multiple novel phyla within a pilot study of a single ecosystem clearly shows the potential of the approach in identifying novel diversities within the rare biosphere.     

Discovering functional novelty in metagenomes: examples from light-mediated processes

The emerging coverage of diverse habitats by metagenomic shotgun data opens new avenues of discovering functional novelty using computational tools. Here, we apply three different concepts for predicting novel functions within light-mediated microbial pathways in five diverse environments. Using phylogenetic approaches, we discovered two novel deep-branching subfamilies of photolyases (involved in light-mediated repair) distributed abundantly in high-UV environments. Using neighborhood approaches, we were able to assign seven novel functional partners in luciferase synthesis, nitrogen metabolism, and quorum sensing to BLUF domain-containing proteins (involved in light sensing). Finally, by domain analysis, for RcaE proteins (involved in chromatic adaptation), we predict 16 novel domain architectures that indicate novel functionalities in habitats with little or no light. Quantification of protein abundance in the various environments supports our findings that bacteria utilize light for sensing, repair, and adaptation far more widely than previously thought. While the discoveries illustrate the opportunities in function discovery, we also discuss the immense conceptual and practical challenges that come along with this new type of data.     

Analysis of multiple compound–protein interactions reveals novel bioactive molecules

The discovery of novel bioactive molecules advances our systems‐level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound–protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold‐hopping compounds. Through a machine‐learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G‐protein‐coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand‐screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.     

Design and synthesis of orally active inhibitors of TNF synthesis as anti-rheumatoid arthritis drugs

A novel series of TNF inhibitors was identified based on the screening of existing MMP inhibitor libraries. Further SAR optimization led to the discovery of a novel lead compound. Its synthesis, efficacy in experimental animal models, and pharmacokinetic data are discussed.     

miReader: Discovering Novel miRNAs in Species without Sequenced Genome

Along with computational approaches, NGS led technologies have caused a major impact upon the discoveries made in the area of miRNA biology, including novel miRNAs identification. However, to this date all microRNA discovery tools compulsorily depend upon the availability of reference or genomic sequences. Here, for the first time a novel approach, miReader, has been introduced which could discover novel miRNAs without any dependence upon genomic/reference sequences. The approach used NGS read data to build highly accurate miRNA models, molded through a Multi-boosting algorithm with Best-First Tree as its base classifier. It was comprehensively tested over large amount of experimental data from wide range of species including human, plants, nematode, zebrafish and fruit fly, performing consistently with >90% accuracy. Using the same tool over Illumina read data for Miscanthus, a plant whose genome is not sequenced; the study reported 21 novel mature miRNA duplex candidates. Considering the fact that miRNA discovery requires handling of high throughput data, the entire approach has been implemented in a standalone parallel architecture. This work is expected to cause a positive impact over the area of miRNA discovery in majority of species, where genomic sequence availability would not be a compulsion any more.     

Discovery of novel substituted benzo-anellated 4-benzylamino pyrrolopyrimidines as dual EGFR and VEGFR2 inhibitors

The quinazoline scaffold is the main part of many marketed EGFR inhibitors. Resistance developments against those inhibitors enforced the search for novel structural lead compounds. We developed novel benzo-anellated 4-benzylamine pyrrolopyrimidines with varied substitution patterns at both the molecular scaffold and the attached residue in the 4-position. The structure-dependent affinities towards EGFR are discussed and first nanomolar derivatives have been identified. Docking studies were carried out for EGFR in order to explore the potential binding mode of the novel inhibitors. As the receptor tyrosine kinase VEGFR2 recently gained an increasing interest as an upregulated signaling kinase in many solid tumors and in tumor metastasis we determined the affinity of our compounds to inhibit VEGFR2. So we identified novel dually acting EGFR and VEGFR2 inhibitors for which first anticancer screening data are reported. Those data indicate a stronger antiproliferative effect of a VEGFR2 inhibition compared to the EGFR inhibition.