GPRC5A is a potential oncogene in pancreatic ductal adenocarcinoma cells that is upregulated by gemcitabine with help from HuR.

GPRC5A is an orphan G-protein coupled receptor with an intriguing dual behavior, acting as an oncogene in some cancers and as a tumor suppressor in other cancers. In the pancreatic cancer context, very little is known about GPRC5A. By analyzing messenger RNA (mRNA) expression data from 675 human cancer cell lines and 10 609 samples from The Cancer Genome Atlas (TCGA) we found that GPRC5A's abundance in pancreatic cancer is highest (cell lines) or second highest (TCGA) among all tissues and cancer types. Further analyses of an independent set of 252 pancreatic normal and cancer samples showed GPRC5A mRNA to be more than twofold upregulated in primary tumor samples compared with normal pancreas (P-value<10(-5)), and even further upregulated in pancreatic cancer metastases to various organs (P-value=0.0021). Immunostaining of 208 cores (103 samples) of a tissue microarray showed generally low expression of GPRC5A protein in normal pancreatic ductal cells; on the other hand, in primary and metastatic samples, GPRC5A protein levels were dramatically increased in pancreatic ductal cells. In vitro studies of multiple pancreatic cancer cell lines showed that an increase in GPRC5A protein levels promoted pancreatic cancer cell growth and migration. Unexpectedly, when we treated pancreatic cancer cell lines with gemcitabine (2',2'-difluorodeoxycytidine), we observed an increase in GPRC5A protein abundance. On the other hand, when we knocked down GPRC5A we sensitized pancreatic cancer cells to gemcitabine. Through further experimentation we showed that the monotonic increase in GPRC5A protein levels that we observe for the first 18 h following gemcitabine treatment results from interactions between GPRC5A's mRNA and the RNA-binding protein HuR, which is an established key mediator of gemcitabine's efficacy in cancer cells. As we discovered, the interaction between GPRC5A and HuR is mediated by at least one HuR-binding site in GPRC5A's mRNA. Our findings indicate that GPRC5A is part of a complex molecular axis that involves gemcitabine and HuR, and, possibly, other genes. Further work is warranted before it can be established unequivocally that GPRC5A is an oncogene in the pancreatic cancer context.

The mRNA-binding protein HuR promotes hypoxia-induced chemoresistance through posttranscriptional regulation of the proto-oncogene PIM1 in pancreatic cancer cells.

Previously, it has been shown that pancreatic ductal adenocarcinoma (PDA) tumors exhibit high levels of hypoxia, characterized by low oxygen pressure (pO2) and decreased O2 intracellular perfusion. Chronic hypoxia is strongly associated with resistance to cytotoxic chemotherapy and chemoradiation in an understudied phenomenon known as hypoxia-induced chemoresistance. The hypoxia-inducible, pro-oncogenic, serine-threonine kinase PIM1 (Proviral Integration site for Moloney murine leukemia virus 1) has emerged as a key regulator of hypoxia-induced chemoresistance in PDA and other cancers. Although its role in therapeutic resistance has been described previously, the molecular mechanism behind PIM1 overexpression in PDA is unknown. Here, we demonstrate that cis-acting AU-rich elements (ARE) present within a 38-base pair region of the PIM1 mRNA 3'-untranslated region mediate a regulatory interaction with the mRNA stability factor HuR (Hu antigen R) in the context of tumor hypoxia. Predominantly expressed in the nucleus in PDA cells, HuR translocates to the cytoplasm in response to hypoxic stress and stabilizes the PIM1 mRNA transcript, resulting in PIM1 protein overexpression. A reverse-phase protein array revealed that HuR-mediated regulation of PIM1 protects cells from hypoxic stress through phosphorylation and inactivation of the apoptotic effector BAD and activation of MEK1/2. Importantly, pharmacological inhibition of HuR by MS-444 inhibits HuR homodimerization and its cytoplasmic translocation, abrogates hypoxia-induced PIM1 overexpression and markedly enhances PDA cell sensitivity to oxaliplatin and 5-fluorouracil under physiologic low oxygen conditions. Taken together, these results support the notion that HuR has prosurvival properties in PDA cells by enabling them with growth advantages in stressful tumor microenvironment niches. Accordingly, these studies provide evidence that therapeutic disruption of HuR's regulation of PIM1 may be a key strategy in breaking an elusive chemotherapeutic resistance mechanism acquired by PDA cells that reside in hypoxic PDA microenvironments.

The miR-17/92 cluster: a comprehensive update on its genomics, genetics, functions and increasingly important and numerous roles in health and disease.

The miR-17/92 cluster is among the best-studied microRNA clusters. Interest in the cluster and its members has been increasing steadily and the number of publications has grown exponentially since its discovery with more than 1000 articles published in 2012 alone. Originally found to be involved in tumorigenesis, research work in recent years has uncovered unexpected roles for its members in a wide variety of settings that include normal development, immune diseases, cardiovascular diseases, neurodegenerative diseases and aging. In light of its ever-increasing importance and ever-widening regulatory roles, we review here the latest body of knowledge on the cluster's involvement in health and disease as well as provide a novel perspective on the full spectrum of protein-coding and non-coding transcripts that are likely regulated by its members.

In silico structural and functional analysis of the human cytomegalovirus (HHV5) genome.

The open reading frames of human cytomegalovirus (human herpesvirus-5, HHV5) encode some 213 unique proteins with mostly unknown functions. Using the threading program, ProCeryon, we calculated possible matches between the amino acid sequences of these proteins and the Protein Data Bank library of three-dimensional structures. Thirty-six proteins were fully identified in terms of their structure and, often, function; 65 proteins were recognized as members of narrow structural/functional families (e.g. DNA-binding factors, cytokines, enzymes, signaling particles, cell surface receptors etc.); and 87 proteins were assigned to broad structural classes (e.g. all-beta, 3-layer-alphabetaalpha, multidomain, etc.). Genes encoding proteins with similar folds, or containing identical structural traits (extreme sequence length, runs of unstructured (Pro and/or Gly-rich) residues, transmembrane segments, etc.) often formed tandem clusters throughout the genome. In the course of this work, benchmarks on about 20 known folds were used to optimize adjustable parameters of threading calculations, i.e. gap penalty weights used in sequence/structure alignments; new scores obtained as simple combinations of existing scoring functions; and number of threading runs conducive to meaningful results. An introduction of summed, per-residue-normalized scores has been essential for discovery of subdomains (EGF-like, SH2, SH3) in longer protein sequences, such as the eight "open sandwich" cytokine domains, 60-70 amino acids long and having the 3beta1alpha fold with one or two disulfide bridges, present in otherwise unrelated proteins.

Non-alpha-helical elements modulate polytopic membrane protein architecture.

In "all alpha-fold" transmembrane proteins, including ion channels, G-protein-coupled receptors (GPCRs), bacterial rhodopsins and photosynthetic reaction centers, relatively long alpha-helices, straight, curved or kinked, pack into compact elliptical or circular domains. Using both existing and newly developed tools to analyze transmembrane segments of all available membrane protein three-dimensional structures, including that very recently elucidated for the GPCR, rhodopsin, we report here the finding of frequent non-alpha-helical components, i.e. 3(10)-helices ("tight turns"), pi-helices ("wide turns") and intrahelical kinks (often due to residues other than proline). Often, diverse helical types and kinks concatenate over long segments and produce complex inclinations of helical axis, and/or diverse frame shifts in the "canonical", alpha-helical side-chain pattern. Marked differences in transmembrane architecture exist even between seemingly structurally related proteins, such as bacteriorhodopsin and rhodopsin. Deconvolution of these non-canonical features into their composite elements is essential for understanding the pleiotropy of polytopic protein structure and function, and must be considered in developing valid macromolecular models.

The emergence of pattern discovery techniques in computational biology.

In the past few years, pattern discovery has been emerging as a generic tool of choice for tackling problems from the computational biology domain. In this presentation, and after defining the problem in its generality, we review some of the algorithms that have appeared in the literature and describe several applications of pattern discovery to problems from computational biology.

Dictionary building via unsupervised hierarchical motif discovery in the sequence space of natural proteins.

Using Teiresias, a pattern discovery method that identifies all motifs present in any given set of protein sequences without requiring alignment or explicit enumeration of the solution space, we have explored the GenPept sequence database and built a dictionary of all sequence patterns with two or more instances. The entries of this dictionary, henceforth named seqlets, cover 98.12% of all amino acid positions in the input database and in essence provide a comprehensive finite set of descriptors for protein sequence space. As such, seqlets can be effectively used to describe almost every naturally occurring protein. In fact, seqlets can be thought of as building blocks of protein molecules that are a necessary (but not sufficient) condition for function or family equivalence memberships. Thus, seqlets can either define conserved family signatures or cut across molecular families and previously undetected sequence signals deriving from functional convergence. Moreover, we show that seqlets also can capture structurally conserved motifs. The availability of a dictionary of seqlets that has been derived in such an unsupervised, hierarchical manner is generating new opportunities for addressing problems that range from reliable classification and the correlation of sequence fragments with functional categories to faster and sensitive engines for homology searches, evolutionary studies, and protein structure prediction.

Building dictionaries of 1D and 3D motifs by mining the Unaligned 1D sequences of 17 archaeal and bacterial genomes.

We have used the Teiresias algorithm to carry out unsupervised pattern discovery in a database containing the unaligned ORFs from the 17 publicly available complete archaeal and bacterial genomes and build a 1D dictionary of motifs. These motifs which we refer to as seqlets account for and cover 97.88% of this genomic input at the level of amino acid positions. Each of the seqlets in this 1D dictionary was located among the sequences in Release 38.0 of the Protein Data Bank and the structural fragments corresponding to each seqlet's instances were identified and aligned in three dimensions: those of the seqlets that resulted in RMSD errors below a pre-selected threshold of 2.5 Angstroms were entered in a 3D dictionary of structurally conserved seqlets. These two dictionaries can be thought of as cross-indices that facilitate the tackling of tasks such as automated functional annotation of genomic sequences, local homology identification, local structure characterization, comparative genomics, etc.

Combinatorial pattern discovery in biological sequences: The TEIRESIAS algorithm.

MOTIVATION: The discovery of motifs in biological sequences is an important problem. RESULTS: This paper presents a new algorithm for the discovery of rigid patterns (motifs) in biological sequences. Our method is combinatorial in nature and able to produce all patterns that appear in at least a (user-defined) minimum number of sequences, yet it manages to be very efficient by avoiding the enumeration of the entire pattern space. Furthermore, the reported patterns are maximal: any reported pattern cannot be made more specific and still keep on appearing at the exact same positions within the input sequences. The effectiveness of the proposed approach is showcased on a number of test cases which aim to: (i) validate the approach through the discovery of previously reported patterns; (ii) demonstrate the capability to identify automatically highly selective patterns particular to the sequences under consideration. Finally, experimental analysis indicates that the algorithm is output sensitive, i.e. its running time is quasi-linear to the size of the generated output.

Molecular moment similarity between clozapine and substituted [(4-phenylpiperazinyl)-methyl] benzamides: selective dopamine D4 agonists.

Moment descriptors of the molecular charge and mass distributions are investigated within the context of molecular similarity. Euclidean distances in the moment descriptor space are shown to yield molecular proximities in accord with chemical intuition for a substituted [(4-phenylpiperazinyl)-methyl] benzamide series of dopamine D4 agonists. The proximity of the dopamine D4 antagonist clozapine to the molecules of this series is also examined in the moment space.

MUSCA: An Algorithm for Constrained Alignment of Multiple Data Sequences.

Given a set of N sequences, the Multiple Sequence Alignment problem is to align these N sequences, possibly with gaps, that brings out the best commonality of the N sequences. MUSCA is a two-stage approach to the alignment problem by identifying two relatively simpler sub-problems whose solutions are used to obtain the alignment of the sequences. We first discover motifs in the N sequences and then extract an appropriate subset of compatible motifs to obtain a good alignment. The motifs of interest to us are the irredundant motifs which are only polynomial in the input size. In practice, however, the number is much smaller (sub-linear). Notice that this step aids in a direct N-wise alignment, as opposed to composing the alignments from lower order (say pairwise) alignments and the solution is also independent of the order of the input sequences; hence the algorithm works very well while dealing with a large number of sequences. The second part of the problem that deals with obtaining a good alignment is solved using a graph-theoretic approach that computes an induced subgraph satisfying certain simple constraints. We reduce a version of this problem to that of solving an instance of a set covering problem, thus offer the best possible approximate solution to the problem (provided P not equalNP). Our experimental results, while being preliminary, indicate that this approach is efficient, particularly on large numbers of long sequences, and, gives good alignments when tested on biological data such as DNA and protein sequences. We introduce the the notion of an alignment number K (2

FLASH: a fast look-up algorithm for string homology.

A key issue in managing today's large amounts of genetic data is the availability of efficient, accurate, and selective techniques for detecting homologies (similarities) between newly discovered and already stored sequences. A common characteristic of today's most advanced algorithms, such as FASTA, BLAST, and BLAZE is the need to scan the contents of the entire database, in order to find one or more matches. This design decision results in either excessively long search times or, as is the case of BLAST, in a sharp trade-off between the achieved accuracy and the required amount of computation. The homology detection algorithm presented in this paper, on the other hand, is based on a probabilistic indexing framework. The algorithm requires minimal access to the database in order to determine matches. This minimal requirement is achieved by using the sequences of interest to generate a highly redundant number of very descriptive tuples; these tuples are subsequently used as indices in a table look-up paradigm. In addition to the description of the algorithm, theoretical and experimental results on the sensitivity and accuracy of the suggested approach are provided. The storage and computational requirements are described and the probability of correct matches and false alarms is derived. Sensitivity and accuracy are shown to be close to those of dynamic programming techniques. A prototype system has been implemented using the described ideas. It contains the full Swiss-Prot database rel 25 (10 MR) and the genome of E. Coli (2 MR). The system is currently being expanded to include the complete Genbank database.(ABSTRACT TRUNCATED AT 250 WORDS)