Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Comparison of Gas Chromatography and Mineralization Experiments for Measuring Loss of Selected Polychlorinated Biphenyl Congeners in Cultures of White Rot Fungi

Two methods were used to compare the biodegradation of six polychlorinated biphenyl (PCB) congeners by 12 white rot fungi. Four fungi were found to be more active than Phanerochaete chrysosporium ATCC 24725. Biodegradation of the following congeners was monitored by gas chromatography: 2,3-dichlorobiphenyl, 4,4′-dichlorobiphenyl, 2,4′,5-trichlorobiphenyl (2,4′,5-TCB), 2,2′,4,4′-tetrachlorobiphenyl, 2,2′,5,5′-tetrachlorobiphenyl, and 2,2′,4,4′,5,5′-hexachlorobiphenyl. The congener tested for mineralization was 2,4′,5-[U-¹⁴C]TCB. Culture supernatants were also assayed for lignin peroxidase and manganese peroxidase activities. Of the fungi tested, two strains of Bjerkandera adusta (UAMH 8258 and UAMH 7308), one strain of Pleurotus ostreatus (UAMH 7964), and Trametes versicolor UAMH 8272 gave the highest biodegradation and mineralization. P. chrysosporium ATCC 24725, a strain frequently used in studies of PCB degradation, gave the lowest mineralization and biodegradation activities of the 12 fungi reported here. Low but detectable levels of lignin peroxidase and manganese peroxidase activity were present in culture supernatants, but no correlation was observed among any combination of PCB congener biodegradation, mineralization, and lignin peroxidase or manganese peroxidase activity. With the exception of P. chrysosporium, congener loss ranged from 40 to 96%; however, these values varied due to nonspecific congener binding to fungal biomass and glassware. Mineralization was much lower, ≤11%, because it measures a complete oxidation of at least part of the congener molecule but the results were more consistent and therefore more reliable in assessment of PCB biodegradation.

Polychlorinated biphenyls (PCBs) are produced by chlorination of biphenyl, resulting in up to 209 different congeners. Commercial mixtures range from light oily fluids to waxes, and their physical properties make them useful as heat transfer fluids, hydraulic fluids, solvent extenders, plasticizers, flame retardants, organic diluents, and dielectric fluids (1, 21). Approximately 24 million lb are in the North American environment (19). The stability and hydrophobic nature of these compounds make them a persistent environmental hazard.To date, bacterial transformations have been the main focus of PCB degradation research. Aerobic bacteria use a biphenyl-induced dioxygenase enzyme system to attack less-chlorinated congeners (mono- to hexachlorobiphenyls) (1, 5, 7, 8, 22). Although more-chlorinated congeners are recalcitrant to aerobic bacterial degradation, microorganisms in anaerobic river sediments reductively dechlorinate these compounds, mainly removing the meta and para chlorines (1, 6, 10, 33, 34).The degradation of PCBs by white rot fungi has been known since 1985 (11, 18). Many fungi have been tested for their ability to degrade PCBs, including the white rot fungi Coriolus versicolor (18), Coriolopsis polysona (41), Funalia gallica (18), Hirneola nigricans (35), Lentinus edodes (35), Phanerochaete chrysosporium (3, 11, 14, 17, 18, 35, 39, 41–43), Phlebia brevispora (18), Pleurotus ostreatus (35, 43), Poria cinerescens (18), Px strain (possibly Lentinus tigrinus) (35), and Trametes versicolor (41, 43). There have also been studies of PCB metabolism by ectomycorrhizal fungi (17) and other fungi such as Aspergillus flavus (32), Aspergillus niger (15), Aureobasidium pullulans (18), Candida boidinii (35), Candida lipolytica (35), Cunninghamella elegans (16), and Saccharomyces cerevisiae (18, 38). The mechanism of PCB biodegradation has not been definitively determined for any fungi. White rot fungi produce several nonspecific extracellular enzymes which have been the subject of extensive research. These nonspecific peroxidases are normally involved in lignin degradation but can oxidize a wide range of aromatic compounds including polycyclic aromatic hydrocarbons (37). Two peroxidases, lignin peroxidase (LiP) and Mn peroxidase (MnP), are secreted into the environment of the fungus under conditions of nitrogen limitation in P. chrysosporium (23, 25, 27, 29) but are not stress related in fungi such as Bjerkandera adusta or T. versicolor (12, 30).Two approaches have been used to determine the biodegradability of PCBs by fungi: (i) loss of the parent congener analyzed by gas chromatography (GC) (17, 32, 35, 42, 43) and (ii) mineralization experiments in which the ¹⁴C of the universally labeled ¹⁴C parent congener is recovered as ¹⁴CO₂ (11, 14, 18, 39, 41). In the first method, the loss of a peak on a chromatogram makes it difficult to decide whether the PCB is being partly degraded, mineralized, adsorbed to the fungal biomass, or bound to glassware, soil particles, or wood chips. Even when experiments with killed-cell and abiotic controls are performed, the extraction efficiency and standard error can make data difficult to interpret. For example, recoveries can range anywhere from 40 to 100% depending on the congener used and the fungus being investigated (17). On the other hand, recovery of significant amounts of ¹⁴CO₂ from the cultures incubated with a ¹⁴C substrate provides definitive proof of fungal metabolism. There appears to be only one report relating data from these two techniques (18), and in that study, [U-¹⁴C]Aroclor 1254, rather than an individual congener, was used.In this study, we examined the ability of 12 white rot fungal strains to metabolize selected PCB congeners to determine which strains were the most active degraders. Included in this group was P. chrysosporium ATCC 24725, a strain used extensively in PCB studies (3, 14, 18, 35, 39, 41–43). Six PCB congeners were selected to give a range of chlorine substitutions and therefore a range of potential biodegradability which was monitored by GC. One of the chosen congeners was ¹⁴C labeled and used in studies to compare the results from a mineralization method with those from the GC method.     

Question Processing and Clustering in INDOC: A Biomedical Question Answering System

The exponential growth in the volume of publications in the biomedical domain has made it impossible for an individual to keep pace with the advances. Even though evidence-based medicine has gained wide acceptance, the physicians are unable to access the relevant information in the required time, leaving most of the questions unanswered. This accentuates the need for fast and accurate biomedical question answering systems. In this paper we introduce INDOC—a biomedical question answering system based on novel ideas of indexing and extracting the answer to the questions posed. INDOC displays the results in clusters to help the user arrive the most relevant set of documents quickly. Evaluation was done against the standard OHSUMED test collection. Our system achieves high accuracy and minimizes user effort.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]     

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Semaphorin3A Signaling Mediated by Fyn-dependent Tyrosine Phosphorylation of Collapsin Response Mediator Protein 2 at Tyrosine 32

Collapsin response mediator protein 2 (CRMP2) is an intracellular protein that mediates signaling of Semaphorin3A (Sema3A), a repulsive axon guidance molecule. Fyn, a Src-type tyrosine kinase, is involved in the Sema3A signaling. However, the relationship between CRMP2 and Fyn in this signaling pathway is still unknown. In our research, we demonstrated that Fyn phosphorylated CRMP2 at Tyr³² residues in HEK293T cells. Immunohistochemical analysis using a phospho-specific antibody at Tyr³² of CRMP showed that Tyr³²-phosphorylated CRMP was abundant in the nervous system, including dorsal root ganglion neurons, the molecular and Purkinje cell layer of adult cerebellum, and hippocampal fimbria. Overexpression of a nonphosphorylated mutant (Tyr³² to Phe³²) of CRMP2 in dorsal root ganglion neurons interfered with Sema3A-induced growth cone collapse response. These results suggest that Fyn-dependent phosphorylation of CRMP2 at Tyr³² is involved in Sema3A signaling.Collapsin response mediator proteins (CRMPs)⁴ have been identified as intracellular proteins that mediate Semaphorin3A (Sema3A) signaling in the nervous system (1). CRMP2 is one of the five members of the CRMP family. CRMPs also mediate signal transduction of NT3, Ephrin, and Reelin (2–4). CRMPs interact with several intracellular molecules, including tubulin, Numb, kinesin1, and Sra1 (5–8). CRMPs are involved in axon guidance, axonal elongation, cell migration, synapse maturation, and the generation of neuronal polarity (1, 2, 4, 5).CRMP family proteins are known to be the major phosphoproteins in the developing brain (1, 9). CRMP2 is phosphorylated by several Ser/Thr kinases, such as Rho kinase, cyclin-dependent kinase 5 (Cdk5), and glycogen synthase kinase 3β (GSK3β) (2, 10–13). The phosphorylation sites of CRMP2 by these kinases are clustered in the C terminus and have already been identified. Rho kinase phosphorylates CRMP2 at Thr⁵⁵⁵ (10). Cdk5 phosphorylates CRMP2 at Ser⁵²², and this phosphorylation is essential for sequential phosphorylations by GSK3β at Ser⁵¹⁸, Thr⁵¹⁴, and Thr⁵⁰⁹ (2, 11–13). These phosphorylations disrupt the interaction of CRMP2 with tubulin or Numb (2, 3, 13). The sequential phosphorylation of CRMP2 by Cdk5 and GSK3β is an essential step in Sema3A signaling (11, 13). Furthermore, the neurofibrillary tangles in the brains of people with Alzheimer disease contain hyperphosphorylated CRMP2 at Thr⁵⁰⁹, Ser⁵¹⁸, and Ser⁵²² (14, 15).CRMPs are also substrates of several tyrosine kinases. The phosphorylation of CRMP2 by Fes/Fps and Fer has been shown to be involved in Sema3A signaling (16, 17). Phosphorylation of CRMP2 at Tyr⁴⁷⁹ by a Src family tyrosine kinase Yes regulates CXCL12-induced T lymphocyte migration (18). We reported previously that Fyn is involved in Sema3A signaling (19). Fyn associates with PlexinA2, one of the components of the Sema3A receptor complex. Fyn also activates Cdk5 through the phosphorylation at Tyr¹⁵ of Cdk5 (19). In dorsal root ganglion (DRG) neurons from fyn-deficient mice, Sema3A-induced growth cone collapse response is attenuated compared with control mice (19). Furthermore, we recently found that Fyn phosphorylates CRMP1 and that this phosphorylation is involved in Reelin signaling (4). Although it has been shown that CRMP2 is involved in Sema3A signaling (1, 11, 13), the relationship between Fyn and CRMP2 in Sema3A signaling and the tyrosine phosphorylation site(s) of CRMPs remain unknown.Here, we show that Fyn phosphorylates CRMP2 at Tyr³². Using a phospho-specific antibody against Tyr³², we determined that the residue is phosphorylated in vivo. A nonphosphorylated mutant CRMP2Y32F inhibits Sema3A-induced growth cone collapse. These results indicate that tyrosine phosphorylation by Fyn at Tyr³² is involved in Sema3A signaling.     

Conserved Glutamate Residues Glu-343 and Glu-519 Provide Mechanistic Insights into Cation/Nucleoside Cotransport by Human Concentrative Nucleoside Transporter hCNT3

Human concentrative nucleoside transporter 3 (hCNT3) utilizes electrochemical gradients of both Na⁺ and H⁺ to accumulate pyrimidine and purine nucleosides within cells. We have employed radioisotope flux and electrophysiological techniques in combination with site-directed mutagenesis and heterologous expression in Xenopus oocytes to identify two conserved pore-lining glutamate residues (Glu-343 and Glu-519) with essential roles in hCNT3 Na⁺/nucleoside and H⁺/nucleoside cotransport. Mutation of Glu-343 and Glu-519 to aspartate, glutamine, and cysteine severely compromised hCNT3 transport function, and changes included altered nucleoside and cation activation kinetics (all mutants), loss or impairment of H⁺ dependence (all mutants), shift in Na⁺:nucleoside stoichiometry from 2:1 to 1:1 (E519C), complete loss of catalytic activity (E519Q) and, similar to the corresponding mutant in Na⁺-specific hCNT1, uncoupled Na⁺ currents (E343Q). Consistent with close-proximity integration of cation/solute-binding sites within a common cation/permeant translocation pore, mutation of Glu-343 and Glu-519 also altered hCNT3 nucleoside transport selectivity. Both residues were accessible to the external medium and inhibited by p-chloromercuribenzene sulfonate when converted to cysteine.Physiologic nucleosides and the majority of synthetic nucleoside analogs with antineoplastic and/or antiviral activity are hydrophilic molecules that require specialized plasma membrane nucleoside transporter (NT)³ proteins for transport into or out of cells (1–4). NT-mediated transport is required for nucleoside metabolism by salvage pathways and is a critical determinant of the pharmacologic actions of nucleoside drugs (3–6). By regulating adenosine availability to purinoreceptors, NTs also modulate a diverse array of physiological processes, including neurotransmission, immune responses, platelet aggregation, renal function, and coronary vasodilation (4, 6, 7). Two structurally unrelated NT families of integral membrane proteins exist in human and other mammalian cells and tissues as follows: the SLC28 concentrative nucleoside transporter (CNT) family and the SLC29 equilibrative nucleoside transporter (ENT) family (3, 4, 6, 8, 9). ENTs are normally present in most, possibly all, cell types (4, 6, 8). CNTs, in contrast, are found predominantly in intestinal and renal epithelia and other specialized cell types, where they have important roles in absorption, secretion, distribution, and elimination of nucleosides and nucleoside drugs (1–3, 5, 6, 9).The CNT protein family in humans is represented by three members, hCNT1, hCNT2, and hCNT3. Belonging to a CNT subfamily phylogenetically distinct from hCNT1/2, hCNT3 utilizes electrochemical gradients of both Na⁺ and H⁺ to accumulate a broad range of pyrimidine and purine nucleosides and nucleoside drugs within cells (10, 11). hCNT1 and hCNT2, in contrast, are Na⁺-specific and transport pyrimidine and purine nucleosides, respectively (11–13). Together, hCNT1–3 account for the three major concentrative nucleoside transport processes of human and other mammalian cells. Nonmammalian members of the CNT protein family that have been characterized functionally include hfCNT, a second member of the CNT3 subfamily from the ancient marine prevertebrate the Pacific hagfish Eptatretus stouti (14), CeCNT3 from Caenorhabditis elegans (15), CaCNT from Candida albicans (16), and the bacterial nucleoside transporter NupC from Escherichia coli (17). hfCNT is Na⁺- but not H⁺-coupled, whereas CeCNT3, CaCNT, and NupC are exclusively H⁺-coupled. Na⁺:nucleoside coupling stoichiometries are 1:1 for hCNT1 and hCNT2 and 2:1 for hCNT3 and hfCNT3 (11, 14). H⁺:nucleoside coupling ratios for hCNT3 and CaCNT are 1:1 (11, 16).Although much progress has been made in molecular studies of ENT proteins (4, 6, 8), studies of structurally and functionally important regions and residues within the CNT protein family are still at an early stage. Topological investigations suggest that hCNT1–3 and other eukaryote CNT family members have a 13 (or possibly 15)-transmembrane helix (TM) architecture, and multiple alignments reveal strong sequence similarities within the C-terminal half of the proteins (18). Prokaryotic CNTs lack the first three TMs of their eukaryotic counterparts, and functional expression of N-terminally truncated human and rat CNT1 in Xenopus oocytes has established that these three TMs are not required for Na⁺-dependent uridine transport activity (18). Consistent with this finding, chimeric studies involving hCNT1 and hfCNT (14) and hCNT1 and hCNT3 (19) have demonstrated that residues involved in Na⁺- and H⁺-coupling reside in the C-terminal half of the protein. Present in this region of the transporter, but of unknown function, is a highly conserved (G/A)XKX₃NEFVA(Y/M/F) motif common to all eukaryote and prokaryote CNTs.By virtue of their negative charge and consequent ability to interact directly with coupling cations and/or participate in cation-induced and other protein conformational transitions, glutamate and aspartate residues play key functional and structural roles in a broad spectrum of mammalian and bacterial cation-coupled transporters (20–30). Little, however, is known about their role in CNTs. This study builds upon a recent mutagenesis study of conserved glutamate and aspartate residues in hCNT1 (31) to undertake a parallel in depth investigation of corresponding residues in hCNT3. By employing the multifunctional capability of hCNT3 as a template for these studies, this study provides novel mechanistic insights into the molecular mechanism(s) of CNT-mediated cation/nucleoside cotransport, including the role of the (G/A)XKX₃NEFVA(Y/M/F) motif.     

Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation

Decomposing a biological sequence into its functional regions is an important prerequisite to understand the molecule. Using the multiple alignments of the sequences, we evaluate a segmentation based on the type of statistical variation pattern from each of the aligned sites. To describe such a more general pattern, we introduce multipattern consensus regions as segmented regions based on conserved as well as interdependent patterns. Thus the proposed consensus region considers patterns that are statistically significant and extends a local neighborhood. To show its relevance in protein sequence analysis, a cancer suppressor gene called p53 is examined. The results show significant associations between the detected regions and tendency of mutations, location on the 3D structure, and cancer hereditable factors that can be inferred from human twin studies.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]     

Size-Selective Predation on Groundwater Bacteria by Nanoflagellates in an Organic-Contaminated Aquifer

Time series incubations were conducted to provide estimates for the size selectivities and rates of protistan grazing that may be occurring in a sandy, contaminated aquifer. The experiments involved four size classes of fluorescently labeled groundwater bacteria (FLB) and 2- to 3-μm-long nanoflagellates, primarily Spumella guttula (Ehrenberg) Kent, that were isolated from contaminated aquifer sediments (Cape Cod, Mass.). The greatest uptake and clearance rates (0.77 bacteria · flagellate⁻¹ · h⁻¹ and 1.4 nl · flagellate⁻¹ · h⁻¹, respectively) were observed for 0.8- to 1.5-μm-long FLB (0.21-μm³ average cell volume), which represent the fastest growing bacteria within the pore fluids of the contaminated aquifer sediments. The 19:1 to 67:1 volume ratios of nanoflagellate predators to preferred bacterial prey were in the lower end of the range commonly reported for other aquatic habitats. The grazing data suggest that the aquifer nanoflagellates can consume as much as 12 to 74% of the unattached bacterial community in 1 day and are likely to have a substantive effect upon bacterial degradation of organic groundwater contaminants.While heterotrophic protists have been found in pristine and contaminated aquifers (3, 29, 34, 37, 54–57), very little research has been performed to elucidate their role in the subsurface. In other environments (e.g., surface and marine waters, topsoil, and wastewater treatment plants), it is well documented that they typically consume bacteria (2, 11, 15, 41, 42, 47), although some have been observed to consume high-molecular-weight organics (48, 59) and even viruses (20, 39). Protists typically graze selectively, depending upon the size (1, 9, 17, 25, 52), growth condition (18, 53), species (16, 17, 35), and motility (18) of their prey. In carbon-limited environments, protists decrease bacterial competition, resulting in a greater bacterial uptake rate for organic substrate per unit of bacterial biomass (27). Based upon indirect field observations, it is also hypothesized that this may be the role they play in organically contaminated aquifers (31). In nutrient-limited environments, protists may release nitrogen or phosphorus needed by bacteria (10, 28, 44, 61).Studies at the U.S. Geological Survey’s (USGS) Toxic Substances Hydrology Program research site at the Massachusetts Military Reservation (MMR) on Cape Cod, Mass., have shown that sandy aquifer sediments can harbor large protistan populations even at relatively low levels (≤2 mg/liter) of dissolved organic matter (30). Protistan abundances in the MMR aquifer plume range from 1 × 10⁴ to 7 × 10⁴ g (dry weight)⁻¹ (30) and consist primarily of nanoflagellates (2 to 3 μm in length) (29) that belong to the genera Bodo, Cercomonas, Cryptaulax, Cyanthomonas, Goniomonas, and Spumella, along with some undescribed species (37). A few amoebae (63) and no ciliates (29) have been observed.Results of a principal-component factor analysis of protistan and bacterial abundances and chemical constituents in the MMR plume suggested that the flagellates were preying upon unattached bacteria (30). Additional evidence of predation was obtained from flowthrough columns of aquifer sediment from which fluorescently labeled unattached bacteria eluted at much lower rates than they did from sterile (protist-free) controls (31). However, these results provide only indirect evidence of predation because no enumerations of the bacteria within the flagellates were performed.The purpose of the research reported in this paper was to directly determine whether the MMR nanoflagellates can consume unattached bacteria in the plume and the extent to which they engage in size-selective grazing. Rates of bacterivory (grazing and clearance rates) were estimated in the laboratory by using fluorescently labeled, monodispersed bacteria (FLB) and nanoflagellates that had been isolated from the MMR aquifer plume. Although other methods exist (19, 26, 38, 43, 45, 62, 64–66), we chose to use fluorescent labeling to study flagellate bacterivory because this procedure requires shorter incubation times and relies upon direct visual observation of the prey within the predator. In addition, experiments could be designed with different sizes of FLB to determine if the nanoflagellates can discriminate between prey. This involved using FLB with cell lengths of 0.1 to 0.5, 0.5 to 0.8, 0.8 to 1.5, and >1.5 μm (average cell volumes of 0.06, 0.14, 0.21, and 0.87 μm³, respectively) in the grazing experiments.     

Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

An Improved Algorithm for the Piecewise-Smooth Mumford and Shah Model in Image Segmentation

An improved algorithm for the piecewise-smooth Mumford and Shah functional is presented. Compared to the previous work of Chan and Vese, and Choi et al., extensions of the key functions are replaced by updating the level set function based on an artificial image that is composed of the diffused image and the original image. The low convergence problem of the classical algorithm is efficiently solved in the proposed approach. The resulting algorithm has also been demonstrated by several cases.[1,2,3,4,5,6,7,8,9,10,11]     

The -Version of the Cramér-von Mises Test for Two-Sample Comparisons in Microarray Data Analysis

Distribution-free statistical tests offer clear advantages in situations where the exact unadjusted -values are required as input for multiple testing procedures. Such situations prevail when testing for differential expression of genes in microarray studies. The Cramér-von Mises two-sample test, based on a certain -distance between two empirical distribution functions, is a distribution-free test that has proven itself as a good choice. A numerical algorithm is available for computing quantiles of the sampling distribution of the Cramér-von Mises test statistic in finite samples. However, the computation is very time- and space-consuming. An counterpart of the Cramér-von Mises test represents an appealing alternative. In this work, we present an efficient algorithm for computing exact quantiles of the -distance test statistic. The performance and power of the -distance test are compared with those of the Cramér-von Mises and two other classical tests, using both simulated data and a large set of microarray data on childhood leukemia. The -distance test appears to be nearly as powerful as its counterpart. The lower computational intensity of the -distance test allows computation of exact quantiles of the null distribution for larger sample sizes than is possible for the Cramér-von Mises test.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25]     

Inference of a Probabilistic Boolean Network from a Single Observed Temporal Sequence

The inference of gene regulatory networks is a key issue for genomic signal processing. This paper addresses the inference of probabilistic Boolean networks (PBNs) from observed temporal sequences of network states. Since a PBN is composed of a finite number of Boolean networks, a basic observation is that the characteristics of a single Boolean network without perturbation may be determined by its pairwise transitions. Because the network function is fixed and there are no perturbations, a given state will always be followed by a unique state at the succeeding time point. Thus, a transition counting matrix compiled over a data sequence will be sparse and contain only one entry per line. If the network also has perturbations, with small perturbation probability, then the transition counting matrix would have some insignificant nonzero entries replacing some (or all) of the zeros. If a data sequence is sufficiently long to adequately populate the matrix, then determination of the functions and inputs underlying the model is straightforward. The difficulty comes when the transition counting matrix consists of data derived from more than one Boolean network. We address the PBN inference procedure in several steps: (1) separate the data sequence into "pure" subsequences corresponding to constituent Boolean networks; (2) given a subsequence, infer a Boolean network; and (3) infer the probabilities of perturbation, the probability of there being a switch between constituent Boolean networks, and the selection probabilities governing which network is to be selected given a switch. Capturing the full dynamic behavior of probabilistic Boolean networks, be they binary or multivalued, will require the use of temporal data, and a great deal of it. This should not be surprising given the complexity of the model and the number of parameters, both transitional and static, that must be estimated. In addition to providing an inference algorithm, this paper demonstrates that the data requirement is much smaller if one does not wish to infer the switching, perturbation, and selection probabilities, and that constituent-network connectivity can be discovered with decent accuracy for relatively small time-course sequences.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]     

Deletion of the Chloride Transporter Slc26a7 Causes Distal Renal Tubular Acidosis and Impairs Gastric Acid Secretion

SLC26A7 (human)/Slc26a7 (mouse) is a recently identified chloride-base exchanger and/or chloride transporter that is expressed on the basolateral membrane of acid-secreting cells in the renal outer medullary collecting duct (OMCD) and in gastric parietal cells. Here, we show that mice with genetic deletion of Slc26a7 expression develop distal renal tubular acidosis, as manifested by metabolic acidosis and alkaline urine pH. In the kidney, basolateral Cl⁻/HCO₃⁻ exchange activity in acid-secreting intercalated cells in the OMCD was significantly decreased in hypertonic medium (a normal milieu for the medulla) but was reduced only mildly in isotonic medium. Changing from a hypertonic to isotonic medium (relative hypotonicity) decreased the membrane abundance of Slc26a7 in kidney cells in vivo and in vitro. In the stomach, stimulated acid secretion was significantly impaired in isolated gastric mucosa and in the intact organ. We propose that SLC26A7 dysfunction should be investigated as a potential cause of unexplained distal renal tubular acidosis or decreased gastric acid secretion in humans.The collecting duct segment of the distal kidney nephron plays a major role in systemic acid base homeostasis by acid secretion and bicarbonate absorption. The acid secretion occurs via H⁺-ATPase and H-K-ATPase into the lumen and bicarbonate is absorbed via basolateral Cl⁻/HCO₃⁻ exchangers (1–4). The tubules, which are located within the outer medullary region of the kidney collecting duct (OMCD),² have the highest rate of acid secretion among the distal tubule segments and are therefore essential to the maintenance of acid base balance (2).The gastric parietal cell is the site of generation of acid and bicarbonate through the action of cytosolic carbonic anhydrase II (5, 6). The intracellular acid is secreted into the lumen via gastric H-K-ATPase, which works in conjunction with a chloride channel and a K⁺ recycling pathway (7–10). The intracellular bicarbonate is transported to the blood via basolateral Cl⁻/HCO₃⁻ exchangers (11–14).SLC26 (human)/Slc26 (mouse) isoforms are members of a conserved family of anion transporters that display tissue-specific patterns of expression in epithelial cells (15–24). Several SLC26 members can function as chloride/bicarbonate exchangers. These include SLC26A3 (DRA), SLC26A4 (pendrin), SLC26A6 (PAT1 or CFEX), SLC26A7, and SLC26A9 (25–31). SLC26A7 and SLC26A9 can also function as chloride channels (32–34).SLC26A7/Slc26a7 is predominantly expressed in the kidney and stomach (28, 29). In the kidney, Slc26a7 co-localizes with AE1, a well-known Cl⁻/HCO₃⁻ exchanger, on the basolateral membrane of (acid-secreting) A-intercalated cells in OMCD cells (29, 35, 36) (supplemental Fig. 1). In the stomach, Slc26a7 co-localizes with AE2, a major Cl⁻/HCO₃⁻ exchanger, on the basolateral membrane of acid secreting parietal cells (28). To address the physiological function of Slc26a7 in the intact mouse, we have generated Slc26a7 ko mice. We report here that Slc26a7 ko mice exhibit distal renal tubular acidosis and impaired gastric acidification in the absence of morphological abnormalities in kidney or stomach.     

The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]