The size distribution of insertions and deletions in human and rodent pseudogenes suggests the logarithmic gap penalty for sequence alignment

The size distributions of deletions, insertions, and indels (i.e., insertions or deletions) were studied, using 78 human processed pseudogenes and other published data sets. The following results were obtained: (1) Deletions occur more frequently than do insertions in sequence evolution; none of the pseudogenes studied shows significantly more insertions than deletions. (2) Empirically, the size distributions of deletions, insertions, and indels can be described well by a power law, i.e., f _k = Ck ^–b , where f _k is the frequency of deletion, insertion, or indel with gap length k, b is the power parameter, and C is the normalization factor. (3) The estimates of b for deletions and insertions from the same data set are approximately equal to each other, indicating that the size distributions for deletions and insertions are approximately identical. (4) The variation in the estimates of b among various data sets is small, indicating that the effect of local structure exists but only plays a secondary role in the size distribution of deletions and insertions. (5) The linear gap penalty, which is most commonly used in sequence alignment, is not supported by our analysis; rather, the power law for the size distribution of indels suggests that an appropriate gap penalty is w _k = a + b ln k, where a is the gap creation cost and blnk is the gap extension cost. (6) The higher frequency of deletion over insertion suggests that the gap creation cost of insertion (a _i ) should be larger than that of deletion (a _d ); that is, a _i – a _d = In R, where R is the frequency ratio of deletions to insertions. Correspondence to: W.-H. Li     

Measurement of Cerebral Glucose Utilization Using Washout After Carotid Injection in the Rat

Abstract: The carotid injection technique, used previously to quantitate the kinetics of blood-brain barrier transport of metabolic substrates, may be modified to analyze the rate of cerebral glucose utilization. A 0.2-ml solution of [¹⁴C]glucose (G_F) and [³H]methylglucose (M), an internal reference, is rapidly injected into the carotid artery, followed by microwave fixation of brain at various times up to 4 min after injection. The brain radioactivity is separated into a fraction containing neutral hexoses (G_F and M) and a fraction containing metabolites of glucose. The G_F/M ratio is related to the rate constant (k₃) of brain glucose utilization by the simple, linear equation: In(G_F/M) = In(G_F°/M°) –k₃t, where G_F°/M°= the brain uptake index of glucose, relative to methylglucose, at 5-15 s after injection, and t= the time after carotid injection, e.g., 1–4 min. It is assumed that (a) the rate of influx due to recirculation of label is minimal during the 4-min circulation period; and (b) the rate constants of glucose efflux (k₂) and methylglucose efflux (k₂*) are identical. Independent estimates of k₂ and k₂* showed these parameters to be identical: k₂= 0.14 + 0.08 min-I; k₂*= 0.14 ± 0.02 min-I. A logarithmic plot of G_F/M ratios versus time was linear (r = 0.99), and was described by the slope k₂= 0.21 ± 0.02 min^?1. Assuming glucose is uniformly distributed in brain, then the glycolytic rate = k₃× brain glucose = (0.21 min^?1) (2.6 μmol g^?1) = 0.55 μmol min^?1 g^?1 for the cortex of the barbiturate-anesthetized rat. These studies provide the basis for a simple method of measurement of regional brain glycolysis that does not require either the use of correction factors, e.g., the lumped constant, or the use of differentially labeled glucose.     

Distance-constraint approach to protein folding. I. Statistical analysis of protein conformations in terms of distances between residues

A statistical analysis of protein conformations in terms of the distance between residues, represented by their C atoms, is presented. We consider four factors that contribute to the determination of the distanced _i,i+k between a given pair ofith and(i+k)th residues in the native conformation of a globular protein: (1) the distancek along the chain, (2) the size of the protein, (3) the conformational states of theith to(i+k)th residues, and (4) the amino acid types of the and(i+k)th residues. In order to account for the dependence on the distancek along the chain, the statistics are taken for three ranges, viz., short, medium, and long ranges (k8; 9k20; andk21; respectively). In the statistics of short-range distances, a mean distanceD _k and its standard deviationS _k are calculated for each value ofk, with and without taking into account the conformational states of all residues fromi toi+k (factors 1 and 3). As an Appendix, the relations for converting from the distances between residues into other conformational parameters are discussed. In the statistics of long-range distances, a reduced distanced* _ij (the actual distance divided by the radius of gyration) is used to scale the data so that they become independent of protein size, and then a mean reduced distanceD _l (a, a) and its standard deviation _l (a, a) are calculated for each amino acid pair (a, a) (factors 2 and 4). The effect of the neighboring residues along the chain on the value of the distanced* _ij is explored by a linear regression analysis between the actual reduced distanced* _ij and the mean value over theD _l for all possible pairs of residues in the two segments of the (i–2)th to the (i+2)th and the (j–2)th to the (j+2)th residues. The effect is assessed in terms of the tangentA _l (a, a) of the calculated regression line for each amino acid pair (a, a). In the statistics of medium-range distances, only factors 1 and 4 are considered, to simplify the analysis. The scaled distanced _i,i+k =(d _i,i+k -D _k )/S _k is used to eliminate the dependence onk, the distance along the chain. The propertiesD _m (a, a), _m (a, a) andA _m (a, a) corresponding toD _l (a, a), _l (a, a), andA _l (a, a), and also calculated for each amino acid pair (a, a). The results are interpreted as follows: the smaller values ofD _l (a, a) andD _m (a, a) indicate a preference of the pair (a, a) for a contact (e.g., pairs between hydrophobic amino acids, and pairs of Cys with aromatic amino acids), and the larger values of these quantities indicate a preference for distant mutual location (e.g., pairs between strong hydrophilic amino acids); the smaller values of _l (a, a) and _m (a, a) indicate a strong preference for either contact or noncontact (e.g., pairs between hydrophobic amino acids, and pairs between strong hydrophobic and hydrophilic amino acids, respectively), and the larger values of these quantities indicate the ambivalent/neutral nature of the preference for contact and noncontact (e.g., pairs containing Ser or Thr); the smaller values ofA _l (a, a) andA _m (a, a) indicate that the distance of an (a, a) pair is determined independently of the amino acid character of the neighboring residues along the chain (e.g., some pairs of Cys or Met with other amino acids) and the larger values of these quantities indicare that such amino acid character contributes strongly to the determination of the distance (e.g., pairs containing Ser or Thr, and pairs between amino acids with small side chains). The difference between the statistics for the long- and medium-range distances is also discussed; the former reflect the difference between the hydrophobic and hydrophilic character of the residues, but the latter cannot be easily interpretable only in terms of hydrophobicity and hydrophilicity. The data analyzed here are used in the optimization of an object function to compute protein conformation in a subsequent paper.     

Application of fuzzy multiobjective optimization for self-sucking impellers in a bioreactor

For three types of self-sucking impellers (fourand six-pipe and disk impellers) mixing power, initial point, amount of gas leaving the impeller and mass transfer coefficient were determined experimentally. Investigations were performed for two systems: water and biomass solution.From the point of view of a minimum mixing power and maximum mass transfer coefficient the best impeller has been chosen. Fuzzy multiobjective optimization for determination of optimum operating conditions is proposed.List of Symbols c concentration of oxygen - D tank diameter - d impeller diameter - g acceleration of gravity - H height of liquid in the tank - H height of liquid above impeller, H=H-y - k consistency coefficient - k _L a volumetric mass transfer coefficient - N rotational speed of impeller - n flow behaviour index - P mixing power for pure liquid - P _G mixing power for aerated liquid - V _G volumetric air flow rate - y distance of impeller from the tank bottom - v _a apparent kinematic viscosity of liquid - density of liquid - time - gas hold-up - Eu=P/N ³ d ⁵ or Eu_G=P _G /N ³ d ⁵ Euler Number for non-gassed or aerated liquid - Fr=N ² d/g Froude Number - Fr^*=N ² d ² /g(H -y) modified Froude Number - K_G=V _G /N d ³ gas flow number - Re=N d ² /v _a Reynolds Number - Sh=k _K a/(g ² /v _a )^1/3 Sherwood Number     

Comparison of oxygen mass transfer coefficient in simple and extractive fermentation systems

Aeration and agitation are important variables to ensure effective oxygen transfer rate during aerobic bioprocesses; therefore, the knowledge of the volumetric mass transfer coefficient (k_La) is required. In view of selecting the optimum oxygen requirements for extractive fermentation in aqueous two-phase system (ATPS), the k_La values in a typical ATPS medium were compared in this work with those in distilled water and in a simple fermentation medium, in the absence of biomass. Aeration and agitation were selected as the independent variables using a 2² full factorial design. Both variables showed statistically significant effects on k_La, and the highest values of this parameter in both media for simple fermentation (241 s⁻¹) and extractive fermentation with ATPS (70.3 s⁻¹) were observed at the highest levels of aeration (5 vvm) and agitation (1200 rpm). The k_La values were then used to establish mathematical correlations of this response as a function of the process variables. The exponents of the power number (N³D²) and superficial gas velocity (V_s) determined in distilled water (α = 0.39 and β = 0.47, respectively) were in reasonable agreement with the ones reported in the literature for several aqueous systems and close to those determined for a simple fermentation medium (α = 0.38 and β = 0.41). On the other hand, as expected by the increased viscosity in the presence of polyethylene glycol, their values were remarkably higher in a typical medium for extractive fermentation (α = 0.50 and β = 1.0). A reasonable agreement was found between the experimental data of k_La for the three selected systems and the values predicted by the theoretical models, under a wide range of operational conditions.     

Automatic detection of anchor points for multiple sequence alignment

Background  

Determining beforehand specific positions to align (anchor points) has proved valuable for the accuracy of automated multiple sequence alignment (MSA) software. This feature can be used manually to include biological expertise, or automatically, usually by pairwise similarity searches. Multiple local similarities are be expected to be more adequate, as more biologically relevant. However, even good multiple local similarities can prove incompatible with the ordering of an alignment.     

Split-inducing indels in phylogenomic analysis

Background

Most phylogenetic studies using molecular data treat gaps in multiple sequence alignments as missing data or even completely exclude alignment columns that contain gaps.

Results

Here we show that gap patterns in large-scale, genome-wide alignments are themselves phylogenetically informative and can be used to infer reliable phylogenies provided the gap data are properly filtered to reduce noise introduced by the alignment method. We introduce here the notion of split-inducing indels (splids) that define an approximate bipartition of the taxon set. We show both in simulated data and in case studies on real-life data that splids can be efficiently extracted from phylogenomic data sets.

Conclusions

Suitably processed gap patterns extracted from genome-wide alignment provide a surprisingly clear phylogenetic signal and an allow the inference of accurate phylogenetic trees.

     

Carbachol-mediated pigment granule dispersion in retinal pigment epithelium requires Ca<Superscript>2+</Superscript> and calcineurin

Background  

Inside bluegill (Lepomis macrochirus) retinal pigment epithelial cells, pigment granules move in response to extracellular signals. During the process of aggregation, pigment motility is directed toward the cell nucleus; in dispersion, pigment is directed away from the nucleus and into long apical processes. A number of different chemicals have been found to initiate dispersion, and carbachol (an acetylcholine analog) is one example. Previous research indicates that the carbachol-receptor interaction activates a G_q-mediated pathway which is commonly linked to Ca²⁺ mobilization. The purpose of the present study was to test for involvement of calcium and to probe calcium-dependent mediators to reveal their role in carbachol-mediated dispersion.     

A rebuttal to the comments on the genome order index and the <Emphasis Type="Italic">Z</Emphasis>-curve

Background  

Elhaik, Graur and Josic recently commented on the genome order index (S) and the Z-curve (Elhaik et al. Biol Direct 2010, 5: 10). S is a quantity defined as S = a ² + c ² + g ² + t ², where a, c, g and t denote corresponding base frequencies. The Z-curve is a three dimensional curve that represents a DNA sequence in the manner that each can be uniquely reconstructed given the other. Elhaik et al. made 4 major claims. 1) In the previous mapping system with the regular tetrahedron, calculation of the radius of the inscribed sphere is "a mathematical error". 2) S follows an exponential distribution and is narrowly distributed with a range of (0.25 - 0.33). 3) Based on the Chargaff's second parity rule (PR2), "S is equivalent to H [Shannon entropy]" and they are derivable from each other. 4) Z-curve "suffers from over dimensionality", because based on the analysis of 235 bacterial genomes, x and y components contributed only less than 1% of the variance and therefore "would be of little use".     

Long-term acid-induced wall extension in an in-vitro system

When frozen-thawed Avena sativa L. coleoptile and Cucumis sativa L. hypocotyl sections, under tension, are acid-treated, they undergo rapid elongation (acid-extension). The acid-extension response consists of two concurrent phases: a burst of extension which decays exponentially over 1–2 h (ExE), and a constant rate of extension (CE) which can persist for at least 6 h. The extension (AL) is closely represented by the equation: L = a – a · e ^kt + c · t where a is the total extension of the exponential phase, k is the rate constant for ExE, and c is the rate of linear extension (CE). Low pH and high tension increased a and c, whereas temperature influenced k. The magnitude of the CE (over 50% extension/10 h), the similarity in its time course to auxin-induced growth, and the apparent yield threshold for CE indicate that CE is more likely than ExE to be the type of extension which cell walls undergo during normal auxin-induced growth.Abbreviations and symbols CAWL capacity for acid-induced wall extension - CE linear phase of acid-extension - ExE exponential phase of acid-extension - IAA indole-3-acetic acid     

Generalizations of Markov model to characterize biological sequences

Background  

The currently used k ^th order Markov models estimate the probability of generating a single nucleotide conditional upon the immediately preceding (gap = 0) k units. However, this neither takes into account the joint dependency of multiple neighboring nucleotides, nor does it consider the long range dependency with gap>0.     

Experimental Design for Bayesian Estimations in the Linear Regression Model Taking Costs into Account

It is assumed that a known, correct, linear regression model (model I) is given. Let the problem be based on a Bayesian estimation of the regression parameter so that any available a priori information regarding this parameter can be used. This Bayesian estimation is, squared loss, an optimal strategy for the overall problem, which is divided into an estimation and a design problem. For practical reasons, the effort involved in performing the experiment will be taken into account as costs. In other words, the experimental design must result in the greatest possible accuracy for a given total cost (restriction of the sample size n). The linear cost function k(x) = 1 + c (x - a)/(b - a) is used to construct costoptimal experimental designs for simple linear regression by means of V = H = [a, b] in a way similar to that used for classical optimality criteria. The complicated structures of these designs and the difficulty in determining them by a direct approach have made it appear advisable to describe an iterative procedure for the construction of cost-optimal designs.     

A generalized affine gap model significantly improves protein sequence alignment accuracy

Sequence alignment underpins common tasks in molecular biology, including genome annotation, molecular phylogenetics, and homology modeling. Fundamental to sequence alignment is the placement of gaps, which represent character insertions or deletions. We assessed the ability of a generalized affine gap cost model to reliably detect remote protein homology and to produce high-quality alignments. Generalized affine gap alignment with optimal gap parameters performed as well as the traditional affine gap model in remote homology detection. Evaluation of alignment quality showed that the generalized affine model aligns fewer residue pairs than the traditional affine model but achieves significantly higher per-residue accuracy. We conclude that generalized affine gap costs should be used when alignment accuracy carries more importance than aligned sequence length.     

An application of conditional logistic regression and multifactor dimensionality reduction for detecting gene-gene Interactions on risk of myocardial infarction: The importance of model validation

Background  

To examine interactions among the angiotensin converting enzyme (ACE) insertion/deletion, plasminogen activator inhibitor-1 (PAI-1) 4G/5G, and tissue plasminogen activator (t-PA) insertion/deletion gene polymorphisms on risk of myocardial infarction using data from 343 matched case-control pairs from the Physicians Health Study. We examined the data using both conditional logistic regression and the multifactor dimensionality reduction (MDR) method. One advantage of the MDR method is that it provides an internal prediction error for validation. We summarize our use of this internal prediction error for model validation.     

Comparative analysis of oxygen transfer rate distribution in stirred bioreactor for simulated and real fermentation broths

Study of the distribution of the oxygen mass transfer coefficient, k _l a, for a stirred bioreactor and simulated (pseudoplastic solutions of carboxymethylcellulose sodium salt) bacterial (P. shermanii), yeast (S. cerevisiae), and fungal (P. chrysogenum free mycelia) broths indicated significant variation of transfer rate with bioreactor height. The magnitude of the influence of the considered factors differed from one region to another. As a consequence of cell adsorption to bubble surface, the results indicated the impossibility of achieving a uniform oxygen transfer rate throughout the whole bulk of the microbial broth, even when respecting the conditions for uniform mixing. Owing to the different affinity of biomass for bubble surface, the positive influence of power input on k _l a is more important for fungal broths, while increasing aeration is favorable only for simulated, bacterial and yeast broths. The influence of the considered factors on k _l a were included in mathematical correlations established based on experimental data. For all considered positions, the proposed equations for real broths have the general expression k_l a = aC_X^b ( \fracP_a V )^g v_S^d , k_{\rm l} a = \alpha C_{\rm X}^{\beta } \left( {{\frac{{P_{\rm a} }}{V}}} \right)^{\gamma } v_{\rm S}^{\delta } , exhibiting good agreement with experimental results (with maximum deviations of ±10.7% for simulated broths, ±8.4% for P. shermanii, ±9.3% for S. cerevisiae, and ±6.6% for P. chrysogenum).     

In situ absorption of aflatoxins in rat small intestine

To evaluate the rate at which the four main aflatoxins (aflatoxins B₁, B₂, G₁ and G₂) are able to cross the luminal membrane of the rat small intestine, a study about intestinal absorption kinetics of these mycotoxins has been made. In situ results obtained showed that the absorption of aflatoxins in rat small intestine is a very fast process that follows first-order kinetics, with an absorption rate constant (k _a ) of 5.84±0.05 (aflatoxin B₁), 4.06±0.09 (aflatoxin B₂), 2.09±0.03 (aflatoxin G₁) and 1.58±0.04 (aflatoxin G₂) h^–1, respectively.     

Theoretical study and rate constant calculations for the reactions of SiHX3 with CF3 and CH3 radicals (X = F, Cl)

Theoretical investigations were carried out on the multi-channel reactions CF₃ + SiHF₃, CF₃ + SiHCl₃, CH₃ + SiHF₃, and CH₃ + SiHCl₃. Electronic structures were calculated at the MP2/6-311+G(d,p) level, and energetic information further refined by the MC-QCISD (single-point) method. The rate constants for major reaction channels were calculated by the canonical variational transition state theory with small-curvature tunneling correction over the temperature range of 200–1,500 K. The theoretical rate constants were in good agreement with the available experimental data and were fitted to the three parameter expression: k _1a(T) = 2.93 × 10^?26 T ^4.25 exp (?318.68/T), and k _2a(T) = 3.67 × 10^?22 T ^2.72 exp (?1,414.22/T), k _3a (T) = 7.00 × 10^?24 T ^3.27 exp (?384.04/T), k _4a(T) = 6.35 × 10^?22 T ^2.59 exp (?603.18/T) (in unit of cm³molecule^?1s^?1) are given. Our calculations indicate that hydrogen abstraction channel is the major channel due to the smaller barrier height among four channels considered.

Kinetic analyses for bindings of concanavalin A to dispersed and condensed mannose surfaces on a quartz crystal microbalance

A biotinylated mannotriose (Man3-bio) was dispersively immobilized in the matrix of biotinylated lactose (Gal-Glc-bio) on a streptavidin-covered, 27-MHz quartz crystal microbalance (QCM), and binding kinetics of concanavalin A (Con A) to Man3-bio in the Gal-Glc-bio matrix could be obtained from frequency decreases (mass increases) of the QCM. Association constants (K_a) and binding and dissociation rate constants (k_on and k_off) could be determined separately as the 1:1 and 1:2 bindings of Con A to Man3-bio on the surface. When Man3-bio was immobilized with content of 1 to 5 mol% in the matrix, the 1:1 binding of Con A to Man3-bio was obtained as K_a = (4 ± 1) × 10⁶ M⁻¹, k_on = (4 ± 1) × 10⁴ M⁻¹ s⁻¹, and k_off = (12 ± 2) × 10^–3 s⁻¹. On the contrary, when Man3-bio was immobilized with content of 20 to 100 mol% in the matrix, the 1:2 binding of Con A to Man3-bio was obtained as K_a = (14 ± 2) × 10⁶ M⁻¹, k_on = (14 ± 2) × 10⁴ M⁻¹ s⁻¹, and k_off = (7 ± 2) × 10^–3 s⁻¹. Thus, K_a for the 1:2 binding was 10 times larger than that for the 1:1 binding, with a three times larger binding rate constant (k_on) and a three times smaller dissociation rate constant (k_off). This is the first example to obtain separate kinetic parameters for the 1:1 and 1:2 bindings of lectins to carbohydrates on the surface.