The power of this approach strongly depends on the quality of the multiple alignments, which thus motivated development of theoretical concepts and strategies to improve alignment of conserved motifs within large sets of distantly related sequences.Certain protein families are highly conserved across distantly related organisms and belong to large and functionally diverse superfamilies. The patterns of conservation present in these protein sequences presumably are due to selective constraints maintaining important but unknown structural mechanisms with some constraints specific to each family and others shared by a larger subset or by the entire superfamily. To exploit these patterns as a source of functional information, we recently devised a statistically based approach called α isubunits, prolyl oligopeptidases, and transitional endoplasmic reticulum (p97) AAA+ ATPases.Here we describe a hidden Markov model (HMM), an algebraic system, and Markov chain Monte Carlo (MCMC) sampling strategies for alignment of multiple sequence motifs. The MCMC sampling strategies are useful both for alignment optimization and for adjusting position specific background amino acid frequencies for alignment uncertainties. Associated statistical formulations provide an objective measure of alignment quality as well as automatic gap penalty optimization. Improved alignments obtained in this way are compared with PSI-BLAST based alignments within the context of CHAIN analysis of three protein families: GWhile not entirely replacing PSI-BLAST based alignments, which likewise may be optimized for CHAIN analysis using this approach, these motif-based methods often more accurately align very distantly related sequences and thus can provide a better measure of selective constraints. In some instances, these new approaches also provide a better understanding of family-specific constraints, as we illustrate for p97 ATPases. Programs implementing these procedures and supplementary information are available from the authors. As the genome projects continue to generate sequence data, it is increasingly common to find protein superfamilies with thousands of members in the protein database. Given sufficient numbers of sequences, sensitive iterative search and alignment procedures, such as PSI-BLAST  and SAM contrast hierarchical alignment and interaction network (CHAIN) analysis [Certain protein families within these large superfamilies are often very highly conserved across distantly related organisms. Such proteins include, for example, certain metabolic enzymes, DNA replication and repair factors, certain structural proteins, such as actin, the motor protein dynein, and regulatory and signalling factors, such as protein kinases and Ras-like GTPases. While many of these proteins seem relatively well characterized, we still cannot account for the strong selective constraints preserving their observed high degree of sequence conservation across major taxonomic groups. Presumably these patterns of conservation contain implicit information regarding still unknown functional mechanisms. To access this information, we recently developed a statistically based approach, called analysis , that idAligning distantly related sequences presents unique algorithmic and statistical challenges because such proteins often only share a minimal structural core with sizable insertions occurring between, and even within, core elements. Classical dynamic programming-based multiple alignment procedures typically have considerable difficulty spanning across these insert regions because the log-odds scores associated with weakly conserved core elements are often too low to offset the substantial gap penalties that such insert regions incur. This problem is further exacerbated when core elements contain short insertions or deletions within them.To address this problems, we previously devised motif (or block) based multiple alignment procedures -6 that cAltschul at the National Center for Biotechnology Information (NCBI) likewise sought to address this problem through generalized affine gap costs , but theMore relevant to our purpose here, another drawback to the use of MUSCLE, MAFFT, and similar programs for CHAIN analysis is that these will align randomly generated sequences – a characteristic incompatible with the statistical basis of CHAIN analysis. MUSCLE and MAFFT perform well on small sets of relatively diverse representative sequences, such as the BALIBASE benchmark sets , becauseTo maintain statistical rigor in our formulations here, we will 'let the data speak' by modelling only those characteristics of the sequences that can be justified by the input data. Such an approach cannot be applied, however, to small benchmark alignment sets, because these lack sufficient sequences – less than the number of amino acids whose parameters are being estimated. Thus, while a rigorous statistical approach has severe limitations when applied to small datasets, it works very well when applied to large, diverse sets of distantly related sequences, as demonstrated, for example, by some of our earlier analyses -16.Two other theoretical issues, which are important to the multiple alignment problem, are devising an objective measure of alignment quality and an efficient strategy for finding the best alignments based on this measure. Our previous methods -6 addresα subunits, which belong to the P loop GTPase class [α,β-hydrolase fold class [Within this MCMC sampler we implemented specific operations on the alignments, including those allowing for realignment of a sequence against the alignment model, shortening or lengthening of blocks, and creation of recombinant alignments. Such operations function like catalysts to help the sampler avoid or more quickly escape from local optima. Here we expand on the number of these operations and modify our Bayesian model to allow for short insertions or deletions within blocks. In theory, such an approach could be used to sample representative multiple alignments from the posterior distribution, which is relevant to CHAIN analysis because this could be used to adjust position-specific amino acid frequencies for alignment uncertainty. Doing so for the model and operations described here, however, is non-trivial and thus is a topic for a future publication built upon this one. Our primary objective here is merely to obtain the optimal alignment. Thus we also introduce various annealing-like strategies for luring the sampler toward optimum alignments. These include simulated annealing, which is applied within sampling routines, and other intervention strategies. Our primary motivation for developing and implementing these concepts and strategies is to improve CHAIN analysis, as is illustrated here for G-protein se class , prolyl ld class ,19, and ld class , which bld class -23 withild class ,24,25.The fundamental problem addressed here is to identify the essential features – the common structural core – characteristic of a large set of distantly related proteins. Given an input sequence set, we build a Bayesian statistical model with adjustable parameters to reflect the relationships among the proteins. We also design a stochastic search algorithm, with an MCMC sampler as its backbone, to explore possible alignments and corresponding model parameters in order to find alignment models that best 'explain' the input data. The model parameters specify, for example, the number and lengths of the motifs, their locations within each sequence, the residue frequencies observed at each position in each motif, and other properties (described below). We may thus envision our sampler as searching through a discrete space where each point, corresponding to a particular alignment, has a probability associated with it. The probability function appears fairly smooth inasmuch as nearby points  have roughly comparable probabilities. As the sampler traverses from one point to another, it favors moves toward the better alignments, that is, toward that part of the alignment space with greater posterior probability. Since it is computationally prohibitive for the sampler to consider many transitions at one time, a key design issue is the selection of allowed transitions between points.We first define the alignment model in precise mathematical terms, which provides a scoring scheme that allows us to judge which alignment is better than another. Here, for the sake of conciseness and readability, we will keep the discussion on a conceptual level whenever possible. Interested readers can consult our earlier publications for further details ,6.m co-linear ungapped motifs, of width w1,... , wm, respectively. Each motif is modelled by a position specific frequency matrix Θi, whereas residues outside the motif blocks follow a common frequency distribution. Independent prior Dirichelet distributions are employed for these frequency parameters.As illustrated in Fig. m and the wi's are unknown, we assume that they are uniformly distributed in a certain range a priori . Then the posterior alignment distribution is:Since both ori (see  for detaP(A | S) ∝ P(A) ∫ P(S | A, Θ)P(Θ)dΘ.P(A | S), the so-called "maximum a posteriori (MAP)" score.Based on this distribution, our algorithm (as implemented in the PROBE program ) attemptg(A, Λ), are given in Methods.A major drawback of the previous block-based alignment approach is that it disallows insertions or deletions within motif blocks. Here we describe hidden Markov model (HMM) ,27 strucp = 1/220) and an extension penalty of 2.5 bits. Then, for example, the overall MAP would need to improve by 25 bits in order to justify a 'surgical operation' on a sequence involving an insertion of three dummy residue (i.e., to 'correct' a deletion in a sequence) or a deletion of three residues (i.e., to 'correct' an insertion in a sequence). The statistical problem is thus that of finding the right penalty so that the sampler only adds insertions or deletions when the data provides sufficient justification. In a Bayesian context, this justification is based on the posterior inference of the overall number of insertions and deletions from what it finds in the aligned sequences.For an intuitive notion of how within-motif penalties influence the total MAP score, consider a gap-opening penalty of say 20 bits  slightly, with the equilibrium distribution of the chain being the target distribution. A MCMC scheme is usually constructed in two steps: (i) propose a new state according to a certain reversible transition rule, and (ii) accept or reject the proposal according to the probability ratio between the proposed and the current states .g(A, Λ) one obtains a set of "typical" alignments according to the posterior distribution, which provides information regarding the most likely alignment(s) supported by the data and its variability. In practice, however, one may wish to find the optimum of this function and explore only around this optimum considering the difficulty of summarizing a set of distinct alignments in a meaningful way. MCMC is also an important ingredient of an optimization technique termed "simulated annealing" [The broad utility and general applicability of these techniques are exemplified and popularized by recent developments in statistics: if one can sample from nealing" , of whicThe elementary mathematical operations of addition and subtraction define a means of transitioning between points in the discrete space of natural numbers. "Global" operations, such as multiplication and integer division, allow transitions between more distant points in this space. Likewise, we define both elementary and global operations on multiple alignments as a means of transitioning between points in alignment space. In this case a set of unaligned sequences  serves the same role as the natural number zero. Formal mathematical descriptions of the alignment and of certain simple operations are provided in our earlier papers ,6. SinceThere are two issues to consider in the design of multiple alignment operations. First, the reversibility of MCMC algorithms requires that every operation have an "inverse" so that the sampler can readily transit in either direction. Second, to help find the optimal alignment according to our Bayesian model, which is our main objective, annealing techniques and less restrictive acceptance rules should be considered for certain complex operations. By doing so the target alignment distribution has to be distorted to some degree, though the global optimum of the distribution remains the same.All alignments described here are collinear multiple alignments (CMAs), which are defined to contain zero or more motif blocks arranged collinearly in each sequence. Partial or complete deletion of any motif from a particular sequence is modelled by aligning that motif against null residues ('-'), which the sampler may insert anywhere in the sequence. Sequences may also contain more than one repeat of the entire protein domain, each of which is modelled by the full set of motifs.  For clarity, we describe operations deterministically, though it should be kept in mind that our sampler applies these stochastically.The HideInsert operation (inverse ShowInsert) is applied to 'surgically' remove a region of the sequence that appears to correspond to a typically short insertion within a conserved motif. This operation thus changes the real sequence into an idealized sequence that, presumably, more closely resembles the canonical characteristics of the protein class. As a result, the sampler needs to maintain both a real and an idealized version of each protein's sequence and to store the operational derivation used to obtain the ideal sequence from the real. Algorithmically it is convenient to deal with insert regions in this way because otherwise the sampler would need to look up the locations of insertions and deletion within each sequence when applying other operations. The FillDeletion operation (inverse UnfillDeletion) likewise converts a sequence that contains a deletion of either part of or all of a motif into an idealized sequence in which the deletion has been filled in with null or 'dummy' ('-') residues. Note that HideInsert and FillDeletion merely define data structure interconversions that allow basic operations, which were initially defined for ungapped motifs, to be efficiently applied to gapped motifs.disallow gaps within motif blocks.The Align operation assigns motif positions within a sequence and thereby adds that sequence to the alignment, UnAlign removes the sequence from the alignment. Note that these operations The AddColumn and DeleteColumn operations add and remove aligned columns, respectively. Note that these operations may add or remove columns internal to a motif as well as at the edges. Moreover, AddColumn may also insert a column an arbitrary number of residues beyond the current edge of a motif. This is important for motif 'fragmentation' ,30, a prElementary operations can be combined in a coordinated manner in various ways to produce compound operations that better facilitate escape from local traps. For example, GapAlign (inverse UngapAlign) combines the row operation Align with the sequence operations HideInsert and FillDeletion in order to add a sequence to an alignment with insertions and deletions. The GapAlign operation is performed using dynamic programming to obtain a gapped alignment of a sequence against a statistical model of the current alignment. The trace back procedure determines how to apply the HideInsert and FillDeletion operations to the true sequence and how the Align operation is then applied to the resultant idealized sequence.We define several compound operations on a motif block: AddBlock, ShiftRight, and TrimRight (with inverses: DeleteBlock, ShiftLeft, and TrimLeft, respectively). Another compound operation, MoveColumn, which transfers a column from one position to another within a block, is its own inverse. Conceptually, AddBlock and DeleteBlock simply iteratively apply the AddColumn and DeleteColumn operations, respectively. Because our motif alignments are collinear, the position of an added block within each idealized sequence must be specified in a manner consistent with this collinear arrangement and, in order to add a new block in this way, the sampler may need to insert null residues at certain positions within some of the idealized sequences. This is an example of operational flexibility. Similar operational flexibility is required for the ShiftRight and ShiftLeft operations, which remove one or more columns from one end and append it to the other end of a motif. TrimRight and TrimLeft allow poorly conserved residues to be trimmed from a motif block based on their relative entropy. These operations thus provide a means to manually edit motif-based alignments as discussed below.Three compound operations involving two motif blocks are: TransferColumn, Splitblock and FuseBlocks. TransferColumn deletes a column from one block and adds it to another block. Splitblock splits a single block into two leaving two contiguous motif blocks in each of the idealized sequences. During future realignment operations the sampler typically induces these abutted blocks to drift apart. Splitblock's inverse operation, FuseBlocks, merges two blocks into one, which typically requires forced realignment of motif positions in each sequence in order to join the blocks together. All such forced realignments are followed by additional optimization via sampling prior to deciding whether to reject or accept this new configuration. We thus typically have to violate the MCMC's acceptance-rejection rule to enable such a move, which distorts the target distribution. The awkwardness of this procedure may be advantageous, however, inasmuch as it forces the sampler out of local traps in alignment space. Fig. As an aid to locating the optimum alignment, we define recombination operations that combine the best features of two distinct, fairly well refined alignments. These operations require that the sampler first generate a population of fairly well refined alignments starting from distinct, randomly selected points in alignment space. All of these input alignments must, of course, contain the same set of sequences.The Recombine operation must be applied to two alignments that are fairly similar because the sampler needs to locate at least one crossover point between them. A crossover point is a set of positions, one position in each aligned sequence, such that the same set of blocks in the first alignment lie to the left of each of those points, while the same set of blocks in the second alignment lie to the right of each point. Because this requirement often proves difficult to satisfy for every sequence, we define the Recombine operation flexibly by allowing a certain number of sequences to violate this rule. In this case, violating sequences are removed prior to recombination and sampled back in afterwards (using the GAPALIGN operation).both input alignments. More precisely, we first find the common blocks shared by the two alignments, where a common block is defined as two aligned motif blocks  that overlap within corresponding sequences. To allow for some flexibility, these are defined as blocks for which at least some minimum fraction (say 50%) of the sequences are consistently aligned in both input alignments.  Then, for each pair of common blocks, we find the sub-block shared by both blocks. Next, we create a new alignment containing only these Intersecting sub-blocks. Finally, sequences that were inconsistently aligned between the two starting alignments are sampled back into the resulting alignment. The Intersect operation allows the sampler to be reinitialized starting with a consensus alignment that aligns only those regions with high likelihood scores and eliminates those regions about which the sampler is less certain. Subsequent sampling will then extend these sub-blocks, add new blocks, and explore more extensively the alignment space.The Intersect operation takes as input two distinct alignments and produces a new alignment containing only those aligned columns common to corresponding motifs in There are no absolute rules on how to choose parameter settings for these algebraic operations, such as, for example, the maximum increase in motif length allowed during the MoveColumn operation or the number of disordered blocks to tolerate for the Recombine operation. We find, in fact, that it often matters little which settings are used and the slight degree to which it does matter depends on the particular protein class being analyzed. As a result, any biologically reasonable parameter settings work well. For example, since weakly conserved motifs are never a hundred residues long, motif blocks typically should be limited to no more than, say, fifty residues in length. Nevertheless our algorithm tolerates unreasonable parameter settings, because then it either simply rejects the corresponding alignment space transitions (though with some degradation in performance) and/or learns to avoid applying useless operations through its memory module, as described below.Having specified various operations on the alignment space, we now need to specify when and how often to apply them, as well as how to escape from local traps and thus to most rapidly converge on an optimum or nearly optimum alignment.Since some of the alignment operations are computationally expensive, it would be helpful to avoid applying them over and over again when this proves to be unfruitful. For example, if the sampler has already converged on the correct number of motifs, applying the AddBlock operation may be a waste of time. On the other hand, we don't want to eliminate any operation entirely, as at some point it may be useful. To do this we define both short-term and long-term sampling memories. The short-term memory allows a rapid response to sudden changes while the long-term memory adds stability so that the sampler does not over respond to short term trends. Details are given in Methods.π (X). As the sampler converges on near optimum alignments, typically it has difficulty 'dropping' into the global optimum of π (X) because the chance of selecting the highest probability alignment is still very small due to the sheer number of near optimum alignments. This is true for the same reason that the most likely outcome of obtaining exactly 5,000 heads and 5,000 tails in 10,000 flips of a fair coin is extremely unlikely.Let the target alignment distribution be denoted generically as π (X) to some exponent, renormalizing it and using the "powered-up" distribution, denoted as πT(X) ∝ πT 1/(X) with the "temperature" parameter varying from a very large value to near-zero, for sampling. This procedure is a key component of simulated annealing [T → 0), we raise the probability of high-density points and lower the probability of low-density points, so as to allow the best alignment to win out over alignments that are nearly as good. If the temperature is lower too abruptly, however, the sampler may get trapped in a sub-optimum alignment, so that the annealing strategy needs to be devised carefully.A standard way around this problem is to take power of nnealing , which hT = 1) probability densities of the sampled alignments. If the variance of log π (X) in a given number K of consecutive iterations at a given temperature is below a certain threshold (so that the posterior probabilities barely change), the sampler may be stuck in a  optimum, and the thermostat raises the temperature a bit. On the other hand, if the log π (X) are varying wildly and, in particular, if they are greatly diverging from the best (i.e., highest probability) alignment found thus far, then the sampler may be wandering away from near optimum alignments and the thermostat lowers the temperature. This approach thus attempts to keep the sampler just above its 'glass transition temperature' [Tg. Details are given in Methods.We have built a 'thermostat' into the sampler that keeps track of variations in the , the user can modify this parameter. As an alternative strategy, two or more programs may also be run in parallel until they both converge on the same alignment.When painting a picture, it is helpful to first draw a rough sketch so that details will end up in the right place relative to each other. Similarly the sampler uses the following progressive refinement strategy to avoid being too "shortsighted."g(A, Λ).There are five stages to this strategy. In the first stage, the sampler applies the Align operation, which aligns the sequences against contiguous ungapped blocks; it also applies compound ungapped motif operations. The initial numbers of block motifs and columns in each block are sampled from binomial distributions with means between roughly 5~10 blocks and 10~30 columns each, respectively. In the second stage, which is introduced after the sampler begins to converge on a local optimum under the ungapped block-motif model, elementary and compound column operations are introduced, which allow these ungapped blocks to 'fragment', thereby permitting nonconserved columns to be ignored by the alignment model . Recomba priori parameter settings chosen before the analysis, fails to split this motif into two blocks. In this case, a biologically more meaningful alignment may be achieved by manually intervening to split this ungapped region . To accommodate such tweaking, we thus allow manual application of various operations. We find that splitting and trimming of aligned blocks are particularly helpful in this regard. Such manually modified alignments then may be reintroduced into a population of similar alignments for recombination and selection via our genetic algorithm [Despite attempts to codify and fully automate optimization of a multiple sequence alignment, the algorithm may still create an alignment model that lacks certain properties observed to be biologically important for a particular class of proteins. Take the situation, for example, where a motif, which occurs as a single block in most of the proteins, is split in two by a sizable insertion in other proteins and where the sampler, due to the lgorithm  followedGibbs-like sampling with multiple operations), GARMA  and GAMBIT . GARMA recombines the output alignments provided by GISMO and then applies simulated annealing strategies on the recombinants. GAMBIT performs on a single alignment the same optimization procedures that GARMA performs on recombinants. Manual application of alignment operations may be performed using another program, TweakAln. These programs along with sample alignments are available from the authors. Multiple alignment of thousands of sequences in this way may take substantial time (e.g., overnight on a 10-processor Linux cluster), but this is not critical because, once performed for a particular protein class, such an alignment can be updated readily by seeding the sampler with a previously optimized alignment. Here we apply these programs to several large protein classes within the context of CHAIN analysis, which is our primary reason for generating such alignments.The theoretical concepts and strategies just described were implemented in the programs GISMO  the background are subject to strong constraints. In Figs α subunits. G proteins [α, a β and a γ subunit, that mediate transduction of extracellular signals to the cellular interior. As do many members of the P loop GTPase class, the Gα subunit functions as a binary switch that is turned on by binding GTP in response to the signal and thereby relays this information to downstream components of the pathway. This switch is turned off by hydrolysis of GTP to GDP, an event mediated by GTPase activating proteins (GAPs).We first examine in this way G protein proteins  are heteα subunits are unique among such GTPase switches inasmuch as their GAP domain is contained within the Gα polypeptide chain itself rather than existing as a distinct protein. This unique arrangement presents particular difficulties for CHAIN analysis because, during subsequent iterations, the PSI-BLAST algorithm tends to slightly overextend the alignment beyond Gα's region of homology to other P loop GTPases and into the C-terminal region of the GAP domain. As a result, the foreground patterns for the Walker A motif are mistakenly aligned against the C-terminal end of the GAP domain . AAA+ Ais known . These Aπ interaction with an alanine  to AAA+ ATPases Fig. , it intrWith a view to improving alignments for CHAIN analysis, we have enhanced our earlier motif-based methods by developing (i) a HMM for insertions and deletions within motifs, (ii) an expanded algebraic system of operations on multiple alignments and (iii) various annealing and sampling strategies that facilitate rapid convergence on optimum or near optimum alignments. Furthermore, our approach, due to its rigorous statistical basis, fills a gap left by current multiple alignment methods inasmuch as it aligns only those characteristics of the input sequences that may be justified statistically. Thus it is useful for statistical analysis of conserved patterns in multiple alignments. Our statistical model likewise provides objective criteria for evaluating curated alignments, thereby guiding manual application of various operations. In the future, our MCMC sampling methods could be used to estimate alignment uncertainties, which will be useful for estimating background amino acid frequencies for CHAIN analysis. These approaches also serve as a starting point for further enhancements that integrate MCMC sampling, HMM and PSI-BLAST methods, which, based on our earlier analyses , seem liWhen this motif-based approach was applied to CHAIN analysis of families belonging to large and diverse protein classes, we found numerous examples, three of which are described here, where this does a better job of revealing subtle, biologically important sequence features than does PSI-BLAST. This is in large part due to the ability of our statistical model and sampling strategies to find weakly conserved islands of homology within a sea of essentially nonconserved regions. While this motif based approach will not become the default method for CHAIN analysis – especially considering that PSI-BLAST alignments also may be optimized using these approaches – it, nevertheless, often more accurately aligns very distantly related sequences and thus can provide a better measure of selective constraints in this situation.ith motif is:We model gaps within motif blocks through the HMM shown in Fig. i ≤ m and 1 ≤ x <wi and where M, I, and D denote match, insertion and deletion states, respectively. The probability matrix for transitions between motifs is:where 1 ≤ i <m and where these transitions each emit a string of zero or more residues. Note that the contribution to the log-posterior probability of the lengths of these strings and of their emission probabilities (as well as those of M and I states) are specified by our ungapped statistical model [where 1 <al model , upon whA, let f(A) be its log-posterior probability as in [i within each sequence Sk is associated with a "path" through the HMM indicating its alignment against motif model Θi. Let the collection of these paths be Λ. Next, we denote the total number of transitions of type M → M, M → I, ..., byFor a given alignment ty as in . If we aNmm, Nmi, Nmd, Nim, Nii, Ndm, Ndd.It then follows that the likelihood of the gap parameters iswith independent prior distributionsαo, βo, 1 - αo - βo) ~ Dirichlet(ao, bo, nm - ao - bo),(αe ~ Beta(ae, ni - ae), and βe ~ Beta(be, nd - be),ao, bo, nm, ae, ni, be, nd are prior pseudo counts given by the user. The corresponding maximum likelihood estimates (MLEs) arewhere The joint posterior distribution for the alignment and gap parameters isg(A, Λ, α, β) ∝ P(S | A, Λ) × P(A) Λ h(Λ | α, β) P(α, β),P(S | A, Λ) × P(A) is computed the same way as in the original block-motif model [where if model , andP(α, β) = Dirichlet(ao, bo, nm - ao - bo) × Beta(ae, ni - ae) × Beta(be, nd - be).Given the alignment Λ, we have the conditional posterior distributionSampling on this distribution can be performed by drawing the following random variables:α and β to getFor computational efficiency, we integrate out the g(A, Λ) with h(Λ) replacing h(Λ | α, β) P(α, β) in our previous formula [This gives rise to a new posterior  formula  and freeK1 residues and one deletion in every K2 residues. Then we setSuppose that we expect to see one insertion in every no to reflect the strength of this conviction. We suggest using priors reflecting conservative gapping where, for example, K1 = K2 = 1000 and no = nM N, where  is the total number of match positions in all of the motifs and N is the total number of aligned sequences.and set L1, and deletion length of L2, then we letFor gap extension prior probabilities, if one expects to see an average insertion length of n1 to be equal to the total number of expected insertions within motifs nM / K1. Likewise, n2 is set equal to the expected number of deletions nM / K2. In order to have different gap parameters for each motif, one need only keep specific counts of insertions and deletions for each motif, as the formula h(Λ) then applies to each motif individually, and we only need to multiply these h functions together when computing the total 'penalty'.We set the prior pseudo counts No  that a type "o" operation has been applied and the number of times no that it was "successful" (i.e., resulted in an increase of the posterior probability). The same is done for short-term memory except that in this case we monitor the number of short-term successes mo over Mo previous applications . At the next iteration, we then assign a probability  of applying this operation, where ws ≥ 0 and wl ≥ 0 are the weights given to the short and long-term memories, respectively, and where wp ≥ 0 specifies the minimum frequency at which this operation is applied. Typically, we set ws = wl = 1 and 0.2 ≤ wp ≤ 0.66, so that operations that previously proved to be unfruitful will only be performed about one-tenth to one-third as often as those that always yield improvements in the alignment.For long-term memory we monitor among the sampler's previous iterations the number of times T' = 300/T and, thus, πT'(X) ∝ πT 300/(X). On this 'pseudo-degrees-Kelvin' scale sampling from the true distribution π (X) (i.e., 300°) corresponds to sampling at 'room temperature'. After a period of sampling at room temperature until 'convergence', which is defined by the sampler's failure to improve the MAP after a specified number of iterations, simulated annealing is initiated. During this stage, whenever the probability densities of the sampled alignments averaged over say 20 iterations fluctuate by more than some maximal value, say Δlog(p) ≥ 50 nats, the temperature is lowered by 1–5°. If, on the other hand, the probability densities of the sampled alignments fluctuate on average less than some minimal value, say Δlog(p) ≤ 5 nats, the temperature is raised by say 1°.  This period of thermostatic sampling is again applied until convergence.We define an intuitive sampling temperature ad hoc approaches conceptually similar to the statistically rigorous procedures described in Methods, which were designed by JSL. AFN implemented the procedures and performed the sequence analyses. Both authors wrote and approved the final manuscript.AFN developed the algorithmic strategies and early


The results demonstrate an inverse correlation of intrathecal TNF and IL-18 levels in head-injured patients and a TNF-dependent inhibition of IL-18 after intracerebral injection in mice. These findings imply a potential new anti-inflammatory mechanism of TNF by attenuation of IL-18, thus confirming the proposed "dual" function of this cytokine in the pathophysiology of traumatic brain injury.Tumor necrosis factor (TNF) and interleukin-(IL)-18 are important mediators of neuroinflammation after closed head injury (CHI). Both mediators have been previously found to be significantly elevated in the intracranial compartment after brain injury, both in patients as well as in experimental model systems. However, the interrelation and regulation of these crucial cytokines within the injured brain has not yet been investigated. The present study was designed to assess a potential regulation of intracranial IL-18 levels by TNF based on a clinical study in head-injured patients and an experimental model in mice. In the first part, we investigated the interrelationship between the daily TNF and IL-18 cerebrospinal fluid levels in 10 patients with severe CHI for up to 14 days after trauma. In the second part of the study, the potential TNF-dependent regulation of intracerebral IL-18 levels was further characterized in an experimental set-up in mice:  Closed head injury (CHI) is the leading cause of mortality and persisting neurological impairment in young people in industrialized countries ,2. The nn = 10, Glasgow Coma Scale score ≤ 8) and indication for intraventricular catheters for cerebrospinal fluid (CSF) drainage due to increased intracranial pressure (ICP > 15 mm Hg) were included in this study. Drained CSF was collected daily for up to 14 days after trauma or until catheters were removed. The patient characteristics are shown in Table n = 10) and revealed no inflammatory CNS disease, based on normal CSF protein and glucose levels and normal white cell counts. All samples were kept on ice at 4°C and immediately centrifuged after collection, aliquoted, and frozen at -70°C until analysis.Patients with isolated severe closed head injury . According to the information provided by the manufacturer, the IL-18 assay recognizes both the mature and the pro-form of IL-18. All concentrations below the detection limit of 5 pg/mL (IL-18) or 1 pg/ml (TNF) were assigned a value of 5 pg/mL, and 1 pg/ml respectively. All samples were run undiluted in duplicate wells. The concentrations were calculated from the mean OD of duplicate samples, determined by spectrophotometer (Dynatech Laboratories Inc., Chantilly, VA, U.S.A.) at an extinction wavelength of 405 nm.The experimental part of the study was set-up on two different protocols with the aim to assess the TNF-dependent regulation of IL-18 in the murine brain:(1) The first part of the experimental study was designed to investigate a potential role of TNF-dependent regulation of intracranial IL-18 expression in a standardized model of CHI, using mice double-deficient in genes for TNF and lymphotoxin-α (TNF/LT-α-/-) [n = 10 per group) using a standardized weight-drop model, as previously described [2 until they awakened and were then brought back to their cages. Control animals were subjected to anesthesia and sham operation only (n = 10 per group). In addition, mice with anesthesia alone (n = 8) were used as internal control and untreated control animals (n = 10) were analyzed for baseline evaluation of intracerebral cytokine profiles in these mice.LT-α-/-) . These kLT-α-/-) -16. The LT-α-/-) . Knockouescribed ,17. In b(2) In the 2nd part of the experimental study, mice of the C57BL/6 strain (n = 10 per group) were treated by stereotactic intracerebroventricular (i.c.v.) injection of either 200 ng mouse recombinant TNF in 10 μl PBS, or vehicle solution only (10 μl PBS), into the left hemisphere using a sterile 27-gauge syringe, under ether anesthesia. According to data from previously published studies [ studies -20, as w studies .For assessment of intracerebral IL-18 levels, the murine brains were immediately removed after decapitation. Tissue homogenization was performed using a Polytron homogenizer (Kinematica, Kriens, Switzerland) with a dilution of 1:4 in ice cold extraction buffer containing 50 mmol/L Tris buffer (pH 7.2), NaCl 150 mmol/L, Triton-X-100 1%, and protease inhibitor cocktail (Roche, Mannheim, Germany). The homogenates were shaken on ice for 90 minutes, centrifuged for 15 minutes at 3,000 g and 4°C. Thereafter, the supernatants were aliquoted and stored at -70°C until analysis. The concentrations of total protein in the brain extracts were measured by Bradford assay (Bio Rad Laboratories, Munich, Germany). The intracerebral protein concentrations were in a similar range in all mice assessed . The quantification of IL-18 levels in murine brain homogenates was performed as described above for the human samples. All mice used in this study were males, in order to avoid a bias in gender, age 12 to 16 weeks with body weights of 25 to 32 g. The animal experiments were performed in compliance with the guidelines of the Federation of European Laboratory Animal Science Association (FELASA) and approval was granted by the Institutional Animal Care Committee of the University of Zurich and of the Hebrew University of Jerusalem.P< 0.05, repeated measures ANOVA; Table r = -0.195 to -0.844). In 7 of 10 patients, this inverse correlation was statistically significant, with a P-value < 0.05 in three patients (# 2,5,10) and P < 0.01 in four patients  remains unclear.In the clinical part of the study on CHI patients, the mean IL-18 concentrations in ventricular CSF collected up to 14 days after trauma were significantly higher than in control lumbar CSF from patients undergoing diagnostic spinal tap , as shown in Fig. In the experimental study, IL-18 was found to be constitutively expressed in the brain of untreated mice  can significantly attenuate neuronal degeneration by induction of antioxidative pathways through activation of the transcription factor NFκb, thus strengthening the notion of neuroprotective effects mediated by these cytokines [The findings from these experimental investigations corroborate the data from the clinical study, where an inverse correlation of intrathecal TNF and IL-18 levels during the first 14 days after severe CHI was found, suggesting that the inhibition of IL-18 may represent a new potential anti-inflammatory mechanism after CHI. Such a "dual role" of TNF has been suggested previously in terms of concomitant pro- and anti-inflammatory effects and detrimental as well as beneficial neuroprotective properties after brain injury . While tytokines . This asytokines . Overallytokines . HoweverCentral nervous system (CNS), cerebrospinal fluid (CSF), closed head injury (CHI), intracerebroventricular (i.c.v.), interleukin (IL), lymphotoxin-α (LT-α / TNF-β), nuclear factor κB (NFκB), tumor necrosis factor (TNF), phosphate-buffered saline (PBS), intracranial pressure (ICP), wild-type (WT), enzyme-linked immunosorbent assay (ELISA).There are no financial interests by any of the authors with regard to the present project.OIS, MCMK, CEH, ES, and PFS were responsible for conception and planning of the experiments, as well as for performing the animal experiments, collection of the human cerebrospinal fluid samples and cytokine measurements in human and murine tissue samples, as well as for writing of the manuscript. DP and IY performed the experimental i.c.v. injection experiments. WE contributed to the interpretation of the results and writing of the manuscript.


Micrococcus species may cause intracranial abscesses, meningitis, pneumonia, and septic arthritis in immunosuppressed or immunocompetent hosts. In addition, strains identified as Micrococcus spp. have been reported recently in infections associated with indwelling intravenous lines, continuous ambulatory peritoneal dialysis fluids, ventricular shunts and prosthetic valves.Kocuria rosea, a gram-positive microorganism belonging to the family Micrococcaceae, in a 39-year-old man undergoing peripheral blood stem cell transplantation due to relapsed Hodgkin disease. This uncommon pathogen may cause opportunistic infections in immunocompromised patients.We report on the first case of a catheter-related bacteremia caused by Kocuria rosea catheter related bacteremia after stem cell transplantation successfully treated with vancomycin and by catheter removal.This report presents a case of  Staphylococcus aureus, coagulase-negative staphylococci, aerobic gram-negative bacilli and Candida albicans are the most frequent causes of catheter related bloodstream infection [Kocuria rosea, is gram-positive, strictly aerobic microorganisms belonging to the family Micrococcaceae that usually grow on simple media and generally considered as non-pathogenic commensals that colonize the oropharynx, skin and mucosa. However, it can be opportunistic pathogen in the immunocompromised patient [Long-term indwelling central venous access devices are indispensable to the supportive care of cancer patients. Infection remains a major complication of Hickman type catheters . Intravanfection . Kocuria patient ,4.Kocuria rosea in a patient undergoing autologous PBSCT. He had a chills, high fever, tachycardia and increased respiratory rate. Persistent bacteremia, which was unresponsive to vancomycin, was successfully treated by catheter removal.We describe the first case of central venous catheter (CVC) related infection associated with Micrococcus spp., which were identified as Kocuria rosea. An antibiogram showed that the pathogen was sensitive to ampicillin/sulbactam, erythromycin and vancomycin. Imipenem and amikacin were discontinued, and intravenous vancomycin, 2 g/day was begun. The patient did not respond satisfactorily to vancomycin therapy for five consecutive days. The catheter was removed and a catheter tip culture was obtained. The cultures demonstrated a massive growth of Kocuria rosea. Blood cultures were thereafter negative. The patient was discharged from the hospital after 14 days of intravenous vancomycin. He was followed for six months; the patient is alive and well and is in complete remission of the primary disease.A 39-year-old man with relapsed Hodgkin disease was admitted to Erciyes University Hospital for autologous PBSCT. A Hickman-type central venous catheter was implanted and the patient received a conditioning regimen consisting ifosfamide, carboplatin and etoposide. On day 3 after PBSCT, he was acutely ill with a chills, high fever (temperature 38.5°C), tachycardia (heart rate 108 beats/min) and increased respiratory rate (26 breaths/min). Clinical, radiological and microbiological diagnostic procedures were performed, and at three blood cultures were obtained from peripheral veins and catheter half an hour apart. The patient was empirically treated with intravenous imipenem/cilastatin, 2 g/day; and amikacin, 1 g/day. On day six after PBSCT, he had a persistent fever and the temperature spiked to 39.5°C. There was no sign of a catheter exit-site infection. Chest radiography was normal. Serial blood cultures taken from the catheter (two cultures) and a peripheral vein (four cultures) were positive for Kocuria rosea on the basis of Bergey's Manual of Systematic Bacteriology [Cultures of blood from the peripheral veins and the CVC were performed with a BACTEC system . The CVC tip was cultured by using the Maki roll technique. A growth of more than 15 cfu on the blood agar plate was considered positive. It was identified as Micrococcus spp. are usually regarded as non-pathogenic skin commensals and their clinical relevance is questionable. However, Micrococcus spp. can be opportunistic pathogens in immunocompromised patients such as PBSCT. It has been reported that Micrococcus luteus has been implicated as the causative agent in cases of intracranial abscesses, meningitis, pneumonia and septic arthritis in immunosuppressed or immunocompetent hosts [Micrococcus spp. have been reported recently in infections associated with indwelling intravenous lines, continuous ambulatory peritoneal dialysis fluids, ventricular shunts and prosthetic valves [nt hosts -12. In ac valves -18.Kocuria rosea" and/or "Kocuria". However, we were unable to find published studies on catheter infections caused by Kocuria rosea in patients undergoing HSCT in febrile neutropenic period.We performed a Medline search using the terms "Micrococcus" and/or "Kocuria rosea from different blood cultures in the absence of other microorganisms, together with isolation of the same organism from the catheter were primarily responsible for the episodes of febrile neutropenia. The catheter was then removed and cultures demonstrated a massive growth of Kocuria rosea.In this patient, the repeated isolation of Kocuria rosea from blood cultures. Although uncommon, the possibility that a central venous line may be the portal of entry should be evaluated. Adherence of bacteria to the silastic tube would possibly explain the failure of treatment by antibiotics alone.The clinician should not underestimate the importance of the repeated isolation of a Kocuria rosea should be considered as a nosocomial pathogen, which may cause of catheter infections in febrile neutropenic patients. In these patients, persistent Kocuria rosea bacteremia unresponsive to medical management should be treated by catheter removal.This report emphasizes that The author(s) declare that they have no competing interests.FA, OY and KG carried out the clinical study of the patient. BS carried out the culture and specific identification of the bacterium. BE and MC drafted the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


As a biological discipline, zoology has one of the longest histories. Today it occasionally appears as though, due to the rapid expansion of life sciences, zoology has been replaced by more or less independent sub-disciplines amongst which exchange is often sparse. However, the recent advance of molecular methodology into "classical" fields of biology, and the development of theories that can explain phenomena on different levels of organisation, has led to a re-integration of zoological disciplines promoting a broader than usual approach to zoological questions. Zoology has re-emerged as an integrative discipline encompassing the most diverse aspects of animal life, from the level of the gene to the level of the ecosystem.Frontiers in Zoology is the first Open Access journal focussing on zoology as a whole. It aims to represent and re-unite the various disciplines that look at animal life from different perspectives and at providing the basis for a comprehensive understanding of zoological phenomena on all levels of analysis. Frontiers in Zoology provides a unique opportunity to publish high quality research and reviews on zoological issues that will be internationally accessible to any reader at no cost.The new journal  Zoology has a long history of more than 2000 years and is one of the natural sciences attracting most public attention. Nevertheless, as a scientific field, zoology occasionally is considered to be old-fashioned and in danger of being replaced by narrower sub-disciplines that focus on a few important and in vogue aspects of animal biology. The remarkable success of such specialized approaches has in the last decades promoted the breakdown of zoology into a number of autonomous research areas, some focussing on molecules and cells and others on whole organisms or ecosystems, each with its own specialized journals and with little exchange of concepts and empirical information between them.The advance of molecular methodology into "classical" fields of biology and the development of theories that have the power to explain phenomena on different levels of organisation urge for the re-integration of zoological disciplines and at the same time highlight the advantages of a broader approach in the study of proximate and ultimate aspects of animal life. Zoology thus fulfils any requirements as an integrative discipline encompassing all aspects of animal life, from the level of the gene to the level of the ecosystem.Frontiers in Zoology, the only Open Access, online journal focussing on zoology.We feel that this renaissance of integrative zoology as a modern and vigorous field of research needs to be reflected in an up-to-date journal dedicated to zoology as a whole. We also believe that the best way to broadly disseminate the results of modern zoological science is provided by an Open Access, online journal. We therefore have launched Frontiers in Zoology aims at re-uniting the various sub-disciplines that investigate animal life from different angles and providing the basis for a more complete understanding of animal life. Initiated and supported by the Deutsche Zoologische Gesellschaft, one of the largest national societies devoted to zoology as a whole, and based on an editorial board  with renowned experts from different research areas, Frontiers in Zoology provides a novel opportunity to publish high quality articles and reviews on zoological phenomena with all the advantages provided by Open Access.Frontiers in Zoology are archived in PubMed Central, the US National Library of Medicine's full-text repository of life science literature, and also in repositories at the University of Potsdam in Germany, at INIST in France and in e-Depot, the National Library of the Netherlands' digital archive of all electronic publications.As Slade et al.  have sumOpen Access has four broad benefits for science and the general public. First, authors are assured that their work is disseminated to the widest possible audience, given that there are no barriers to access their work. This is accentuated by the authors being free to reproduce and distribute their work, for example by placing it on their institution's website. It has been suggested that free online articles are more highly cited because of their easier availability . Second,formats . The Editors-in-Chief or one of the Specialist Editors will forward the manuscript to two or three referees for peer-review. Upon receiving the reports from the referees, the respective Editor may suggest rejection or acceptance without, with major or with minor changes to the Editors-in-Chief, who will finalise the decision based upon the reports and the editor's opinion. Articles will be published online immediately upon acceptance and soon after listed in PubMed. Decisions about a manuscript will be exclusively based on the quality of the work, not on whether the authors can pay the article-processing charge. Note that processing charges will be waived for all authors whose institution is a BioMed Central member. Furthermore, editors can grant discretionary waivers for 35% of articles submitted in any given period of 90 days.Manuscripts with all figures and tables shall be submitted online using any of the listed Frontiers in Zoology provides an optimal way for the broad and fast dissemination of outstanding research from all areas of Zoology. We therefore invite all researchers in zoology to experience the manifold advantages provided by Open Access, online journals and to submit their high quality manuscripts to this new journal.


Despite the widespread use of microarrays, much ambiguity regarding data analysis, interpretation and correlation of the different technologies exists. There is a considerable amount of interest in correlating results obtained between different microarray platforms. To date, only a few cross-platform evaluations have been published and unfortunately, no guidelines have been established on the best methods of making such correlations. To address this issue we conducted a thorough evaluation of two commercial microarray platforms to determine an appropriate methodology for making cross-platform correlations.® and Amersham CodeLink™ UniSet Human 20 K microarrays were compared. For each microarray platform, five technical replicates, derived from the same total RNA samples, were labeled, hybridized, and quantified according to each manufacturers' standard protocols. The correlation coefficient (r) of differential expression ratios for the entire set of 10,763 overlapping genes was 0.62 between platforms. However, the correlation improved significantly (r = 0.79) when genes within noise were excluded. In addition to levels of inter-platform correlation, we evaluated precision, statistical-significance profiles, power, and noise levels for each microarray platform. Accuracy of differential expression was measured against real-time PCR for 25 genes and both platforms correlated well with r values of 0.92 and 0.79 for CodeLink and GeneChip, respectively.In this study, expression measurements for 10,763 genes uniquely represented on Affymetrix U133A/B GeneChipsAs a result of this study, we recommend using only genes called 'present' in cross-platform correlations. However, as in this study, a large number of genes may be lost from the correlation due to differing levels of noise between platforms. This is an important consideration given the apparent difference in sensitivity of the two platforms. Data from microarray analysis need to be interpreted cautiously and therefore, we provide guidelines for making cross-platform correlations. In all, this study represents the most comprehensive and specifically designed comparison of short-oligonucleotide microarray platforms to date using the largest set of overlapping genes. There are several commercial microarray systems currently available on the market for genome-scale gene expression analysis. Different microarray manufacturers provide distinct underlying technologies, protocols and reagents specific to each system . Despitein situ synthesized oligonucleotides vs. pre-synthesized, non-contact oligonucleotide deposition. We restricted our comparative analysis to these two platforms because these systems are most similar with respect to oligonucleotide length, target preparation, and single color indirect labeling methodology. Since these commercial assays are similar, and systematic variables were isolated by using the same total RNA starting material for all target preparations, we expected disparity in performance to reflect differences inherent to the microarray platforms. To provide data for comparison of the platforms, five technical replicates of brain and pancreas were processed on each platform and the results were compared for reproducibility, sensitivity, and similarity of results. Standard manufacturer-recommended protocols and settings were employed to obtain the raw data from each platform. In the case of Affymetrix GeneChip, a recent cross-platform microarray evaluation [There are two commercially available high-density microarray platforms that use short oligonucleotides for expression profiling. CodeLink  and GeneChip (Affymetrix, Santa Clara, CA) microarray platforms utilize oligonucleotide gene target probes of 30 and 25 bases, respectively. Some of the notable differences between the GeneChip and CodeLink systems are, respectively, multiple probes vs. one pre-validated probe per gene target, two-dimensional surface vs. three-dimensional array matrix, and aluation  used twoaluation ,9 for analuation . We theraluation . This meTwo different tissue types were compared in this study to ensure a large number of differentially expressed genes, and provide expression ratios across a wide dynamic range for derivation of the correlation coefficient between the two platforms. The array-to-array precision of each microarray platform was calculated from the five replicates within each tissue sample.The pair-wise array-to-array precision of each microarray platform is illustrated in Figure 2 ratio| > 1), is defined as the false-change rate. Table Precision measurements were calculated from signals above noise across the arrays being compared Tables  and 2 toIn addition to pair-wise array precision, we calculated coefficients of variation (CV) for each platform as a function of intensity, across all replicates. In Figure Differential expression ratios were compared between platforms to determine the cross-platform correlation. As shown in Figure 10 ratio of expression (brain/pancreas) is shown on the x-axis and the p-value, from a two-tailed Student's t-tests on normalized log-transformed intensities, is shown on the y-axis. The vertical dashed lines represent 2-fold change ratios, which are commonly used in the field as significance levels for non-replicated array experiments. The horizontal dashed line represents the statistical-significance level where p = 0.01 (an uncorrected lenient level, used to error on the side of inclusion). The lower right- and left-hand corners of each graph contain the genes that showed a large fold-change but fail to achieve statistical significance (p > 0.01). GeneChip results show a larger number of genes in these regions as compared to the CodeLink data. The data points located in the upper-central region of each graph represent genes that were statistically significant (p < 0.01) despite modest fold-changes (< 2-fold). The minimal-detectable statistically significant fold-change was tighter for CodeLink relative to GeneChip as illustrated by the distance across the 'volcano' plot at the 0.01 significance level. In addition, the number of genes above the 0.01 significance level was greater for CodeLink relative to GeneChip. The distribution difference between the red and blue data points demonstrates the advantage of identifying signals above noise for making ratio calculations.The improvement in the correlation coefficient from 0.62 to 0.79 achieved by excluding noise underscores the value in identifying the population of signals above noise for cross-platform comparisons. The 'volcano plots' in Figure The 'volcano' plots are translated into Venn diagrams of statistically significant differentially expressed genes for each platform in Figure A power analysis was conducted on each microarray platform to estimate the number of technical replicates needed to achieve a reasonable level of statistical confidence when noise was either included or excluded from the dataset Figure . EvaluatThe accuracy of CodeLink and GeneChip differential-expression ratios were compared to quantitative real-time PCR (qrtPCR). Microarray expression ratios were measured against results from qrtPCR for a randomly selected subset of 25 genes Table  and plotIncreased access and utilization of microarray data through core facilities and affordable commercial microarray systems is driving the need for direct comparisons of data between the different available platform technologies. The ability to exchange data across different platforms gives the research community the ability to cross-validate results and extend understanding of biological processes through integration of published data collected with different technologies. The results presented here demonstrate that we are closer to reaching this goal than previously reported -7.We have compared two commercial platforms and in doing so present several steps required for making comparisons between short oligonucleotide microarray data sets. First, one must normalize annotation. Unfortunately, despite the completion of the human, rat and mouse genome sequencing projects, accurate and stable gene annotation information is not available. The existence of inaccurate sequence information, absence of an exact gene count, incomplete understanding of splicing variations, and the complexity of highly homologous gene sequences all contribute to the challenges of generating a controlled vocabulary for uniquely and constantly annotating genes at the present time. In addition, when considering commercially available arrays, the consumer is left to rely on the manufacturer to provide a probe with a one to one correlation to the intended gene target. Furthermore, until recently manufacturers have withheld the release of the exact probe sequences to researchers . Now thaet al. 2003 [et al. made a limited but similar attempt to reduce noise by using what they termed a "variance filter" [When comparing between the two platforms using tissue ratio data without regard for noise, the correlation between platforms is not very strong  Concordantly 'present' which was used to calculate the correlation comparisons; (2.) Concordantly 'absent', where both platforms agree that the transcript is not 'present' in the samples tested; and (3.) 'Present' on one microarray platform but not the other, which are considered a separate set of discrepant results. In the studies presented here, the CodeLink platform generates a higher percentage of detectable signals above noise  contains a collection of approximately 20,289 probes within a single reaction chamber on each individual slide. All oligonucleotide probes are 30 bases in length. The core of the CodeLink platform is a glass slide coated with a polyacrylamide gel matrix to create a three-dimensional aqueous hybridization environment. Modified 5'-amine-terminated oligonucleotides are deposited onto the polymer using piezoelectric dispensing robots and then covalently attached to activated functional groups within the gel matrix. Oligonucleotides are co-dispensed with a fluorescein-derivative dye, which enables scanning and inspection of every feature element on every slide after the dispensing. Additional sites are then blocked and slides are washed, rinsed and dried prior to attachment of an integrated, proprietary, polypropylene hybridization chamber. All probes appearing on the final product have been pre-validated for performance and screened from an original panel of up to three probes per gene.in situ synthesis. Individual probes consist of 25 base DNA sequences.The HG-U133 GeneChip Set from Affymetrix (Santa Clara, CA, USA) contains 44,928 probes, on 2 chips, that represent 42,676 unique sequences from the GenBank database corresponding to 28,036 unique UniGene clusters. The GeneChip technology is based on a photolithographic One lot of human brain and pancreas total RNA (brain lot#033P010402009A and pancreas lot#022P0102B from Ambion) was assessed for quality using the Agilent 2100 Bioanalyzer and split equally between Amersham Biosciences in Chandler, Arizona and the Genomics Shared Service at the Arizona Cancer Center. The Affymetrix target preparations and hybridizations were performed entirely at the Arizona Cancer Center to ensure that these microarrays were run by an independent party with GeneChip expertise. In addition, an aliquot from these lots of total RNA was saved and subsequently used in qrtPCR reactions for verifying the expression profiles obtained by each microarray platform.in vitro transcription using the BioArray High Yield RNA Transcript Labeling Kit (Enzo). The dsDNA was mixed with 1× HY reaction buffer, 1× biotin labeled ribonucleotides (NTPs with Bio-UTP and Bio-CTP), 1× DTT, 1× RNase inhibitor mix and 1× T7 RNA polymerase. The mixture was incubated at 37°C for 5 hours. The labeled cRNA was then purified using an RNeasy mini kit (Qiagen) according to the manufacturer's protocol and ethanol precipitated. Fragmentation of cRNA, hybridization, washing, staining, and scanning were performed as described in the Affymetrix GeneChip Expression Analysis Technical Manual [BioB, 5 pM BioC, 25 pM BioD, and 100 pM cre), 0.1 mg/ml herring sperm DNA, 0.5 mg/ml acetylated BSA and 1× manufacturer recommended hybridization buffer, and hybridization was performed with a GeneChip Fluidic Station (Affymetrix) using the appropriate antibody amplification, washing and staining protocol. The phycoerythin-stained array was scanned, resulting in a digital image file. In all, 5 replicates of U133A and U133B were processed for each total RNA sample. Therefore, 10 target preparation reactions were performed for each of the two tissues to generate the necessary cRNA for this study.For each Affymetrix GeneChip, double-stranded cDNA was synthesized from 5 ug of total RNA with the SuperScript Double-Stranded cDNA Synthesis Kit (Invitrogen) and dT24-T7 primer (Operon) according to the manufacturer's instructions. Biotin-labeled cRNA was prepared by in vitro transcription using a single, labeled nucleotide, biotin-11-UTP in the IVT reaction at a concentration of 1.25 mM. Unlabeled UTP was present at 3.75 mM, while GTP, ATP, and CTP were at 5 mM. The mixture was incubated at 37°C overnight for 14 hours. The labeled cRNA was then purified using an RNeasy® mini kit (Qiagen). Fragmentation of cRNA, hybridization, washing, staining, and scanning were performed as described [® 4000 B scanner (Axon Instruments).For each CodeLink Bioarray, double-stranded cDNA and subsequent cRNA was synthesized from 5 ug of total RNA using the CodeLink Expression Assay Kit (Amersham Biosciences) according to manufacturer's instructions . Brieflyescribed . Brieflyth) base of each 25-mer probe is incorrect. The MM probe is designed to give an indication of the degree of nonspecific hybridization [p-value and the comparison of each Discrimination score to the user-definable threshold (Tau). Tau is a small positive number that can be adjusted to increase or decrease sensitivity and/or specificity of the analysis . The One-sided Wilcoxon's Signed Rank test is the statistical method employed to generate the Detection p-value. It assigns each probe pair a rank based on how far the probe pair discrimination score is from Tau. The user-modifiable Detection p-value cut-offs, Alpha 1 (α1) and Alpha 2 (α2) provide boundaries for defining 'Present', 'Marginal' or 'Absent' calls. At the default settings (α1 = 0.04 and α2 = 0.06), any p-value that falls below α1 is assigned a 'Present' call, and above α2 is assigned an 'Absent' call. 'Marginal' calls are given to probe sets which have p-values between α1 and α2. In our study, the MAS 5.0 default parameters were retained. For a complete description of the MAS 5.0 algorithms and statistical tests please refer to the Affymetrix manuals [For the U133 GeneChip technology, each gene is represented by 11 probe pairs containing both a perfect match probe (PM) and a mismatch probe (MM) where the middle . The mean intensity is taken for each spot and background corrected by subtracting the surrounding median local background intensity. A spot is considered 'absent' (within noise) if the spot's signal mean is less than its corresponding local background mean plus one standard deviation of local background pixels. For each probe the local background is comprised of a circular area of pixels surrounding the segmented signal. The image segmentation and quantification process is outlined in the ImaGene 5.5 user's manual .To facilitate comparisons between data sets, CodeLink probes and GeneChip probe sets were mapped to specific sequence clusters according to the NCBI Human UniGene build #166 relative to the manufacturer's provided NCBI accession numbers. Multiple probe or probe sets targeting a single UniGene cluster or single probe or probe sets targeting multiple clusters were removed from consideration. The overlapping and uniquely represented UniGene clusters were used to identify 10,763 gene probes for comparison between platforms. Gene-expression values were global linearly normalized according to manufacturers' standard normalization procedure ,26. The 1 = μ2). The power analysis was modeled from log2 transformed ratios derived from all pair-wise array-to-array combinations across the five replicates within the brain sample, since this tissue had the greatest similarity in performance between microarray platforms. Expression profiling of the pancreas sample showed many more genes within noise ('absent') for the GeneChip platform relative to CodeLink. The power analysis was conducted as previously described [A power analysis is a computational tool used to determine the replication needed to achieve a desired level of confidence in results from a particular experiment -32. Deteescribed ,36 for t® One-Step RT-PCR Master Mix Reagent Kit (Applied Biosystems, Foster City, CA, USA) was used with each custom designed, gene-specific primer/probe set to amplify and quantify each transcript of interest. Optimal primer/probe sets were selected using Primer Express software version 1.0 B6 (Applied Biosystems). Reactions (25 ul) contained 100 ng of total RNA, 300 nM forward and reverse primers, 200 nM TaqMan probe, 12.5 uL 2X Master Mix without the enzyme uracil DNA glycosylase (UNG), 0.625 mL MultiScribe™ and RNAase Inhibitor Mix, and 6.875 uL RNAse-free water. RT-PCR amplification and real-time detection were performed using an ABI PRISM 7700 Sequence Detection System (Applied Biosystems) for 30 min at 48°C (reverse transcription), 10 min at 95°C (AmpliTaq Gold activation), 38 cycles of denaturation (15 s at 95°C), and annealing/extension (60 s at 60°C). Data were analyzed using ABI PRISM Sequence Detection Software version 1.6.3 and then further processed using Microsoft® Excel (Microsoft, Redmond, WA). Cyclophilin (PPIE) served as the endogenous control for the normalization of input target RNA. Raw CT values, qrtPCR primer/probe sequences, and corresponding array probe names are available in supplementary material [see The TaqManeactions 5 ul contRS, TJS, RL, TK-K and CP are employees of GE Healthcare.RS planned and designed the study, conducted the micoarray experiments with the CodeLink platform, analyzed the data, generated all of the figures, and drafted the paper. TJS helped with writing of the paper, deriving the overlap between platforms, provided overall technical guidance and coordination. RL designed, conducted, and analyzed the quantitative PCR experiments, performed bioinformatics support, and edited the manuscript. CP read the manuscript and provided comments. TK-K edited the manuscript. GW helped with the experimental design and was responsible for generating all of the GeneChip data as well as editing the manuscript. JA provided guidance with the statistical power analysis and additions to the manuscript. All authors read and approved the final manuscript.T values for triplicates, mean CT values, median CT values, standard deviation of replicates, and coefficient of variation of replicates for each gene evaluated by qrtPCR.This file contains gene names, sample designations, CClick here for fileThis file contains gene names, oligonucleotide sequences, and oligonucleotide type (FORWARD PRIMER, HYB OLIGO, and REVERSE PRIMER) for each gene evaluated by qrtPCR.Click here for fileThis file contains gene names, descriptions, GenBank accessions, log2 ratios (qrtPCR, CodeLink, and GeneChip), CodeLink probe names, and GeneChip probe set names for each gene examined by all three technologies.Click here for file


A comprehensive analysis of the occurrence of pseudogenes in a diverse selection of 64 prokaryote genomes identified around 7,000 candidate pseudogenes. A large fraction of prokaryote pseudogenes seems to have arisen from failed horizontal-transfer events.   Pseudogenes often manifest themselves as disabled copies of known genes. In prokaryotes, it was generally believed (with a few well-known exceptions) that they were rare.We have carried out a comprehensive analysis of the occurrence of pseudogenes in a diverse selection of 64 prokaryote genomes. Overall, we find a total of around 7,000 candidate pseudogenes. Moreover, in all the genomes surveyed, pseudogenes occur in at least 1 to 5% of all gene-like sequences, with some genomes having considerably higher occurrence. Although many large populations of pseudogenes arise from large, diverse protein families (for example, the ABC transporters), notable numbers of pseudogenes are associated with specific families that do not occur that widely. These include the cytochrome P450 and PPE families (PF00067 and PF00823) and others that have a direct role in DNA transposition.Escherichia coli.We find suggestive evidence that a large fraction of prokaryote pseudogenes arose from failed horizontal transfer events. In particular, we find that pseudogenes are more than twice as likely as genes to have anomalous codon usage associated with horizontal transfer. Moreover, we found a significant difference in the number of horizontally transferred pseudogenes in pathogenic and non-pathogenic strains of  Mycobacterium leprae, where around 1,100 pseudogenes (compared to around 1,600 genes) were found, with pseudogene formation providing a 'fossil record' of recent wholesale loss of pathways involved in lipid metabolism and anaerobic respiration [Genes that have recently fallen out of use for an organism are often detectable in the genome as pseudogenes - disabled copies of genes characterizable by disruptions of their reading frames due to frameshifts and premature stop codons -3. Survepiration .Here we want to address the question of whether these large populations are exceptional, or whether there are substantial populations of pseudogenes in other prokaryotic genomes. If so, from a holistic 'polygenomic' perspective, what sorts of proteins tend to form prokaryotic pseudogenes? And are there any themes in common with the occurrence of pseudogenes in eukaryotes?To address these broad questions, we have adapted a pipeline developed for eukaryotic pseudogene identification to 64 prokaryotic genomes .To identify pseudogenes in prokaryotic genomes, we performed a conservative and comprehensive search, as outlined in Figure M. leprae, Escherichia coli and Rickettsia prowazekii [Previously, the pseudogene fraction was defined as the ratio of the number of pseudogenes to the number of all gene-like sequences (genes plus pseudogenes) . By thisowazekii ,16-19. IM. leprae has a very large pseudogene fraction (36.5%) and is clearly a unique outlier. When this genome is set aside, the three groups have similar pseudogene fractions (3.6%, 3.9% and 3.3%). Note that three other pathogenic species/strains have relatively large pseudogene fractions, including Neisseria meningitidis MC58 (12.4%), N. meningitidis Z2491 (11.6%) and Rickettsia conorii (9.7%). The higher pseudogene fractions of some pathogenic species have previously been suggested to be a result of a rapidly changing environmental niche, with loss of metabolic and respiratory pathways [To check for a correlation with microbial 'lifestyle', we classified the 64 species into three categories: archaea, pathogenic bacteria and non-pathogenic bacteria. The pseudogene fractions for these groupings were assessed. dis MC58 .4%, N. mWe found that about 2,300 of our 6,895 candidate pseudogenes overlap with more than 2,600 annotated hypothetical open reading frames (ORFs), whose fractions were indicated in Figure We used the Pfam classification  to analyThere are notable protein families that rank high in pseudogene number, but low in terms of gene number. They include the PPE family (PF00823) which is thought to be linked to antigenic variation in mycobacteria and is highly polymorphic ; the cytN. meningitidis (MC58 and Z2491) carry 26 and 22 copies of transposase pseudogenes, respectively, and have only 11 and 5 copies of transposase genes. In the MC58 strain, transposase pseudogenes have been found in most of the 29 remnant insertion sequences [N. meningitidis strains probably undergo high selection pressure for transposases. The integrase core domain family (PF00665) is the catalytic domain of integrase, which mediates integration of a DNA copy of a viral/bacteriophage genome into the host genome [Streptococcus pneumoniae, M. leprae, M. tuberculosis, and E. coli strain O157:H7. The large number of pseudogenes relative to genes for these two gene families may reflect an overall high selective pressure for them - that is, a gene family that is rapidly duplicating and evolving may generate many pseudogenes.Figure equences . This sut genome . It catat genome . The larE. coli and M. leprae [Retrotransposition and genomic DNA duplication generate pseudogenes in mammals and other eukaryotes ,3. In co. leprae ,16, pseuHowever, the complete extent of the processes forming prokaryotic pseudogenes is not yet well understood. We realize that there are many methods of defining horizontal transfer -36 and aTo confirm our findings based on a method relying on GC content bias we applied the GeneTrace method . We analyzed a subset of pseudogenes and found that 18% result from failed horizontal transfer events, consistent with the previous method. Note that GeneTrace and the GC-content method are very different in the criteria they use to assess horizontal transfer and thus make for good independent verification of each other.In summary, we report here for the first time an estimate of how often horizontal transfer in prokaryotes introduces genes that are redundant, useless or even detrimental. Firstly, ORFs from dangerous genetic elements are under strong selection pressure to be deleted from the host's genome . SecondlM. tuberculosis (strains H37Rv and CDC1551), N. meningitidis (strains Z1491 and MC8), and Helicobacter pylori (strains 26695 and J99) share similar index values within species. However, E. coli has different index values in the three strains studied. The free-living E. coli K12 strain has an index value of 4.6, comparable to values calculated from previous results [E. coli strains O157:H7 and O157:H7 EDL933 have much lower values (1.8 and 0.8). This can be readily explained in two ways: the intracellular pathogenic E. coli strains could have moved into a different environment that results in lower exposure to incoming DNA and thus to a lower rate of horizontal gene transfer [By examining closely related strains of the same species, we found that most close strains have a similar value for the failed horizontal transfer index. In particular,  results , while ttransfer ; or thesM. leprae and three closely related mycoplasma species: Mycoplasma pneumoniae, Mycoplasma pulmonis and Ureaplasma urealyticum. Pseudogenes in these four pathogenic bacteria carry several times more disablements, suggesting that these bacteria have an accelerated disabling mutation rate. It is known that M. leprae has lost the dnaQ-mediated proofreading activities of DNA polymerase III [To characterize the overall rate of decay of pseudogene populations, we plotted the fraction of disablements versus the average number of matching residues (to their closest homologs) per pseudogene for each species. This measure shows how the overall level of decay of a pseudogene population relates to age . There is a general power-law-like behavior governing this measure, with recent pseudogenes having few disablements and divergent pseudogenes having many Figure . Archaearase III ,42, whicIt is important to note here that the current sequence databases are derived from an uneven sampling of genomes. Therefore, genomes of organisms with more sequenced relatives may appear to have, on average, a seemingly younger population of pseudogenes, while others may appear to have older and fewer identifiable pseudogenes. Using data from 64 genomes, our results indicate an overall trend for pseudogenes observed in most of the genomes studied. However, these results have to be viewed as preliminary until more genome data is available.We have shown that pseudogenes in prokaryotes are not uncommon, occupying 1-5% of all gene-like sequences. We find that specific gene families with clear links to DNA transposition and environmental responses have higher pseudogene/gene ratios.The pseudogene data has many implications for the study of genome reduction and expansion ,44. A siIn terms of methodological implications, it is evidently necessary to include prokaryote pseudogenes as part of systematic annotation pipelines in the future. In addition, it was also shown to be helpful to identify potential short ORFs . FurtherWe used the following datasets in our prokaryotic pseudogene analysis: Swiss-Prot (release 40.19 and updated to 27 May, 2002)  containiFigure To ensure that spurious disablements were not introduced at ends of sequences as an alignment artifact, we excluded homology matches whose disablements occurred only within a 'cutoff region' at either end. We used 16 residues for the cutoff region for short sequences (160 amino acids or fewer) - a parameter that has been applied previously [We also assessed the potential pseudogenes by examining the distribution of the disablements within pseudogene sequences. Given that mutations within pseudogenes are unconstrained, we would expect disablements on pseudogenes to be evenly distributed. Figure Mycoplasma pneumoniae, M. pulmonis and U. urealyticum. By manual examination of E. coli genes with translational frameshifts in the RECODE database [We considered non-standard codon usage in some bacteria, such as when TGA encodes tryptophan rather than a stop codon in mycoplasma species, including database , we founE. coli strains indicated that only about 7% of candidate pseudogenes could be due to sequencing error [S. pneumoniae genome (frameshift positions are not considered as they are difficult to locate.). This genome and eight others found in the trace archive of the National Center for Biotechnology Information (NCBI) [S. pneumoniae as a case study as it is a relatively big genome available in the archive. By adapting a previous method [-2 as a cutoff of the error rate (err = 10SUM(-0.1Q)) for all nucleic acids. The stop codons with all three nucleic acids above the cutoff were validated. Out of 116 pseudogenes in this genome, 73 were found to contain 150 stop codons in total. Using the available data in the trace archive, we identified 54 pseudogenes with stop codons being aligned with the original sequences, and validated 47 of these (87%). In addition, a similar fraction of stop codons (101 out of 116) was confirmed.Sequencing errors could also be a potential problem in the detection of pseudogenes. However, this effect is expected to be small, as comparison of independently sequenced isolates of the same ng error . To furtn (NCBI)  and Ensen (NCBI)  were alls method , we examAll genes in the 64 genomes were assigned to Pfam families by cross-referencing of their Swiss-Prot ID. Pseudogenes were assigned to Pfam families through ID of their closest homologs. Only the homologs that cover more than 70% of the Pfam domain were selected. A pseudogene could be assigned to multiple Pfam families if it contains multiple domains.Here we used a method (GC-content) to estimate horizontal transferred genes on the basis of their base compositions ,39. We aTo ensure that we had the codon positions accurately assigned for the GC-content method, we only analyzed codons for pseudogenes that aligned well with annotated protein sequences, specifically excluding the regions of the alignment around frameshifts. While it is true that the local alignment in some regions of a pseudogene may be ambiguous, causing some difference in the GC-content calculation in that region, the impact on the overall GC-content estimation is minimal, given how many positions we average over to calculate the failed transfer index score.The results for the 64 genomes are shown in Table ,E. coli [similar to the measure previously used in E. coli . This esNote that to minimize the effect of more divergent sequence alignments, for the horizontal-transfer calculations we only analyzed 1,748 'recent' pseudogenes, which have more than 50% sequence identity to their closest matches over an aligned subsequence of more than 100 residues.We have investigated the statistical robustness of the failed transfer index using resampling approaches . For eacWe also applied an alternative method (GeneTrace) to estimate horizontally transferred pseudogenes . In this