Splice sites (SSs) are short sequences that are crucial for proper mRNA splicing in eukaryotic cells, and therefore can be expected to be shaped by strong selection. Nevertheless, in mammals and in other intron-rich organisms, many of the SSs often involve nonconsensus (Nc), rather than consensus (Cn), nucleotides, and beyond the two critical nucleotides, the SSs are not perfectly conserved between species. Here, we compare the SS sequences between primates, and between Drosophila fruit flies, to reveal the pattern of selection acting at SSs. Cn-to-Nc substitutions are less frequent, and Nc-to-Cn substitutions are more frequent, than neutrally expected, indicating, respectively, negative and positive selection. This selection is relatively weak (1 < |4Nes| < 4), and has a similar efficiency in primates and in Drosophila. Within some nucleotide positions, the positive selection in favor of Nc-to-Cn substitutions is weaker than the negative selection maintaining already established Cn nucleotides; this difference is due to site-specific negative selection favoring current Nc nucleotides. In general, however, the strength of negative selection protecting the Cn alleles is similar in magnitude to the strength of positive selection favoring replacement of Nc alleles, as expected under the simple nearly neutral turnover. In summary, although a fraction of the Nc nucleotides within SSs is maintained by selection, the abundance of deleterious nucleotides in this class suggests a substantial genome-wide drift load.
Sample source, procurement process and other technical variations introduce batch effects into genomics data. Algorithms to remove these artifacts enhance differences between known biological covariates, but also carry potential concern of removing intragroup biological heterogeneity and thus any personalized genomic signatures. As a result, accurate identification of novel subtypes from batch-corrected genomics data is challenging using standard algorithms designed to remove batch effects for class comparison analyses. Nor can batch effects be corrected reliably in future applications of genomics-based clinical tests, in which the biological groups are by definition unknown a priori.
Various diseases require the selection of preferable treatment out of available alternatives. Multiple sclerosis (MS), an autoimmune inflammatory/neurodegenerative disease of the CNS, requires long-term medication with either specific disease-modifying therapy (DMT) - IFN-? or glatiramer acetate (GA) - which remain the only first-line DMTs in all countries. A significant share of MS patients are resistant to treatment with one or the other DMT; therefore, the earliest choice of preferable DMT is of particular importance. A number of conventional pharmacogenetic studies performed up to the present day have identified the treatment-sensitive genetic biomarkers that might be specific for the particular drug; however, the suitable biomarkers for selection of one or another first-line DMT are remained to be found. Comparative pharmacogenetic analysis may allow the identification of the discriminative genetic biomarkers, which may be more informative for an a priori DMT choice than those found in conventional pharmacogenetic studies. The search for discriminative markers of preferable first-line DMT, which differ in carriage between IFN-? responders and GA responders as well as between IFN-? nonresponders and GA nonresponders, has been performed in 253 IFN-?-treated MS patients and 285 GA-treated MS patients. A bioinformatics algorithm for identification of composite biomarkers (allelic sets) was applied on a unified set of immune-response genes, which are relevant for IFN-? and/or GA modes of action, and identical clinical criteria of treatment response. We found the range of discriminative markers, which include polymorphic variants of CCR5, IFNAR1, TGFB1, DRB1 or CTLA4 genes, in different combinations. Every allelic set includes the CCR5 genetic variant, which probably suggests its crucial role in the modulation of the DMT response. Special attention should be given to the (CCR5*d+ IFNAR1*G) discriminative combination, which clearly points towards IFN-? treatment choice for carriers of this combination. As a whole the comparative approach provides an option for the identification of prognostic composite biomarkers for a preferable medication among available alternatives.
The mechanisms triggering most of autoimmune diseases are still obscure. Autoreactive B cells play a crucial role in the development of such pathologies and, in particular, production of autoantibodies of different specificities. The combination of deep-sequencing technology with functional studies of antibodies selected from highly representative immunoglobulin combinatorial libraries may provide unique information on specific features in the repertoires of autoreactive B cells. Here, we have analyzed cross-combinations of the variable regions of human immunoglobulins against the myelin basic protein (MBP) previously selected from a multiple sclerosis (MS)-related scFv phage-display library. On the other hand, we have performed deep sequencing of the sublibraries of scFvs against MBP, Epstein-Barr virus (EBV) latent membrane protein 1 (LMP1), and myelin oligodendrocyte glycoprotein (MOG). Bioinformatics analysis of sequencing data and surface plasmon resonance (SPR) studies have shown that it is the variable fragments of antibody heavy chains that mainly determine both the affinity of antibodies to the parent autoantigen and their cross-reactivity. It is suggested that LMP1-cross-reactive anti-myelin autoantibodies contain heavy chains encoded by certain germline gene segments, which may be a hallmark of the EBV-specific B cell subpopulation involved in MS triggering.
Numerous methodologies, assays, and databases presently provide candidate targets of transcription factors (TFs). However, TFs rarely regulate their targets universally. The context of activation of a TF can change the transcriptional response of targets. Direct multiple regulation typical to mammalian genes complicates direct inference of TF targets from gene expression data. We present a novel statistic that infers context-specific TF regulation based upon the CoGAPS algorithm, which infers overlapping gene expression patterns resulting from coregulation. Numerical experiments with simulated data showed that this statistic correctly inferred targets that are common to multiple TFs, except in cases where the signal from a TF is negligible relative to noise level and signal from other TFs. The statistic is robust to moderate levels of error in the simulated gene sets, identifying fewer false positives than false negatives. Significantly, the regulatory statistic refines the number of TF targets relevant to cell signaling in gastrointestinal stromal tumors (GIST) to genes consistent with the phosphorylation patterns of TFs identified in previous studies. As formulated, the proposed regulatory statistic has wide applicability to inferring set membership in integrated datasets. This statistic could be naturally extended to account for prior probabilities of set membership or to add candidate gene targets.
Sanger sequencing is a common method of reading DNA sequences. It is less expensive than high-throughput methods, and it is appropriate for numerous applications including molecular diagnostics. However, sequencing mixtures of similar DNA of pathogens with this method is challenging. This is important because most clinical samples contain such mixtures, rather than pure single strains. The traditional solution is to sequence selected clones of PCR products, a complicated, time-consuming, and expensive procedure. Here, we propose the base-calling with vocabulary (BCV) method that computationally deciphers Sanger chromatograms obtained from mixed DNA samples. The inputs to the BCV algorithm are a chromatogram and a dictionary of sequences that are similar to those we expect to obtain. We apply the base-calling function on a test dataset of chromatograms without ambiguous positions, as well as one with 3-14% sequence degeneracy. Furthermore, we use BCV to assemble a consensus sequence for an HIV genome fragment in a sample containing a mixture of viral DNA variants and to determine the positions of the indels. Finally, we detect drug-resistant Mycobacterium tuberculosis strains carrying frameshift mutations mixed with wild-type bacteria in the pncA gene, and roughly characterize bacterial communities in clinical samples by direct 16S rRNA sequencing.
The problem of reconstruction of ancestral states given a phylogeny and data from extant species arises in a wide range of biological studies. The continuous-time Markov model for the discrete states evolution is generally used for the reconstruction of ancestral states. We modify this model to account for a case when the states of the extant species are uncertain. This situation appears, for example, if the states for extant species are predicted by some program and thus are known only with some level of reliability; it is common for bioinformatics field. The main idea is formulation of the problem as a hidden Markov model on a tree (tree HMM, tHMM), where the basic continuous-time Markov model is expanded with the introduction of emission probabilities of observed data (e.g. prediction scores) for each underlying discrete state. Our tHMM decoding algorithm allows us to predict states at the ancestral nodes as well as to refine states at the leaves on the basis of quantitative comparative genomics. The test on the simulated data shows that the tHMM approach applied to the continuous variable reflecting the probabilities of the states (i.e. prediction score) appears to be more accurate then the reconstruction from the discrete states assignment defined by the best score threshold. We provide examples of applying our model to the evolutionary analysis of N-terminal signal peptides and transcription factor binding sites in bacteria. The program is freely available at http://bioinf.fbb.msu.ru/~nadya/tHMM and via web-service at http://bioinf.fbb.msu.ru/treehmmweb.
Glatiramer acetate (GA) is widely used as a first-line disease-modifying treatment for multiple sclerosis (MS). However, a significant proportion of MS patient appears to experience modest benefit from GA-treatment. Genetic variants affecting the clinical response to GA are believed to be relevant as biomarkers of GA-treatment efficiency.
Modeling of signal driven transcriptional reprogramming is critical for understanding of organism development, human disease, and cell biology. Many current modeling techniques discount key features of the biological sub-systems when modeling multiscale, organism-level processes. We present a mechanistic hybrid model, GESSA, which integrates a novel pooled probabilistic Boolean network model of cell signaling and a stochastic simulation of transcription and translation responding to a diffusion model of extracellular signals. We apply the model to simulate the well studied cell fate decision process of the vulval precursor cells (VPCs) in C. elegans, using experimentally derived rate constants wherever possible and shared parameters to avoid overfitting. We demonstrate that GESSA recovers (1) the effects of varying scaffold protein concentration on signal strength, (2) amplification of signals in expression, (3) the relative external ligand concentration in a known geometry, and (4) feedback in biochemical networks. We demonstrate that setting model parameters based on wild-type and LIN-12 loss-of-function mutants in C. elegans leads to correct prediction of a wide variety of mutants including partial penetrance of phenotypes. Moreover, the model is relatively insensitive to parameters, retaining the wild-type phenotype for a wide range of cell signaling rate parameters.
Coordinated Gene Activity in Pattern Sets (CoGAPS) provides an integrated package for isolating gene expression driven by a biological process, enhancing inference of biological processes from transcriptomic data. CoGAPS improves on other enrichment measurement methods by combining a Markov chain Monte Carlo (MCMC) matrix factorization algorithm (GAPS) with a threshold-independent statistic inferring activity on gene sets. The software is provided as open source C++ code built on top of JAGS software with an R interface.
IFN-beta is widely used as first-line immunomodulatory treatment for multiple sclerosis. Response to treatment is variable (30-50% of patients are nonresponders) and requires a long treatment duration for accurate assessment to be possible. Information about genetic variations that predict responsiveness would allow appropriate treatment selection early after diagnosis, improve patient care, with time saving consequences and more efficient use of resources.
Footprint data is an important source of information on transcription factor recognition motifs. However, a footprinting fragment can contain no sequences similar to known protein recognition sites. Inspection of genome fragments nearby can help to identify missing site positions.
Multiple sclerosis (MS) is a chronic inflammatory, disabling disease of the CNS. A recent study has estimated the annual cost of MS in Europe at euro12.5 billion. There is no definitive cure for the disease. Immunomodulatory therapies, such as IFN-beta and glatiramer acetate, are only partially effective. Various new therapies in the final stages of clinical trials are being developed in the absence of efficacy biomarkers. Hence, there is a pressing need for identification of MS treatment response biomarkers. The focus of the multicenter research initiative United Europeans for the development of pharmacogenomics in MS (UEPHA*MS) is to promote and improve training opportunities in the novel supradisciplinary area of pharmacogenomics, biomarker research and systems biology applied to MS. UEPHA*MS is a Marie Curie Initial Training network funded by the 7th Framework Programme of the European Commission. The main scientific goals of this network are both to enhance our knowledge of the mechanisms determining response outcomes of existing immunomodulatory therapies and to identify novel therapeutic opportunities. UEPHA*MS is composed of 11 internationally recognized research teams from five countries with an assortment of expertise in complementary disciplines. The UEPHA*MS network will provide a coherent and internationally competitive platform for the training of young scientists based on multidisciplinary state-of-the-art laboratory-based and network-wide activities. This network will be instrumental in priming young scientists for Europes collective effort toward improved provision of healthcare based on personalized medicine.
IFN-? is widely used as the first-line disease-modifying treatment for multiple sclerosis. However, 30-50% of multiple sclerosis patients do not respond to this therapy. Identification of genetic variants and their combinations that predict responsiveness to IFN-? could be useful for treatment prognosis.
We have created a statistically grounded tool for determining the correlation of genomewide data with other datasets or known biological features, intended to guide biological exploration of high-dimensional datasets, rather than providing immediate answers. The software enables several biologically motivated approaches to these data and here we describe the rationale and implementation for each approach. Our models and statistics are implemented in an R package that efficiently calculates the spatial correlation between two sets of genomic intervals (data and/or annotated features), for use as a metric of functional interaction. The software handles any type of pointwise or interval data and instead of running analyses with predefined metrics, it computes the significance and direction of several types of spatial association; this is intended to suggest potentially relevant relationships between the datasets.
Identification of transcriptional regulatory regions and tracing their internal organization are important for understanding the eukaryotic cell machinery. Cis-regulatory modules (CRMs) of higher eukaryotes are believed to possess a regulatory grammar, or preferred arrangement of binding sites, that is crucial for proper regulation and thus tends to be evolutionarily conserved. Here, we present a method CORECLUST (COnservative REgulatory CLUster STructure) that predicts CRMs based on a set of positional weight matrices. Given regulatory regions of orthologous and/or co-regulated genes, CORECLUST constructs a CRM model by revealing the conserved rules that describe the relative location of binding sites. The constructed model may be consequently used for the genome-wide prediction of similar CRMs, and thus detection of co-regulated genes, and for the investigation of the regulatory grammar of the system. Compared with related methods, CORECLUST shows better performance at identification of CRMs conferring muscle-specific gene expression in vertebrates and early-developmental CRMs in Drosophila.
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