Larry N. Singh

Dysregulation of synaptogenesis genes antecedes motor neuron pathology in spinal muscular atrophy

Significance Spinal muscular atrophy (SMA), a common genetic motor neuron (MN) degenerative disease and leading hereditary cause of infant mortality, results from survival of motor neuron (SMN) protein deficiency. However, SMN’s ubiquitous expression and housekeeping functions in biogenesis of snRNPs, the spliceosome’s subunits, seems difficult to reconcile with SMA’s MN selective pathology. Here, we sequenced transcriptomes of MNs and adjacent white matter microdissected from spinal cords of presymptomatic SMA mice. This process revealed selective and MN-specific splicing and expression-level perturbations of mRNAs, including those essential for establishing neuromuscular junctions, the first structures that degenerate in SMA. We suggest that SMN’s central role in transcriptome regulation explains the gene-expression perturbations that impair MN function and survival in SMA. The motor neuron (MN) degenerative disease, spinal muscular atrophy (SMA) is caused by deficiency of SMN (survival motor neuron), a ubiquitous and indispensable protein essential for biogenesis of snRNPs, key components of pre-mRNA processing. However, SMA’s hallmark MN pathology, including neuromuscular junction (NMJ) disruption and sensory-motor circuitry impairment, remains unexplained. Toward this end, we used deep RNA sequencing (RNA-seq) to determine if there are any transcriptome changes in MNs and surrounding spinal cord glial cells (white matter, WM) microdissected from SMN-deficient SMA mouse model at presymptomatic postnatal day 1 (P1), before detectable MN pathology (P4–P5). The RNA-seq results, previously unavailable for SMA at any stage, revealed cell-specific selective mRNA dysregulations (∼300 of 11,000 expressed genes in each, MN and WM), many of which are known to impair neurons. Remarkably, these dysregulations include complete skipping of agrin’s Z exons, critical for NMJ maintenance, strong up-regulation of synapse pruning-promoting complement factor C1q, and down-regulation of Etv1/ER81, a transcription factor required for establishing sensory-motor circuitry. We propose that dysregulation of such specific MN synaptogenesis genes, compounded by many additional transcriptome abnormalities in MNs and WM, link SMN deficiency to SMA’s signature pathology.

CTCF binding site classes exhibit distinct evolutionary, genomic, epigenomic and transcriptomic features

BackgroundCTCF (CCCTC-binding factor) is an evolutionarily conserved zinc finger protein involved in diverse functions ranging from negative regulation of MYC, to chromatin insulation of the beta-globin gene cluster, to imprinting of the Igf2 locus. The 11 zinc fingers of CTCF are known to differentially contribute to the CTCF-DNA interaction at different binding sites. It is possible that the differences in CTCF-DNA conformation at different binding sites underlie CTCFs functional diversity. If so, the CTCF binding sites may belong to distinct classes, each compatible with a specific functional role.ResultsWe have classified approximately 26,000 CTCF binding sites in CD4+ T cells into three classes based on their similarity to the well-characterized CTCF DNA-binding motif. We have comprehensively characterized these three classes of CTCF sites with respect to several evolutionary, genomic, epigenomic, transcriptomic and functional features. We find that the low-occupancy sites tend to be cell type specific. Furthermore, while the high-occupancy sites associate with repressive histone marks and greater gene co-expression within a CTCF-flanked block, the low-occupancy sites associate with active histone marks and higher gene expression. We found that the low-occupancy sites have greater conservation in their flanking regions compared to high-occupancy sites. Interestingly, based on a novel class-conservation metric, we observed that human low-occupancy sites tend to be conserved as low-occupancy sites in mouse (and vice versa) more frequently than expected.ConclusionsOur work reveals several key differences among CTCF occupancy-based classes and suggests a critical, yet distinct functional role played by low-occupancy sites.

Functional diversification of paralogous transcription factors via divergence in DNA binding site motif and in expression.

Background Gene duplication is a major driver of evolutionary innovation as it allows for an organism to elaborate its existing biological functions via specialization or diversification of initially redundant gene paralogs. Gene function can diversify in several ways. Transcription factor gene paralogs in particular, can diversify either by changes in their tissue-specific expression pattern or by changes in the DNA binding site motif recognized by their protein product, which in turn alters their gene targets. The relationship between these two modes of functional diversification of transcription factor paralogs has not been previously investigated, and is essential for understanding adaptive evolution of transcription factor gene families. Findings Based on a large set of human paralogous transcription factor pairs, we show that when the DNA binding site motifs of transcription factor paralogs are similar, the expressions of the genes that encode the paralogs have diverged, so in general, at most one of the paralogs is highly expressed in a tissue. Moreover, paralogs with diverged DNA binding site motifs tend to be diverged in their function. Conversely, two paralogs that are highly expressed in a tissue tend to have dissimilar DNA binding site motifs. We have also found that in general, within a paralogous family, tissue-specific decrease in gene expression is more frequent than what is expected by chance. Conclusions While previous investigations of paralogous gene diversification have only considered coding sequence divergence, by explicitly quantifying divergence in DNA binding site motif, our work presents a new paradigm for investigating functional diversification. Consistent with evolutionary expectation, our quantitative analysis suggests that paralogous transcription factors have survived extinction in part, either through diversification of their DNA binding site motifs or through alterations in their tissue-specific expression levels.

Mimosa: Mixture model of co-expression to detect modulators of regulatory interaction

BackgroundFunctionally related genes tend to be correlated in their expression patterns across multiple conditions and/or tissue-types. Thus co-expression networks are often used to investigate functional groups of genes. In particular, when one of the genes is a transcription factor (TF), the co-expression-based interaction is interpreted, with caution, as a direct regulatory interaction. However, any particular TF, and more importantly, any particular regulatory interaction, is likely to be active only in a subset of experimental conditions. Moreover, the subset of expression samples where the regulatory interaction holds may be marked by presence or absence of a modifier gene, such as an enzyme that post-translationally modifies the TF. Such subtlety of regulatory interactions is overlooked when one computes an overall expression correlation.ResultsHere we present a novel mixture modeling approach where a TF-Gene pair is presumed to be significantly correlated (with unknown coefficient) in an (unknown) subset of expression samples. The parameters of the model are estimated using a Maximum Likelihood approach. The estimated mixture of expression samples is then mined to identify genes potentially modulating the TF-Gene interaction. We have validated our approach using synthetic data and on four biological cases in cow, yeast, and humans.ConclusionsWhile limited in some ways, as discussed, the work represents a novel approach to mine expression data and detect potential modulators of regulatory interactions.

Correlated changes between regulatory cis elements and condition-specific expression in paralogous gene families

Gene duplication is integral to evolution, providing novel opportunities for organisms to diversify in function. One fundamental pathway of functional diversification among initially redundant gene copies, or paralogs, is via alterations in their expression patterns. Although the mechanisms underlying expression divergence are not completely understood, transcription factor binding sites and nucleosome occupancy are known to play a significant role in the process. Previous attempts to detect genomic variations mediating expression divergence in orthologs have had limited success for two primary reasons. First, it is inherently challenging to compare expressions among orthologs due to variable trans-acting effects and second, previous studies have quantified expression divergence in terms of an overall similarity of expression profiles across multiple samples, thereby obscuring condition-specific expression changes. Moreover, the inherently inter-correlated expressions among homologs present statistical challenges, not adequately addressed in many previous studies. Using rigorous statistical tests, here we characterize the relationship between cis element divergence and condition-specific expression divergence among paralogous genes in Saccharomyces cerevisiae. In particular, among all combinations of gene family and TFs analyzed, we found a significant correlation between TF binding and the condition-specific expression patterns in over 20% of the cases. In addition, incorporating nucleosome occupancy reveals several additional correlations. For instance, our results suggest that GAL4 binding plays a major role in the expression divergence of the genes in the sugar transporter family. Our work presents a novel means of investigating the cis regulatory changes potentially mediating expression divergence in paralogous gene families under specific conditions.

TREMOR—a tool for retrieving transcriptional modules by incorporating motif covariance

A transcriptional module (TM) is a collection of transcription factors (TF) that as a group, co-regulate multiple, functionally related genes. The task of identifying TMs poses an important biological challenge. Since TFs belong to evolutionarily and structurally related families, TF family members often bind to similar DNA motifs and can confound sequence-based approaches to TM identification. A previous approach to TM detection addresses this issue by pre-selecting a single representative from each TF family. One problem with this approach is that closely related transcription factors can still target sufficiently distinct genes in a biologically meaningful way, and thus, pre-selecting a single family representative may in principle miss certain TMs. Here we report a method—TREMOR (Transcriptional Regulatory Module Retriever). This method uses the Mahalanobis distance to assess the validity of a TM and automatically incorporates the inter-TF binding similarity without resorting to pre-selecting family representatives. The application of TREMOR on human muscle-specific, liver-specific and cell-cycle-related genes reveals TFs and TMs that were validated from literature and also reveals additional related genes.

Estimation of the Hyperexponential Density with Applications in Sensor Networks

This paper solves the problem of estimation of the parameters of a hyperexponential density and presents a practical application of the solution in sensor networks. Two novel algorithms for estimating the parameters of the density are formulated. In the first algorithm, an objective function is constructed as a function of the unknown component means and an estimate of the cumulative distribution function (cdf) of the hyperexponential density. The component means are obtained by minimizing this objective function, using quasi-Newtonian techniques. The mixing probabilities are then computed using these known means and linear least squares analysis. In the second algorithm, an objective function of the unknown component means, mixing probabilities, and an estimate of the cdf is constructed. All the 2M parameters are computed by minimizing this objective function, using quasi-Newtonian techniques. The developed algorithms are also compared to the basic EM algorithm, and their relative advantages over the EM algorithm are discussed. The algorithms developed are computationally efficient and easily implemented, and hence, are suitable for low-power and sensor nodes with limited storage and computational capacity. In particular, we demonstrate how the structure of these algorithms may be exploited to be effectively utilized in practical situations, and are hence ideal for sensor networks.

Correlated evolution of positions within mammalian cis elements.

Transcriptional regulation critically depends on proper interactions between transcription factors (TF) and their cognate DNA binding sites. The widely used model of TF-DNA binding – the Positional Weight Matrix (PWM) – presumes independence between positions within the binding site. However, there is evidence to show that the independence assumption may not always hold, and the extent of interposition dependence is not completely known. We hypothesize that the interposition dependence should partly be manifested as correlated evolution at the positions. We report a Maximum-Likelihood (ML) approach to infer correlated evolution at any two positions within a PWM, based on a multiple alignment of 5 mammalian genomes. Application to a genome-wide set of putative cis elements in human promoters reveals a prevalence of correlated evolution within cis elements. We found that the interdependence between two positions decreases with increasing distance between the positions. The interdependent positions tend to be evolutionarily more constrained and moreover, the dependence patterns are relatively similar across structurally related transcription factors. Although some of the detected mutational dependencies may be due to context-dependent genomic hyper-mutation, notably CG to TG, the majority is likely due to context-dependent preferences for specific nucleotide combinations within the cis elements. Patterns of evolution at individual nucleotide positions within mammalian TF binding sites are often significantly correlated, suggesting interposition dependence. The proposed methodology is also applicable to other classes of non-coding functional elements. A detailed investigation of mutational dependencies within specific motifs could reveal preferred nucleotide combinations that may help refine the DNA binding models.

Estimation of channel and data statistics in some digital wireless communication systems

Blind estimation of noise variance and data statistics in some classes of M-symbol wireless digital communication systems is studied. Automatic gain control in pulse amplitude modulation systems und the reference carrier in coherent demodulation systems provide the noise-free signal values for the set of symbols at the receiver. This reduces the problem of channel and data statistics estimation to the estimation of the common component variance and mixing probabilities in a finite Gaussian mixture, with known values for component means. Using these known component means, /spl mu//sub 1/...../spl mu//sub M/, a set of non-linear transformations, sinh(/spl mu//sub i/x) and cosh(/spl mu//sub i/x) of the data (mixture random variable X) are used to develop convergent and computationally efficient estimators for both the noise variance and the vector of symbol probabilities. The estimation equations can be implemented recursively or with a batch processing algorithm. Asymptotic variances of the estimates are derived and the developed estimator is simulated for a specific problem.

Mimosa: mixture model of co-expression to detect modulators of regulatory interaction

Functionally related genes tend to be correlated in their expression patterns across multiple conditions and/or tissue-types. Thus co-expression networks are often used to investigate functional groups of genes. In particular, when one of the genes is a transcription factor (TF), the co-expression-based interaction is interpreted, with caution, as a direct regulatory interaction. However, any particular TF, and more importantly, any particular regulatory interaction, is likely to be active only in a subset of experimental conditions. Moreover, the subset of expression samples where the regulatory interaction holds may be marked by presence or absence of a modifier gene, such as an enzyme that post-translationally modifies the TF. Such subtlety of regulatory interactions is overlooked when one computes an overall expression correlation. Here we present a novel mixture modeling approach where a TF-Gene pair is presumed to be significantly correlated (with unknown coefficient) in a (unknown) subset of expression samples. The parameters of the model are estimated using a Maximum Likelihood approach. The estimated mixture of expression samples is then mined to identify genes potentially modulating the TF-Gene interaction. We have validated our approach using synthetic data and on three biological cases in cow and in yeast. While limited in some ways, as discussed, the work represents a novel approach to mine expression data and detect potential modulators of regulatory interactions.