PATTERNA: transcriptome-wide search for functional RNA elements via structural data signatures.

Establishing a link between RNA structure and function remains a great challenge in RNA biology. The emergence of high-throughput structure profiling experiments is revolutionizing our ability to decipher structure, yet principled approaches for extracting information on structural elements directly from these data sets are lacking. We present PATTERNA, an unsupervised pattern recognition algorithm that rapidly mines RNA structure motifs from profiling data. We demonstrate that PATTERNA detects motifs with an accuracy comparable to commonly used thermodynamic models and highlight its utility in automating data-directed structure modeling from large data sets. PATTERNA is versatile and compatible with diverse profiling techniques and experimental conditions

Global Functional Atlas of \u3cem\u3eEscherichia coli\u3c/em\u3e Encompassing Previously Uncharacterized Proteins

One-third of the 4,225 protein-coding genes of Escherichia coli K-12 remain functionally unannotated (orphans). Many map to distant clades such as Archaea, suggesting involvement in basic prokaryotic traits, whereas others appear restricted to E. coli, including pathogenic strains. To elucidate the orphans’ biological roles, we performed an extensive proteomic survey using affinity-tagged E. coli strains and generated comprehensive genomic context inferences to derive a high-confidence compendium for virtually the entire proteome consisting of 5,993 putative physical interactions and 74,776 putative functional associations, most of which are novel. Clustering of the respective probabilistic networks revealed putative orphan membership in discrete multiprotein complexes and functional modules together with annotated gene products, whereas a machine-learning strategy based on network integration implicated the orphans in specific biological processes. We provide additional experimental evidence supporting orphan participation in protein synthesis, amino acid metabolism, biofilm formation, motility, and assembly of the bacterial cell envelope. This resource provides a “systems-wide” functional blueprint of a model microbe, with insights into the biological and evolutionary significance of previously uncharacterized proteins

Engineering biological networks using cooperative transcriptional assembly

Eukaryotic genes are often regulated by multivalent transcription factor (TF) complexes. Through the process of cooperative self-assembly, these complexes carry out non-linear regulatory operations involved in cellular decision-making and signal processing. In this thesis, we apply this natural design principle to artificial networks, testing whether engineered cooperative TF assemblies can be used to program non-linear synthetic circuit behavior in yeast. Using a model-guided approach, we show that specifying strength and number of interactions in an assembly enables predictive tuning between regimes of linear and non-linear regulatory response for single- and multi-input circuits. We demonstrate that synthetic assemblies can be adjusted to control circuit dynamics, shaping the timing of activation. We harness this capability to engineer circuits that perform dynamic filtering, enabling frequency-dependent decoding in cell populations. Thru this work, we find that cooperative assembly provides a versatile way to tune nonlinearity of network connections, dramatically expanding the range engineerable behaviors available to synthetic circuits. We then extend our modeling-framework to predict genome-wide binding of our TF assemblies and find that cooperative complexes made of weakly-interacting proteins can reduce unintended activation of endogenous genes. Thus, we are able to introduce synthetic regulatory components with low fitness costs on the cell, ensuring long-term stability of our integrated circuits over time. Taken together, this dissertation outlines a synthetic framework for building cooperative transcriptional complexes in vivo in order to engineer complex regulatory behaviors that are functionally orthogonal to the host cell.2019-10-22T00:00:00

A combination of transcriptional and microRNA regulation improves the stability of the relative concentrations of target genes

It is well known that, under suitable conditions, microRNAs are able to fine tune the relative concentration of their targets to any desired value. We show that this function is particularly effective when one of the targets is a Transcription Factor (TF) which regulates the other targets. This combination defines a new class of feed-forward loops (FFLs) in which the microRNA plays the role of master regulator. Using both deterministic and stochastic equations we show that these FFLs are indeed able not only to fine-tune the TF/target ratio to any desired value as a function of the miRNA concentration but also, thanks to the peculiar topology of the circuit, to ensures the stability of this ratio against stochastic fluctuations. These two effects are due to the interplay between the direct transcriptional regulation and the indirect TF/Target interaction due to competition of TF and target for miRNA binding (the so called "sponge effect"). We then perform a genome wide search of these FFLs in the human regulatory network and show that they are characterizedby a very peculiar enrichment pattern. In particular they are strongly enriched in all the situations in which the TF and its target have to be precisely kept at the same concentration notwithstanding the environmental noise. As an example we discuss the FFL involving E2F1 as Transcription Factor, RB1 as target and miR-17 family as master regulator. These FFLs ensure a tight control of the E2F/RB ratio which in turns ensures the stability of the transition from the G0/G1 to the S phase in quiescent cells.Comment: 23 pages, 10 figure

Joint Loop End Modeling Improves Covariance Model Based Non-coding RNA Gene Search

The effect of more detailed modeling of the interface between stem and loop in non-coding RNA hairpin structures on efficacy of covariance-model-based non-coding RNA gene search is examined. Currently, the prior probabilities of the two stem nucleotides and two loop-end nucleotides at the interface are treated the same as any other stem and loop nucleotides respectively. Laboratory thermodynamic studies show that hairpin stability is dependent on the identities of these four nucleotides, but this is not taken into account in current covariance models. It is shown that separate estimation of emission priors for these nucleotides and joint treatment of substitution probabilities for the two loop-end nucleotides leads to improved non-coding RNA gene search

Statistical evidence for conserved, local secondary structure in the coding regions of eukaryotic mRNAs and pre-mRNAs

Owing to the degeneracy of the genetic code, protein-coding regions of mRNA sequences can harbour more than only amino acid information. We search the mRNA sequences of 11 human protein-coding genes for evolutionarily conserved secondary structure elements using RNA-Decoder, a comparative secondary structure prediction program that is capable of explicitly taking the known protein-coding context of the mRNA sequences into account. We detect well-defined, conserved RNA secondary structure elements in the coding regions of the mRNA sequences and show that base-paired codons strongly correlate with sparse codons. We also investigate the role of repetitive elements in the formation of secondary structure and explain the use of alternate start codons in the caveolin-1 gene by a conserved secondary structure element overlapping the nominal start codon. We discuss the functional roles of our novel findings in regulating the gene expression on mRNA level. We also investigate the role of secondary structure on the correct splicing of the human CFTR gene. We study the wild-type version of the pre-mRNA as well as 29 variants with synonymous mutations in exon 12. By comparing our predicted secondary structures to the experimentally determined splicing efficiencies, we find with weak statistical significance that pre-mRNAs with high-splicing efficiencies have different predicted secondary structures than pre-mRNAs with low-splicing efficiencies

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Analysis of Antisense Expression by Whole Genome Tiling Microarrays and siRNAs Suggests Mis-Annotation of Arabidopsis Orphan Protein-Coding Genes

MicroRNAs (miRNAs) and trans-acting small-interfering RNAs (tasi-RNAs) are small (20-22 nt long) RNAs (smRNAs) generated from hairpin secondary structures or antisense transcripts, respectively, that regulate gene expression by Watson-Crick pairing to a target mRNA and altering expression by mechanisms related to RNA interference. The high sequence homology of plant miRNAs to their targets has been the mainstay of miRNA prediction algorithms, which are limited in their predictive power for other kingdoms because miRNA complementarity is less conserved yet transitive processes (production of antisense smRNAs) are active in eukaryotes. We hypothesize that antisense transcription and associated smRNAs are biomarkers which can be computationally modeled for gene discovery.We explored rice (Oryza sativa) sense and antisense gene expression in publicly available whole genome tiling array transcriptome data and sequenced smRNA libraries (as well as C. elegans) and found evidence of transitivity of MIRNA genes similar to that found in Arabidopsis. Statistical analysis of antisense transcript abundances, presence of antisense ESTs, and association with smRNAs suggests several hundred Arabidopsis 'orphan' hypothetical genes are non-coding RNAs. Consistent with this hypothesis, we found novel Arabidopsis homologues of some MIRNA genes on the antisense strand of previously annotated protein-coding genes. A Support Vector Machine (SVM) was applied using thermodynamic energy of binding plus novel expression features of sense/antisense transcription topology and siRNA abundances to build a prediction model of miRNA targets. The SVM when trained on targets could predict the "ancient" (deeply conserved) class of validated Arabidopsis MIRNA genes with an accuracy of 84%, and 76% for "new" rapidly-evolving MIRNA genes.Antisense and smRNA expression features and computational methods may identify novel MIRNA genes and other non-coding RNAs in plants and potentially other kingdoms, which can provide insight into antisense transcription, miRNA evolution, and post-transcriptional gene regulation