Short Abstract: Dendritic cells are the major specialized antigen-presenting cells, thereby connecting innate and adaptive immunity. Because of their role in establishing adaptive immunity, they have been used as targets for immunotherapy. Monocytes can differentiate into dendritic cells in vitro in the presence of colony-stimulating factor 2 (CSF2) and interleukin 4 (IL4), activating four signalling pathways (MAPK, JAK/STAT, NFKB, and PI3K). However, the transcriptional regulation responsible for dendritic cell differentiation from monocytes (moDCs) remains unknown. By curating scientific literature on moDCs differentiation, we established a preliminary logical model that helped us identify missing information for the activation of genes responsible for this differentiation, including missing targets for key transcription factors (TFs). Using ChIP-seq and RNA-seq data from the Blueprint consortium, we defined active and inactive promoters, together with differentially expressed genes in monocytes, moDCs, and macrophages (which correspond to an alternative cell fate). We then used this functional genomic information to predict novel targets for the identified TFs. We established a second logical model integrating this information, which enabled us to recapitulate the main established facts regarding moDCs differentiation. Prospectively, the resulting model should be useful to develop novel immunotherapies based on moDCs regulatory network.

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Short Abstract: Leishmania spp. is the causal agent of several diseases called leishmaniasis, which is one of the neglected diseases that seek to be eradicated in the coming years. We aimed to study the genomic structure and function of ncRNAs (non-coding RNAs) from Leishmania spp. and to get some insights into its RNAome. We studied 26 strains corresponding to 16 different species of the genera. The analysis of RNAome revealed the presence of several ncRNAs that are shared through different species and were differentially expressed in the same developmental stage of the parasite, that coexpressed to several other coding genes involved in chromatin structure and host interaction. This work constitutes the first effort to characterize the Leishmania RNAome, supporting further approaches to better understand the role of ncRNAs in the gene regulation, infective process and host-parasite interaction.

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Short Abstract: The study of long non-coding RNAs (lncRNAs), > 200 nucleotides, is central to understanding the development and progression of many complex diseases. Unlike proteins, the functionality of lncRNAs is only subtly encoded in their primary sequence. Hence, current in-silico lncRNA annotation methods mostly rely on annotations inferred from interaction networks. But extensive experimental studies are required to build these networks. In this work, we present a graph-based Machine Learning method called FGGA-lnc for the automatic Gene Ontology (GO) annotation of lncRNAs across the three GO sub-domains. We build upon FGGA (Factor Graph GO Annotation), a computational method originally developed to annotate protein sequences from non-model organisms. In the FGGA-lnc version, a coding-based approach is introduced to fuse primary sequence and secondary structure information of lncRNA molecules. As a result, lncRNA sequences become sequences of a higher-order alphabet allowing supervised learning methods to assess individual GO-term annotations. The set of likely inconsistent GO annotations is then polished by the message passing machinery embodied in the factor graph model of the target ontology. Evaluations of FGGA-lnc on zebrafish lncRNA data showed promising results suggesting it as a candidate to satisfy the huge demand of functional annotations arising from high-throughput sequencing technologies.

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Short Abstract: Genomic and transcriptomic variation in health and disease have been extensively studied. Post-transcriptional regulation is less explored due to its higher complexity which requires more sophisticated experimental technology and analytical methods. Ribosome profiling (RP) offers a way to quantify translation efficiency across the whole transcriptome. We have developed Ribolog, a ribosome profiling data analysis tool set which has several distinct advantages over other existing methods: (i) It detects and eliminates the translation stalling bias, (ii) It does not require estimation of dispersion parameter (does not rely on negative binomial distribution), (iii) It has more statistical power and is more robust, (iv) It can work with few replicates and yield reliable results even from low-coverage libraries, (v) It is easily adaptable for experiments with synthetic spike-in standards, (vi) It introduces new RP-specific QC measures, (vii) It can accommodate complex experimental designs involving multiple samples and covariates in one model, and is not limited to pairwise comparisons. Our preliminary results applying Ribolog to a dataset comparing two poorly metastatic and two highly metastatic cell lines (CN34 and MDA vs. LM1a and LM2) indicated that this method is indeed highly sensitive and 80-90% reproducible among biological replicates. It also revealed the roles of codon optimality, tRNA abundance and RNA-binding protein binding sites on translation dynamics. Translation rate of more transcripts was influenced by metastatic state compared to genetic background. We observed patterns of co-translational regulation in downstream targets of post-transcriptional master modulators such as HNRNPC. In addition to providing a sensitive bias-corrected test of translation efficiency, Ribolog contains a method for empirical null testing to further reduce false positives, a meta-analysis tool, and an experimental design module for power analysis and sample size calculation of RP studies. Ribolog allows seamless integration of various omic data types e.g. tRNA abundances, and offers a feature selection module to identify the set of factors influencing translation efficiency of individual transcripts. Another Ribolog module quantifies the usage of upstream ORFs from annotated and unannotated translation initiation sites, and measure their regulatory effects on translation rate from the main ORF. Ribolog is available on github, and is being regularly updated, improving its performance and adding more functionalities.

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Short Abstract: Motivation: The regulation of gene expression is a key factor in the development and maintenance of life in all organisms. This process is carried out mainly through the action of transcription factors (TFs), although other actors such as ncRNAs are involved. In this work, we propose a new method to construct Gene Regulatory Networks (GRNs) depicting regulatory events in a certain context for Drosophila melanogaster. Our approach is based on known relationships between epigenetics and the activity of transcription factors. Results: We developed method, Tool for Weighted Epigenomic Networks in D. melanogaster (Fly T-WEoN), which generates GRNs starting from a reference network that contains all known gene regulations in the fly. Regulations that are unlikely taking place are removed by applying a series of knowledge-based filters. Each of these filters is implemented as an independent module that considers a type of experimental evidence, including DNA methylation, chromatin accessibility, histone modifications, and gene expression. Fly T-WEoN is based on heuristic rules that reflect current knowledge on gene regulation in D. melanogaster obtained from literature. Experimental data files can be generated with several standard procedures and used solely when and if available.Fly T-WEoN is available as a Cytoscape application that permits integration with other tools, and facilitates downstream network analysis. In this work, we first demonstrate the reliability of our method to then provide a relevant application case of our tool: early development of D. melanogaster. Availability: Fly T-WEoN, together with its step-by-step guide is available at https://weon.readthedocs.io

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Short Abstract: Trans-acting expression quantitative trait loci (trans-eQTLs) are SNPs regulating the expression of distant target genes (>1Mb). Trans-eQTLs account for =70% heritability of gene expression levels and have great potential to uncover the underlying biological mechanisms of complex diseases. However, trans-eQTLs are more challenging to identify than locally-acting cis-eQTLs because of their small effect sizes and the severe multiple-testing burden. Furthermore, strong gene expression correlations entails strong correlations among SNP-gene association P-values. Our method Tejaas can discover trans-eQTLs by performing L2-regularized ‘reverse’ multiple regression of each SNP on all gene expression levels, aggregating evidence from many small trans-effects while being unaffected by the strong expression correlations. Tejaas, coupled with a novel k-nearest neighbors confounder correction, predicts 18851 unique trans-eQTLs across 49 tissues from the GTEx(v8) data. They are enriched in various functional regions of the genome, including DHS sites (1.32x enrichment), open chromatin (1.19x), enhancer (1.31x) and promoter (1.10x) regions. They overlap with known cis-eQTLs (1.24x enrichment) from the GTEx analysis and are 1.09x enriched to be located within 100kb of known transcription factors. They are also enriched in reporter assay QTL regions of K562 (1.79x) and HepG2 (3.29x) cells indicating regulatory activity. Many trans-eQTLs overlap with disease-associated SNPs from GWAS, revealing tissue-specific transcriptional regulation mechanisms that drive disease etiology. For example, trans-eQTLs predicted in whole blood show enrichment in GWAS for blood traits, while heart and aorta are enriched in cardiometabolic traits. Thyroid and pancreas trans-eQTLs show enrichments for endocrine system diseases. Tejaas is open-source and available online (https://github.com/soedinglab/tejaas).

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Keywords: Differential expression small RNAseq ncRNAs Mapping Problem Dengue

Poster:

Short Abstract: In the last years it has become apparent that small RNA-mediated gene regulation may be even more complex than previously believed. For example, studies have found small fragments derived from ncRNAs (sfd-RNAs) varying between 20 and 30nt with silencing or interference activity derived from tRNAs and snoRNAs. Despite their structural and functional resemblance to miRNAs, current tools developed to test the differential expression of miRNAs may not be readily applied to sfd-RNAs. Two of the major causes of this problem is a lack of accurate annotation of small RNAs (ncRNAs) and the effect of the multi-mapping reads (MMR) placement problem in differential expression analysis. To solve these issues, we generated an integrated copy-number corrected ncRNA annotation and a reproducibility-qualifier methodology that allows the detection of differential expression of sfd-RNAs including canonical miRNAs. We present sixteen different computational strategies composed of combinations of four aligners and four normalization methods to detect differential expressed small RNAs derived from ncRNAs in dengue virus infected human dermal microvascular endothelial cells (HMEC-1). Expression blocks associated with sfd-RNAs have been ranked based on the sixteen different strategies. Notably, our approach let us to detect differential expressed small RNAs that has not been previously annotated in the human genome. Those strategies simultaneously detect differential expression of miRNAs and sfd-RNAs in DENV-2 infected HMEC-1 cells, systematically addressing the MMR placement problem. A set of diferential expressed sfd-RNAs during DENV-2 infection was detected for subsequent experimental validation.

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Keywords: Yoga Exercise Integrative genomics Protein protein interaction

Poster:

Short Abstract: Background: Yoga is a multifaceted spiritual tool that helps in maintaining health, peace of mind and positive thoughts. The crucial dimensions of yoga are asana (physical posture), pranayama (regulated breathing) and meditation. In the context of asana, yoga is similar to physical exercise. Introduction: This study aims to construct a molecular network to find molecules that play essential roles in both physical exercise and yoga. Method: Several keywords, including “yoga”, “exercise”, “Blood”, “Liver”, “Homo sapiens” and “20200820 to 20200830” were searched in GEO datasets. For gene ontology (GO), and KEGG pathway analysis. Protein-protein interaction (PPI) network and cluster analysis detect the potential regulatory seed genes in yoga and exercise, protecting the body from diseases and disorders. Result: We downloaded two gene expression profiles (GSE44777 and GSE6053) from the GEO database and analyzed with GEO2R. There were 666 and 361 DEGs in GSE44777 and GSE6053 respectively. A total of 32 genes co-exist in the DEGs of the two data sets. KEGG pathway analysis showed that 32 DEGs have enriched in immune system regulation and process and novel biological process (ROS, RNS production in phagocytes). We found ECT2, ILR1+, CASP9 were the most important mRNAs in the network analyzed by Cytoscape APP centiscape by MCODE Cluster formation. We constructed a miRNA-mRNA regulatory network seven targetings microRNA control the expression of CASP9. Conclusion: This integrative genomics study identified both well-characterized immune system regulatory process and novel biological process (ROS, RNS production in phagocytes) in DEGs of yoga and exercise, thus showing potential for protection against diseases and maintenance of homeostasis.

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Keywords: Gene Regulatory Networks pattern-matching motif Transcription Factor Regulatory Sequence Analysis Tools (RSAT)

Poster:

Short Abstract: Gene Regulatory Networks (GRNs) are responsible for fundamental aspects in biological systems such as environment response and physiological processes, being key in development and disease progression [1,2]. Several efforts have been done to infer them, with approaches based in coexpression, orthology, sequence motifs, etc [3]. However, there is not one perfect method and in the means of further improving the inferred GRNs, a motif-based correction underlying putative interactions can be applied as a second step. Here, we present network-interactions, a user-friendly GRN reconstruction pipeline based on pattern-matching integrated in the Regulatory Sequence Analysis Tools (RSAT) [4] platform that can help refine GRNs obtained by other tools. It takes as input a list of transcriptional factors (TFs) of interest, a file of regulatory genomic regions, the genome version of the organism of interest, and an optional previously obtained network. network-interactions runs matrix-scan using, on its default mode, JASPAR’s 2020 vertebrates nonredundant motif collection to search for TF-gene target interactions; and thereby computes several networks: a complete network for all TF-gene interactions, another for only TF-TF interactions, one with indirect interactions, and when provided an input GRN, the complements and the intersection between it and the complete network, where the intersection would contain the common results from both tools including putative TF binding information. The presented tool extends RSAT’s suite and offers a straightforward and flexible method, which facilitates the expansion and refinement of GRNs. [1] Levine, M., & Davidson, E. H. (2005). Gene regulatory networks for development. Proceedings of the National Academy of Sciences, 102(14), 4936-4942. [2] Fazilaty, H., Rago, L., Youssef, K. K., Ocaña, O. H., Garcia-Asencio, F., Arcas, A., ... & Nieto, M. A. (2019). A gene regulatory network to control EMT programs in development and disease. Nature communications, 10(1), 1-16. [3] Mercatelli, D., Scalambra, L., Triboli, L., Ray, F., & Giorgi, F. M. (2020). Gene regulatory network inference resources: A practical overview. Biochimica et Biophysica Acta (BBA)-Gene Regulatory Mechanisms, 1863(6), 194430. [4] Nguyen, N. T. T., Contreras-Moreira, B., Castro-Mondragon, J. A., Santana-Garcia, W., Ossio, R., Robles-Espinoza, C. D., ... & van Helden, J. (2018). RSAT 2018: regulatory sequence analysis tools 20th anniversary. Nucleic acids research, 46(W1), W209-W214.

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Keywords: bark beetles semiochemicals olfactory proteins host colonization

Poster:

Short Abstract: The round-headed pine beetle Dendroctonus adjunctus is one of the five most critical primary pests in forest ecosystems in Mexico, their dispersion and colonization behaviors are linked to a communication system mediated by semiochemicals. Antennae are the primary sensory organs of insects, olfactory processing includes different perireceptor events where a set of nonreceptor olfactory proteins are involved, and whose study has sharply increased in the last decade; their size, stability, resistance to high temperature, and proteolytic digestion make them candidates for the development of different biotechnological tools and applications in agriculture. This study provides the first head transcriptome analysis of D. adjunctus collected in infested trees during their higher incidence period and the identification of olfactory genes involved in the perception of odors. De novo assembly yielded 44,420 unigenes, and GO annotations were similar to those of antennal transcriptomes, which reflect metabolic processes related to smell and signal transduction. A total of 36 new transcripts of nonreceptor olfactory genes were identified, of which 27 encode OBPs, 7 encode CSPs, and 2 encode SNMP candidates. The inclusion of a comparative analysis of sequence motifs of OBPs, CSPs, and SNMPs provides clear information on the distinct characteristics of each family and their subclasses. The integration of motif patterns into phylogenetic analysis allowing not only an improved understanding of the evolutionary process but also the conservation of motif patterns between nonreceptor protein families of different Scolytinae and Coleoptera species may suggest distinct regions with functional or structural importance. Our study provides information on genes encoding nonreceptor proteins in D. adjunctus and broadens the knowledge of olfactory genes in bark beetle species, and will help to understand colonization and aggregation behaviors for the development of tools that complement management strategies.

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Keywords: network analysis RNA mediated regulation non-coding RNA plant reproductive development microprotein predictions siRNAs predictions plant-pathogen interactions

Poster:

Short Abstract: Viroids are minimal pathogens of angiosperms, consisting of non-coding RNA (239-401nt) that cause severe diseases in agronomic interest crops. In tomato, the infection symptoms by Pospiviroid species include dwarfism, reduction in vigor, abortion of flowers, and reduced size and number in fruits. The transition from vegetative growth to reproductive development requires gene network coordination, where transcription factors (TFs) act as essential organ morphogenesis components. Symptoms associated with viroid infection are linked to reproductive development in hosts, related to hormone signaling pathways, affecting the expression levels of involved TFs, including the possibility that vd-siRNAs or host factors may be involved in genetic regulation. We proposed a network of gene associations between hormonal pathways to infer a mechanism of interaction during viroid infection. Signaling pathways of hormones are primarily implicit within this network. The objective was to break down the nodes of the proposed network to analyze the transcription factors and critical targets related to the host symptoms. Our analysis highlighted that the tomato SolBigPetal1:SIBHLH036 gene ortholog of Arabidopsis thaliana BIGPETAL-1 (petal size-morphology) contains 20 of 21 nts of exon sequence corresponding to the region within the P domain of the viroid genome, and it is highly induced in tomato during infection. Another striking finding was that two PIF-TFs (SlBHLH06, SlBHLH052) are potentially linked with microproteins (miPs) regulation in brassinosteroid pathways. Besides, we identified key interactor genes in jasmonic acid (Omega 3 fatty acid desaturase/Fad7), gibberellins (Gibberellin B hydroxylase/ 3OH-1), and auxins (indoleacetic acid 3/ IAA3) hormone pathways, being negatively regulated via predicted Pospiviroid derived siRNAs. Overall, our study confirms the participation of vd-siRNAs as modulators of target genes in hormone pathways and represents the first approach to study the role of BHLH-miPs and their interactors in the genetic regulation of reproductive developmental processes during viroid infection.