The advent of recent high throughput sequencing technologies resulted in an unexplored big data of genomics and transcriptomics that might help to answer various research questions in Parkinson’s disease(PD) progression. While the literature has revealed various predictive models that use longitudinal clinical data for disease progression, there is no predictive model based on RNA-Sequence data of PD patients. This study investigates how to predict the PD Progression for a patient’s next medical visit by capturing longitudinal temporal patterns in the RNA-Seq data. Data provided by Parkinson Progression Marker Initiative (PPMI) includes 423 PD patients with a variable number of visits for a period of 4 years. We propose a predictive model based on a Recurrent Neural Network (RNN) with dense connections. The results show that the proposed architecture is able to predict PD progression from high dimensional RNA-seq data with a Root Mean Square Error (RMSE) of 6.0 and rank-order correlation of (r=0.83, p<0.0001) between the predicted and actual disease status of PD. We show empirical evidence that the addition of dense connections and batch normalization into RNN layers boosts its training and generalization capability.

No description available.

In cancer research, class discovery is the first process for investigating a new dataset for which hidden groups there are by similar attributes. However datasets from gene expressions, RNA microarray or RNA-sequence, are high-dimensional. Which makes it hard to perform clusteranalysis and to get clusters that are well separated. Well separated clusters are wanted because that tells that objects are most likely not placed in wrong clusters. This report investigate in an experiment whether using K-Means and hierarchical are suitable for clustering gene expressions in RNA-sequence data from various tumors. Dimensionality reduction methods are also applied to see whether that helps create well-separated clusters. The results tell that well separated clusters are only achieved by using PCA as dimensionality reduction and K-Means on correlation. The main contribution of this paper is determining that using K-Means or hierarchical clustering on the full natural dimensionality of RNA-sequence data returns unwanted silhouette average width, under 0,4.

Sequence-function relationship is a fundamental question for many branches of modern biomedical research. It connects the primary sequence of proteins to the function of proteins and fitness of organisms, holding answers for critical questions such as functional consequences of mutations identified in whole genome sequencing and adaptive potential of fast evolving pathogenic viruses and microbes. Many different approaches have been developed to delineate the genotype-phenotype map for different proteins, but are generally limited by their throughput or precision. To systematically quantify the fitness of large numbers of mutations, I modified a novel high throughput mutational scanning approach (EMPIRIC) to investigate the fitness landscape of mutations in important regions of essential proteins from the yeast or RNA viruses. Using EMPIRIC, I analyzed the interplay of the expression level and sequence of Hsp90 on the yeast growth and revealed latent effect of mutations at reduced expression levels of Hsp90. I also examined the functional constraint on the receptor binding site of the Env of Human Immunodeficiency Virus (HIV) and uncovered enhanced receptor binding capacity as a common pathway for adaptation of HIV to laboratory conditions. Moreover, I explored the adaptive potential of neuraminidase (NA) of influenza A virus to a NA inhibitor, oseltamivir, and identified novel oseltamivir resistance mutations with distinct molecular mechanisms. In summary, I applied a high throughput functional genomics approach to map the sequence-function relationship in various systems and examined the evolutionary constraints and adaptive potential of essential proteins ranging from molecular chaperones to drug-targetable viral proteins.

A síndrome de Bloom (SB) é uma síndrome de instabilidade cromossômica rara, transmitida por herança autossômica recessiva. Caracteriza-se por deficiência de crescimento pré e pós-natal, microcefalia, hipoplasia malar, eritema telangiectásico em face e comprometimento do sistema imunológico, entre outras manifestações clínicas. Os pacientes com SB apresentam predisposição aumentada para o desenvolvimento de neoplasias em idade precoce, sendo esta, a principal causa de óbito. Ao estudo citogenético observa-se aumento de quebras cromossômicas espontâneas e trocas entre cromátides irmãs (TCI), que são utilizadas como marcador diagnóstico para a SB. Além disso, a literatura mostra que a maioria dos pacientes também apresenta mutações no gene BLM, que estão relacionadas a defeitos no mecanismo de reparo do DNA. No entanto, os mecanismos fisiopatológicos não são completamente compreendidos. Nesse sentido estudamos o transcriptoma de duas pacientes portadoras da síndrome de Bloom e de três controles utilizando a metodologia RNA-seq (Illumina, Inc., San Diego, CA). A análise de expressão diferencial revelou 216 genes diferencialmente expressos relacionados a vias relacionadas à resposta imune como replicação negativa da regulação da replicação do genoma viral, regulação positiva da proliferação de células B, via de sinalização mediada por interferon gama, ativação de células B, resposta a vírus, resposta imune adaptativa e processo efetor imune, e nenhuma diferença da expressão em genes de reparo de DNA. Concomitantemente, observamos a hiperexpressão do gene BLM para ambas as pacientes, contribuindo para a desestabilização de genes envolvidos em vias imunológicas, fenômeno também observado em alguns tumores. Dessa forma, sugerimos que a combinação de defeitos de proliferação linfocitária e defeitos de sinalização celular somados a outros, como perda celular e expressão alterada do gene BLM, podem contribuir diretamente para as principais características observadas na síndrome de Bloom, como a deficiência de crescimento e o elevado risco de câncer. Futuramente, o estudo do transcriptoma, aplicado a outros portadores da SB e outras síndromes de instabilidade, possibilitará uma análise mais acurada das interações gênicas relevantes para a desestabilização do genoma / Bloom Syndrome (BS) is a rare chromosome instability syndrome, with recessive autosomal inheritance. The main clinical manifestations are pre and postnatal growth deficiency, microcephaly, malar hypoplasia, telangiectasic facial erythema and compromised immune system, among others. Patients with BS present increased risk to the development of neoplasias at an early age, which is the main cause of death. Cytogenetic test is used as a diagnostic marker for BS since the patient\'s cells present increase in spontaneous chromosomal breaks and sister chromatid exchange (SCE). In addition, the literature reveals that most patients also present mutations in the BLM gene, which are related to defects in the DNA repair mechanism; however, it is still not completely understood. In this sense, we studied the transcriptome of two patients with Bloom\'s syndrome and three controls using the RNA-seq methodology (Illumina, Inc., San Diego, CA). Differential expression analysis revealed 216 differentially expressed genes related to immunological pathways such as: negative replication of the regulation of the viral genome replication, positive regulation of B cells proliferation, gama-interferon mediated signalization pathway, B cells activation, virus response, adaptive immune response and immune effector process, and absence of difference of DNA repair genes expression. At the same time, we observed the hyperexpression of the BLM gene for both patients contributing for the destabilization of genes involved in immunological pathways, a phenomenon also observed in some tumors. Thus, we suggest that the combination of lymphoid proliferation defects and cell signaling defects added to others such as cell loss and altered expression of the BLM gene may contribute directly to the main characteristics observed in Bloom\'s syndrome, such as growth failure and high risk of cancer. In the future, the study of the transcriptome applied to other BS carriers and other instability syndromes, will allow a more accurate analysis of the relevant gene interactions to the destabilization of the genome

A síndrome de Bloom (SB) é uma síndrome de instabilidade cromossômica rara, transmitida por herança autossômica recessiva. Caracteriza-se por deficiência de crescimento pré e pós-natal, microcefalia, hipoplasia malar, eritema telangiectásico em face e comprometimento do sistema imunológico, entre outras manifestações clínicas. Os pacientes com SB apresentam predisposição aumentada para o desenvolvimento de neoplasias em idade precoce, sendo esta, a principal causa de óbito. Ao estudo citogenético observa-se aumento de quebras cromossômicas espontâneas e trocas entre cromátides irmãs (TCI), que são utilizadas como marcador diagnóstico para a SB. Além disso, a literatura mostra que a maioria dos pacientes também apresenta mutações no gene BLM, que estão relacionadas a defeitos no mecanismo de reparo do DNA. No entanto, os mecanismos fisiopatológicos não são completamente compreendidos. Nesse sentido estudamos o transcriptoma de duas pacientes portadoras da síndrome de Bloom e de três controles utilizando a metodologia RNA-seq (Illumina, Inc., San Diego, CA). A análise de expressão diferencial revelou 216 genes diferencialmente expressos relacionados a vias relacionadas à resposta imune como replicação negativa da regulação da replicação do genoma viral, regulação positiva da proliferação de células B, via de sinalização mediada por interferon gama, ativação de células B, resposta a vírus, resposta imune adaptativa e processo efetor imune, e nenhuma diferença da expressão em genes de reparo de DNA. Concomitantemente, observamos a hiperexpressão do gene BLM para ambas as pacientes, contribuindo para a desestabilização de genes envolvidos em vias imunológicas, fenômeno também observado em alguns tumores. Dessa forma, sugerimos que a combinação de defeitos de proliferação linfocitária e defeitos de sinalização celular somados a outros, como perda celular e expressão alterada do gene BLM, podem contribuir diretamente para as principais características observadas na síndrome de Bloom, como a deficiência de crescimento e o elevado risco de câncer. Futuramente, o estudo do transcriptoma, aplicado a outros portadores da SB e outras síndromes de instabilidade, possibilitará uma análise mais acurada das interações gênicas relevantes para a desestabilização do genoma / Bloom Syndrome (BS) is a rare chromosome instability syndrome, with recessive autosomal inheritance. The main clinical manifestations are pre and postnatal growth deficiency, microcephaly, malar hypoplasia, telangiectasic facial erythema and compromised immune system, among others. Patients with BS present increased risk to the development of neoplasias at an early age, which is the main cause of death. Cytogenetic test is used as a diagnostic marker for BS since the patient\'s cells present increase in spontaneous chromosomal breaks and sister chromatid exchange (SCE). In addition, the literature reveals that most patients also present mutations in the BLM gene, which are related to defects in the DNA repair mechanism; however, it is still not completely understood. In this sense, we studied the transcriptome of two patients with Bloom\'s syndrome and three controls using the RNA-seq methodology (Illumina, Inc., San Diego, CA). Differential expression analysis revealed 216 differentially expressed genes related to immunological pathways such as: negative replication of the regulation of the viral genome replication, positive regulation of B cells proliferation, gama-interferon mediated signalization pathway, B cells activation, virus response, adaptive immune response and immune effector process, and absence of difference of DNA repair genes expression. At the same time, we observed the hyperexpression of the BLM gene for both patients contributing for the destabilization of genes involved in immunological pathways, a phenomenon also observed in some tumors. Thus, we suggest that the combination of lymphoid proliferation defects and cell signaling defects added to others such as cell loss and altered expression of the BLM gene may contribute directly to the main characteristics observed in Bloom\'s syndrome, such as growth failure and high risk of cancer. In the future, the study of the transcriptome applied to other BS carriers and other instability syndromes, will allow a more accurate analysis of the relevant gene interactions to the destabilization of the genome

Indiana University-Purdue University Indianapolis (IUPUI) / RNA-binding proteins (RBPs) are vital post-transcriptional regulatory molecules in transcriptome of mammalian species. It necessitates studying their expression dynamics to extract how post-transcriptional networks work in various mammalian tissues. RNA binding proteins (RBPs) play important roles in controlling the post-transcriptional fate of RNA molecules, yet their evolutionary dynamics remains largely unknown. As expression profiles of genes encoding for RBPs can yield insights about their evolutionary trajectories on the post-transcriptional regulatory networks across species, we performed a comparative analyses of RBP expression profiles across 8 tissues (brain, cerebellum, heart, lung, liver, lung, skeletal muscle, testis) in 11 mammals (human, chimpanzee, gorilla, orangutan, macaque, rat, mouse, platypus, opossum, cow) and chicken & frog (evolutionary outgroups). Noticeably, orthologous gene expression profiles suggest a significantly higher expression level for RBPs than their non-RBP gene counterparts, which include other protein-coding and non-coding genes, across all the mammalian tissues studied here. This trend is significant irrespective of the tissue and species being compared, though RBP gene expression distribution patterns were found to be generally diverse in nature. Our analysis also shows that RBPs are expressed at a significantly lower level in human and mouse tissues compared to their expression levels in equivalent tissues in other mammals: chimpanzee, orangutan, rat, etc., which are all likely exposed to diverse natural habitats and ecological settings compared to more stable ecological environment humans and mice might have been exposed, thus reducing the need for complex and extensive post-transcriptional control. Further analysis of the similarity of orthologous RBP expression profiles between all pairs of tissue-mammal combinations clearly showed the grouping of RBP expression profiles across tissues in a given mammal, in contrast to the clustering of expression profiles for non-RBPs, which frequently grouped equivalent tissues across diverse mammalian species together, suggesting a significant evolution of RBPs expression after speciation events. Calculation of species specificity indices (SSIs) for RBPs across various tissues, to identify those that exhibited restricted expression to few mammals, revealed that about 30% of the RBPs are species-specific in at least one tissue studied here, with lung, liver, kidney & testis exhibiting a significantly higher proportion of species specifically expressed RBPs. We conducted a differential expression analysis of RBPs in human, mouse and chicken tissues to study the evolution of expression levels in recently evolved species (i.e., humans and mice) than evolutionarily-distant species (i.e., chickens). We identified more than 50% of the orthologous RBPs to be differentially expressed in at least one tissue, compared between human and mouse, but not so between human and an outgroup chicken, in which RBP expression levels are relatively conserved. Among the studied tissues (brain, liver and kidney) showed a higher fraction of differentially expressed RBPs, which may suggest hyper- regulatory activities by RBPs in these tissues with species evolution. Overall, this study forms a foundation for understanding the evolution of expression levels of RBPs in mammals, facilitating a snapshot of the wiring patterns of post-transcriptional regulatory networks in mammalian genomes. In our second study, we focused on elucidating novel features of post-transcriptional regulatory molecules called as circRNA from LongPolyA RNA-sequence data. The debate over presence of nonlinear exon splicing such as exon-shuffling or formation of circularized forms has finally come to an end as numerous repertoires have shown of their occurrence and presence through transcriptomic analyses. It is evident from previous studies that along with consensus-site splicing non-consensus site splicing is robustly occurring in the cell. Also, in spite of applying different high-throughput approaches (both computational and experimental) to determine their abundance, the signal is consistent and strongly conforming the plausible circularization mechanisms. Earlier studies hypothesized and hence focused on the ribo-minus non-polyA RNA-sequence data to identify circular RNA structures in cell and compared their abundance levels with their linear counterparts. Thus far, the studies show their conserved nature across tissues and species also that they are not translated and preferentially are without poly (A) tail, with one to five exons long. Much of this initial work has been performed using non-polyA sequencing thus probably underestimates the abundance of circular RNAs originating from long poly (A) RNA isoforms. Our hypothesis is if the circular RNA events are not the artifact of random events, but has a structured and defined mechanism for their formation, then there would not be biases on preferential selection / leaving of polyA tails, while forming the circularized isoforms. We have applied an existing computational pipeline from earlier studies by Memczack et. al., on ENCODE cell-lines long poly (A) RNA-sequence data. With the same pipeline, we achieve a significant number of circular RNA isoforms in the data, some of which are overlapping with known circular RNA isoforms from the literature. We identified an approach and worked upon to identify the precise structure of circular RNA, which is not plausible from the existing computational approaches. We aim to study their expression profiles in normal and cancer cell-lines, and see if there exists any pattern and functional significance based on their abundance levels in the cell.