Transcriptome analysis of Taenia solium cysticerci using Open reading Frame ESTS (ORESTES)

<p>Abstract</p> <p>Background</p> <p>Human infection by the pork tapeworm <it>Taenia solium </it>affects more than 50 million people worldwide, particularly in underdeveloped and developing countries. Cysticercosis which arises from larval encystation can be life threatening and difficult to treat. Here, we investigate for the first time the transcriptome of the clinically relevant cysticerci larval form.</p> <p>Results</p> <p>Using Expressed Sequence Tags (ESTs) produced by the ORESTES method, a total of 1,520 high quality ESTs were generated from 20 ORESTES cDNA mini-libraries and its analysis revealed fragments of genes with promising applications including 51 ESTs matching antigens previously described in other species, as well as 113 sequences representing proteins with potential extracellular localization, with obvious applications for immune-diagnosis or vaccine development.</p> <p>Conclusion</p> <p>The set of sequences described here will contribute to deciphering the expression profile of this important parasite and will be informative for the genome assembly and annotation, as well as for studies of intra- and inter-specific sequence variability. Genes of interest for developing new diagnostic and therapeutic tools are described and discussed.</p

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Gene expression analysis in microdissected renal tissue - Current challenges and strategies

The architecture and compartmentalization of the kidney has stimulated the development of an array of microtechniques to study the functional differences between the distinct nephron segments. With the vast amounts of genomic sequence data now available, the groundwork has been laid for a comprehensive characterization of the molecular pathways defining the differences in nephron function. With the development of sensitive gene expression techniques the tools for a comprehensive molecular analysis of specific renal microenvironments have been provided: Quantitative RT-PCR technologies now allow the analysis of specific mRNAs from as little as single microdissected renal cells. A more global view of gene expression regulation is a logical development from the application of large scale profiling techniques. In this review, we will discuss the power and pitfalls of these approaches, including their potential for the functional characterization of nephron heterogeneity and diagnostic application in renal disease. Copyright (C) 2002 S. Karger AG, Basel

Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems

A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

A Genetic Screen for Attenuated Growth Identifies Genes Crucial for Intraerythrocytic Development of Plasmodium falciparum

A majority of the Plasmodium falciparum genome codes for genes with unknown functions, which presents a major challenge to understanding the parasite's biology. Large-scale functional analysis of the parasite genome is essential to pave the way for novel therapeutic intervention strategies against the disease and yet difficulties in genetic manipulation of this deadly human malaria parasite have been a major hindrance for functional analysis of its genome. Here, we used a forward functional genomic approach to study P. falciparum and identify genes important for optimal parasite development in the disease-causing, intraerythrocytic stages. We analyzed 123 piggyBac insertion mutants of P. falciparum for proliferation efficiency in the intraerythrocytic stages, in vitro. Almost 50% of the analyzed mutants showed significant reduction in proliferation efficiency, with 20% displaying severe defects. Functional categorization of genes in the severely attenuated mutants revealed significant enrichment for RNA binding proteins, suggesting the significance of post-transcriptional gene regulation in parasite development and emphasizing its importance as an antimalarial target. This study demonstrates the feasibility of much needed forward genetics approaches for P. falciparum to better characterize its genome and accelerate drug and vaccine development