IκBε deficiency accelerates disease development in chronic lymphocytic leukemia.

The NFKBIE gene, which encodes the NF-κB inhibitor IκBε, is mutated in 3-7% of patients with chronic lymphocytic leukemia (CLL). The most recurrent alteration is a 4-bp frameshift deletion associated with NF-κB activation in leukemic B cells and poor clinical outcome. To study the functional consequences of NFKBIE gene inactivation, both in vitro and in vivo, we engineered CLL B cells and CLL-prone mice to stably down-regulate NFKBIE expression and investigated its role in controlling NF-κB activity and disease expansion. We found that IκBε loss leads to NF-κB pathway activation and promotes both migration and proliferation of CLL cells in a dose-dependent manner. Importantly, NFKBIE inactivation was sufficient to induce a more rapid expansion of the CLL clone in lymphoid organs and contributed to the development of an aggressive disease with a shortened survival in both xenografts and genetically modified mice. IκBε deficiency was associated with an alteration of the MAPK pathway, also confirmed by RNA-sequencing in NFKBIE-mutated patient samples, and resistance to the BTK inhibitor ibrutinib. In summary, our work underscores the multimodal relevance of the NF-κB pathway in CLL and paves the way to translate these findings into novel therapeutic options.

Intranuclear inclusions of polyQ-expanded ATXN1 sequester RNA molecules.

Spinocerebellar ataxia type 1 (SCA1) is an autosomal dominant neurodegenerative disease caused by a trinucleotide (CAG) repeat expansion in the ATXN1 gene. It is characterized by the presence of polyglutamine (polyQ) intranuclear inclusion bodies (IIBs) within affected neurons. In order to investigate the impact of polyQ IIBs in SCA1 pathogenesis, we generated a novel protein aggregation model by inducible overexpression of the mutant ATXN1(Q82) isoform in human neuroblastoma SH-SY5Y cells. Moreover, we developed a simple and reproducible protocol for the efficient isolation of insoluble IIBs. Biophysical characterization showed that polyQ IIBs are enriched in RNA molecules which were further identified by next-generation sequencing. Finally, a protein interaction network analysis indicated that sequestration of essential RNA transcripts within ATXN1(Q82) IIBs may affect the ribosome resulting in error-prone protein synthesis and global proteome instability. These findings provide novel insights into the molecular pathogenesis of SCA1, highlighting the role of polyQ IIBs and their impact on critical cellular processes.

Combining Multiple RNA-Seq Data Analysis Algorithms Using Machine Learning Improves Differential Isoform Expression Analysis.

RNA sequencing has become the standard technique for high resolution genome-wide monitoring of gene expression. As such, it often comprises the first step towards understanding complex molecular mechanisms driving various phenotypes, spanning organ development to disease genesis, monitoring and progression. An advantage of RNA sequencing is its ability to capture complex transcriptomic events such as alternative splicing which results in alternate isoform abundance. At the same time, this advantage remains algorithmically and computationally challenging, especially with the emergence of even higher resolution technologies such as single-cell RNA sequencing. Although several algorithms have been proposed for the effective detection of differential isoform expression from RNA-Seq data, no widely accepted golden standards have been established. This fact is further compounded by the significant differences in the output of different algorithms when applied on the same data. In addition, many of the proposed algorithms remain scarce and poorly maintained. Driven by these challenges, we developed a novel integrative approach that effectively combines the most widely used algorithms for differential transcript and isoform analysis using state-of-the-art machine learning techniques. We demonstrate its usability by applying it on simulated data based on several organisms, and using several performance metrics; we conclude that our strategy outperforms the application of the individual algorithms. Finally, our approach is implemented as an R Shiny application, with the underlying data analysis pipelines also available as docker containers.

TRIP - T cell receptor/immunoglobulin profiler.

BACKGROUND: Antigen receptors are characterized by an extreme diversity of specificities, which poses major computational and analytical challenges, particularly in the era of high-throughput immunoprofiling by next generation sequencing (NGS). The T cell Receptor/Immunoglobulin Profiler (TRIP) tool offers the opportunity for an in-depth analysis based on the processing of the output files of the IMGT/HighV-Quest tool, a standard in NGS immunoprofiling, through a number of interoperable modules. These provide detailed information about antigen receptor gene rearrangements, including variable (V), diversity (D) and joining (J) gene usage, CDR3 amino acid and nucleotide composition and clonality of both T cell receptors (TR) and B cell receptor immunoglobulins (BcR IG), and characteristics of the somatic hypermutation within the BcR IG genes. TRIP is a web application implemented in R shiny. RESULTS: Two sets of experiments have been performed in order to evaluate the efficiency and performance of the TRIP tool. The first used a number of synthetic datasets, ranging from 250k to 1M sequences, and established the linear response time of the tool (about 6 h for 1M sequences processed through the entire BcR IG data pipeline). The reproducibility of the tool was tested comparing the results produced by the main TRIP workflow with the results from a previous pipeline used on the Galaxy platform. As expected, no significant differences were noted between the two tools; although the preselection process seems to be stricter within the TRIP pipeline, about 0.1% more rearrangements were filtered out, with no impact on the final results. CONCLUSIONS: TRIP is a software framework that provides analytical services on antigen receptor gene sequence data. It is accurate and contains functions for data wrangling, cleaning, analysis and visualization, enabling the user to build a pipeline tailored to their needs. TRIP is publicly available at https://bio.tools/TRIP_-_T-cell_Receptor_Immunoglobulin_Profiler .

Ancestral state reconstruction of metabolic pathways across pangenome ensembles.

As genome sequencing efforts are unveiling the genetic diversity of the biosphere with an unprecedented speed, there is a need to accurately describe the structural and functional properties of groups of extant species whose genomes have been sequenced, as well as their inferred ancestors, at any given taxonomic level of their phylogeny. Elaborate approaches for the reconstruction of ancestral states at the sequence level have been developed, subsequently augmented by methods based on gene content. While these approaches of sequence or gene-content reconstruction have been successfully deployed, there has been less progress on the explicit inference of functional properties of ancestral genomes, in terms of metabolic pathways and other cellular processes. Herein, we describe PathTrace, an efficient algorithm for parsimony-based reconstructions of the evolutionary history of individual metabolic pathways, pivotal representations of key functional modules of cellular function. The algorithm is implemented as a five-step process through which pathways are represented as fuzzy vectors, where each enzyme is associated with a taxonomic conservation value derived from the phylogenetic profile of its protein sequence. The method is evaluated with a selected benchmark set of pathways against collections of genome sequences from key data resources. By deploying a pangenome-driven approach for pathway sets, we demonstrate that the inferred patterns are largely insensitive to noise, as opposed to gene-content reconstruction methods. In addition, the resulting reconstructions are closely correlated with the evolutionary distance of the taxa under study, suggesting that a diligent selection of target pangenomes is essential for maintaining cohesiveness of the method and consistency of the inference, serving as an internal control for an arbitrary selection of queries. The PathTrace method is a first step towards the large-scale analysis of metabolic pathway evolution and our deeper understanding of functional relationships reflected in emerging pangenome collections.

BioPAXViz: a cytoscape application for the visual exploration of metabolic pathway evolution.

Summary: BioPAXViz is a Cytoscape (version 3) application, providing a comprehensive framework for metabolic pathway visualization. Beyond the basic parsing, viewing and browsing roles, the main novel function that BioPAXViz provides is a visual comparative analysis of metabolic pathway topologies across pre-computed pathway phylogenomic profiles given a species phylogeny. Furthermore, BioPAXViz supports the display of hierarchical trees that allow efficient navigation through sets of variants of a single reference pathway. Thus, BioPAXViz can significantly facilitate, and contribute to, the study of metabolic pathway evolution and engineering. Availability and Implementation: BioPAXViz has been developed as a Cytoscape app and is available at: https://github.com/CGU-CERTH/BioPAX.Viz. The software is distributed under the MIT License and is accompanied by example files and data. Additional documentation is available at the aforementioned GitHub repository. Contact: ouzounis@certh.gr.

Future opportunities and trends for e-infrastructures and life sciences: going beyond the grid to enable life science data analysis.

With the increasingly rapid growth of data in life sciences we are witnessing a major transition in the way research is conducted, from hypothesis-driven studies to data-driven simulations of whole systems. Such approaches necessitate the use of large-scale computational resources and e-infrastructures, such as the European Grid Infrastructure (EGI). EGI, one of key the enablers of the digital European Research Area, is a federation of resource providers set up to deliver sustainable, integrated and secure computing services to European researchers and their international partners. Here we aim to provide the state of the art of Grid/Cloud computing in EU research as viewed from within the field of life sciences, focusing on key infrastructures and projects within the life sciences community. Rather than focusing purely on the technical aspects underlying the currently provided solutions, we outline the design aspects and key characteristics that can be identified across major research approaches. Overall, we aim to provide significant insights into the road ahead by establishing ever-strengthening connections between EGI as a whole and the life sciences community.

Detection of genomic idiosyncrasies using fuzzy phylogenetic profiles.

Phylogenetic profiles express the presence or absence of genes and their homologs across a number of reference genomes. They have emerged as an elegant representation framework for comparative genomics and have been used for the genome-wide inference and discovery of functionally linked genes or metabolic pathways. As the number of reference genomes grows, there is an acute need for faster and more accurate methods for phylogenetic profile analysis with increased performance in speed and quality. We propose a novel, efficient method for the detection of genomic idiosyncrasies, i.e. sets of genes found in a specific genome with peculiar phylogenetic properties, such as intra-genome correlations or inter-genome relationships. Our algorithm is a four-step process where genome profiles are first defined as fuzzy vectors, then discretized to binary vectors, followed by a de-noising step, and finally a comparison step to generate intra- and inter-genome distances for each gene profile. The method is validated with a carefully selected benchmark set of five reference genomes, using a range of approaches regarding similarity metrics and pre-processing stages for noise reduction. We demonstrate that the fuzzy profile method consistently identifies the actual phylogenetic relationship and origin of the genes under consideration for the majority of the cases, while the detected outliers are found to be particular genes with peculiar phylogenetic patterns. The proposed method provides a time-efficient and highly scalable approach for phylogenetic stratification, with the detected groups of genes being either similar to their own genome profile or different from it, thus revealing atypical evolutionary histories.

The chlamydiales pangenome revisited: structural stability and functional coherence.

The entire publicly available set of 37 genome sequences from the bacterial order Chlamydiales has been subjected to comparative analysis in order to reveal the salient features of this pangenome and its evolutionary history. Over 2,000 protein families are detected across multiple species, with a distribution consistent to other studied pangenomes. Of these, there are 180 protein families with multiple members, 312 families with exactly 37 members corresponding to core genes, 428 families with peripheral genes with varying taxonomic distribution and finally 1,125 smaller families. The fact that, even for smaller genomes of Chlamydiales, core genes represent over a quarter of the average protein complement, signifies a certain degree of structural stability, given the wide range of phylogenetic relationships within the group. In addition, the propagation of a corpus of manually curated annotations within the discovered core families reveals key functional properties, reflecting a coherent repertoire of cellular capabilities for Chlamydiales. We further investigate over 2,000 genes without homologs in the pangenome and discover two new protein sequence domains. Our results, supported by the genome-based phylogeny for this group, are fully consistent with previous analyses and current knowledge, and point to future research directions towards a better understanding of the structural and functional properties of Chlamydiales.