Chromatin activation profiling of stereotyped chronic lymphocytic leukemias reveals a subset 8-specific signature.

The chromatin activation landscape of chronic lymphocytic leukemia (CLL) with stereotyped B-cell receptor immunoglobulin is currently unknown. In this study, we report the results of a whole-genome chromatin profiling of histone 3 lysine 27 acetylation of 22 CLLs from major subsets, which were compared against nonstereotyped CLLs and normal B-cell subpopulations. Although subsets 1, 2, and 4 did not differ much from their nonstereotyped CLL counterparts, subset 8 displayed a remarkably distinct chromatin activation profile. In particular, we identified 209 de novo active regulatory elements in this subset, which showed similar patterns with U-CLLs undergoing Richter transformation. These regions were enriched for binding sites of 9 overexpressed transcription factors. In 78 of 209 regions, we identified 113 candidate overexpressed target genes, 11 regions being associated with more than 2 adjacent genes. These included blocks of up to 7 genes, suggesting local coupregulation within the same genome compartment. Our findings further underscore the uniqueness of subset 8 CLL, notable for the highest risk of Richter's transformation among all CLLs and provide additional clues to decipher the molecular basis of its clinical behavior.

N-Glycosylation of the Ig Receptors Shapes the Antigen Reactivity in Chronic Lymphocytic Leukemia Subset #201.

Subset #201 is a clinically indolent subgroup of patients with chronic lymphocytic leukemia defined by the expression of stereotyped, mutated IGHV4-34/IGLV1-44 BCR Ig. Subset #201 is characterized by recurrent somatic hypermutations (SHMs) that frequently lead to the creation and/or disruption of N-glycosylation sites within the Ig H and L chain variable domains. To understand the relevance of this observation, using next-generation sequencing, we studied how SHM shapes the subclonal architecture of the BCR Ig repertoire in subset #201, particularly focusing on changes in N-glycosylation sites. Moreover, we profiled the Ag reactivity of the clonotypic BCR Ig expressed as rmAbs. We found that almost all analyzed cases from subset #201 carry SHMs potentially affecting N-glycosylation at the clonal and/or subclonal level and obtained evidence for N-glycan occupancy in SHM-induced novel N-glycosylation sites. These particular SHMs impact (auto)antigen recognition, as indicated by differences in Ag reactivity between the authentic rmAbs and germline revertants of SHMs introducing novel N-glycosylation sites in experiments entailing 1) flow cytometry for binding to viable cells, 2) immunohistochemistry against various human tissues, 3) ELISA against microbial Ags, and 4) protein microarrays testing reactivity against multiple autoantigens. On these grounds, N-glycosylation appears as relevant for the natural history of at least a fraction of Ig-mutated chronic lymphocytic leukemia. Moreover, subset #201 emerges as a paradigmatic case for the role of affinity maturation in the evolution of Ag reactivity of the clonotypic BCR Ig.

IgIDivA: immunoglobulin intraclonal diversification analysis.

Intraclonal diversification (ID) within the immunoglobulin (IG) genes expressed by B cell clones arises due to ongoing somatic hypermutation (SHM) in a context of continuous interactions with antigen(s). Defining the nature and order of appearance of SHMs in the IG genes can assist in improved understanding of the ID process, shedding light into the ontogeny and evolution of B cell clones in health and disease. Such endeavor is empowered thanks to the introduction of high-throughput sequencing in the study of IG gene repertoires. However, few existing tools allow the identification, quantification and characterization of SHMs related to ID, all of which have limitations in their analysis, highlighting the need for developing a purpose-built tool for the comprehensive analysis of the ID process. In this work, we present the immunoglobulin intraclonal diversification analysis (IgIDivA) tool, a novel methodology for the in-depth qualitative and quantitative analysis of the ID process from high-throughput sequencing data. IgIDivA identifies and characterizes SHMs that occur within the variable domain of the rearranged IG genes and studies in detail the connections between identified SHMs, establishing mutational pathways. Moreover, it combines established and new graph-based metrics for the objective determination of ID level, combined with statistical analysis for the comparison of ID level features for different groups of samples. Of importance, IgIDivA also provides detailed visualizations of ID through the generation of purpose-built graph networks. Beyond the method design, IgIDivA has been also implemented as an R Shiny web application. IgIDivA is freely available at https://bio.tools/igidiva.

Galaxy Training: A powerful framework for teaching!

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis, and stewardship are still rarely taught in life science educational programs, resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org), an open access, community-driven framework for the collection of FAIR (Findable, Accessible, Interoperable, Reusable) training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics, and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments.

Higher-order immunoglobulin repertoire restrictions in CLL: the illustrative case of stereotyped subsets 2 and 169.

Chronic lymphocytic leukemia (CLL) major stereotyped subset 2 (IGHV3-21/IGLV3-21, ∼2.5% of all cases of CLL) is an aggressive disease variant, irrespective of the somatic hypermutation (SHM) status of the clonotypic IGHV gene. Minor stereotyped subset 169 (IGHV3-48/IGLV3-21, ∼0.2% of all cases of CLL) is related to subset 2, as it displays a highly similar variable antigen-binding site. We further explored this relationship through next-generation sequencing and crystallographic analysis of the clonotypic B-cell receptor immunoglobulin. Branching evolution of the predominant clonotype through intraclonal diversification in the context of ongoing SHM was evident in both heavy and light chain genes of both subsets. Molecular similarities between the 2 subsets were highlighted by the finding of shared SHMs within both the heavy and light chain genes in all analyzed cases at either the clonal or subclonal level. Particularly noteworthy in this respect was a ubiquitous SHM at the linker region between the variable and the constant domain of the IGLV3-21 light chains, previously reported as critical for immunoglobulin homotypic interactions underlying cell-autonomous signaling capacity. Notably, crystallographic analysis revealed that the IGLV3-21-bearing CLL subset 169 immunoglobulin retains the same geometry and contact residues for the homotypic intermolecular interaction observed in subset 2, including the SHM at the linker region, and, from a molecular standpoint, belong to a common structural mode of autologous recognition. Collectively, our findings document that stereotyped subsets 2 and 169 are very closely related, displaying shared immunoglobulin features that can be explained only in the context of shared functional selection.

Ten simple rules for making training materials FAIR.

Everything we do today is becoming more and more reliant on the use of computers. The field of biology is no exception; but most biologists receive little or no formal preparation for the increasingly computational aspects of their discipline. In consequence, informal training courses are often needed to plug the gaps; and the demand for such training is growing worldwide. To meet this demand, some training programs are being expanded, and new ones are being developed. Key to both scenarios is the creation of new course materials. Rather than starting from scratch, however, it's sometimes possible to repurpose materials that already exist. Yet finding suitable materials online can be difficult: They're often widely scattered across the internet or hidden in their home institutions, with no systematic way to find them. This is a common problem for all digital objects. The scientific community has attempted to address this issue by developing a set of rules (which have been called the Findable, Accessible, Interoperable and Reusable [FAIR] principles) to make such objects more findable and reusable. Here, we show how to apply these rules to help make training materials easier to find, (re)use, and adapt, for the benefit of all.

A framework to assess the quality and impact of bioinformatics training across ELIXIR.

ELIXIR is a pan-European intergovernmental organisation for life science that aims to coordinate bioinformatics resources in a single infrastructure across Europe; bioinformatics training is central to its strategy, which aims to develop a training community that spans all ELIXIR member states. In an evidence-based approach for strengthening bioinformatics training programmes across Europe, the ELIXIR Training Platform, led by the ELIXIR EXCELERATE Quality and Impact Assessment Subtask in collaboration with the ELIXIR Training Coordinators Group, has implemented an assessment strategy to measure quality and impact of its entire training portfolio. Here, we present ELIXIR's framework for assessing training quality and impact, which includes the following: specifying assessment aims, determining what data to collect in order to address these aims, and our strategy for centralised data collection to allow for ELIXIR-wide analyses. In addition, we present an overview of the ELIXIR training data collected over the past 4 years. We highlight the importance of a coordinated and consistent data collection approach and the relevance of defining specific metrics and answer scales for consortium-wide analyses as well as for comparison of data across iterations of the same course.

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 .

Integrated epigenomic and transcriptomic analysis reveals TP63 as a novel player in clinically aggressive chronic lymphocytic leukemia.

Chronic lymphocytic leukemia (CLL) stereotyped subsets #6 and #8 include cases expressing unmutated B cell receptor immunoglobulin (BcR IG) (U-CLL). Subset #6 (IGHV1-69/IGKV3-20) is less aggressive compared to subset #8 (IGHV4-39/IGKV1(D)-39) which has the highest risk for Richter's transformation among all CLL. The underlying reasons for this divergent clinical behavior are not fully elucidated. To gain insight into this issue, here we focused on epigenomic signatures and their links with gene expression, particularly investigating genome-wide DNA methylation profiles in subsets #6 and #8 as well as other U-CLL cases not expressing stereotyped BcR IG. We found that subset #8 showed a distinctive DNA methylation profile compared to all other U-CLL cases, including subset #6. Integrated analysis of DNA methylation and gene expression revealed significant correlation for several genes, particularly highlighting a relevant role for the TP63 gene which was hypomethylated and overexpressed in subset #8. This observation was validated by quantitative PCR, which also revealed TP63 mRNA overexpression in additional nonsubset U-CLL cases. BcR stimulation had distinct effects on p63 protein expression, particularly leading to induction in subset #8, accompanied by increased CLL cell survival. This pro-survival effect was also supported by siRNA-mediated downregulation of p63 expression resulting in increased apoptosis. In conclusion, we report that DNA methylation profiles may vary even among CLL patients with similar somatic hypermutation status, supporting a compartmentalized approach to dissecting CLL biology. Furthermore, we highlight p63 as a novel prosurvival factor in CLL, thus identifying another piece of the complex puzzle of clinical aggressiveness.

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.

A mini-review on perturbation modelling across single-cell omic modalities.

Recent advances in single-cell omics technology have transformed the landscape of cellular and molecular research, enriching the scope and intricacy of cellular characterisation. Perturbation modelling seeks to comprehensively grasp the effects of external influences like disease onset or molecular knock-outs or external stimulants on cellular physiology, specifically on transcription factors, signal transducers, biological pathways, and dynamic cell states. Machine and deep learning tools transform complex perturbational phenomena in algorithmically tractable tasks to formulate predictions based on various types of single-cell datasets. However, the recent surge in tools and datasets makes it challenging for experimental biologists and computational scientists to keep track of the recent advances in this rapidly expanding filed of single-cell modelling. Here, we recapitulate the main objectives of perturbation modelling and summarise novel single-cell perturbation technologies based on genetic manipulation like CRISPR or compounds, spanning across omic modalities. We then concisely review a burgeoning group of computational methods extending from classical statistical inference methodologies to various machine and deep learning architectures like shallow models or autoencoders, to biologically informed approaches based on gene regulatory networks, and to combinatorial efforts reminiscent of ensemble learning. We also discuss the rising trend of large foundational models in single-cell perturbation modelling inspired by large language models. Lastly, we critically assess the challenges that underline single-cell perturbation modelling while pointing towards relevant future perspectives like perturbation atlases, multi-omics and spatial datasets, causal machine learning for interpretability, multi-task learning for performance and explainability as well as prospects for solving interoperability and benchmarking pitfalls.

Microbial co-occurrence network demonstrates spatial and climatic trends for global soil diversity.

Despite recent research efforts to explore the co-occurrence patterns of diverse microbes within soil microbial communities, a substantial knowledge-gap persists regarding global climate influences on soil microbiota behaviour. Comprehending co-occurrence patterns within distinct geoclimatic groups is pivotal for unravelling the ecological structure of microbial communities, that are crucial for preserving ecosystem functions and services. Our study addresses this gap by examining global climatic patterns of microbial diversity. Using data from the Earth Microbiome Project, we analyse a meta-community co-occurrence network for bacterial communities. This method unveils substantial shifts in topological features, highlighting regional and climatic trends. Arid, Polar, and Tropical zones show lower diversity but maintain denser networks, whereas Temperate and Cold zones display higher diversity alongside more modular networks. Furthermore, it identifies significant co-occurrence patterns across diverse climatic regions. Central taxa associated with different climates are pinpointed, highlighting climate's pivotal role in community structure. In conclusion, our study identifies significant correlations between microbial interactions in diverse climatic regions, contributing valuable insights into the intricate dynamics of soil microbiota.

Good machine learning practices: Learnings from the modern pharmaceutical discovery enterprise.

Machine Learning (ML) and Artificial Intelligence (AI) have become an integral part of the drug discovery and development value chain. Many teams in the pharmaceutical industry nevertheless report the challenges associated with the timely, cost effective and meaningful delivery of ML and AI powered solutions for their scientists. We sought to better understand what these challenges were and how to overcome them by performing an industry wide assessment of the practices in AI and Machine Learning. Here we report results of the systematic business analysis of the personas in the modern pharmaceutical discovery enterprise in relation to their work with the AI and ML technologies. We identify 23 common business problems that individuals in these roles face when they encounter AI and ML technologies at work, and describe best practices (Good Machine Learning Practices) that address these issues.

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.