Mass Spectrometry Identification of Biomarkers in Extracellular Vesicles From Plasmodium vivax Liver Hypnozoite Infections.

Latent liver stages termed hypnozoites cause relapsing Plasmodium vivax malaria infection and represent a major obstacle in the goal of malaria elimination. Hypnozoites are clinically undetectable, and presently, there are no biomarkers of this persistent parasite reservoir in the human liver. Here, we have identified parasite and human proteins associated with extracellular vesicles (EVs) secreted from in vivo infections exclusively containing hypnozoites. We used P. vivax-infected human liver-chimeric (huHEP) FRG KO mice treated with the schizonticidal experimental drug MMV048 as hypnozoite infection model. Immunofluorescence-based quantification of P. vivax liver forms showed that MMV048 removed schizonts from chimeric mice livers. Proteomic analysis of EVs derived from FRG huHEP mice showed that human EV cargo from infected FRG huHEP mice contain inflammation markers associated with active schizont replication and identified 66 P. vivax proteins. To identify hypnozoite-specific proteins associated with EVs, we mined the proteome data from MMV048-treated mice and performed an analysis involving intragroup and intergroup comparisons across all experimental conditions followed by a peptide compatibility analysis with predicted spectra to warrant robust identification. Only one protein fulfilled this stringent top-down selection, a putative filamin domain-containing protein. This study sets the stage to unveil biological features of human liver infections and identify biomarkers of hypnozoite infection associated with EVs.

Hierarchical chromatin organization detected by TADpole.

The rapid development of Chromosome Conformation Capture (3C-based techniques), as well as imaging together with bioinformatics analyses, has been fundamental for unveiling that chromosomes are organized into the so-called topologically associating domains or TADs. While TADs appear as nested patterns in the 3C-based interaction matrices, the vast majority of available TAD callers are based on the hypothesis that TADs are individual and unrelated chromatin structures. Here we introduce TADpole, a computational tool designed to identify and analyze the entire hierarchy of TADs in intra-chromosomal interaction matrices. TADpole combines principal component analysis and constrained hierarchical clustering to provide a set of significant hierarchical chromatin levels in a genomic region of interest. TADpole is robust to data resolution, normalization strategy and sequencing depth. Domain borders defined by TADpole are enriched in main architectural proteins (CTCF and cohesin complex subunits) and in the histone mark H3K4me3, while their domain bodies, depending on their activation-state, are enriched in either H3K36me3 or H3K27me3, highlighting that TADpole is able to distinguish functional TAD units. Additionally, we demonstrate that TADpole's hierarchical annotation, together with the new DiffT score, allows for detecting significant topological differences on Capture Hi-C maps between wild-type and genetically engineered mouse.

Zerone: a ChIP-seq discretizer for multiple replicates with built-in quality control.

MOTIVATION: Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the standard method to investigate chromatin protein composition. As the number of community-available ChIP-seq profiles increases, it becomes more common to use data from different sources, which makes joint analysis challenging. Issues such as lack of reproducibility, heterogeneous quality and conflicts between replicates become evident when comparing datasets, especially when they are produced by different laboratories. RESULTS: Here, we present Zerone, a ChIP-seq discretizer with built-in quality control. Zerone is powered by a Hidden Markov Model with zero-inflated negative multinomial emissions, which allows it to merge several replicates into a single discretized profile. To identify low quality or irreproducible data, we trained a Support Vector Machine and integrated it as part of the discretization process. The result is a classifier reaching 95% accuracy in detecting low quality profiles. We also introduce a graphical representation to compare discretization quality and we show that Zerone achieves outstanding accuracy. Finally, on current hardware, Zerone discretizes a ChIP-seq experiment on mammalian genomes in about 5 min using less than 700 MB of memory. AVAILABILITY AND IMPLEMENTATION: Zerone is available as a command line tool and as an R package. The C source code and R scripts can be downloaded from https://github.com/nanakiksc/zerone The information to reproduce the benchmark and the figures is stored in a public Docker image that can be downloaded from https://hub.docker.com/r/nanakiksc/zerone/ CONTACT: : guillaume.filion@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Starcode: sequence clustering based on all-pairs search.

MOTIVATION: The increasing throughput of sequencing technologies offers new applications and challenges for computational biology. In many of those applications, sequencing errors need to be corrected. This is particularly important when sequencing reads from an unknown reference such as random DNA barcodes. In this case, error correction can be done by performing a pairwise comparison of all the barcodes, which is a computationally complex problem. RESULTS: Here, we address this challenge and describe an exact algorithm to determine which pairs of sequences lie within a given Levenshtein distance. For error correction or redundancy reduction purposes, matched pairs are then merged into clusters of similar sequences. The efficiency of starcode is attributable to the poucet search, a novel implementation of the Needleman-Wunsch algorithm performed on the nodes of a trie. On the task of matching random barcodes, starcode outperforms sequence clustering algorithms in both speed and precision. AVAILABILITY AND IMPLEMENTATION: The C source code is available at http://github.com/gui11aume/starcode.

The temporal mutational and immune tumour microenvironment remodelling of HER2-negative primary breast cancers.

The biology of breast cancer response to neoadjuvant therapy is underrepresented in the literature and provides a window-of-opportunity to explore the genomic and microenvironment modulation of tumours exposed to therapy. Here, we characterised the mutational, gene expression, pathway enrichment and tumour-infiltrating lymphocytes (TILs) dynamics across different timepoints of 35 HER2-negative primary breast cancer patients receiving neoadjuvant eribulin therapy (SOLTI-1007 NEOERIBULIN-NCT01669252). Whole-exome data (N = 88 samples) generated mutational profiles and candidate neoantigens and were analysed along with RNA-Nanostring 545-gene expression (N = 96 samples) and stromal TILs (N = 105 samples). Tumour mutation burden varied across patients at baseline but not across the sampling timepoints for each patient. Mutational signatures were not always conserved across tumours. There was a trend towards higher odds of response and less hazard to relapse when the percentage of subclonal mutations was low, suggesting that more homogenous tumours might have better responses to neoadjuvant therapy. Few driver mutations (5.1%) generated putative neoantigens. Mutation and neoantigen load were positively correlated (R2 = 0.94, p = <0.001); neoantigen load was weakly correlated with stromal TILs (R2 = 0.16, p = 0.02). An enrichment in pathways linked to immune infiltration and reduced programmed cell death expression were seen after 12 weeks of eribulin in good responders. VEGF was downregulated over time in the good responder group and FABP5, an inductor of epithelial mesenchymal transition (EMT), was upregulated in cases that recurred (p < 0.05). Mutational heterogeneity, subclonal architecture and the improvement of immune microenvironment along with remodelling of hypoxia and EMT may influence the response to neoadjuvant treatment.

Dynamics of genome architecture and chromatin function during human B cell differentiation and neoplastic transformation.

To investigate the three-dimensional (3D) genome architecture across normal B cell differentiation and in neoplastic cells from different subtypes of chronic lymphocytic leukemia and mantle cell lymphoma patients, here we integrate in situ Hi-C and nine additional omics layers. Beyond conventional active (A) and inactive (B) compartments, we uncover a highly-dynamic intermediate compartment enriched in poised and polycomb-repressed chromatin. During B cell development, 28% of the compartments change, mostly involving a widespread chromatin activation from naive to germinal center B cells and a reversal to the naive state upon further maturation into memory B cells. B cell neoplasms are characterized by both entity and subtype-specific alterations in 3D genome organization, including large chromatin blocks spanning key disease-specific genes. This study indicates that 3D genome interactions are extensively modulated during normal B cell differentiation and that the genome of B cell neoplasias acquires a tumor-specific 3D genome architecture.

Characterization of an eutherian gene cluster generated after transposon domestication identifies Bex3 as relevant for advanced neurological functions.

BACKGROUND: One of the most unusual sources of phylogenetically restricted genes is the molecular domestication of transposable elements into a host genome as functional genes. Although these kinds of events are sometimes at the core of key macroevolutionary changes, their origin and organismal function are generally poorly understood. RESULTS: Here, we identify several previously unreported transposable element domestication events in the human and mouse genomes. Among them, we find a remarkable molecular domestication that gave rise to a multigenic family in placental mammals, the Bex/Tceal gene cluster. These genes, which act as hub proteins within diverse signaling pathways, have been associated with neurological features of human patients carrying genomic microdeletions in chromosome X. The Bex/Tceal genes display neural-enriched patterns and are differentially expressed in human neurological disorders, such as autism and schizophrenia. Two different murine alleles of the cluster member Bex3 display morphological and physiopathological brain modifications, such as reduced interneuron number and hippocampal electrophysiological imbalance, alterations that translate into distinct behavioral phenotypes. CONCLUSIONS: We provide an in-depth understanding of the emergence of a gene cluster that originated by transposon domestication and gene duplication at the origin of placental mammals, an evolutionary process that transformed a non-functional transposon sequence into novel components of the eutherian genome. These genes were integrated into existing signaling pathways involved in the development, maintenance, and function of the CNS in eutherians. At least one of its members, Bex3, is relevant for higher brain functions in placental mammals and may be involved in human neurological disorders.

Machine Learning: How Much Does It Tell about Protein Folding Rates?

The prediction of protein folding rates is a necessary step towards understanding the principles of protein folding. Due to the increasing amount of experimental data, numerous protein folding models and predictors of protein folding rates have been developed in the last decade. The problem has also attracted the attention of scientists from computational fields, which led to the publication of several machine learning-based models to predict the rate of protein folding. Some of them claim to predict the logarithm of protein folding rate with an accuracy greater than 90%. However, there are reasons to believe that such claims are exaggerated due to large fluctuations and overfitting of the estimates. When we confronted three selected published models with new data, we found a much lower predictive power than reported in the original publications. Overly optimistic predictive powers appear from violations of the basic principles of machine-learning. We highlight common misconceptions in the studies claiming excessive predictive power and propose to use learning curves as a safeguard against those mistakes. As an example, we show that the current amount of experimental data is insufficient to build a linear predictor of logarithms of folding rates based on protein amino acid composition.