Mutational and transcriptional landscape of pediatric B-cell precursor lymphoblastic lymphoma.

ABSTRACT: Pediatric B-cell precursor (BCP) lymphoblastic malignancies are neoplasms with manifestation either in the bone marrow or blood (BCP acute lymphoblastic leukemia [BCP-ALL]) or are less common in extramedullary tissue (BCP lymphoblastic lymphoma [BCP-LBL]). Although both presentations are similar in morphology and immunophenotype, molecular studies have been virtually restricted to BCP-ALL so far. The lack of molecular studies on BCP-LBL is due to its rarity and restriction on small, mostly formalin-fixed paraffin-embedded (FFPE) tissues. Here, to our knowledge, we present the first comprehensive mutational and transcriptional analysis of what we consider the largest BCP-LBL cohort described to date (n = 97). Whole-exome sequencing indicated a mutational spectrum of BCP-LBL, strikingly similar to that found in BCP-ALL. However, epigenetic modifiers were more frequently mutated in BCP-LBL, whereas BCP-ALL was more frequently affected by mutation in genes involved in B-cell development. Integrating copy number alterations, somatic mutations, and gene expression by RNA sequencing revealed that virtually all molecular subtypes originally defined in BCP-ALL are present in BCP-LBL, with only 7% of lymphomas that were not assigned to a subtype. Similar to BCP-ALL, the most frequent subtypes of BCP-LBL were high hyperdiploidy and ETV6::RUNX1. Tyrosine kinase/cytokine receptor rearrangements were detected in 7% of BCP-LBL. These results indicate that genetic subtypes can be identified in BCP-LBL using next-generation sequencing, even in FFPE tissue, and may be relevant to guide treatment.

Developmental trajectories and cooperating genomic events define molecular subtypes of BCR::ABL1-positive ALL.

ABSTRACT: Distinct diagnostic entities within BCR::ABL1-positive acute lymphoblastic leukemia (ALL) are currently defined by the International Consensus Classification of myeloid neoplasms and acute leukemias (ICC): "lymphoid only", with BCR::ABL1 observed exclusively in lymphatic precursors, vs "multilineage", where BCR::ABL1 is also present in other hematopoietic lineages. Here, we analyzed transcriptomes of 327 BCR::ABL1-positive patients with ALL (age, 2-84 years; median, 46 years) and identified 2 main gene expression clusters reproducible across 4 independent patient cohorts. Fluorescence in situ hybridization analysis of fluorescence-activated cell-sorted hematopoietic compartments showed distinct BCR::ABL1 involvement in myeloid cells for these clusters (n = 18/18 vs n = 3/16 patients; P < .001), indicating that a multilineage or lymphoid BCR::ABL1 subtype can be inferred from gene expression. Further subclusters grouped samples according to cooperating genomic events (multilineage: HBS1L deletion or monosomy 7; lymphoid: IKZF1-/- or CDKN2A/PAX5 deletions/hyperdiploidy). A novel HSB1L transcript was highly specific for BCR::ABL1 multilineage cases independent of HBS1L genomic aberrations. Treatment on current German Multicenter Study Group for Adult ALL (GMALL) protocols resulted in comparable disease-free survival (DFS) for multilineage vs lymphoid cluster patients (3-year DFS: 70% vs 61%; P = .530; n = 91). However, the IKZF1-/- enriched lymphoid subcluster was associated with inferior DFS, whereas hyperdiploid cases showed a superior outcome. Thus, gene expression clusters define underlying developmental trajectories and distinct patterns of cooperating events in BCR::ABL1-positive ALL with prognostic relevance.

Virulence-related comparative transcriptomics of infectious and non-infectious chlamydial particles.

BACKGROUND: Members of the phylum Chlamydiae are obligate intracellular pathogens of humans and animals and have a serious impact on host health. They comprise several zoonotic species with varying disease outcomes and prevalence. To investigate differences in virulence, we focused on Chlamydia psittaci, C. abortus and Waddlia chondrophila. Most threatening is C. psittaci, which frequently infects humans and causes psittacosis associated with severe pneumonia. The closest relative of C. psittaci is C. abortus, which shares the vast majority of genes but less frequently infects humans, and causes stillbirth and sepsis. W. chondrophila is more distantly related, and occasional human infections are associated with respiratory diseases or miscarriage. One possible explanation for differences in virulence originate from species-specific genes as well as differentially expressed homologous virulence factors. RESULTS: RNA-sequencing (RNA-Seq) was applied to purified infectious elementary bodies (EBs) and non-infectious reticulate bodies (RBs) in order to elucidate the transcriptome of the infectious and replicative chlamydial states. The results showed that approximately half of all genes were differentially expressed. For a descriptive comparison, genes were categorised according to their function in the RAST database. This list was extended by the inclusion of inclusion membrane proteins, outer membrane proteins, polymorphic membrane proteins and type III secretion system effectors. In addition, the expression of fifty-six known and a variety of predicted virulence and immunogenic factors with homologs in C. psittaci, C. abortus and W. chondrophila was analysed. To confirm the RNA-Seq results, the expression of nine factors was validated using real-time quantitative polymerase chain reaction (RT-qPCR). Comparison of RNA-Seq and RT-qPCR results showed a high mean Pearson correlation coefficient of 0.95. CONCLUSIONS: It was shown that both the replicative and infectious chlamydial state contained distinctive transcriptomes and the cellular processes emphasised in EBs and RBs differed substantially based on the chlamydial species. In addition, the very first interspecies transcriptome comparison is presented here, and the considerable differences in expression of homologous virulence factors might contribute to the differing infection rates and disease outcomes of the pathogens. The RNA-Seq results were confirmed by RT-qPCR and demonstrate the feasibility of interspecies transcriptome comparisons in chlamydia.

Combination of computational techniques and RNAi reveal targets in Anopheles gambiae for malaria vector control.

Increasing reports of insecticide resistance continue to hamper the gains of vector control strategies in curbing malaria transmission. This makes identifying new insecticide targets or alternative vector control strategies necessary. CLassifier of Essentiality AcRoss EukaRyote (CLEARER), a leave-one-organism-out cross-validation machine learning classifier for essential genes, was used to predict essential genes in Anopheles gambiae and selected predicted genes experimentally validated. The CLEARER algorithm was trained on six model organisms: Caenorhabditis elegans, Drosophila melanogaster, Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Schizosaccharomyces pombe, and employed to identify essential genes in An. gambiae. Of the 10,426 genes in An. gambiae, 1,946 genes (18.7%) were predicted to be Cellular Essential Genes (CEGs), 1716 (16.5%) to be Organism Essential Genes (OEGs), and 852 genes (8.2%) to be essential as both OEGs and CEGs. RNA interference (RNAi) was used to validate the top three highly expressed non-ribosomal predictions as probable vector control targets, by determining the effect of these genes on the survival of An. gambiae G3 mosquitoes. In addition, the effect of knockdown of arginase (AGAP008783) on Plasmodium berghei infection in mosquitoes was evaluated, an enzyme we computationally inferred earlier to be essential based on chokepoint analysis. Arginase and the top three genes, AGAP007406 (Elongation factor 1-alpha, Elf1), AGAP002076 (Heat shock 70kDa protein 1/8, HSP), AGAP009441 (Elongation factor 2, Elf2), had knockdown efficiencies of 91%, 75%, 63%, and 61%, respectively. While knockdown of HSP or Elf2 significantly reduced longevity of the mosquitoes (p<0.0001) compared to control groups, Elf1 or arginase knockdown had no effect on survival. However, arginase knockdown significantly reduced P. berghei oocytes counts in the midgut of mosquitoes when compared to LacZ-injected controls. The study reveals HSP and Elf2 as important contributors to mosquito survival and arginase as important for parasite development, hence placing them as possible targets for vector control.

Molecular subgroups of T-cell acute lymphoblastic leukemia in adults treated according to pediatric-based GMALL protocols.

In contrast to B-cell precursor acute lymphoblastic leukemia (ALL), molecular subgroups are less well defined in T-lineage ALL. Comprehensive studies on molecular T-ALL subgroups have been predominantly performed in pediatric ALL patients. Currently, molecular characteristics are rarely considered for risk stratification. Herein, we present a homogenously treated cohort of 230 adult T-ALL patients characterized on transcriptome, and partly on DNA methylation and gene mutation level in correlation with clinical outcome. We identified nine molecular subgroups based on aberrant oncogene expression correlating to four distinct DNA methylation patterns. The subgroup distribution differed from reported pediatric T-ALL cohorts with higher frequencies of prognostic unfavorable subgroups like HOXA or LYL1/LMO2. A small subset (3%) of HOXA adult T-ALL patients revealed restricted expression of posterior HOX genes with aberrant activation of lncRNA HOTTIP. With respect to outcome, TLX1 (n = 44) and NKX2-1 (n = 4) had an exceptionally favorable 3-year overall survival (3y-OS) of 94%. Within thymic T-ALL, the non TLX1 patients had an inferior but still good prognosis. To our knowledge this is the largest cohort of adult T-ALL patients characterized by transcriptome sequencing with meaningful clinical follow-up. Risk classification based on molecular subgroups might emerge and contribute to improvements in outcome.

Machine learning on large scale perturbation screens for SARS-CoV-2 host factors identifies ß-catenin/CBP inhibitor PRI-724 as a potent antiviral.

Expanding antiviral treatment options against SARS-CoV-2 remains crucial as the virus evolves under selection pressure which already led to the emergence of several drug resistant strains. Broad spectrum host-directed antivirals (HDA) are promising therapeutic options, however the robust identification of relevant host factors by CRISPR/Cas9 or RNA interference screens remains challenging due to low consistency in the resulting hits. To address this issue, we employed machine learning, based on experimental data from several knockout screens and a drug screen. We trained classifiers using genes essential for virus life cycle obtained from the knockout screens. The machines based their predictions on features describing cellular localization, protein domains, annotated gene sets from Gene Ontology, gene and protein sequences, and experimental data from proteomics, phospho-proteomics, protein interaction and transcriptomic profiles of SARS-CoV-2 infected cells. The models reached a remarkable performance suggesting patterns of intrinsic data consistency. The predicted HDF were enriched in sets of genes particularly encoding development, morphogenesis, and neural processes. Focusing on development and morphogenesis-associated gene sets, we found ß-catenin to be central and selected PRI-724, a canonical ß-catenin/CBP disruptor, as a potential HDA. PRI-724 limited infection with SARS-CoV-2 variants, SARS-CoV-1, MERS-CoV and IAV in different cell line models. We detected a concentration-dependent reduction in cytopathic effects, viral RNA replication, and infectious virus production in SARS-CoV-2 and SARS-CoV-1-infected cells. Independent of virus infection, PRI-724 treatment caused cell cycle deregulation which substantiates its potential as a broad spectrum antiviral. Our proposed machine learning concept supports focusing and accelerating the discovery of host dependency factors and identification of potential host-directed antivirals.

Insights into IGH clonal evolution in BCP-ALL: frequency, mechanisms, associations, and diagnostic implications.

Introduction: The malignant transformation leading to a maturation arrest in B-cell precursor acute lymphoblastic leukemia (BCP-ALL) occurs early in B-cell development, in a pro-B or pre-B cell, when somatic recombination of variable (V), diversity (D), and joining (J) segment immunoglobulin (IG) genes and the B-cell rescue mechanism of VH replacement might be ongoing or fully active, driving clonal evolution. In this study of newly diagnosed BCP-ALL, we sought to understand the mechanistic details of oligoclonal composition of the leukemia at diagnosis, clonal evolution during follow-up, and clonal distribution in different hematopoietic compartments. Methods: Utilizing high-throughput sequencing assays and bespoke bioinformatics we identified BCP-ALL-derived clonally-related IGH sequences by their shared 'DNJ-stem'. Results: We introduce the concept of 'marker DNJ-stem' to cover the entirety of, even lowly abundant, clonally-related family members. In a cohort of 280 adult patients with BCP-ALL, IGH clonal evolution at diagnosis was identified in one-third of patients. The phenomenon was linked to contemporaneous recombinant and editing activity driven by aberrant ongoing DH/VH-DJH recombination and VH replacement, and we share insights and examples for both. Furthermore, in a subset of 167 patients with molecular subtype allocation, high prevalence and high degree of clonal evolution driven by ongoing DH/VH-DJH recombination were associated with the presence of KMT2A gene rearrangements, while VH replacements occurred more frequently in Ph-like and DUX4 BCP-ALL. Analysis of 46 matched diagnostic bone marrow and peripheral blood samples showed a comparable clonal and clonotypic distribution in both hematopoietic compartments, but the clonotypic composition markedly changed in longitudinal follow-up analysis in select cases. Thus, finally, we present cases where the specific dynamics of clonal evolution have implications for both the initial marker identification and the MRD monitoring in follow-up samples. Discussion: Consequently, we suggest to follow the marker DNJ-stem (capturing all family members) rather than specific clonotypes as the MRD target, as well as to follow both VDJH and DJH family members since their respective kinetics are not always parallel. Our study further highlights the intricacy, importance, and present and future challenges of IGH clonal evolution in BCP-ALL.

FAT1 expression in T-cell acute lymphoblastic leukemia (T-ALL) modulates proliferation and WNT signaling.

FAT atypical cadherin 1 (FAT1), a transmembrane protein, is frequently mutated in various cancer types and has been described as context-dependent tumor suppressor or oncogene. The FAT1 gene is mutated in 12-16% of T-cell acute leukemia (T-ALL) and aberrantly expressed in about 54% of T-ALL cases contrasted with absent expression in normal T-cells. Here, we characterized FAT1 expression and profiled the methylation status from T-ALL patients. In our T-ALL cohort, 53% of patient samples were FAT1 positive (FAT1pos) compared to only 16% FAT1 positivity in early T-ALL patient samples. Aberrant expression of FAT1 was strongly associated with FAT1 promotor hypomethylation, yet a subset, mainly consisting of TLX1-driven T-ALL patient samples showed methylation-independent high FAT1 expression. Genes correlating with FAT1 expression revealed enrichment in WNT signaling genes representing the most enriched single pathway. FAT1 knockdown or knockout led to impaired proliferation and downregulation of WNT pathway target genes (CCND1, MYC, LEF1), while FAT1 overexpressing conveyed a proliferative advantage. To conclude, we characterized a subtype pattern of FAT1 gene expression in adult T-ALL patients correlating with promotor methylation status. FAT1 dependent proliferation and WNT signaling discloses an impact on deeper understanding of T-ALL leukemogenesis as a fundament for prospective therapeutic strategies.

Isocitrate dehydrogenase 1 mutation drives leukemogenesis by PDGFRA activation due to insulator disruption in acute myeloid leukemia (AML).

Acute myeloid leukemia (AML) is characterized by complex molecular alterations and driver mutations. Elderly patients show increased frequencies of IDH mutations with high chemoresistance and relapse rates despite recent therapeutic advances. Besides being associated with global promoter hypermethylation, IDH1 mutation facilitated changes in 3D DNA-conformation by CTCF-anchor methylation and upregulated oncogene expression in glioma, correlating with poor prognosis. Here, we investigated the role of IDH1 p.R132H mutation in altering 3D DNA-architecture and subsequent oncogene activation in AML. Using public RNA-Seq data, we identified upregulation of tyrosine kinase PDGFRA in IDH1-mutant patients, correlating with poor prognosis. DNA methylation analysis identified CpG hypermethylation within a CTCF-anchor upstream of PDGFRA in IDH1-mutant patients. Increased PDGFRA expression, PDGFRA-CTCF methylation and decreased CTCF binding were confirmed in AML CRISPR cells with heterozygous IDH1 p.R132H mutation and upon exogenous 2-HG treatment. IDH1-mutant cells showed higher sensitivity to tyrosine kinase inhibitor dasatinib, which was supported by reduced blast count in a patient with refractory IDH1-mutant AML after dasatinib treatment. Our data illustrate that IDH1 p.R132H mutation leads to CTCF hypermethylation, disrupting DNA-looping and insulation of PDGFRA, resulting in PDGFRA upregulation in IDH1-mutant AML. Treatment with dasatinib may offer a novel treatment strategy for IDH1-mutant AML.

The Gene Expression Classifier ALLCatchR Identifies B-cell Precursor ALL Subtypes and Underlying Developmental Trajectories Across Age.

Current classifications (World Health Organization-HAEM5/ICC) define up to 26 molecular B-cell precursor acute lymphoblastic leukemia (BCP-ALL) disease subtypes by genomic driver aberrations and corresponding gene expression signatures. Identification of driver aberrations by transcriptome sequencing (RNA-Seq) is well established, while systematic approaches for gene expression analysis are less advanced. Therefore, we developed ALLCatchR, a machine learning-based classifier using RNA-Seq gene expression data to allocate BCP-ALL samples to all 21 gene expression-defined molecular subtypes. Trained on n = 1869 transcriptome profiles with established subtype definitions (4 cohorts; 55% pediatric / 45% adult), ALLCatchR allowed subtype allocation in 3 independent hold-out cohorts (n = 1018; 75% pediatric / 25% adult) with 95.7% accuracy (averaged sensitivity across subtypes: 91.1% / specificity: 99.8%). High-confidence predictions were achieved in 83.7% of samples with 98.9% accuracy. Only 1.2% of samples remained unclassified. ALLCatchR outperformed existing tools and identified novel driver candidates in previously unassigned samples. Additional modules provided predictions of samples blast counts, patient's sex, and immunophenotype, allowing the imputation in cases where these information are missing. We established a novel RNA-Seq reference of human B-lymphopoiesis using 7 FACS-sorted progenitor stages from healthy bone marrow donors. Implementation in ALLCatchR enabled projection of BCP-ALL samples to this trajectory. This identified shared proximity patterns of BCP-ALL subtypes to normal lymphopoiesis stages, extending immunophenotypic classifications with a novel framework for developmental comparisons of BCP-ALL. ALLCatchR enables RNA-Seq routine application for BCP-ALL diagnostics with systematic gene expression analysis for accurate subtype allocation and novel insights into underlying developmental trajectories.

Predicting host dependency factors of pathogens in Drosophila melanogaster using machine learning.

Pathogens causing infections, and particularly when invading the host cells, require the host cell machinery for efficient regeneration and proliferation during infection. For their life cycle, host proteins are needed and these Host Dependency Factors (HDF) may serve as therapeutic targets. Several attempts have approached screening for HDF producing large lists of potential HDF with, however, only marginal overlap. To get consistency into the data of these experimental studies, we developed a machine learning pipeline. As a case study, we used publicly available lists of experimentally derived HDF from twelve different screening studies based on gene perturbation in Drosophila melanogaster cells or in vivo upon bacterial or protozoan infection. A total of 50,334 gene features were generated from diverse categories including their functional annotations, topology attributes in protein interaction networks, nucleotide and protein sequence features, homology properties and subcellular localization. Cross-validation revealed an excellent prediction performance. All feature categories contributed to the model. Predicted and experimentally derived HDF showed a good consistency when investigating their common cellular processes and function. Cellular processes and molecular function of these genes were highly enriched in membrane trafficking, particularly in the trans-Golgi network, cell cycle and the Rab GTPase binding family. Using our machine learning approach, we show that HDF in organisms can be predicted with high accuracy evidencing their common investigated characteristics. We elucidated cellular processes which are utilized by invading pathogens during infection. Finally, we provide a list of 208 novel HDF proposed for future experimental studies.

Identifying essential genes across eukaryotes by machine learning.

Identifying essential genes on a genome scale is resource intensive and has been performed for only a few eukaryotes. For less studied organisms essentiality might be predicted by gene homology. However, this approach cannot be applied to non-conserved genes. Additionally, divergent essentiality information is obtained from studying single cells or whole, multi-cellular organisms, and particularly when derived from human cell line screens and human population studies. We employed machine learning across six model eukaryotes and 60 381 genes, using 41 635 features derived from the sequence, gene function information and network topology. Within a leave-one-organism-out cross-validation, the classifiers showed high generalizability with an average accuracy close to 80% in the left-out species. As a case study, we applied the method to Tribolium castaneum and Bombyx mori and validated predictions experimentally yielding similar performances. Finally, using the classifier based on the studied model organisms enabled linking the essentiality information of human cell line screens and population studies.

Essential gene prediction in Drosophila melanogaster using machine learning approaches based on sequence and functional features.

Genes are termed to be essential if their loss of function compromises viability or results in profound loss of fitness. On the genome scale, these genes can be determined experimentally employing RNAi or knockout screens, but this is very resource intensive. Computational methods for essential gene prediction can overcome this drawback, particularly when intrinsic (e.g. from the protein sequence) as well as extrinsic features (e.g. from transcription profiles) are considered. In this work, we employed machine learning to predict essential genes in Drosophila melanogaster. A total of 27,340 features were generated based on a large variety of different aspects comprising nucleotide and protein sequences, gene networks, protein-protein interactions, evolutionary conservation and functional annotations. Employing cross-validation, we obtained an excellent prediction performance. The best model achieved in D. melanogaster a ROC-AUC of 0.90, a PR-AUC of 0.30 and a F1 score of 0.34. Our approach considerably outperformed a benchmark method in which only features derived from the protein sequences were used (P < 0.001). Investigating which features contributed to this success, we found all categories of features, most prominently network topological, functional and sequence-based features. To evaluate our approach we performed the same workflow for essential gene prediction in human and achieved an ROC-AUC = 0.97, PR-AUC = 0.73, and F1 = 0.64. In summary, this study shows that using our well-elaborated assembly of features covering a broad range of intrinsic and extrinsic gene and protein features enabled intelligent systems to predict well the essentiality of genes in an organism.

Anopheles metabolic proteins in malaria transmission, prevention and control: a review.

The increasing resistance to currently available insecticides in the malaria vector, Anopheles mosquitoes, hampers their use as an effective vector control strategy for the prevention of malaria transmission. Therefore, there is need for new insecticides and/or alternative vector control strategies, the development of which relies on the identification of possible targets in Anopheles. Some known and promising targets for the prevention or control of malaria transmission exist among Anopheles metabolic proteins. This review aims to elucidate the current and potential contribution of Anopheles metabolic proteins to malaria transmission and control. Highlighted are the roles of metabolic proteins as insecticide targets, in blood digestion and immune response as well as their contribution to insecticide resistance and Plasmodium parasite development. Furthermore, strategies by which these metabolic proteins can be utilized for vector control are described. Inhibitors of Anopheles metabolic proteins that are designed based on target specificity can yield insecticides with no significant toxicity to non-target species. These metabolic modulators combined with each other or with synergists, sterilants, and transmission-blocking agents in a single product, can yield potent malaria intervention strategies. These combinations can provide multiple means of controlling the vector. Also, they can help to slow down the development of insecticide resistance. Moreover, some metabolic proteins can be modulated for mosquito population replacement or suppression strategies, which will significantly help to curb malaria transmission.

Purification of infectious and non-infectious chlamydial particles using iodixanol for density gradient preparation.

Chlamydiae are obligate intracellular bacteria with two distinct morphological stages, the infectious elementary bodies (EBs) and non-infectious reticulate bodies (RBs). Here we describe a rapid and straightforward protocol for the purification of EBs and RBs involving special density gradients. It has been successfully applied to three chlamydial species.