Chronic myelomonocytic leukaemia stem cell transcriptomes anticipate disease morphology and outcome.

BACKGROUND: Chronic myelomonocytic leukaemia (CMML) is a clinically heterogeneous stem cell malignancy with overlapping features of myelodysplasia and myeloproliferation. Over 90% of patients carry mutations in epigenetic and/or splicing genes, typically detectable in the Lin-CD34+CD38- immunophenotypic stem cell compartment in which the leukaemia-initiating cells reside. Transcriptional dysregulation at the stem cell level is likely fundamental to disease onset and progression. METHODS: We performed single-cell RNA sequencing on 6826 Lin-CD34+CD38-stem cells from CMML patients and healthy controls using the droplet-based, ultra-high-throughput 10x platform. FINDINGS: We found substantial inter- and intra-patient heterogeneity, with CMML stem cells displaying distinctive transcriptional programs. Compared with normal controls, CMML stem cells exhibited transcriptomes characterized by increased expression of myeloid-lineage and cell cycle genes, and lower expression of genes selectively expressed by normal haematopoietic stem cells. Neutrophil-primed progenitor genes and a MYC transcription factor regulome were prominent in stem cells from CMML-1 patients, whereas CMML-2 stem cells exhibited strong expression of interferon-regulatory factor regulomes, including those associated with IRF1, IRF7 and IRF8. CMML-1 and CMML-2 stem cells (stages distinguished by proportion of downstream blasts and promonocytes) differed substantially in both transcriptome and pseudotime, indicating fundamentally different biology underpinning these disease states. Gene expression and pathway analyses highlighted potentially tractable therapeutic vulnerabilities for downstream investigation. Importantly, CMML patients harboured variably-sized subpopulations of transcriptionally normal stem cells, indicating a potential reservoir to restore functional haematopoiesis. INTERPRETATION: Our findings provide novel insights into the CMML stem cell compartment, revealing an unexpected degree of heterogeneity and demonstrating that CMML stem cell transcriptomes anticipate disease morphology, and therefore outcome. FUNDING: Project funding was supported by Oglesby Charitable Trust, Cancer Research UK, Blood Cancer UK, and UK Medical Research Council.

Targeting the IL33-NLRP3 axis improves therapy for experimental cerebral malaria.

Cerebral malaria (CM) is a serious neurological complication caused by Plasmodium falciparum infection. Currently, the only treatment for CM is the provision of antimalarial drugs; however, such treatment by itself often fails to prevent death or development of neurological sequelae. To identify potential improved treatments for CM, we performed a nonbiased whole-brain transcriptomic time-course analysis of antimalarial drug chemotherapy of murine experimental CM (ECM). Bioinformatics analyses revealed IL33 as a critical regulator of neuroinflammation and cerebral pathology that is down-regulated in the brain during fatal ECM and in the acute period following treatment of ECM. Consistent with this, administration of IL33 alongside antimalarial drugs significantly improved the treatment success of established ECM. Mechanistically, IL33 treatment reduced inflammasome activation and IL1ß production in microglia and intracerebral monocytes in the acute recovery period following treatment of ECM. Moreover, treatment with the NLRP3-inflammasome inhibitor MCC950 alongside antimalarial drugs phenocopied the protective effect of IL33 therapy in improving the recovery from established ECM. We further showed that IL1ß release from macrophages was stimulated by hemozoin and antimalarial drugs and that this was inhibited by MCC950. Our results therefore demonstrate that manipulation of the IL33-NLRP3 axis may be an effective therapy to suppress neuroinflammation and improve the efficacy of antimalarial drug treatment of CM.

Comparison of different algorithms for simultaneous estimation of multiple parameters in kinetic metabolic models.

Computational models in systems biology are usually characterized by a lack of reliable parameter values. This is especially true for kinetic metabolic models. Experimental data can be used to estimate these missing parameters. Different optimization techniques have been explored to solve this challenging task but none has proved to be superior to the other. In this paper we review the problem of parameter estimation in kinetic models. We focus on the suitability of four commonly used optimization techniques of parameter estimation in biochemical pathways and make a comparison between those methods. The suitability of each technique is evaluated based on the ability of converging to a solution within a reasonable amount of time. As most local optimization methods fail to arrive at a satisfactory solution we only considered the global optimization techniques. A case study of the upper part of Glycolysis consisting 15 parameters is taken as the benchmark model for evaluating these methods.