Early initiation of second-line therapy in primary immune thrombocytopenia: insights from real-world evidence.

To compare patients with primary immune thrombocytopenia (ITP) prescribed early (within 3 months of initial ITP treatment) second-line treatment (eltrombopag, romiplostim, rituximab, immunosuppressive agents, splenectomy) with or without concomitant first-line therapy to those who received only first-line therapy. This real-world retrospective cohort study of 8268 patients with primary ITP from a large US-based database (Optum® de-identified Electronic Health Record [EHR] dataset) combined electronic claims and EHR data. Outcomes included platelet count, bleeding events, and corticosteroid exposure 3 to 6 months after initial treatment. Baseline platelet counts were lower in patients receiving early second-line therapy (10‒28 × 109/L) versus those who did not (67 × 109/L). Counts improved and bleeding events decreased from baseline in all treatment groups 3 to 6 months after the start of therapy. Among the very few patients for whom follow-up treatment data were available (n = 94), corticosteroid use was reduced during the 3- to 6-month follow-up period in patients who received early second-line therapy versus those who did not (39% vs 87%, p < 0.001). Early second-line treatment was prescribed for more severe cases of ITP and appeared to be associated with improved platelet counts and bleeding outcomes 3 to 6 months after initial therapy. Early second-line therapy also appeared to reduce corticosteroid use after 3 months, although the small number of patients with follow-up data on treatment precludes any substantive conclusions. Further research is needed to determine whether early second-line therapy has an effect on the long-term course of ITP.

External validation of a claims-based model to predict left ventricular ejection fraction class in patients with heart failure.

BACKGROUND: Ejection fraction (EF) is an important prognostic factor in heart failure (HF), but administrative claims databases lack information on EF. We previously developed a model to predict EF class from Medicare claims. Here, we evaluated the performance of this model in an external validation sample of commercial insurance enrollees. METHODS: Truven MarketScan claims linked to electronic medical records (EMR) data (IBM Explorys) containing EF measurements were used to identify a cohort of US patients with HF between 01-01-2012 and 10-31-2019. By applying the previously developed model, patients were classified into HF with reduced EF (HFrEF) or preserved EF (HFpEF). EF values recorded in EMR data were used to define gold-standard HFpEF (LVEF ≥45%) and HFrEF (LVEF<45%). Model performance was reported in terms of overall accuracy, positive predicted values (PPV), and sensitivity for HFrEF and HFpEF. RESULTS: A total of 7,001 HF patients with an average age of 71 years were identified, 1,700 (24.3%) of whom had HFrEF. An overall accuracy of 0.81 (95% CI: 0.80-0.82) was seen in this external validation sample. For HFpEF, the model had sensitivity of 0.96 (95%CI, 0.95-0.97) and PPV of 0.81 (95% CI, 0.81-0.82); while for HFrEF, the sensitivity was 0.32 (95%CI, 0.30-0.34) and PPV was 0.73 (95%CI, 0.69-0.76). These results were consistent with what was previously published in US Medicare claims data. CONCLUSIONS: The successful validation of the Medicare claims-based model provides evidence that this model may be used to identify patient subgroups with specific EF class in commercial claims databases as well.

Perturbation of Methionine/S-adenosylmethionine Metabolism as a Novel Vulnerability in MLL Rearranged Leukemia.

Leukemias bearing mixed lineage leukemia (MLL) rearrangement (MLL-R) resulting in expression of oncogenic MLL fusion proteins (MLL-FPs) represent an especially aggressive disease subtype with the worst overall prognoses and chemotherapeutic response. MLL-R leukemias are uniquely dependent on the epigenetic function of the H3K79 methyltransferase DOT1L, which is misdirected by MLL-FPs activating gene expression, driving transformation and leukemogenesis. Given the functional necessity of these leukemias to maintain adequate methylation potential allowing aberrant activating histone methylation to proceed, driving leukemic gene expression, we investigated perturbation of methionine (Met)/S-adenosylmethionine (SAM) metabolism as a novel therapeutic paradigm for MLL-R leukemia. Disruption of Met/SAM metabolism, by either methionine deprivation or pharmacologic inhibition of downstream metabolism, reduced overall cellular methylation potential, reduced relative cell numbers, and induced apoptosis selectively in established MLL-AF4 cell lines or MLL-AF6-expressing patient blasts but not in BCR-ABL-driven K562 cells. Global histone methylation dynamics were altered, with a profound loss of requisite H3K79 methylation, indicating inhibition of DOT1L function. Relative occupancy of the repressive H3K27me3 modification was increased at the DOT1L promoter in MLL-R cells, and DOT1L mRNA and protein expression was reduced. Finally, pharmacologic inhibition of Met/SAM metabolism significantly prolonged survival in an advanced, clinically relevant patient-derived MLL-R leukemia xenograft model, in combination with cytotoxic induction chemotherapy. Our findings provide support for further investigation into the development of highly specific allosteric inhibitors of enzymatic mediators of Met/SAM metabolism or dietary manipulation of methionine levels. Such inhibitors may lead to enhanced treatment outcomes for MLL-R leukemia, along with cytotoxic chemotherapy or DOT1L inhibitors.

Cell Type Purification by Single-Cell Transcriptome-Trained Sorting.

Much of current molecular and cell biology research relies on the ability to purify cell types by fluorescence-activated cell sorting (FACS). FACS typically relies on the ability to label cell types of interest with antibodies or fluorescent transgenic constructs. However, antibody availability is often limited, and genetic manipulation is labor intensive or impossible in the case of primary human tissue. To date, no systematic method exists to enrich for cell types without a priori knowledge of cell-type markers. Here, we propose GateID, a computational method that combines single-cell transcriptomics with FACS index sorting to purify cell types of choice using only native cellular properties such as cell size, granularity, and mitochondrial content. We validate GateID by purifying various cell types from zebrafish kidney marrow and the human pancreas to high purity without resorting to specific antibodies or transgenes.

Primary lung cancer samples cultured under microenvironment-mimetic conditions enrich for mesenchymal stem-like cells that promote metastasis.

The tumor microenvironment (TME) is composed of a heterogeneous biological ecosystem of cellular and non-cellular elements including transformed tumor cells, endothelial cells, immune cells, activated fibroblasts or myofibroblasts, stem and progenitor cells, as well as the cytokines and matrix that they produce. The constituents of the TME stroma are multiple and varied, however cancer associated fibroblasts (CAF) and their contribution to the TME are important in tumor progression. CAF are hypothesized to arise from multiple progenitor cell types, including mesenchymal stem cells. Currently, isolation of TME stroma from patients is complicated by issues such as limited availability of biopsy material and cell stress incurred during lengthy adaptation to atmospheric oxygen (20% O2) in cell culture, limiting pre-clinical studies of patient tumor stromal interactions. Here we describe a microenvironment mimetic in vitro cell culturing system that incorporates elements of the in vivo lung environment, including lung fibroblast derived extracellular matrix and physiological hypoxia (5% O2). Using this system, we easily isolated and rapidly expanded stromal progenitors from patient lung tumor resections without complex sorting methods or growth supplements. These progenitor populations retained expression of pluripotency markers, secreted factors associated with cancer progression, and enhanced tumor cell growth and metastasis. An understanding of the biology of these progenitor cell populations in a TME-like environment may advance our ability to target these cells and limit their effects on promoting cancer metastasis.

Comparative utility of NRG and NRGS mice for the study of normal hematopoiesis, leukemogenesis, and therapeutic response.

Cell-line-derived xenografts (CDXs) or patient-derived xenografts (PDXs) in immune-deficient mice have revolutionized our understanding of normal and malignant human hematopoiesis. Transgenic approaches further improved in vivo hematological research, allowing the development of human-cytokine-producing mice, which show superior human cell engraftment. The most popular mouse strains used in research, the NOG (NOD.Cg-Prkdcscid Il2rγtm1Sug/Jic) and the NSG (NOD/SCID-IL2Rγ-/-, NOD.Cg-PrkdcscidIl2rγtm1Wjl/SzJ) mouse, and their human-cytokine-producing (interleukin-3, granulocyte-macrophage colony-stimulating factor, and stem cell factor) counterparts (huNOG and NSGS), rely partly on a mutation in the DNA repair protein PRKDC, causing a severe combined immune deficiency (SCID) phenotype and rendering the mice less tolerant to DNA-damaging therapeutics, thereby limiting their usefulness in the investigation of novel acute myeloid leukemia (AML) therapeutics. NRG (NOD/RAG1/2-/-IL2Rγ-/-) mice show equivalent immune ablation through a defective recombination activation gene (RAG), leaving DNA damage repair intact, and human-cytokine-producing NRGS (NRG-SGM3) mice were generated, improving myeloid engraftment. Our findings indicate that unconditioned NRG and NRGS mice can harbor established AML CDXs and can tolerate aggressive induction chemotherapy at higher doses than NSG mice without overt toxicity. However, unconditioned NRGS mice developed less clinically relevant disease, with CDXs forming solid tumors throughout the body, whereas unconditioned NRG mice were incapable of efficiently supporting PDX or human hematopoietic stem cell engraftment. These findings emphasize the contextually dependent utility of each of these powerful new strains in the study of normal and malignant human hematopoiesis. Therefore, the choice of mouse strain cannot be random, but must be based on the experimental outcomes and questions to be addressed.

The Antiarrhythmic Drug, Amiodarone, Decreases AKT Activity and Sensitizes Human Acute Myeloid Leukemia Cells to Apoptosis by ABT-263.

BACKGROUND: Successful treatment of leukemia requires new medications to combat drug resistance, but the development of novel therapies is an arduous and risky endeavor. Repurposing currently approved drugs or those already in clinical development to treat other indications is a more practical approach. Moreover, combinatorial therapeutics are often more efficacious than single agent therapeutics because the former can simultaneously target multiple pathways that mitigate tumor aggressiveness and induce cancer cell death. MATERIAL AND METHODS: In this study, we combined the class III antiarrhythmic agent amiodarone and the BH3 mimetic ABT-263 based on data from a prior drug screen to assess the degree of apoptotic induction in 2 human leukemia cell lines. RESULTS: The combination yielded statistically significant increases in apoptosis in both cell lines by downregulating AKT activity and increasing cleaved caspase-3. CONCLUSIONS: Overall, our findings suggest that combining K+ channel blockers with prosurvival Bcl-2 family inhibitors is a promising therapeutic approach in treating leukemia.

Exhaustive Analysis of a Genotype Space Comprising 10(15 )Central Carbon Metabolisms Reveals an Organization Conducive to Metabolic Innovation.

All biological evolution takes place in a space of possible genotypes and their phenotypes. The structure of this space defines the evolutionary potential and limitations of an evolving system. Metabolism is one of the most ancient and fundamental evolving systems, sustaining life by extracting energy from extracellular nutrients. Here we study metabolism's potential for innovation by analyzing an exhaustive genotype-phenotype map for a space of 10(15) metabolisms that encodes all possible subsets of 51 reactions in central carbon metabolism. Using flux balance analysis, we predict the viability of these metabolisms on 10 different carbon sources which give rise to 1024 potential metabolic phenotypes. Although viable metabolisms with any one phenotype comprise a tiny fraction of genotype space, their absolute numbers exceed 10(9) for some phenotypes. Metabolisms with any one phenotype typically form a single network of genotypes that extends far or all the way through metabolic genotype space, where any two genotypes can be reached from each other through a series of single reaction changes. The minimal distance of genotype networks associated with different phenotypes is small, such that one can reach metabolisms with novel phenotypes--viable on new carbon sources--through one or few genotypic changes. Exceptions to these principles exist for those metabolisms whose complexity (number of reactions) is close to the minimum needed for viability. Increasing metabolic complexity enhances the potential for both evolutionary conservation and evolutionary innovation.

Historical contingency and the gradual evolution of metabolic properties in central carbon and genome-scale metabolisms.

BACKGROUND: A metabolism can evolve through changes in its biochemical reactions that are caused by processes such as horizontal gene transfer and gene deletion. While such changes need to preserve an organism's viability in its environment, they can modify other important properties, such as a metabolism's maximal biomass synthesis rate and its robustness to genetic and environmental change. Whether such properties can be modulated in evolution depends on whether all or most viable metabolisms - those that can synthesize all essential biomass precursors - are connected in a space of all possible metabolisms. Connectedness means that any two viable metabolisms can be converted into one another through a sequence of single reaction changes that leave viability intact. If the set of viable metabolisms is disconnected and highly fragmented, then historical contingency becomes important and restricts the alteration of metabolic properties, as well as the number of novel metabolic phenotypes accessible in evolution. RESULTS: We here computationally explore two vast spaces of possible metabolisms to ask whether viable metabolisms are connected. We find that for all but the simplest metabolisms, most viable metabolisms can be transformed into one another by single viability-preserving reaction changes. Where this is not the case, alternative essential metabolic pathways consisting of multiple reactions are responsible, but such pathways are not common. CONCLUSIONS: Metabolism is thus highly evolvable, in the sense that its properties could be fine-tuned by successively altering individual reactions. Historical contingency does not strongly restrict the origin of novel metabolic phenotypes.

A latent capacity for evolutionary innovation through exaptation in metabolic systems.

Some evolutionary innovations may originate non-adaptively as exaptations, or pre-adaptations, which are by-products of other adaptive traits. Examples include feathers, which originated before they were used in flight, and lens crystallins, which are light-refracting proteins that originated as enzymes. The question of how often adaptive traits have non-adaptive origins has profound implications for evolutionary biology, but is difficult to address systematically. Here we consider this issue in metabolism, one of the most ancient biological systems that is central to all life. We analyse a metabolic trait of great adaptive importance: the ability of a metabolic reaction network to synthesize all biomass from a single source of carbon and energy. We use novel computational methods to sample randomly many metabolic networks that can sustain life on any given carbon source but contain an otherwise random set of known biochemical reactions. We show that when we require such networks to be viable on one particular carbon source, they are typically also viable on multiple other carbon sources that were not targets of selection. For example, viability on glucose may entail viability on up to 44 other sole carbon sources. Any one adaptation in these metabolic systems typically entails multiple potential exaptations. Metabolic systems thus contain a latent potential for evolutionary innovations with non-adaptive origins. Our observations suggest that many more metabolic traits may have non-adaptive origins than is appreciated at present. They also challenge our ability to distinguish adaptive from non-adaptive traits.

Growth temperature and genome size in bacteria are negatively correlated, suggesting genomic streamlining during thermal adaptation.

Prokaryotic genomes are small and compact. Either this feature is caused by neutral evolution or by natural selection favoring small genomes-genome streamlining. Three separate prior lines of evidence argue against streamlining for most prokaryotes. We find that the same three lines of evidence argue for streamlining in the genomes of thermophile bacteria. Specifically, with increasing habitat temperature and decreasing genome size, the proportion of genomic DNA in intergenic regions decreases. Furthermore, with increasing habitat temperature, generation time decreases. Genome-wide selective constraints do not decrease as in the reduced genomes of host-associated species. Reduced habitat variability is not a likely explanation for the smaller genomes of thermophiles. Genome size may be an indirect target of selection due to its association with cell volume. We use metabolic modeling to demonstrate that known changes in cell structure and physiology at high temperature can provide a selective advantage to reduce cell volume at high temperatures.

Superessential reactions in metabolic networks.

The metabolic genotype of an organism can change through loss and acquisition of enzyme-coding genes, while preserving its ability to survive and synthesize biomass in specific environments. This evolutionary plasticity allows pathogens to evolve resistance to antimetabolic drugs by acquiring new metabolic pathways that bypass an enzyme blocked by a drug. We here study quantitatively the extent to which individual metabolic reactions and enzymes can be bypassed. To this end, we use a recently developed computational approach to create large metabolic network ensembles that can synthesize all biomass components in a given environment but contain an otherwise random set of known biochemical reactions. Using this approach, we identify a small connected core of 124 reactions that are absolutely superessential (that is, required in all metabolic networks). Many of these reactions have been experimentally confirmed as essential in different organisms. We also report a superessentiality index for thousands of reactions. This index indicates how easily a reaction can be bypassed. We find that it correlates with the number of sequenced genomes that encode an enzyme for the reaction. Superessentiality can help choose an enzyme as a potential drug target, especially because the index is not highly sensitive to the chemical environment that a pathogen requires. Our work also shows how analyses of large network ensembles can help understand the evolution of complex and robust metabolic networks.

A kinetic platform for in silico modeling of the metabolic dynamics in Escherichia coli.

BACKGROUND: A prerequisite for a successful design and discovery of an antibacterial drug is the identification of essential targets as well as potent inhibitors that adversely affect the survival of bacteria. In order to understand how intracellular perturbations occur due to inhibition of essential metabolic pathways, we have built, through the use of ordinary differential equations, a mathematical model of 8 major Escherichia coli pathways. RESULTS: Individual in vitro enzyme kinetic parameters published in the literature were used to build the network of pathways in such a way that the flux distribution matched that reported from whole cells. Gene regulation at the transcription level as well as feedback regulation of enzyme activity was incorporated as reported in the literature. The unknown kinetic parameters were estimated by trial and error through simulations by observing network stability. Metabolites, whose biosynthetic pathways were not represented in this platform, were provided at a fixed concentration. Unutilized products were maintained at a fixed concentration by removing excess quantities from the platform. This approach enabled us to achieve steady state levels of all the metabolites in the cell. The output of various simulations correlated well with those previously published. CONCLUSION: Such a virtual platform can be exploited for target identification through assessment of their vulnerability, desirable mode of target enzyme inhibition, and metabolite profiling to ascribe mechanism of action following a specific target inhibition. Vulnerability of targets in the biosynthetic pathway of coenzyme A was evaluated using this platform. In addition, we also report the utility of this platform in understanding the impact of a physiologically relevant carbon source, glucose versus acetate, on metabolite profiles of bacterial pathogens.

Inhibition of methionine adenosyltransferase II induces FasL expression, Fas-DISC formation and caspase-8-dependent apoptotic death in T leukemic cells.

Methionine adenosyltransferase II (MAT II) is a key enzyme in cellular metabolism and catalyzes the formation of S-adenosylmethionine (SAMe) from L-methionine and ATP. Normal resting T lymphocytes have minimal MAT II activity, whereas activated proliferating T lymphocytes and transformed T leukemic cells show significantly enhanced MAT II activity. This work was carried out to examine the role of MAT II activity and SAMe biosynthesis in the survival of leukemic T cells. Inhibition of MAT II and the resultant decrease in SAMe levels enhanced expression of FasL mRNA and protein, and induced DISC (Death Inducing Signaling Complex) formation with FADD (Fas-associated Death Domain) and procaspase-8 recruitment, as well as concomitant increase in caspase-8 activation and decrease in c-FLIP(s) levels. Fas-initiated signaling induced by MAT II inhibition was observed to link to the mitochondrial pathway via Bid cleavage and to ultimately lead to increased caspase-3 activation and DNA fragmentation in these cells. Furthermore, blocking MAT 2A mRNA expression, which encodes the catalytic subunits of MAT II, using a small-interfering RNA approach enhanced FasL expression and cell death, validating the essential nature of MAT II activity in the survival of T leukemic cells.