B-cell progenitor fate determinant interferon regulatory factor 4 (IRF4) exerts key roles in the pathogenesis and progression of multiple myeloma (MM), a currently incurable plasma cell malignancy. Aberrant expression of IRF4 and the establishment of a positive auto-regulatory loop with oncogene MYC, drives a MM specific gene-expression programme leading to the abnormal expansion of malignant immature plasma cells. Targeting the IRF4-MYC oncogenic loop has the potential to provide a selective and effective therapy for MM. Here we evaluate the use of bromodomain inhibitors to target the IRF4-MYC axis through combined inhibition of their known epigenetic regulators, BRD4 and CBP/EP300. Although all inhibitors induced cell death, we found no synergistic effect of targeting both of these regulators on the viability of MM cell-lines. Importantly, for all inhibitors over a time period up to 72 hours, we detected reduced IRF4 mRNA, but a limited decrease in IRF4 protein expression or mRNA levels of downstream target genes. This indicates that inhibitor-induced loss of cell viability is not mediated through reduced IRF4 protein expression, as previously proposed. Further analysis revealed a long half-life of IRF4 protein in MM cells. In support of our experimental observations, gene network modelling of MM suggests that bromodomain inhibition is exerted primarily through MYC and not IRF4. These findings suggest that despite the autofeedback positive regulatory loop between IRF4 and MYC, bromodomain inhibitors are not effective at targeting IRF4 in MM and that novel therapeutic strategies should focus on the direct inhibition or degradation of IRF4.
Brain organoids represent a powerful tool for studying human neurological diseases, particularly those that affect brain growth and structure. However, many diseases manifest with clear evidence of physiological and network abnormality in the absence of anatomical changes, raising the question of whether organoids possess sufficient neural network complexity to model these conditions. Here, we explore the network-level functions of brain organoids using calcium sensor imaging and extracellular recording approaches that together reveal the existence of complex network dynamics reminiscent of intact brain preparations. We demonstrate highly abnormal and epileptiform-like activity in organoids derived from induced pluripotent stem cells from individuals with Rett syndrome, accompanied by transcriptomic differences revealed by single-cell analyses. We also rescue key physiological activities with an unconventional neuroregulatory drug, pifithrin-$α$. Together, these findings provide an essential foundation for the utilization of brain organoids to study intact and disordered human brain network formation and illustrate their utility in therapeutic discovery.
Humoral immunity depends on efficient activation of B cells and their subsequent differentiation into antibody-secreting cells (ASCs). The transcription factor NFκB cRel is critical for B cell proliferation, but incorporating its known regulatory interactions into a mathematical model of the ASC differentiation circuit prevented ASC generation in simulations. Indeed, experimental ectopic cRel expression blocked ASC differentiation by inhibiting the transcription factor Blimp1, and in wild-type (WT) cells cRel was dynamically repressed during ASC differentiation by Blimp1 binding the Rel locus. Including this bi-stable circuit of mutual cRel-Blimp1 antagonism into a multi-scale model revealed that dynamic repression of cRel controls the switch from B cell proliferation to ASC generation phases and hence the respective cell population dynamics. Our studies provide a mechanistic explanation of how dysregulation of this bi-stable circuit might result in pathologic B cell population phenotypes and thus offer new avenues for diagnostic stratification and treatment.
Rapid antibody production in response to invading pathogens requires the dramatic expansion of pathogen-derived antigen-specific B lymphocyte populations. Whether B cell population dynamics are based on stochastic competition between competing cell fates, as in the development of competence by the bacterium Bacillus subtilis, or on deterministic cell fate decisions that execute a predictable program, as during the development of the worm Caenorhabditis elegans, remains unclear. Here, we developed long-term live-cell microscopy of B cell population expansion and multiscale mechanistic computational modeling to characterize the role of molecular noise in determining phenotype heterogeneity. We show that the cell lineage trees underlying B cell population dynamics are mediated by a largely predictable decision-making process where the heterogeneity of cell proliferation and death decisions at any given timepoint largely derives from nongenetic heterogeneity in the founder cells. This means that contrary to previous models, only a minority of genetically identical founder cells contribute the majority to the population response. We computationally predict and experimentally confirm nongenetic molecular determinants that are predictive of founder cells’ proliferative capacity. While founder cell heterogeneity may arise from different exposure histories, we show that it may also be due to the gradual accumulation of small amounts of intrinsic noise during the lineage differentiation process of hematopoietic stem cells to mature B cells. Our finding of the largely deterministic nature of B lymphocyte responses may provide opportunities for diagnostic and therapeutic development.
Phenotypic differences often occur even in clonal cell populations. Many potential sources of such variation have been identified, from biophysical rate variance intrinsic to all chemical processes to asymmetric division of molecular components extrinsic to any particular signaling pathway. Identifying the sources of phenotypic variation and quantifying their contributions to cell fate variation is not possible without accurate single cell data. By combining such data with mathematical models of potential noise sources it is possible to characterize the impact of varying levels of each noise source and identify which sources of variation best explain the experimental observations. The mathematical framework of information theory provides metrics of the impact of noise on the reliability of a cell to sense its environment. While the presence of noise in a single cellular system reduces the reliability of signal transduction its impact on a population of varied single cells remains unclear.