The tumor suppressor MIR139 is silenced by POLR2M to promote AML oncogenesis

MIR139 is a tumor suppressor and is commonly silenced in acute myeloid leukemia (AML). However, the tumor-suppressing activities of miR-139 and molecular mechanisms of MIR139-silencing remain largely unknown. Here, we studied the poorly prognostic MLL-AF9 fusion protein-expressing AML. We show that MLL-AF9 expression in hematopoietic precursors caused epigenetic silencing of MIR139, whereas overexpression of MIR139 inhibited in vitro and in vivo AML outgrowth. We identified novel miR-139 targets that mediate the tumor-suppressing activities of miR-139 in MLL-AF9 AML. We revealed that two enhancer regions control MIR139 expression and found that the polycomb repressive complex 2 (PRC2) downstream of MLL-AF9 epigenetically silenced MIR139 in AML. Finally, a genome-wide CRISPR-Cas9 knockout screen revealed RNA Polymerase 2 Subunit M (POLR2M) as a novel MIR139-regulatory factor. Our findings elucidate the molecular control of tumor suppressor MIR139 and reveal a role for POLR2M in the MIR139-silencing mechanism, downstream of MLL-AF9 and PRC2 in AML. In addition, we confirmed these findings in human AML cell lines with different oncogenic aberrations, suggesting that this is a more common oncogenic mechanism in AML. Our results may pave the way for new targeted therapy in AML.Proteomic

Distributional vowel training is less effective for adults than for infants. A study using the mismatch response

Distributional learning of speech sounds (i.e., learning from simple exposure to frequency distributions of speech sounds in the environment) has been observed in the lab repeatedly in both infants and adults. The current study is the first attempt to examine whether the capacity for using the mechanism is different in adults than in infants. To this end, a previous event-related potential study that had shown distributional learning of the English vowel contrast /æ/~/ε/ in 2-to-3-month old Dutch infants was repeated with Dutch adults. Specifically, the adults were exposed to either a bimodal distribution that suggested the existence of the two vowels (as appropriate in English), or to a unimodal distribution that did not (as appropriate in Dutch). After exposure the participants were tested on their discrimination of a representative [æ] and a representative [ε], in an oddball paradigm for measuring mismatch responses (MMRs). Bimodally trained adults did not have a significantly larger MMR amplitude, and hence did not show significantly better neural discrimination of the test vowels, than unimodally trained adults. A direct comparison between the normalized MMR amplitudes of the adults with those of the previously tested infants showed that within a reasonable range of normalization parameters, the bimodal advantage is reliably smaller in adults than in infants, indicating that distributional learning is a weaker mechanism for learning speech sounds in adults (if it exists in that group at all) than in infants

The mismatch negativity: A review of underlying mechanisms

The mismatch negativity (MMN) is a brain response to violations of a rule, established by a sequence of sensory stimuli (typically in the auditory domain) [Näätänen R. Attention and brain function. Hillsdale, NJ: Lawrence Erlbaum; 1992]. The MMN reflects the brain’s ability to perform automatic comparisons between consecutive stimuli and provides an electrophysiological index of sensory learning and perceptual accuracy. Although the MMN has been studied extensively, the neurophysiological mechanisms underlying the MMN are not well understood. Several hypotheses have been put forward to explain the generation of the MMN; amongst these accounts, the “adaptation hypothesis” and the “model adjustment hypothesis” have received the most attention. This paper presents a review of studies that focus on neuronal mechanisms underlying the MMN generation, discusses the two major explanatory hypotheses, and proposes predictive coding as a general framework that attempts to unify both

Outlier responses reflect sensitivity to statistical structure in the human brain

We constantly look for patterns in the environment that allow us to learn its key regularities. These regularities are fundamental in enabling us to make predictions about what is likely to happen next. The physiological study of regularity extraction has focused primarily on repetitive sequence-based rules within the sensory environment, or on stimulus-outcome associations in the context of reward-based decision-making. Here we ask whether we implicitly encode non-sequential stochastic regularities, and detect violations therein. We addressed this question using a novel experimental design and both behavioural and magnetoencephalographic (MEG) metrics associated with responses to pure-tone sounds with frequencies sampled from a Gaussian distribution. We observed that sounds in the tail of the distribution evoked a larger response than those that fell at the centre. This response resembled the mismatch negativity (MMN) evoked by surprising or unlikely events in traditional oddball paradigms. Crucially, responses to physically identical outliers were greater when the distribution was narrower. These results show that humans implicitly keep track of the uncertainty induced by apparently random distributions of sensory events. Source reconstruction suggested that the statistical-context-sensitive responses arose in a temporo-parietal network, areas that have been associated with attention orientation to unexpected events. Our results demonstrate a very early neurophysiological marker of the brain's ability to implicitly encode complex statistical structure in the environment. We suggest that this sensitivity provides a computational basis for our ability to make perceptual inferences in noisy environments and to make decisions in an uncertain world