Corncob bedding alters the effects of estrogens on aggressive behavior and reduces estrogen receptor-α expression in the brain

There is growing appreciation that estrogen signaling pathways can be modulated by naturally occurring environmental compounds such as phytoestrogens and the more recently discovered xenoestrogens. Many researchers studying the effects of estrogens on brain function or behavior in animal models choose to use phytoestrogen-free food for this reason. Corncob bedding is commonly used in animal facilities across the United States and has been shown to inhibit estrogen-dependent reproductive behavior in rats. The mechanism for this effect was unclear, because the components of corncob bedding mediating this effect did not bind estrogen receptors. Here, we show in the California mouse (Peromyscus californicus) that estrogens decrease aggression when cardboard-based bedding is used but that this effect is absent when corncob bedding is used. California mice housed on corncob bedding also had fewer estrogen receptor-α-positive cells in the bed nucleus of the stria terminalis and ventromedial hypothalamus compared with mice housed on cardboard-based bedding. In addition, corncob bedding suppressed the expression of phosphorylated ERK in these brain regions as well as in the medial amygdala and medial preoptic area. Previous reports of the effects of corncob bedding on reproductive behavior are not widely appreciated. Our observations on the effects of corncob bedding on behavior and brain function should draw attention to the importance that cage bedding can exert on neuroendocrine research.     

Müllerian inhibiting substance contributes to sex-linked biases in the brain and behavior

Many behavioral traits and most brain disorders are common to males and females but are more evident in one sex than the other. The control of these subtle sex-linked biases is largely unstudied and has been presumed to mirror that of the highly dimorphic reproductive nuclei. Sexual dimorphism in the reproductive tract is a product of Müllerian inhibiting substance (MIS), as well as the sex steroids. Males with a genetic deficiency in MIS signaling are sexually males, leading to the presumption that MIS is not a neural regulator. We challenge this presumption by reporting that most immature neurons in mice express the MIS-specific receptor (MISRII) and that male Mis^−/− and Misrii^−/− mice exhibit subtle feminization of their spinal motor neurons and of their exploratory behavior. Consequently, MIS may be a broad regulator of the subtle sex-linked biases in the nervous system.     

From the Cover: What the fly’s nose tells the fly’s brain

The fly olfactory system has a three-layer architecture: The fly’s olfactory receptor neurons send odor information to the first layer (the encoder) where this information is formatted as combinatorial odor code, one which is maximally informative, with the most informative neurons firing fastest. This first layer then sends the encoded odor information to the second layer (decoder), which consists of about 2,000 neurons that receive the odor information and “break” the code. For each odor, the amplitude of the synaptic odor input to the 2,000 second-layer neurons is approximately normally distributed across the population, which means that only a very small fraction of neurons receive a large input. Each odor, however, activates its own population of large-input neurons and so a small subset of the 2,000 neurons serves as a unique tag for the odor. Strong inhibition prevents most of the second-stage neurons from firing spikes, and therefore spikes from only the small population of large-input neurons is relayed to the third stage. This selected population provides the third stage (the user) with an odor label that can be used to direct behavior based on what odor is present.A hallmark of at least three major brain structures found in essentially all vertebrates—cerebellum, hippocampus, and olfactory system—is an architecture with three stages of information processing (Fig. 1). In the first stage (the encoder), information arriving from other brain areas is assembled into a combinatorial code and relayed, with a massive expansion of neuron number, to the second stage. This code is “broken” by the second stage (the decoder), and passed to the third stage, where the desired pieces of decoded information are selected for use in other brain regions.Open in a separate windowFig. 1.Schematic representation of the Marr motif. Four sensory neurons at the left (circles represent their cell bodies) send their axons to two regions of neuropil (dotted circles) in the first (encoder) stage of the three-stage circuit. Additional circuitry (not illustrated) produces interactions between the two neuropil regions. Dendrites of the two stage 1 projection neurons (cell bodies of precerebellar neurons are the circles) collect and format the sensory information as a combinatorial code. This coded information is then sent over the precerebellar neuron axons to stage 2 (decoder). Synaptic connections (dark dots) are made on the dendrites of four stage 2 neurons (granule cell bodies represented by four circles), and the output, the broken code, is sent at the right of the diagram to stage 3 (not represented). Additional circuitry responsible for breaking the combinatorial code in the second stage is not shown.The first proposal for the operation of this three-stage processing architecture was made by Marr (1), over four decades ago, to explain the function of the cerebellum, and I shall refer to the first two (encoder/decoder) stages of the architecture as the Marr motif. According to Marr, the encoder stage provides the pattern (neuronal activity compiled in precerebellar nuclei) that is relayed to cerebellar granule cells (the decoder stage). In granule cells, the pattern provided by the precerebellar neurons is separated by spreading the information over many more neurons (there are many more granule cells than precerebellar neurons), and by quieting most of the granule cells with strong inhibition from Golgi inhibitory neurons. These inhibitory neurons collect the output of many granule cells and feed it back to them. Finally, in the third stage (Purkinje cells), some parts of the separated pattern relayed by the granule cells are selected for labeling by concurrent climbing fiber activity that adjusts the strength of synapses conveying the chosen signals to Purkinje cells. Whenever labeled signals happened to recur, the modified synapses cause selected Purkinje cells change their firing rates and provide an output based on the tagged signals.Although Marr’s proposal has been enormously influential, we still do not understand the combinatorial code (Marr’s pattern) generated by the precerebellar neurons, nor do we know how it is decoded by the granule cells (Marr’s pattern separation). What appears to be this same three-stage architecture is used by Drosophila for the first three levels of its olfactory system (2), but the fly system is much smaller, simpler, and more completely understood than any vertebrate version. Here I exploit the simplicity and extensive knowledge about the fly olfactory system to learn the properties of the combinatorial odor code, and how it is decoded. My hope is that what is learned from the fly will help us understand the similar three-level architecture in vertebrates.     

Lag threads organize the brain’s intrinsic activity

It has been widely reported that intrinsic brain activity, in a variety of animals including humans, is spatiotemporally structured. Specifically, propagated slow activity has been repeatedly demonstrated in animals. In human resting-state fMRI, spontaneous activity has been understood predominantly in terms of zero-lag temporal synchrony within widely distributed functional systems (resting-state networks). Here, we use resting-state fMRI from 1,376 normal, young adults to demonstrate that multiple, highly reproducible, temporal sequences of propagated activity, which we term “lag threads,” are present in the brain. Moreover, this propagated activity is largely unidirectional within conventionally understood resting-state networks. Modeling experiments show that resting-state networks naturally emerge as a consequence of shared patterns of propagation. An implication of these results is that common physiologic mechanisms may underlie spontaneous activity as imaged with fMRI in humans and slowly propagated activity as studied in animals.Spontaneous (intrinsic) neural activity is ubiquitously present in the mammalian brain, as first noted by Hans Berger (1). Spontaneous activity persists in all physiological states, although the statistical properties of this activity are modified by level of arousal and ongoing behavior (2–8). Invasive studies in animals using diverse techniques—for example, local field potentials (9–11), voltage-sensitive dyes (12–15), and calcium imaging (4, 16, 17)—have demonstrated richly organized intrinsic activity at multiple temporal and spatial scales. The most used technique for studying whole-brain intrinsic activity in humans is resting-state functional magnetic resonance imaging (rs-fMRI). Biswal et al. first reported that slow (<0.1 Hz) spontaneous fluctuations of the blood oxygen level-dependent (BOLD) signal are temporally synchronous within the somatomotor system (18). This basic result has since been extended to multiple functional systems spanning the entire brain (19–22). Synchrony of intrinsic activity is widely referred to as functional connectivity; the associated topographies are known as resting-state networks (RSNs) (23) and, equivalently, intrinsic connectivity networks (24).Almost all prior rs-fMRI studies have used either seed-based correlation mapping (25) or spatial independent components analysis (sICA) (26). Critically, neither or these techniques provide for the possibility that activity within RSNs may exhibit temporal lags on a time scale finer than the temporal sampling density. However, we recently demonstrated highly reproducible lags on the order of ∼1 s by application of parabolic interpolation to rs-fMRI data acquired at a rate of one volume every 3 s (SI Appendix, Fig. S1) (27). Moreover, this lag structure can be modified, with appropriate focality, by a variety of task paradigms (27).Investigations of rs-fMRI lag structure previously have been limited by the concern that observed lags may reflect regional differences in the kinetics of neurovascular coupling rather than primary neural processes (28, 29). However, our previous dimensionality analysis demonstrated that there are at least two independent lag processes within the brain (27). The neurovascular model can account for only one of these. Hence, there must be at least one lag process that is genuinely of neural origin. We have since made significant methodological improvements (Theory and Fig. 1) that enable a more detailed characterization of lag structure in BOLD rs-fMRI data. We report our results in two parts.Fig. 1.Illustration of lag threads. A shows three patterns of propagation (lag threads) through six nodes. The objective is to demonstrate the mapping between lag structure and PCA. The illustration is not intended as a model of propagation in neural tissue. ...In part I, we present an expanded view of the lag structure within the normal adult human brain derived from BOLD rs-fMRI data in 1,376 individuals. Specifically, we show that at least eight orthogonal lag processes can be reproducibly demonstrated. We refer to these processes as “threads” by way of analogy with modern computer programming practice in which single applications contain multiple, independent thread sequences.In part II, we investigate the relation between lag threads and zero-lag temporal correlations—that is, conventional, resting-state functional connectivity. We find that, although there is no simple relation between lag and zero-lag temporal correlation over all pairs of voxels, apparent propagation is largely unidirectional within RSNs. We also show that the zero-lag temporal correlation structure of rs-fMRI arises as a consequence of lags, whereas the reverse is not true. These results suggest that lag threads account for observed patterns of zero-lag temporal synchrony and that RSNs are an emergent property of lag structure.     

GTP energy dependence of endocytosis and autophagy in the aging brain and Alzheimer’s disease

Increased interest in the aging and Alzheimer’s disease (AD)-related impairments in autophagy in the brain raise important questions about regulation and treatment. Since many steps in endocytosis and autophagy depend on GTPases, new measures of cellular GTP levels are needed to evaluate energy regulation in aging and AD. The recent development of ratiometric GTP sensors (GEVALS) and findings that GTP levels are not homogenous inside cells raise new issues of regulation of GTPases by the local availability of GTP. In this review, we highlight the metabolism of GTP in relation to the Rab GTPases involved in formation of early endosomes, late endosomes, and lysosomal transport to execute the autophagic degradation of damaged cargo. Specific GTPases control macroautophagy (mitophagy), microautophagy, and chaperone-mediated autophagy (CMA). By inference, local GTP levels would control autophagy, if not in excess. Additional levels of control are imposed by the redox state of the cell, including thioredoxin involvement. Throughout this review, we emphasize the age-related changes that could contribute to deficits in GTP and AD. We conclude with prospects for boosting GTP levels and reversing age-related oxidative redox shift to restore autophagy. Therefore, GTP levels could regulate the numerous GTPases involved in endocytosis, autophagy, and vesicular trafficking. In aging, metabolic adaptation to a sedentary lifestyle could impair mitochondrial function generating less GTP and redox energy for healthy management of amyloid and tau proteostasis, synaptic function, and inflammation.

     

The hematology laboratory’s response to the COVID-19 pandemic: A scoping review

The ongoing COVID-19 pandemic has had a profound worldwide impact on the laboratory hematology community. Nevertheless, the pace of COVID-19 hematology-related research has continued to accelerate and has established the role of laboratory hematology data for many purposes including disease prognosis and outcome. The purpose of this scoping review was to assess the current state of COVID-19 laboratory hematology research. A comprehensive search of the literature published between December 1, 2019, and July 3, 2020, was performed, and we analyzed the sources, publication dates, study types, and topics of the retrieved studies. Overall, 402 studies were included in this scoping review. Approximately half of these studies (n = 202, 50.37%) originated in China. Retrospective cohort studies comprised the largest study type (n = 176, 43.89%). Prognosis/ risk factors, epidemiology, and coagulation were the most common topics. The number of studies published per day has increased through the end of May. The studies were heavily biased in favor of papers originating in China and on retrospective clinical studies with limited use of and reporting of laboratory data. Despite the major improvements in our understanding of the role of coagulation, automated hematology, and cell morphology in COVID-19, there are gaps in the literature, including biosafety and the laboratory role in screening and prevention of COVID-19. There is a gap in the publication of papers focused on guidelines for the laboratory. Our findings suggest that, despite the large number of publications related to laboratory data and their use in COVID-19 disease, many areas remain unexplored or under-reported.