RNA helicase DDX6 in P-bodies is essential for the assembly of stress granules

Two prominent cytoplasmic RNA granules, ubiquitous RNA-processing bodies (PB) and inducible stress granules (SG), regulate storage of translationally arrested mRNAs and are intimately related. In this study, we found the dependence of SG formation on PB in the cells under arsenite (ARS) stress, but not the other way around. GW182, 4E-T and DDX6 essential for PB formation differentially affect SG formation in the cells under ARS stress, with DDX6 being the most prominent. The cells with DDX6 deficiency display irregular shape of SG which could be rescued by ectopic wt DDX6, but not its helicase mutant E247A DDX6, which induces SG in the cells without stress, indicating that DDX6 helicase activity is essential for PB, but suppressive for SG. DDX6's dual roles are independent of DDX6 interactors EDC3, CNOT1, and PAT1B. This study provides a conceptual advance of how DDX6 involves in the biogenesis of PB and SG.

Autoimmunity increases susceptibility to and mortality from sepsis

Our prior publication detailing how sepsis influences subsequent development of EAE presented a conceptual advance in understanding the post-sepsis chronic immunoparalysis state (Jensen et al., 2020). However, the reverse scenario (autoimmunity prior to sepsis) defines a high-risk patient population whose susceptibility to sepsis remains poorly defined. Herein, we present a retrospective analysis of University of Iowa Hospital and Clinics patients demonstrating increased sepsis incidence among MS, relative to non-MS, patients. To interrogate how autoimmune disease influences host susceptibility to sepsis well-established murine models of MS and sepsis, EAE and CLP, respectively, were utilized. EAE, relative to non-EAE, mice were highly susceptible to sepsis-induced mortality with elevated cytokine storms. These results were further recapitulated in LPS and S. pneumoniae sepsis models. This work highlights both the relevance of identifying highly susceptible patient populations and expands the growing body of literature that host immune status at the time of septic insult is a potent mortality determinant.

Evolutionary velocity with protein language models

Predicting the order of biological homologs is a fundamental task in evolutionary biology. For protein evolution, this order is often determined by first arranging sequences into a phylogenetic tree, which has limiting assumptions and can suffer from substantial ambiguity. Here, we demonstrate how machine learning algorithms called language models can learn mutational likelihoods that predict the directionality of evolution, thereby enabling phylogenetic analysis that addresses key limitations of existing methods. Our main conceptual advance is to construct a "vector field" of protein evolution through local evolutionary predictions that we refer to as evolutionary velocity (evo-velocity). We show that evo-velocity can successfully predict evolutionary order at vastly different timescales, from viral proteins evolving over years to eukaryotic proteins evolving over geologic eons. Evo-velocity also yields new evolutionary insights, predicting strategies of viral-host immune escape, resolving conflicting theories on the evolution of serpins, and revealing a key role of horizontal gene transfer in the evolution of eukaryotic glycolysis. In doing so, our work suggests that language models can learn sufficient rules of natural protein evolution to enable evolutionary predictability.

“Aging-and-Tech Job Vulnerability”: A proposed framework on the dual impact of aging and AI, robotics, and automation among older workers

As the aging population and workforce constitute a worldwide concern, it is becoming necessary to predict how the dual threat of aging and technology at work increases the job vulnerability of older workers and jeopardizes their employability and permanence in the labor market. The objective of this paper is twofold: (1) to analyze perceptions of artificial intelligence, robotics, and automation in work settings and the expected impact of these technologies on older workers to contextualize this emergent phenomenon; and (2) to propose a general model related to “Aging-and-Tech Job Vulnerability” to explain and predict the combined effect of aging and AI/robotics/automation on job insecurity and additional outcomes among older workers. The propositions of the Age-and-Tech Job Vulnerability model developed in this paper seek to present a first approach for the conceptual advance and research on this emerging phenomenon and entails several theoretical and practical implications for organizational psychology.

The many bits of positional information

ABSTRACTHalf a century after Lewis Wolpert's seminal conceptual advance on how cellular fates distribute in space, we provide a brief historical perspective on how the concept of positional information emerged and influenced the field of developmental biology and beyond. We focus on a modern interpretation of this concept in terms of information theory, largely centered on its application to cell specification in the early Drosophila embryo. We argue that a true physical variable (position) is encoded in local concentrations of patterning molecules, that this mapping is stochastic, and that the processes by which positions and corresponding cell fates are determined based on these concentrations need to take such stochasticity into account. With this approach, we shift the focus from biological mechanisms, molecules, genes and pathways to quantitative systems-level questions: where does positional information reside, how it is transformed and accessed during development, and what fundamental limits it is subject to?

The hybrid nature of task-evoked activity: Inside-out neural dynamics in intracranial EEG and Deep Learning

A.AbstractThe standard approach in neuroscience research infers from the external stimulus (outside) to the brain (inside) through stimulus-evoked activity. Recently challenged by Buzsáki, he advocates the reverse; an inside-out approach inferring from the brain’s activity to the neural effects of the stimulus. If so, stimulus-evoked activity should be a hybrid of internal and external components. Providing direct evidence for this hybrid nature, we measured human intracranial stereo-electroencephalography (sEEG) to investigate how prestimulus variability, i.e., standard deviation, shapes poststimulus activity through trial-to-trial variability. We first observed greater poststimulus variability quenching in trials exhibiting high prestimulus variability. Next, we found that the relative effect of the stimulus was higher in the later (300-600ms) than the earlier (0-300ms) poststimulus period. These results were extended by our Deep Learning LSTM network models at the single trial level. The accuracy to classify single trials (prestimulus low/high) increased greatly when the models were trained and tested with real trials compared to trials that exclude the effects of the prestimulus-related ongoing dynamics (corrected trials). Lastly, we replicated our findings showing that trials with high prestimulus variability in theta and alpha bands exhibits faster reaction times. Together, our results support the inside-out approach by demonstrating that stimulus-related activity is a hybrid of two factors: 1) the effects of the external stimulus itself, and 2) the effects of the ongoing dynamics spilling over from the prestimulus period, with the second, i.e., the inside, dwarfing the influence of the first, i.e., the outside.B.Significance StatementOur findings signify a significant conceptual advance in the relationship between pre- and poststimulus dynamics in humans. These findings are important as they show that we miss an essential component - the impact of the ongoing dynamics - when restricting our analyses to the effects of the external stimulus alone. Consequently, these findings may be crucial to fully understand higher cognitive functions and their impairments, as can be seen in psychiatric illnesses. In addition, our Deep Learning LSTM models show a second conceptual advance: high classification accuracy of a single trial to its prestimulus state. Finally, our replicated results in an independent dataset and task showed that this relationship between pre- and poststimulus dynamics exists across tasks and is behaviorally relevant.

The Legacy of Sickness Behaviors

Systemic infections of all types lead to a syndrome known as sickness behaviors. Changes in the behavior of febrile humans and animals formed the original basis for this concept. Body temperature is behaviorally regulated in both endotherms and ectotherms. However, infections cause other changes in body functions, including sleep disruption, anorexia, cognitive and memory deficits and disorientation. The brain mediates this entire cluster of symptoms, even though most major infections occur outside the brain. The true importance of sickness behaviors is not the numerous discoveries of symptoms that affect all of us when we get sick. Instead, the legacy of 30 years of research in sickness behaviors is that it established the physiologic importance of reciprocal communication systems between the immune system and the brain. This conceptual advance remains in its infancy.

Small molecule inhibition of Dynamin-dependent endocytosis targets multiple niche signals and impairs leukemia stem cells

Intensive chemotherapy for acute leukemia can usually induce complete remission, but fails in many patients to eradicate the leukemia stem cells responsible for relapse. There is accumulating evidence that these relapse-inducing cells are maintained and protected by signals provided by the microenvironment. Thus, inhibition of niche signals is a proposed strategy to target leukemia stem cells but this requires knowledge of the critical signals and may be subject to compensatory mechanisms. Signals from the niche require receptor-mediated endocytosis, a generic process dependent on the Dynamin family of large GTPases. Here, we show that Dynole 34-2, a potent inhibitor of Dynamin GTPase activity, can block transduction of key signalling pathways and overcome chemoresistance of leukemia stem cells. Our results provide a significant conceptual advance in therapeutic strategies for acute leukemia that may be applicable to other malignancies in which signals from the niche are involved in disease progression and chemoresistance.

Site-specific spectroscopic measurement of spin and charge in (LuFeO3)m/(LuFe2O4)1 multiferroic superlattices

Interface materials offer a means to achieve electrical control of ferrimagnetism at room temperature as was recently demonstrated in (LuFeO3)m/(LuFe2O4)1 superlattices. A challenge to understanding the inner workings of these complex magnetoelectric multiferroics is the multitude of distinct Fe centres and their associated environments. This is because macroscopic techniques characterize average responses rather than the role of individual iron centres. Here, we combine optical absorption, magnetic circular dichroism and first-principles calculations to uncover the origin of high-temperature magnetism in these superlattices and the charge-ordering pattern in the m = 3 member. In a significant conceptual advance, interface spectra establish how Lu-layer distortion selectively enhances the Fe2+ → Fe3+ charge-transfer contribution in the spin-up channel, strengthens the exchange interactions and increases the Curie temperature. Comparison of predicted and measured spectra also identifies a non-polar charge ordering arrangement in the LuFe2O4 layer. This site-specific spectroscopic approach opens the door to understanding engineered materials with multiple metal centres and strong entanglement.