Molecular basis of anaphylatoxin binding, activation, and signaling bias at complement receptors.

The complement system is a critical part of our innate immune response, and the terminal products of this cascade, anaphylatoxins C3a and C5a, exert their physiological and pathophysiological responses primarily via two GPCRs, C3aR and C5aR1. However, the molecular mechanism of ligand recognition, activation, and signaling bias of these receptors remains mostly elusive. Here, we present nine cryo-EM structures of C3aR and C5aR1 activated by their natural and synthetic agonists, which reveal distinct binding pocket topologies of complement anaphylatoxins and provide key insights into receptor activation and transducer coupling. We also uncover the structural basis of a naturally occurring mechanism to dampen the inflammatory response of C5a via proteolytic cleavage of the terminal arginine and the G-protein signaling bias elicited by a peptide agonist of C3aR identified here. In summary, our study elucidates the innerworkings of the complement anaphylatoxin receptors and should facilitate structure-guided drug discovery to target these receptors in a spectrum of disorders.

Crystal Structure of Silkworm PIWI-Clade Argonaute Siwi Bound to piRNA.

PIWI-clade Argonaute proteins associate with PIWI-interacting RNAs (piRNAs) and silence transposable elements in animal gonads. Here, we report the crystal structure of a silkworm PIWI-clade Argonaute, Siwi, bound to the endogenous piRNA, at 2.4 Å resolution. Siwi adopts a bilobed architecture consisting of N-PAZ and MID-PIWI lobes, in which the 5' and 3' ends of the bound piRNA are anchored by the MID-PIWI and PAZ domains, respectively. A structural comparison of Siwi with AGO-clade Argonautes reveals notable differences in their nucleic-acid-binding channels, likely reflecting the distinct lengths of their guide RNAs and their mechanistic differences in guide RNA loading and cleavage product release. In addition, the structure reveals that Siwi and prokaryotic, but not eukaryotic, AGO-clade Argonautes share unexpected similarities, such as metal-dependent 5'-phosphate recognition and a potential structural transition during the catalytic-tetrad formation. Overall, this study provides a critical starting point toward a mechanistic understanding of piRNA-mediated transposon silencing.

Genotoxic aldehyde stress prematurely ages hematopoietic stem cells in a p53-driven manner.

Aged hematopoietic stem cells (HSCs) display diminished self-renewal and a myeloid differentiation bias. However, the drivers and mechanisms that underpin this fundamental switch are not understood. HSCs produce genotoxic formaldehyde that requires protection by the detoxification enzymes ALDH2 and ADH5 and the Fanconi anemia (FA) DNA repair pathway. We find that the HSCs in young Aldh2-/-Fancd2-/- mice harbor a transcriptomic signature equivalent to aged wild-type HSCs, along with increased epigenetic age, telomere attrition, and myeloid-biased differentiation quantified by single HSC transplantation. In addition, the p53 response is vigorously activated in Aldh2-/-Fancd2-/- HSCs, while p53 deletion rescued this aged HSC phenotype. To further define the origins of the myeloid differentiation bias, we use a GFP genetic reporter to find a striking enrichment of Vwf+ myeloid and megakaryocyte-lineage-biased HSCs. These results indicate that metabolism-derived formaldehyde-DNA damage stimulates the p53 response in HSCs to drive accelerated aging.

Versatile JMJD proteins: juggling histones and much more.

Jumonji C domain-containing (JMJD) proteins are found in bacteria, fungi, animals, and plants. They belong to the 2-oxoglutarate-dependent oxygenase superfamily and are endowed with various enzymatic activities, including demethylation of histones and hydroxylation of non-histone proteins. Many JMJD proteins are involved in the epigenetic control of gene expression, yet they also modulate a myriad other cellular processes. In this review we focus on the 33 human JMJD proteins and their established and controversial catalytic properties, survey their epigenetic and non-epigenetic functions, emphasize their contribution to sex-specific disease differences, and highlight how they sense metabolic changes. All this underlines not only their key roles in development and homeostasis, but also that JMJD proteins are destined to become drug targets in multiple diseases.

Decoding anaphylatoxins: unveiling the molecular mechanisms of complement receptor activation and signaling.

Recent advances in cryo-electron microscopy (Cryo-EM) have revolutionized our understanding of the complement C5a/C3a receptors that are crucial in inflammation. A recent report by Yadav et al. has elucidated the activation, ligand binding, selectivity, and signaling bias of these receptors, thereby enhancing structure-guided drug discovery. This paves the way for more effective anti-inflammatory therapies that target these receptors with unprecedented precision.

Robust Hi-C Maps of Enhancer-Promoter Interactions Reveal the Function of Non-coding Genome in Neural Development and Diseases.

Genome-wide mapping of chromatin interactions at high resolution remains experimentally and computationally challenging. Here we used a low-input "easy Hi-C" protocol to map the 3D genome architecture in human neurogenesis and brain tissues and also demonstrated that a rigorous Hi-C bias-correction pipeline (HiCorr) can significantly improve the sensitivity and robustness of Hi-C loop identification at sub-TAD level, especially the enhancer-promoter (E-P) interactions. We used HiCorr to compare the high-resolution maps of chromatin interactions from 10 tissue or cell types with a focus on neurogenesis and brain tissues. We found that dynamic chromatin loops are better hallmarks for cellular differentiation than compartment switching. HiCorr allowed direct observation of cell-type- and differentiation-specific E-P aggregates spanning large neighborhoods, suggesting a mechanism that stabilizes enhancer contacts during development. Interestingly, we concluded that Hi-C loop outperforms eQTL in explaining neurological GWAS results, revealing a unique value of high-resolution 3D genome maps in elucidating the disease etiology.

A Balancing Act: Learning from the Past to Build a Future-Focused Opioid Strategy.

The harmful side effects of opioid drugs such as respiratory depression, tolerance, dependence, and abuse potential have limited the therapeutic utility of opioids for their entire clinical history. However, no previous attempt to develop effective pain drugs that substantially ameliorate these effects has succeeded, and the current opioid epidemic affirms that they are a greater hindrance to the field of pain management than ever. Recent attempts at new opioid development have sought to reduce these side effects by minimizing engagement of the regulatory protein arrestin-3 at the mu-opioid receptor, but there is significant controversy around this approach. Here, we discuss the ongoing effort to develop safer opioids and its relevant historical context. We propose a new model that reconciles results previously assumed to be in direct conflict to explain how different signaling profiles at the mu-opioid receptor contribute to opioid tolerance and dependence. Our goal is for this framework to inform the search for a new generation of lower liability opioid analgesics.

Gut microbiota as a sensor of autoimmune response and treatment for rheumatoid arthritis.

Rheumatoid arthritis (RA) is considered a multifactorial condition where interaction between the genetic and environmental factors lead to immune dysregulation causing autoreactivity. While among the various genetic factors, HLA-DR4 and DQ8, have been reported to be the strongest risk factors, the role of various environmental factors has been unclear. Though events initiating autoreactivity remain unknown, a mucosal origin of RA has gained attention based on the recent observations with the gut dysbiosis in patients. However, causality of gut dysbiosis has been difficult to prove in humans. Mouse models, especially mice expressing RA-susceptible and -resistant HLA class II genes have helped unravel the complex interactions between genetic factors and gut microbiome. This review describes the interactions between HLA genes and gut dysbiosis in sex-biased preclinical autoreactivity and discusses the potential use of endogenous commensals as indicators of treatment efficacy as well as therapeutic tool to suppress pro-inflammatory response in rheumatoid arthritis.

Microbial influences on severity and sex bias of systemic autoimmunity.

Commensal microbes have the capacity to affect development and severity of autoimmune diseases. Germ-free (GF) animals have proven to be a fine tool to obtain definitive answers to the queries about the microbial role in these diseases. Moreover, GF and gnotobiotic animals can be used to dissect the complex symptoms and determine which are regulated (enhanced or attenuated) by microbes. These include disease manifestations that are sex biased. Here, we review comparative analyses conducted between GF and Specific-Pathogen Free (SPF) mouse models of autoimmunity. We present data from the B6;NZM-Sle1NZM2410/AegSle2NZM2410/AegSle3NZM2410/Aeg-/LmoJ (B6.NZM) mouse model of systemic lupus erythematosus (SLE) characterized by multiple measurable features. We compared the severity and sex bias of SPF, GF, and ex-GF mice and found variability in the severity and sex bias of some manifestations. Colonization of GF mice with the microbiotas taken from B6.NZM mice housed in two independent institutions variably affected severity and sexual dimorphism of different parameters. Thus, microbes regulate both the severity and sexual dimorphism of select SLE traits. The sensitivity of particular trait to microbial influence can be used to further dissect the mechanisms driving the disease. Our results demonstrate the complexity of the problem and open avenues for further investigations.

Queuosine-tRNA promotes sex-dependent learning and memory formation by maintaining codon-biased translation elongation speed.

Queuosine (Q) is a modified nucleoside at the wobble position of specific tRNAs. In mammals, queuosinylation is facilitated by queuine uptake from the gut microbiota and is introduced into tRNA by the QTRT1-QTRT2 enzyme complex. By establishing a Qtrt1 knockout mouse model, we discovered that the loss of Q-tRNA leads to learning and memory deficits. Ribo-Seq analysis in the hippocampus of Qtrt1-deficient mice revealed not only stalling of ribosomes on Q-decoded codons, but also a global imbalance in translation elongation speed between codons that engage in weak and strong interactions with their cognate anticodons. While Q-dependent molecular and behavioral phenotypes were identified in both sexes, female mice were affected more severely than males. Proteomics analysis confirmed deregulation of synaptogenesis and neuronal morphology. Together, our findings provide a link between tRNA modification and brain functions and reveal an unexpected role of protein synthesis in sex-dependent cognitive performance.

Identification and correction for collider bias in a genome-wide association study of diabetes-related heart failure.

Type 2 diabetes (T2D) is a major risk factor for heart failure (HF) and has elevated incidence among individuals with HF. Since genetics and HF can independently influence T2D, collider bias may occur when T2D (i.e., collider) is controlled for by design or analysis. Thus, we conducted a genome-wide association study (GWAS) of diabetes-related HF with correction for collider bias. We first performed a GWAS of HF to identify genetic instrumental variables (GIVs) for HF and to enable bidirectional Mendelian randomization (MR) analysis between T2D and HF. We identified 61 genomic loci, significantly associated with all-cause HF in 114,275 individuals with HF and over 1.5 million controls of European ancestry. Using a two-sample bidirectional MR approach with 59 and 82 GIVs for HF and T2D, respectively, we estimated that T2D increased HF risk (odds ratio [OR] 1.07, 95% confidence interval [CI] 1.04-1.10), while HF also increased T2D risk (OR 1.60, 95% CI 1.36-1.88). Then we performed a GWAS of diabetes-related HF corrected for collider bias due to the study design of index cases. After removing the spurious association of TCF7L2 locus due to collider bias, we identified two genome-wide significant loci close to PITX2 (chromosome 4) and CDKN2B-AS1 (chromosome 9) associated with diabetes-related HF in the Million Veteran Program and replicated the associations in the UK Biobank. Our MR findings provide strong evidence that HF increases T2D risk. As a result, collider bias leads to spurious genetic associations of diabetes-related HF, which can be effectively corrected to identify true positive loci.

Overcoming gender bias in STEM.

Despite prevalent diversity and inclusion programs in STEM, gender biases and stereotypes persist across educational and professional settings. Recognizing this enduring bias is crucial for achieving transformative change on gender equity and can help orient policy toward more effective strategies to address ongoing disparities.

Conformational Complexity and Dynamics in a Muscarinic Receptor Revealed by NMR Spectroscopy.

The M2 muscarinic acetylcholine receptor (M2R) is a prototypical GPCR that plays important roles in regulating heart rate and CNS functions. Crystal structures provide snapshots of the M2R in inactive and active states, but the allosteric link between the ligand binding pocket and cytoplasmic surface remains poorly understood. Here we used solution NMR to examine the structure and dynamics of the M2R labeled with 13CH3-ε-methionine upon binding to various orthosteric and allosteric ligands having a range of efficacy for both G protein activation and arrestin recruitment. We observed ligand-specific changes in the NMR spectra of 13CH3-ε-methionine probes in the M2R extracellular domain, transmembrane core, and cytoplasmic surface, allowing us to correlate ligand structure with changes in receptor structure and dynamics. We show that the M2R has a complex energy landscape in which ligands with different efficacy profiles stabilize distinct receptor conformations.

People see more of their biases in algorithms.

Algorithmic bias occurs when algorithms incorporate biases in the human decisions on which they are trained. We find that people see more of their biases (e.g., age, gender, race) in the decisions of algorithms than in their own decisions. Research participants saw more bias in the decisions of algorithms trained on their decisions than in their own decisions, even when those decisions were the same and participants were incentivized to reveal their true beliefs. By contrast, participants saw as much bias in the decisions of algorithms trained on their decisions as in the decisions of other participants and algorithms trained on the decisions of other participants. Cognitive psychological processes and motivated reasoning help explain why people see more of their biases in algorithms. Research participants most susceptible to bias blind spot were most likely to see more bias in algorithms than self. Participants were also more likely to perceive algorithms than themselves to have been influenced by irrelevant biasing attributes (e.g., race) but not by relevant attributes (e.g., user reviews). Because participants saw more of their biases in algorithms than themselves, they were more likely to make debiasing corrections to decisions attributed to an algorithm than to themselves. Our findings show that bias is more readily perceived in algorithms than in self and suggest how to use algorithms to reveal and correct biased human decisions.

Motor "laziness" constrains fixation selection in real-world tasks.

Humans coordinate their eye, head, and body movements to gather information from a dynamic environment while maximizing reward and minimizing biomechanical and energetic costs. However, such natural behavior is not possible in traditional experiments employing head/body restraints and artificial, static stimuli. Therefore, it is unclear to what extent mechanisms of fixation selection discovered in lab studies, such as inhibition-of-return (IOR), influence everyday behavior. To address this gap, participants performed nine real-world tasks, including driving, visually searching for an item, and building a Lego set, while wearing a mobile eye tracker (169 recordings; 26.6 h). Surprisingly, in all tasks, participants most often returned to what they just viewed and saccade latencies were shorter preceding return than forward saccades, i.e., consistent with facilitation, rather than inhibition, of return. We hypothesize that conservation of eye and head motor effort ("laziness") contributes. Correspondingly, we observed center biases in fixation position and duration relative to the head's orientation. A model that generates scanpaths by randomly sampling these distributions reproduced all return phenomena we observed, including distinct 3-fixation sequences for forward versus return saccades. After controlling for orbital eccentricity, one task (building a Lego set) showed evidence for IOR. This, along with small discrepancies between model and data, indicates that the brain balances minimization of motor costs with maximization of rewards (e.g., accomplished by IOR and other mechanisms) and that the optimal balance varies according to task demands. Supporting this account, the orbital range of motion used in each task traded off lawfully with fixation duration.

Understanding the Asian water tower requires a redesigned precipitation observation strategy.

The Asian water tower (AWT) serves as the source of 10 major Asian river systems and supports the lives of ~2 billion people. Obtaining reliable precipitation data over the AWT is a prerequisite for understanding the water cycle within this pivotal region. Here, we quantitatively reveal that the "observed" precipitation over the AWT is considerably underestimated in view of observational evidence from three water cycle components, namely, evapotranspiration, runoff, and accumulated snow. We found that three paradoxes appear if the so-called observed precipitation is corrected, namely, actual evapotranspiration exceeding precipitation, unrealistically high runoff coefficients, and accumulated snow water equivalent exceeding contemporaneous precipitation. We then explain the cause of precipitation underestimation from instrumental error caused by wind-induced gauge undercatch and the representativeness error caused by sparse-uneven gauge density and the complexity of local surface conditions. These findings require us to rethink previous results concerning the water cycle, prompting the study to discuss potential solutions.

Do digital technologies reduce racially biased reporting? Evidence from NYPD administrative data.

Recent work has emphasized the disproportionate bias faced by minorities when interacting with law enforcement. However, research on the topic has been hampered by biased sampling in administrative data, namely that records of police interactions with citizens only reflect information on the civilians that police elect to investigate, and not civilians that police observe but do not investigate. In this work, we address a related bias in administrative police data which has received less empirical attention, namely reporting biases around investigations that have taken place. Further, we investigate whether digital monitoring tools help mitigate this reporting bias. To do so, we examine changes in reports of interactions between law enforcement and citizens in the wake of the New York City Police Department's replacement of analog memo books with mobile smartphones. Results from a staggered difference in differences estimation indicate a significant increase in reports of citizen stops once the new smartphones are deployed. Importantly, we observe that the rise is driven by increased reports of "unproductive" stops, stops involving non-White citizens, and stops occurring in areas characterized by a greater concentration of crime and non-White residents. These results reinforce the recent observation that prior work has likely underestimated the extent of racial bias in policing. Further, they highlight that the implementation of digital monitoring tools can mitigate the issue to some extent.

A predisposed motor bias shapes individuality in vocal learning.

The development of individuality during learned behavior is a common trait observed across animal species; however, the underlying biological mechanisms remain understood. Similar to human speech, songbirds develop individually unique songs with species-specific traits through vocal learning. In this study, we investigate the developmental and molecular mechanisms underlying individuality in vocal learning by utilizing F1 hybrid songbirds (Taeniopygia guttata cross with Taeniopygia bichenovii), taking an integrating approach combining experimentally controlled systematic song tutoring, unbiased discriminant analysis of song features, and single-cell transcriptomics. When tutoring with songs from both parental species, F1 hybrid individuals exhibit evident diversity in their acquired songs. Approximately 30% of F1 hybrids selectively learn either song of the two parental species, while others develop merged songs that combine traits from both species. Vocal acoustic biases during vocal babbling initially appear as individual differences in songs among F1 juveniles and are maintained through the sensitive period of song vocal learning. These vocal acoustic biases emerge independently of the initial auditory experience of hearing the biological father's and passive tutored songs. We identify individual differences in transcriptional signatures in a subset of cell types, including the glutamatergic neurons projecting from the cortical vocal output nucleus to the hypoglossal nuclei, which are associated with variations of vocal acoustic features. These findings suggest that a genetically predisposed vocal motor bias serves as the initial origin of individual variation in vocal learning, influencing learning constraints and preferences.

On the different regimes of stochastic gradient descent.

Modern deep networks are trained with stochastic gradient descent (SGD) whose key hyperparameters are the number of data considered at each step or batch size [Formula: see text], and the step size or learning rate [Formula: see text]. For small [Formula: see text] and large [Formula: see text], SGD corresponds to a stochastic evolution of the parameters, whose noise amplitude is governed by the "temperature" [Formula: see text]. Yet this description is observed to break down for sufficiently large batches [Formula: see text], or simplifies to gradient descent (GD) when the temperature is sufficiently small. Understanding where these cross-overs take place remains a central challenge. Here, we resolve these questions for a teacher-student perceptron classification model and show empirically that our key predictions still apply to deep networks. Specifically, we obtain a phase diagram in the [Formula: see text]-[Formula: see text] plane that separates three dynamical phases: i) a noise-dominated SGD governed by temperature, ii) a large-first-step-dominated SGD and iii) GD. These different phases also correspond to different regimes of generalization error. Remarkably, our analysis reveals that the batch size [Formula: see text] separating regimes (i) and (ii) scale with the size [Formula: see text] of the training set, with an exponent that characterizes the hardness of the classification problem.

The Black Lives Matter movement mitigates bias against racial minority actors.

Watching movies is among the most popular entertainment and cultural activities. How do viewers react when a movie sequel increases racial minority actors in the main cast ("minority increase")? On the one hand, such sequels may receive better evaluations if viewers appreciate racially inclusive casting for its novel elements (the value-in-diversity perspective) and moral appeal (the fairness perspective on diversity). On the other hand, discrimination research suggests that if viewers harbor biases against racial minorities, sequels with minority increase may receive worse evaluations. To examine these competing possibilities, we analyze a unique panel dataset of movie series released from 1998 to 2021 and conduct text analysis of 312,457 reviews of these movies. Consistent with discrimination research, we find that movies with minority increase receive lower ratings and more toxic reviews. Importantly, these effects weaken after the advent of the Black Lives Matter (BLM) movement, especially when the movement's intensity is high. These results are reliable across various robustness checks (e.g., propensity score matching, random implementation test). We conceptually replicate the bias mitigation effect of BLM in a preregistered experiment: Heightening the salience of BLM increases White individuals' acceptance of racial minority increase in a movie sequel. This research demonstrates the power of social movements in fostering diversity, equality, and inclusion.