Systematic mapping of organism-scale gene-regulatory networks in aging using population asynchrony.

In aging, physiologic networks decline in function at rates that differ between individuals, producing a wide distribution of lifespan. Though 70% of human lifespan variance remains unexplained by heritable factors, little is known about the intrinsic sources of physiologic heterogeneity in aging. To understand how complex physiologic networks generate lifespan variation, new methods are needed. Here, we present Asynch-seq, an approach that uses gene-expression heterogeneity within isogenic populations to study the processes generating lifespan variation. By collecting thousands of single-individual transcriptomes, we capture the Caenorhabditis elegans "pan-transcriptome"-a highly resolved atlas of non-genetic variation. We use our atlas to guide a large-scale perturbation screen that identifies the decoupling of total mRNA content between germline and soma as the largest source of physiologic heterogeneity in aging, driven by pleiotropic genes whose knockdown dramatically reduces lifespan variance. Our work demonstrates how systematic mapping of physiologic heterogeneity can be applied to reduce inter-individual disparities in aging.

The long-term genetic stability and individual specificity of the human gut microbiome.

By following up the gut microbiome, 51 human phenotypes and plasma levels of 1,183 metabolites in 338 individuals after 4 years, we characterize microbial stability and variation in relation to host physiology. Using these individual-specific and temporally stable microbial profiles, including bacterial SNPs and structural variations, we develop a microbial fingerprinting method that shows up to 85% accuracy in classifying metagenomic samples taken 4 years apart. Application of our fingerprinting method to the independent HMP cohort results in 95% accuracy for samples taken 1 year apart. We further observe temporal changes in the abundance of multiple bacterial species, metabolic pathways, and structural variation, as well as strain replacement. We report 190 longitudinal microbial associations with host phenotypes and 519 associations with plasma metabolites. These associations are enriched for cardiometabolic traits, vitamin B, and uremic toxins. Finally, mediation analysis suggests that the gut microbiome may influence cardiometabolic health through its metabolites.

Sensing of individual stalled 80S ribosomes by Fap1 for nonfunctional rRNA turnover.

Cells can respond to stalled ribosomes by sensing ribosome collisions and employing quality control pathways. How ribosome stalling is resolved without collisions, however, has remained elusive. Here, focusing on noncolliding stalling exhibited by decoding-defective ribosomes, we identified Fap1 as a stalling sensor triggering 18S nonfunctional rRNA decay via polyubiquitination of uS3. Ribosome profiling revealed an enrichment of Fap1 at the translation initiation site but also an association with elongating individual ribosomes. Cryo-EM structures of Fap1-bound ribosomes elucidated Fap1 probing the mRNA simultaneously at both the entry and exit channels suggesting an mRNA stasis sensing activity, and Fap1 sterically hinders the formation of canonical collided di-ribosomes. Our findings indicate that individual stalled ribosomes are the potential signal for ribosome dysfunction, leading to accelerated turnover of the ribosome itself.

Sources of Individual Differences in Pain.

Pain is an immense clinical and societal challenge, and the key to understanding and treating it is variability. Robust interindividual differences are consistently observed in pain sensitivity, susceptibility to developing painful disorders, and response to analgesic manipulations. This review examines the causes of this variability, including both organismic and environmental sources. Chronic pain development is a textbook example of a gene-environment interaction, requiring both chance initiating events (e.g., trauma, infection) and more immutable risk factors. The focus is on genetic factors, since twin studies have determined that a plurality of the variance likely derives from inherited genetic variants, but sex, age, ethnicity, personality variables, and environmental factors are also considered.

Individual differences in human gaze behavior generalize from faces to objects.

Individuals differ in where they fixate on a face, with some looking closer to the eyes while others prefer the mouth region. These individual biases are highly robust, generalize from the lab to the outside world, and have been associated with social cognition and associated disorders. However, it is unclear, whether these biases are specific to faces or influenced by domain-general mechanisms of vision. Here, we juxtaposed these hypotheses by testing whether individual face fixation biases generalize to inanimate objects. We analyzed >1.8 million fixations toward faces and objects in complex natural scenes from 405 participants tested in multiple labs. Consistent interindividual differences in fixation positions were highly inter-correlated across faces and objects in all samples. Observers who fixated closer to the eye region also fixated higher on inanimate objects and vice versa. Furthermore, the inter-individual spread of fixation positions scaled with target size in precisely the same, non-linear manner for faces and objects. These findings contradict a purely domain-specific account of individual face gaze. Instead, they suggest significant domain-general contributions to the individual way we look at faces, a finding with potential relevance for basic vision, face perception, social cognition, and associated clinical conditions.

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.

Individual gaze shapes diverging neural representations.

Complex visual stimuli evoke diverse patterns of gaze, but previous research suggests that their neural representations are shared across brains. Here, we used hyperalignment to compare visual responses between observers viewing identical stimuli. We find that individual eye movements enhance cortical visual responses but also lead to representational divergence. Pairwise differences in the spatial distribution of gaze and in semantic salience predict pairwise representational divergence in V1 and inferior temporal cortex, respectively. This suggests that individual gaze sculpts individual visual worlds.

Longitudinal stability of individual brain plasticity patterns in blindness.

The primary visual cortex (V1) in blindness is engaged in a wide spectrum of tasks and sensory modalities, including audition, touch, language, and memory. This widespread involvement raises questions regarding the constancy of its role and whether it might exhibit flexibility in its function over time, connecting to diverse network functions specific to task demands. This would suggest that reorganized V1 assumes a role like multiple-demand system regions. Alternatively, varying patterns of plasticity in blind V1 may be attributed to individual factors, with different blind individuals recruiting V1 preferentially for different functions. In support of this, we recently showed that V1 functional connectivity (FC) varies greatly across blind individuals. But do these represent stable individual patterns of plasticity, or are they driven more by instantaneous changes, like a multiple-demand system now inhabiting V1? Here, we tested whether individual FC patterns from the V1 of blind individuals are stable over time. We show that over two years, FC from the V1 is unique and highly stable in a small sample of repeatedly sampled congenitally blind individuals. Further, using multivoxel pattern analysis, we demonstrate that the unique reorganization patterns of these individuals allow decoding of participant identity. Together with recent evidence for substantial individual differences in V1 connectivity, this indicates that there may be a consistent role for V1 in blindness, which may differ for each individual. Further, it suggests that the variability in visual reorganization in blindness across individuals could be used to seek stable neuromarkers for sight rehabilitation and assistive approaches.

Callousness, exploitativeness, and tracking of cooperation incentives in the human default network.

Human cognitive capacities that enable flexible cooperation may have evolved in parallel with the expansion of frontoparietal cortical networks, particularly the default network. Conversely, human antisocial behavior and trait antagonism are broadly associated with reduced activity, impaired connectivity, and altered structure of the default network. Yet, behaviors like interpersonal manipulation and exploitation may require intact or even superior social cognition. Using a reinforcement learning model of decision-making on a modified trust game, we examined how individuals adjusted their cooperation rate based on a counterpart's cooperation and social reputation. We observed that learning signals in the default network updated the predicted utility of cooperation or defection and scaled with reciprocal cooperation. These signals were weaker in callous (vs. compassionate) individuals but stronger in those who were more exploitative (vs. honest and humble). Further, they accounted for associations between exploitativeness, callousness, and reciprocal cooperation. Separately, behavioral sensitivity to prior reputation was reduced in callous but not exploitative individuals and selectively scaled with responses of the medial temporal subsystem of the default network. Overall, callousness was characterized by blunted behavioral and default network sensitivity to cooperation incentives. Exploitativeness predicted heightened sensitivity to others' cooperation but not social reputation. We speculate that both compassion and exploitativeness may reflect cognitive adaptations to social living, enabled by expansion of the default network in anthropogenesis.

Onscreen presence of instructors in video lectures affects learners' neural synchrony and visual attention during multimedia learning.

COVID-19 forced students to rely on online learning using multimedia tools, and multimedia learning continues to impact education beyond the pandemic. In this study, we combined behavioral, eye-tracking, and neuroimaging paradigms to identify multimedia learning processes and outcomes. College students viewed four video lectures including slides with either an onscreen human instructor, an animated instructor, or no onscreen instructor. Brain activity was recorded via fMRI, visual attention was recorded via eye-tracking, and learning outcome was assessed via post-tests. Onscreen presence of instructor, compared with no instructor presence, resulted in superior post-test performance, less visual attention on the slide, more synchronized eye movements during learning, and higher neural synchronization in cortical networks associated with socio-emotional processing and working memory. Individual variation in cognitive and socio-emotional abilities and intersubject neural synchronization revealed different levels of cognitive and socio-emotional processing in different learning conditions. The instructor-present condition evoked increased synchronization, likely reflecting extra processing demands in attentional control, working memory engagement, and socio-emotional processing. Although human instructors and animated instructors led to comparable learning outcomes, the effects were due to the dynamic interplay of information processing vs. attentional distraction. These findings reflect a benefit-cost trade-off where multimedia learning outcome is enhanced only when the cognitive benefits motivated by the social presence of onscreen instructor outweigh the cognitive costs brought about by concurrent attentional distraction unrelated to learning.

Puppy whines mediate maternal behavior in domestic dogs.

In mammals, offspring vocalizations typically encode information about identity and body condition, allowing parents to limit alloparenting and adjust care. But how do these vocalizations mediate parental behavior in species faced with the problem of rearing not one, but multiple offspring, such as domestic dogs? Comprehensive acoustic analyses of 4,400 whines recorded from 220 Beagle puppies in 40 litters revealed litter and individual (within litter) differences in call acoustic structure. By then playing resynthesized whines to mothers, we showed that they provided more care to their litters, and were more likely to carry the emitting loudspeaker to the nest, in response to whine variants derived from their own puppies than from strangers. Importantly, care provisioning was attenuated by experimentally moving the fundamental frequency (fo, perceived as pitch) of their own puppies' whines outside their litter-specific range. Within most litters, we found a negative relationship between puppies' whine fo and body weight. Consistent with this, playbacks showed that maternal care was stronger in response to high-pitched whine variants simulating relatively small offspring within their own litter's range compared to lower-pitched variants simulating larger offspring. We thus show that maternal care in a litter-rearing species relies on a dual assessment of offspring identity and condition, largely based on level-specific inter- and intra-litter variation in offspring call fo. This dual encoding system highlights how, even in a long-domesticated species, vocalizations reflect selective pressures to meet species-specific needs. Comparative work should now investigate whether similar communication systems have convergently evolved in other litter-rearing species.

A cognitive pathway to punishment insensitivity.

Individuals differ in their sensitivity to the adverse consequences of their actions, leading some to persist in maladaptive behaviors. Two pathways have been identified for this insensitivity: a motivational pathway based on excessive reward valuation and a behavioral pathway based on autonomous stimulus-response mechanisms. Here, we identify a third, cognitive pathway based on differences in punishment knowledge and use of that knowledge to suppress behavior. We show that distinct phenotypes of punishment sensitivity emerge from differences in what people learn about their actions. Exposed to identical punishment contingencies, some people (sensitive phenotype) form correct causal beliefs that they use to guide their behavior, successfully obtaining rewards and avoiding punishment, whereas others form incorrect but internally coherent causal beliefs that lead them to earn punishment they do not like. Incorrect causal beliefs were not inherently problematic because we show that many individuals benefit from information about why they are being punished, revaluing their actions and changing their behavior to avoid further punishment (unaware phenotype). However, one condition where incorrect causal beliefs were problematic was when punishment is infrequent. Under this condition, more individuals show punishment insensitivity and detrimental patterns of behavior that resist experience and information-driven updating, even when punishment is severe (compulsive phenotype). For these individuals, rare punishment acted as a "trap," inoculating maladaptive behavioral preferences against cognitive and behavioral updating.

Dynamics in a behavioral-epidemiological model for individual adherence to a nonpharmaceutical intervention.

The SARS-CoV-2 pandemic has highlighted the importance of behavioral drivers in epidemic dynamics. With the relaxation of mandated nonpharmaceutical interventions (NPIs) formerly in place to decrease transmission, such as mask-wearing or social distancing, adherence to an NPI is now the result of individual decision-making. To study these coupled dynamics, we embed a game-theoretic model for individual NPI adherence within an epidemiological model. When the disease is endemic, we find that our model has multiple (but none concurrently stable) equilibria: one each with zero, complete, or partial NPI adherence. Surprisingly, for the equilibrium with partial NPI adherence, the number of infections is independent of the transmission rate. Therefore, in that regime, a change in the rate of pathogen transmission, e.g., due to another (mandated) NPI or a new variant, has no effect on endemic infection levels. On the other hand, we show that vaccination successfully decreases endemic infection levels, and, unexpectedly, also reduces the number of susceptibles at equilibrium when there is partial adherence. From a game-theoretic perspective, we find that highly effective NPIs lead at most to partial adherence. As this effectiveness decreases, partially effective NPIs initially lead to increases in population-level adherence, especially if the risk is high enough. However, a completely ineffective NPI results in no adherence. Furthermore, we identify parameter regions where the individual incentives may not align with those of society as a whole. Overall, our findings illustrate complexities that can arise due to behavioral-epidemiological feedback and suggest appropriate measures to avoid more pessimistic population-level outcomes.

Mapping human brain charts cross-sectionally and longitudinally.

Brain scans acquired across large, age-diverse cohorts have facilitated recent progress in establishing normative brain aging charts. Here, we ask the critical question of whether cross-sectional estimates of age-related brain trajectories resemble those directly measured from longitudinal data. We show that age-related brain changes inferred from cross-sectionally mapped brain charts can substantially underestimate actual changes measured longitudinally. We further find that brain aging trajectories vary markedly between individuals and are difficult to predict with population-level age trends estimated cross-sectionally. Prediction errors relate modestly to neuroimaging confounds and lifestyle factors. Our findings provide explicit evidence for the importance of longitudinal measurements in ascertaining brain development and aging trajectories.

Sensitivity analysis of individual treatment effects: A robust conformal inference approach.

We propose a model-free framework for sensitivity analysis of individual treatment effects (ITEs), building upon ideas from conformal inference. For any unit, our procedure reports the Γ-value, a number which quantifies the minimum strength of confounding needed to explain away the evidence for ITE. Our approach rests on the reliable predictive inference of counterfactuals and ITEs in situations where the training data are confounded. Under the marginal sensitivity model of [Z. Tan, J. Am. Stat. Assoc. 101, 1619-1637 (2006)], we characterize the shift between the distribution of the observations and that of the counterfactuals. We first develop a general method for predictive inference of test samples from a shifted distribution; we then leverage this to construct covariate-dependent prediction sets for counterfactuals. No matter the value of the shift, these prediction sets (resp. approximately) achieve marginal coverage if the propensity score is known exactly (resp. estimated). We describe a distinct procedure also attaining coverage, however, conditional on the training data. In the latter case, we prove a sharpness result showing that for certain classes of prediction problems, the prediction intervals cannot possibly be tightened. We verify the validity and performance of the methods via simulation studies and apply them to analyze real datasets.

Cultural threat perceptions predict violent extremism via need for cognitive closure.

Understanding the psychological processes that drive violent extremism is a pressing global issue. Across six studies, we demonstrate that perceived cultural threats lead to violent extremism because they increase people's need for cognitive closure (NFC). In general population samples (from Denmark, Afghanistan, Pakistan, France, and an international sample) and a sample of former Mujahideen in Afghanistan, single-level and multilevel mediation analyses revealed that NFC mediated the association between perceived cultural threats and violent extremist outcomes. Further, in comparisons between the sample of former Afghan Mujahideen and the general population sample from Afghanistan following the known-group paradigm, the former Mujahideen scored significantly higher on cultural threat, NFC, and violent extremist outcomes. Moreover, the proposed model successfully differentiated former Afghan Mujahideen participants from the general Afghan participants. Next, two preregistered experiments provided causal support for the model. Experimentally manipulating the predictor (cultural threat) in Pakistan led to higher scores on the mediator (NFC) and dependent variables (violent extremist outcomes). Finally, an experiment conducted in France demonstrated the causal effect of the mediator (NFC) on violent extremist outcomes. Two internal meta-analyses using state-of-the-art methods (i.e., meta-analytic structural equation modeling and pooled indirect effects analyses) further demonstrated the robustness of our results across the different extremist outcomes, designs, populations, and settings. Cultural threat perceptions seem to drive violent extremism by eliciting a need for cognitive closure.

Neuronal morphology enhances robustness to perturbations of channel densities.

Biological neurons show significant cell-to-cell variability but have the striking ability to maintain their key firing properties in the face of unpredictable perturbations and stochastic noise. Using a population of multi-compartment models consisting of soma, neurites, and axon for the lateral pyloric neuron in the crab stomatogastric ganglion, we explore how rebound bursting is preserved when the 14 channel conductances in each model are all randomly varied. The coupling between the axon and other compartments is critical for the ability of the axon to spike during bursts and consequently determines the set of successful solutions. When the coupling deviates from a biologically realistic range, the neuronal tolerance of conductance variations is lessened. Thus, the gross morphological features of these neurons enhance their robustness to perturbations of channel densities and expand the space of individual variability that can maintain a desired output pattern.

Resting EEG Periodic and Aperiodic Components Predict Cognitive Decline Over 10 Years.

Measures of intrinsic brain function at rest show promise as predictors of cognitive decline in humans, including EEG metrics such as individual α peak frequency (IAPF) and the aperiodic exponent, reflecting the strongest frequency of α oscillations and the relative balance of excitatory/inhibitory neural activity, respectively. Both IAPF and the aperiodic exponent decrease with age and have been associated with worse executive function and working memory. However, few studies have jointly examined their associations with cognitive function, and none have examined their association with longitudinal cognitive decline rather than cross-sectional impairment. In a preregistered secondary analysis of data from the longitudinal Midlife in the United States (MIDUS) study, we tested whether IAPF and aperiodic exponent measured at rest predict cognitive function (N = 235; age at EEG recording M = 55.10, SD = 10.71) over 10 years. The IAPF and the aperiodic exponent interacted to predict decline in overall cognitive ability, even after controlling for age, sex, education, and lag between data collection time points. Post hoc tests showed that "mismatched" IAPF and aperiodic exponents (e.g., higher exponent with lower IAPF) predicted greater cognitive decline compared to "matching" IAPF and aperiodic exponents (e.g., higher exponent with higher IAPF; lower IAPF with lower aperiodic exponent). These effects were largely driven by measures of executive function. Our findings provide the first evidence that IAPF and the aperiodic exponent are joint predictors of cognitive decline from midlife into old age and thus may offer a useful clinical tool for predicting cognitive risk in aging.

Arid5a uses disordered extensions of its core ARID domain for distinct DNA- and RNA-recognition and gene regulation.

AT-rich interacting domain (ARID)-containing proteins, Arids, are a heterogeneous DNA-binding protein family involved in transcription regulation and chromatin processing. For the member Arid5a, no exact DNA-binding preference has been experimentally defined so far. Additionally, the protein binds to mRNA motifs for transcript stabilization, supposedly through the DNA-binding ARID domain. To date, however, no unbiased RNA motif definition and clear dissection of nucleic acid-binding through the ARID domain have been undertaken. Using NMR-centered biochemistry, we here define the Arid5a DNA preference. Further, high-throughput in vitro binding reveals a consensus RNA-binding motif engaged by the core ARID domain. Finally, transcriptome-wide binding (iCLIP2) reveals that Arid5a has a weak preference for (A)U-rich regions in pre-mRNA transcripts of factors related to RNA processing. We find that the intrinsically disordered regions flanking the ARID domain modulate the specificity and affinity of DNA binding, while they appear crucial for RNA interactions. Ultimately, our data suggest that Arid5a uses its extended ARID domain for bifunctional gene regulation and that the involvement of IDR extensions is a more general feature of Arids in interacting with different nucleic acids at the chromatin-mRNA interface.

DRdriver: identifying drug resistance driver genes using individual-specific gene regulatory network.

Drug resistance is one of principal limiting factors for cancer treatment. Several mechanisms, especially mutation, have been validated to implicate in drug resistance. In addition, drug resistance is heterogeneous, which makes an urgent need to explore the personalized driver genes of drug resistance. Here, we proposed an approach DRdriver to identify drug resistance driver genes in individual-specific network of resistant patients. First, we identified the differential mutations for each resistant patient. Next, the individual-specific network, which included the genes with differential mutations and their targets, was constructed. Then, the genetic algorithm was utilized to identify the drug resistance driver genes, which regulated the most differentially expressed genes and the least non-differentially expressed genes. In total, we identified 1202 drug resistance driver genes for 8 cancer types and 10 drugs. We also demonstrated that the identified driver genes were mutated more frequently than other genes and tended to be associated with the development of cancer and drug resistance. Based on the mutational signatures of all driver genes and enriched pathways of driver genes in brain lower grade glioma treated by temozolomide, the drug resistance subtypes were identified. Additionally, the subtypes showed great diversity in epithelial-mesenchyme transition, DNA damage repair and tumor mutation burden. In summary, this study developed a method DRdriver for identifying personalized drug resistance driver genes, which provides a framework for unlocking the molecular mechanism and heterogeneity of drug resistance.