Genetic Identification of Vagal Sensory Neurons That Control Feeding.

Energy homeostasis requires precise measurement of the quantity and quality of ingested food. The vagus nerve innervates the gut and can detect diverse interoceptive cues, but the identity of the key sensory neurons and corresponding signals that regulate food intake remains unknown. Here, we use an approach for target-specific, single-cell RNA sequencing to generate a map of the vagal cell types that innervate the gastrointestinal tract. We show that unique molecular markers identify vagal neurons with distinct innervation patterns, sensory endings, and function. Surprisingly, we find that food intake is most sensitive to stimulation of mechanoreceptors in the intestine, whereas nutrient-activated mucosal afferents have no effect. Peripheral manipulations combined with central recordings reveal that intestinal mechanoreceptors, but not other cell types, potently and durably inhibit hunger-promoting AgRP neurons in the hypothalamus. These findings identify a key role for intestinal mechanoreceptors in the regulation of feeding.

Using birth-death processes to infer tumor subpopulation structure from live-cell imaging drug screening data.

Tumor heterogeneity is a complex and widely recognized trait that poses significant challenges in developing effective cancer therapies. In particular, many tumors harbor a variety of subpopulations with distinct therapeutic response characteristics. Characterizing this heterogeneity by determining the subpopulation structure within a tumor enables more precise and successful treatment strategies. In our prior work, we developed PhenoPop, a computational framework for unravelling the drug-response subpopulation structure within a tumor from bulk high-throughput drug screening data. However, the deterministic nature of the underlying models driving PhenoPop restricts the model fit and the information it can extract from the data. As an advancement, we propose a stochastic model based on the linear birth-death process to address this limitation. Our model can formulate a dynamic variance along the horizon of the experiment so that the model uses more information from the data to provide a more robust estimation. In addition, the newly proposed model can be readily adapted to situations where the experimental data exhibits a positive time correlation. We test our model on simulated data (in silico) and experimental data (in vitro), which supports our argument about its advantages.

Shared genetic effects of emotion and subcortical volumes in healthy adults.

Ample studies have reported a strong association between emotion and subcortical volumes; still, the underlying mechanism regarding this relation remains unclear. Using a twin design, the current study aimed to explore the intrinsic association between emotion and subcortical volumes by examining their phenotypic, genetic, and environmental correlations. We used a group dataset of 960 individuals from the Human Connectome Project (234 monozygotic twins, 145 dizygotic twins, 581 not twins, males = 454, age = 22-37 years). We found that both emotion and subcortical volumes were heritable. Of the 17 emotional traits, 13 were significantly phenotypically correlated with the volumes of multiple subcortical regions. There was no environmental correlation between emotion and subcortical volumes; however, we found a genetic overlap between overall emotional traits and caudate volume. Taken together, our results showed that emotion and subcortical volumes were heritable and closely related. Although the caudate has been often studied with execution of movement, given that the caudate volume is genetically associated with diverse emotional domains, such as negative affect, psychological well-being, and social relationships, it may suggest that the caudate volume might also be an important factor when studying the brain basis of emotion.

Comparing empirical kinship derived heritability for imaging genetics traits in the UK biobank and human connectome project.

Imaging genetics analyses use neuroimaging traits as intermediate phenotypes to infer the degree of genetic contribution to brain structure and function in health and/or illness. Coefficients of relatedness (CR) summarize the degree of genetic similarity among subjects and are used to estimate the heritability - the proportion of phenotypic variance explained by genetic factors. The CR can be inferred directly from genome-wide genotype data to explain the degree of shared variation in common genetic polymorphisms (SNP-heritability) among related or unrelated subjects. We developed a central processing and graphics processing unit (CPU and GPU) accelerated Fast and Powerful Heritability Inference (FPHI) approach that linearizes likelihood calculations to overcome the ∼N2-3 computational effort dependency on sample size of classical likelihood approaches. We calculated for 60 regional and 1.3 × 105 voxel-wise traits in N = 1,206 twin and sibling participants from the Human Connectome Project (HCP) (550 M/656 F, age = 28.8 ± 3.7 years) and N = 37,432 (17,531 M/19,901 F; age = 63.7 ± 7.5 years) participants from the UK Biobank (UKBB). The FPHI estimates were in excellent agreement with heritability values calculated using Genome-wide Complex Trait Analysis software (r = 0.96 and 0.98 in HCP and UKBB sample) while significantly reducing computational (102-4 times). The regional and voxel-wise traits heritability estimates for the HCP and UKBB were likewise in excellent agreement (r = 0.63-0.76, p < 10-10). In summary, the hardware-accelerated FPHI made it practical to calculate heritability values for voxel-wise neuroimaging traits, even in very large samples such as the UKBB. The patterns of additive genetic variance in neuroimaging traits measured in a large sample of related and unrelated individuals showed excellent agreement regardless of the estimation method. The code and instruction to execute these analyses are available at www.solar-eclipse-genetics.org.

Heterogeneity coordinates bacterial multi-gene expression in single cells.

For a genetically identical microbial population, multi-gene expression in various environments requires effective allocation of limited resources and precise control of heterogeneity among individual cells. However, it is unclear how resource allocation and cell-to-cell variation jointly shape the overall performance. Here we demonstrate a Simpson's paradox during overexpression of multiple genes: two competing proteins in single cells correlated positively for every induction condition, but the overall correlation was negative. Yet this phenomenon was not observed between two competing mRNAs in single cells. Our analytical framework shows that the phenomenon arises from competition for translational resource, with the correlation modulated by both mRNA and ribosome variability. Thus, heterogeneity plays a key role in single-cell multi-gene expression and provides the population with an evolutionary advantage, as demonstrated in this study.

Offspring genetic effects on maternal care.

Parental care is found widely across animal taxa and is manifest in a range of behaviours from basic provisioning in cockroaches to highly complex behaviours seen in mammals. The evolution of parental care is viewed as the outcome of an evolutionary cost/benefit trade-off between investing in current and future offspring, leading to the selection of traits in offspring that influence parental behaviour. Thus, level and quality of parental care are affected by both parental and offspring genetic differences that directly and indirectly influence parental care behaviour. While significant research effort has gone into understanding how parental genomes affect parental, and mostly maternal, behaviour, few studies have investigated how offspring genomes affect parental care. In this review, we bring together recent findings across different fields focussing on the mechanism and genetics of offspring effects on maternal care in mammals.

Cerebral cortex expansion and folding: what have we learned?

One of the most prominent features of the human brain is the fabulous size of the cerebral cortex and its intricate folding. Cortical folding takes place during embryonic development and is important to optimize the functional organization and wiring of the brain, as well as to allow fitting a large cortex in a limited cranial volume. Pathological alterations in size or folding of the human cortex lead to severe intellectual disability and intractable epilepsy. Hence, cortical expansion and folding are viewed as key processes in mammalian brain development and evolution, ultimately leading to increased intellectual performance and, eventually, to the emergence of human cognition. Here, we provide an overview and discuss some of the most significant advances in our understanding of cortical expansion and folding over the last decades. These include discoveries in multiple and diverse disciplines, from cellular and molecular mechanisms regulating cortical development and neurogenesis, genetic mechanisms defining the patterns of cortical folds, the biomechanics of cortical growth and buckling, lessons from human disease, and how genetic evolution steered cortical size and folding during mammalian evolution.

Genetic Music System with Synthetic Biology.

This article introduces GeMS, a system for music composition informed by synthetic biology. GeMS generates music with simulations of genetic processes, such as transcription, translation, and protein folding, with which biological systems render chains of amino acids from DNA strands. The system comprises the following components: the Miranda machine, the rhythmator, and the pitch processor. The Miranda machine is an abstract Turing-machine-like processor, which manipulates a sequence of DNA symbols according to a set of programming instructions. This process generates a pool of new DNA strands, which are subsequently translated into rhythms. GeMS represents the musical equivalent of amino acids in terms of rhythms, referred to as rhythmic codons. This enables the rhythmator to convert DNA sequences into rhythmic sequences. The pitch processor generates pitches for such rhythmic sequences. It is inspired by the phenomenon of protein folding. The pitch processor considers orientation information of DNA instructions yielded by the Miranda machine in order to activate algorithms for generating pitches. A musical composition, entitled Artibiotics, for percussion ensemble and electronic instruments, is presented to demonstrate the system.

Reconsidering the Meaning of Concepts in Biology: Why Distinctions Are So Important.

Concepts have a central and important place in science, therefore, it is important that their meanings are always made clear. However, such clarity does not always exist, even in the case of such fundamental biological concepts as "gene" and "adaptation." A quick look at textbooks reveals that different meanings may be attributed to the same concept, even within the same textbook, without explicitly discussing the differences of those meanings. This can be misleading, and mask important conceptual differences. Therefore, the differences between the various meanings of the same concept should be discussed and explained in order for conceptual understanding to be achieved.

Evolutionary Image Transition and Painting Using Random Walks.

We present a study demonstrating how random walk algorithms can be used for evolutionary image transition. We design different mutation operators based on uniform and biased random walks and study how their combination with a baseline mutation operator can lead to interesting image transition processes in terms of visual effects and artistic features. Using feature-based analysis we investigate the evolutionary image transition behaviour with respect to different features and evaluate the images constructed during the image transition process. Afterwards, we investigate how modifications of our biased random walk approaches can be used for evolutionary image painting. We introduce an evolutionary image painting approach whose underlying biased random walk can be controlled by a parameter influencing the bias of the random walk and thereby creating different artistic painting effects.

Genetic Programming for Evolving Similarity Functions for Clustering: Representations and Analysis.

Clustering is a difficult and widely studied data mining task, with many varieties of clustering algorithms proposed in the literature. Nearly all algorithms use a similarity measure such as a distance metric (e.g., Euclidean distance) to decide which instances to assign to the same cluster. These similarity measures are generally predefined and cannot be easily tailored to the properties of a particular dataset, which leads to limitations in the quality and the interpretability of the clusters produced. In this article, we propose a new approach to automatically evolving similarity functions for a given clustering algorithm by using genetic programming. We introduce a new genetic programming-based method which automatically selects a small subset of features (feature selection) and then combines them using a variety of functions (feature construction) to produce dynamic and flexible similarity functions that are specifically designed for a given dataset. We demonstrate how the evolved similarity functions can be used to perform clustering using a graph-based representation. The results of a variety of experiments across a range of large, high-dimensional datasets show that the proposed approach can achieve higher and more consistent performance than the benchmark methods. We further extend the proposed approach to automatically produce multiple complementary similarity functions by using a multi-tree approach, which gives further performance improvements. We also analyse the interpretability and structure of the automatically evolved similarity functions to provide insight into how and why they are superior to standard distance metrics.

Evolved Transistor Array Robot Controllers.

For the first time, a field programmable transistor array (FPTA) was used to evolve robot control circuits directly in analog hardware. Controllers were successfully incrementally evolved for a physical robot engaged in a series of visually guided behaviours, including finding a target in a complex environment where the goal was hidden from most locations. Circuits for recognising spoken commands were also evolved and these were used in conjunction with the controllers to enable voice control of the robot, triggering behavioural switching. Poor quality visual sensors were deliberately used to test the ability of evolved analog circuits to deal with noisy uncertain data in realtime. Visual features were coevolved with the controllers to automatically achieve dimensionality reduction and feature extraction and selection in an integrated way. An efficient new method was developed for simulating the robot in its visual environment. This allowed controllers to be evaluated in a simulation connected to the FPTA. The controllers then transferred seamlessly to the real world. The circuit replication issue was also addressed in experiments where circuits were evolved to be able to function correctly in multiple areas of the FPTA. A methodology was developed to analyse the evolved circuits which provided insights into their operation. Comparative experiments demonstrated the superior evolvability of the transistor array medium.

Conservation genetics: Linking science with practice.

Conservation genetics is a well-established scientific field. However, limited information transfer between science and practice continues to hamper successful implementation of scientific knowledge in conservation practice and management. To mitigate this challenge, we have established a conservation genetics community, which entails an international exchange-and-skills platform related to genetic methods and approaches in conservation management. First, it allows for scientific exchange between researchers during annual conferences. Second, personal contact between conservation professionals and scientists is fostered by organising workshops and by popularising knowledge on conservation genetics methods and approaches in professional journals in national languages. Third, basic information on conservation genetics has been made accessible by publishing an easy-to-read handbook on conservation genetics for practitioners. Fourth, joint projects enabled practitioners and scientists to work closely together from the start of a project in order to establish a tight link between applied questions and scientific background. Fifth, standardised workflows simplifying the implementation of genetic tools in conservation management have been developed. By establishing common language and trust between scientists and practitioners, all these measures help conservation genetics to play a more prominent role in future conservation planning and management.

Exploring the drivers of population structure across desert snakes can help to link micro and macroevolution.

To understand the underlying mechanisms generating population genetic divergence and structure is a critical step towards understanding how biodiversity evolves at both micro- and macroevolutionary scales. At the population-level, geographic isolation as well as adaptation to local environmental conditions can generate different patterns of spatial genetic variation among populations. Specific organismal traits as well as the characteristics of the environment might influence the process under which populations become spatially structured. In a From the Cover article in this issue of Molecular Ecology, Myers et al. (2019) present an integrative approach to investigate if the Cochise filter barrier (CFB), lying between the Sonoran and Chihuahuan Deserts, and the surrounding river networks were relevant in driving the population structure of 13 snake species. While local environmental conditions seem to predominantly contribute to lineage divergence, traditionally studied vicariant barriers seem to have played a minor role in shaping population structure across the studied species. This study brings insights into how population-level processes could contribute to the formation of incipient species, which ultimately might affect the speciation rates measured at macroevolutionary scales. Hence, Myers et al. (2019) not only represents an integrative study aiming to understand the drivers of population genetic divergence, but also a potentially important contribution to our ongoing challenge in linking micro- and macroevolution.

Environmental heterogeneity and not vicariant biogeographic barriers generate community-wide population structure in desert-adapted snakes.

Genetic structure can be influenced by local adaptation to environmental heterogeneity and biogeographic barriers, resulting in discrete population clusters. Geographic distance among populations, however, can result in continuous clines of genetic divergence that appear as structured populations. Here, we evaluate the relevant importance of these three factors over a landscape characterized by environmental heterogeneity and the presence of a hypothesized biogeographic barrier in producing population genetic structure within 13 codistributed snake species using a genomic data set. We demonstrate that geographic distance and environmental heterogeneity across western North America contribute to population genomic divergence. Surprisingly, landscape features long thought to contribute to biogeographic barriers play little role in divergence community wide. Our results suggest that isolation by environment is the most important contributor to genomic divergence. Furthermore, we show that models of population clustering that incorporate spatial information consistently outperform nonspatial models, demonstrating the importance of considering geographic distances in population clustering. We argue that environmental and geographic distances as drivers of community-wide divergence should be explored before assuming the role of biogeographic barriers.

Studying genomic processes at the single-molecule level: introducing the tools and applications.

To understand genomic processes such as transcription, translation or splicing, we need to be able to study their spatial and temporal organization at the molecular level. Single-molecule approaches provide this opportunity, allowing researchers to monitor molecular conformations, interactions or diffusion quantitatively and in real time in purified systems and in the context of the living cell. This Review introduces the types of application of single-molecule approaches that can enhance our understanding of genome function.

Heritability estimates of cortical anatomy: The influence and reliability of different estimation strategies.

Twin study designs have been previously used to investigate the heritability of neuroanatomical measures, such as regional cortical volumes. Volume can be fractionated into surface area and cortical thickness, where both measures are considered to have independent genetic and environmental bases. Region of interest (ROI) and vertex-wise approaches have been used to calculate heritability of cortical thickness and surface area in twin studies. In our study, we estimate heritability using the Human Connectome Project magnetic resonance imaging dataset composed of healthy young twin and non-twin siblings (mean age of 29, sample size of 757). Both ROI and vertex-wise methods were used to compare regional heritability of cortical thickness and surface area. Heritability estimates were controlled for age, sex, and total ipsilateral surface area or mean cortical thickness. In both approaches, heritability estimates of cortical thickness and surface area were lower when accounting for average ipsilateral cortical thickness and total surface area respectively. When comparing both approaches at a regional level, the vertex-wise approach showed higher surface area and lower cortical thickness heritability estimates compared to the ROI approach. The calcarine fissure had the highest surface area heritability estimate (ROI: 44%, vertex-wise: 50%) and posterior cingulate gyrus had the highest cortical thickness heritability (ROI: 50%, vertex-wise 40%). We also observed that limitations in image processing and variability in spatial averaging errors based on regional size may make obtaining true estimates of cortical thickness and surface area challenging in smaller regions. It is important to identify which approach is best suited to estimate heritability based on the research hypothesis and the size of the regions being investigated.

Open questions: Tackling Darwin's "instincts": the genetic basis of behavioral evolution.

All of us have marveled at the remarkable diversity of animal behaviors in nature.None of us has much idea of how these have evolved.

Hospital nursing leadership-led interventions increased genomic awareness and educational intent in Magnet settings.

BACKGROUND: The Precision Medicine Initiative will accelerate genomic discoveries that improve health care, necessitating a genomic competent workforce. PURPOSE: This study assessed leadership team (administrator/educator) year-long interventions to improve registered nurses' (RNs) capacity to integrate genomics into practice. METHODS: We examined genomic competency outcomes in 8,150 RNs. FINDINGS: Awareness and intention to learn more increased compared with controls. Findings suggest achieving genomic competency requires a longer intervention and support strategies such as infrastructure and policies. Leadership played a role in mobilizing staff, resources, and supporting infrastructure to sustain a large-scale competency effort on an institutional basis. DISCUSSION: Results demonstrate genomic workforce competency can be attained with leadership support and sufficient time. Our study provides evidence of the critical role health-care leaders play in facilitating genomic integration into health care to improve patient outcomes. Genomics' impact on quality, safety, and cost indicate a leader-initiated national competency effort is achievable and warranted.

Sequence analysis of chromosome 1 revealed different selection patterns between Chinese wild mice and laboratory strains.

Both natural and artificial selection play a critical role in animals' adaptation to the environment. Detection of the signature of selection in genomic regions can provide insights for understanding the function of specific phenotypes. It is generally assumed that laboratory mice may experience intense artificial selection while wild mice more natural selection. However, the differences of selection signature in the mouse genome and underlying genes between wild and laboratory mice remain unclear. In this study, we used two mouse populations: chromosome 1 (Chr 1) substitution lines (C1SLs) derived from Chinese wild mice and mouse genome project (MGP) sequenced inbred strains and two selection detection statistics: Fst and Tajima's D to identify the signature of selection footprint on Chr 1. For the differentiation between the C1SLs and MGP, 110 candidate selection regions containing 47 protein coding genes were detected. A total of 149 selection regions which encompass 7.215 Mb were identified in the C1SLs by Tajima's D approach. While for the MGP, we identified nearly twice selection regions (243) compared with the C1SLs which accounted for 13.27 Mb Chr 1 sequence. Through functional annotation, we identified several biological processes with significant enrichment including seven genes in the olfactory transduction pathway. In addition, we searched the phenotypes associated with the 47 candidate selection genes identified by Fst. These genes were involved in behavior, growth or body weight, mortality or aging, and immune systems which align well with the phenotypic differences between wild and laboratory mice. Therefore, the findings would be helpful for our understanding of the phenotypic differences between wild and laboratory mice and applications for using this new mouse resource (C1SLs) for further genetics studies.