Mitigating Pandemic Risk with Influenza A Virus Field Surveillance at a Swine-Human Interface, bioRxiv, 2019-03-22
Working overnight at a large swine exhibition, we identified an influenza A virus (IAV) outbreak in swine, nanopore-sequenced 13 IAV genomes from samples collected, and in real-time, determined that these viruses posed a novel risk to humans due to genetic mismatches between the viruses and current pre-pandemic candidate vaccine viruses (CVV). We developed and used a portable IAV sequencing and analysis platform called Mia (Mobile Influenza Analysis) to complete and characterize full-length consensus genomes approximately 18 hours after unpacking the mobile lab. Swine are important animal IAV reservoirs that have given rise to pandemic viruses via zoonotic transmission. Genomic analyses of IAV in swine are critical to understanding pandemic risk of viruses in this reservoir, and characterization of viruses circulating in exhibition swine enables rapid comparison to current seasonal influenza vaccines and CVVs. The Mia system rapidly identified three genetically distinct swine IAV lineages from three subtypes A(H1N1), A(H3N2) and A(H1N2). Additional analysis of the HA protein sequences of the A(H1N2) viruses identified >30 amino acid differences between the HA1 portion of the hemagglutinin of these viruses and the most closely related pre-2009 CVV. All virus sequences were emailed to colleagues at CDC who initiated development of a synthetically derived CVV designed to provide an optimal antigenic match with the viruses detected in the exhibition. In subsequent months, this virus caused 13 infections in humans, and was the dominant variant virus in the US detected in 2018. Had this virus caused a severe outbreak or pandemic, our proactive surveillance efforts and CVV derivation would have provided an approximate 8 week time advantage for vaccine manufacturing. This is the first report of the use of field-derived nanopore sequencing data to initiate a real-time, actionable public health countermeasure.
biorxiv microbiology 0-100-users 2019Relationship between cardiac cycle and the timing of actions during action execution and observation, bioRxiv, 2019-03-22
AbstractPrevious research suggests that there may be a relationship between the timing of motor events and phases of the cardiac cycle. However, this relationship has thus far only been researched using simple isolated movements such as key-presses in reaction-time tasks and only in a single subject acting alone. Here, we investigated how the cardiac cycle relates to ongoing self-paced movements in both action execution and observation using a novel dyadic paradigm. We recorded electrocardiography (ECG) in 26 subjects who formed 13 dyads containing an action executioner and observer as they performed a self-paced sequence of movements. We demonstrated that heartbeats are timed to movements during both action execution and observation. Specifically, movements were more likely to culminate between heartbeats than simultaneously with the heartbeat. The same pattern was observed for action observation, with the observer’s heartbeats occurring off-phase with movement culmination. These findings demonstrate that there is synchronicity between an action executioner’s cardiac cycle and the timing of their movements, and that the same relationship is mirrored in an observer. This suggests that interpersonal synchronicity may be caused by the mirroring of a phasic relationship between movement and the heart.
biorxiv neuroscience 100-200-users 2019Structural color in Junonia butterflies evolves by tuning scale lamina thickness, bioRxiv, 2019-03-22
AbstractStructural color is a pervasive natural phenomenon, caused by photonic nanostructures that refract light. Diverse organisms employ structural color to mediate ecological interactions and create specific optical effects such as iridescence. Despite its importance for living systems, the developmental, genetic, and evolutionary processes that generate structural color largely remain mysterious. Here, we focus on simple photonic structures, thin film reflectors, in the lower lamina of Junonia butterfly scales. We present multiple lines of evidence that the thickness of the lamina quantitatively controls lamina color, which is an important determinant of overall wing color, even when pigments are also present. First, in a lineage of buckeye butterflies artificially selected for blue wing color for 12 generations, a thicker lamina resulted in a color shift from brown to blue. A similar lamina thickness increase explains the appearance of blue scales in butterflies with mutations in the optix wing patterning gene. Finally, lamina thickness variation underlies the color diversity that distinguishes seasonal variants, sexes, and species throughout the genus Junonia. Thus, quantitatively tuning a single dimension of the existing scale architecture allows butterflies to evolve a broad spectrum of hues over both microevolutionary and macroevolutionary time frames. Because the lower lamina is an intrinsic component of typical butterfly scales, our findings imply that lamina structural color influences wing color in most butterflies.Significance StatementStructural colors, which result from photonic nanostructures that refract light and can create iridescence, are an important tool for many organisms. We use thin films, which are morphologically simple nanostructures that generate structural color in the lower lamina of butterfly scales, to dissect how photonic structures evolve. By combining interspecies comparisons with two different experimental approaches—artificial selection on wing color, and genetically engineered mutation of the optix wing patterning gene—we demonstrate that lamina thickness controls the wavelength (hue) of the structural color. These lamina structural colors are ubiquitous in the genus Junonia, and determine wing color along with pigments. Our results suggest that lamina structural colors probably exist in most butterflies, and that tuning lamina thickness facilitates wing color evolution.
biorxiv evolutionary-biology 0-100-users 2019The Arrival of Steppe and Iranian Related Ancestry in the Islands of the Western Mediterranean, bioRxiv, 2019-03-21
A series of studies have documented how Steppe pastoralist-related ancestry reached central Europe by at least 2500 BCE, while Iranian farmer-related ancestry was present in Aegean Europe by at least 1900 BCE. However, the spread of these ancestries into the western Mediterranean where they have contributed to many populations living today remains poorly understood. We generated genome-wide ancient DNA from the Balearic Islands, Sicily, and Sardinia, increasing the number of individuals with reported data from these islands from 3 to 52. We obtained data from the oldest skeleton excavated from the Balearic islands (dating to ∼2400 BCE), and show that this individual had substantial Steppe pastoralist-derived ancestry; however, later Balearic individuals had less Steppe heritage reflecting geographic heterogeneity or immigration from groups with more European first farmer-related ancestry. In Sicily, Steppe pastoralist ancestry arrived by ∼2200 BCE and likely came at least in part from Spain as it was associated with Iberian-specific Y chromosomes. In Sicily, Iranian-related ancestry also arrived by the Middle Bronze Age, thus revealing that this ancestry type, which was ubiquitous in the Aegean by this time, also spread further west prior to the classical period of Greek expansion. In Sardinia, we find no evidence of either eastern ancestry type in the Nuragic Bronze Age, but show that Iranian-related ancestry arrived by at least ∼300 BCE and Steppe ancestry arrived by ∼300 CE, joined at that time or later by North African ancestry. These results falsify the view that the people of Sardinia are isolated descendants of Europe’s first farmers. Instead, our results show that the island’s admixture history since the Bronze Age is as complex as that in many other parts of Europe.
biorxiv genomics 0-100-users 2019A Critique of Pure Learning What Artificial Neural Networks can Learn from Animal Brains, bioRxiv, 2019-03-20
ABSTRACTOver the last decade, artificial neural networks (ANNs), have undergone a revolution, catalyzed in large part by better tools for supervised learning. However, training such networks requires enormous data sets of labeled examples, whereas young animals (including humans) typically learn with few or no labeled examples. This stark contrast with biological learning has led many in the ANN community posit that instead of supervised paradigms, animals must rely instead primarily on unsupervised learning, leading the search for better unsupervised algorithms. Here we argue that much of an animal’s behavioral repertoire is not the result of clever learning algorithms—supervised or unsupervised—but arises instead from behavior programs already present at birth. These programs arise through evolution, are encoded in the genome, and emerge as a consequence of wiring up the brain. Specifically, animals are born with highly structured brain connectivity, which enables them learn very rapidly. Recognizing the importance of the highly structured connectivity suggests a path toward building ANNs capable of rapid learning.
biorxiv neuroscience 200-500-users 2019A Systematic Evaluation of Single Cell RNA-Seq Analysis Pipelines Library preparation and normalisation methods have the biggest impact on the performance of scRNA-seq studies, bioRxiv, 2019-03-20
AbstractThe recent rapid spread of single cell RNA sequencing (scRNA-seq) methods has created a large variety of experimental and computational pipelines for which best practices have not been established yet. Here, we use simulations based on five scRNA-seq library protocols in combination with nine realistic differential expression (DE) setups to systematically evaluate three mapping, four imputation, seven normalisation and four differential expression testing approaches resulting in ~ 3,000 pipelines, allowing us to also assess interactions among pipeline steps.We find that choices of normalisation and library preparation protocols have the biggest impact on scRNA-seq analyses. Specifically, we find that library preparation determines the ability to detect symmetric expression differences, while normalisation dominates pipeline performance in asymmetric DE-setups. Finally, we illustrate the importance of informed choices by showing that a good scRNA-seq pipeline can have the same impact on detecting a biological signal as quadrupling the sample size.
biorxiv bioinformatics 100-200-users 2019