Maloney, R. | bioRxiv Science

bioRxiv2021

Precise quantification of behavioral individuality from 80 million decisions across 183,000 flies

Individual animals behave differently from each other. This variability is a component of personality and arises even when genetics and environment are held constant. Discovering the biological mechanisms underlying behavioral variability depends on efficiently measuring individual behavioral bias, a requirement that is facilitated by automated, high-throughput experiments. We compiled a large data set of individual locomotor behavior measures, acquired from over 183,000 fruit flies walking in Y-shaped mazes. With this data set we first conducted a "computational ethology natural history" study to quantify the distribution of individual behavioral biases with unprecedented precision and examine correlations between behavioral measures with high power. We discovered a slight, but highly significant, left-bias in spontaneous locomotor decision-making. We then used the data to evaluate standing hypotheses about biological mechanisms affecting behavioral variability, specifically: the neuromodulator serotonin and its precursor transporter, heterogametic sex, and temperature. We found a variety of significant effects associated with each of these mechanisms that were behavior-dependent. This indicates that the relationship between biological mechanisms and behavioral variability may be highly context dependent. Going forward, automation of behavioral experiments will likely be essential in teasing out the complex causality of individuality.

neuroscience↗

bioRxiv2020

High-Throughput Human Primary Cell-Based Airway Model for Evaluating Influenza, Coronavirus, or other Respiratory Viruses in vitro

Influenza and other respiratory viruses represent a significant threat to public health, national security, and the world economy, and can lead to the emergence of global pandemics such as the current COVID-19 crisis. One of the greatest barriers to the development of effective therapeutic agents to treat influenza, coronaviruses, and many other infections of the respiratory tract is the absence of a robust preclinical model. Preclinical studies currently rely on high-throughput, low-fidelity in vitro screening with cell lines and/or low-throughput animal models that often provide a poor correlation to human clinical responses. Here, we introduce a human primary airway epithelial cell-based model integrated into a high-throughput platform where tissues are cultured at an air-liquid interface (PREDICT96-ALI). We present results on the application of this platform to influenza and coronavirus infections, providing multiple readouts capable of evaluating viral infection kinetics and potentially the efficacy of therapeutic agents in an in vitro system. Several strains of influenza A virus are shown to successfully infect the human primary cell-based airway tissue cultured at an air-liquid interface (ALI), and as a proof-of-concept, the effect of the antiviral oseltamivir on one strain of Influenza A is evaluated. Human coronaviruses NL63 (HCoV-NL63) and SARS-CoV-2 enter host cells via ACE2 and utilize the protease TMPRSS2 for protein priming, and we confirm expression of both in our ALI model. We also demonstrate coronavirus infection in this system with HCoV-NL63, observing sufficient viral propagation over 96 hours post-infection to indicate successful infection of the primary cell-based model. This new capability has the potential to address a gap in the rapid assessment of therapeutic efficacy of various small molecules and antiviral agents against influenza and other respiratory viruses including coronaviruses.

microbiology↗