Biology subjects

Hart, T.

Publications and source records attributed to Hart, T..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

Sparse and stereotyped encoding implicates a core glomerulus for ant alarm behavior

Ants communicate via large arrays of pheromones and possess expanded, highly complex olfactory systems, with antennal lobes in the brain comprising ~500 glomeruli. This expansion implies that odors could activate hundreds of glomeruli, which would pose challenges for higher order processing. To study this problem, we generated the first transgenic ants, expressing the genetically encoded calcium indicator GCaMP6s in olfactory sensory neurons. Using two-photon imaging, we mapped complete glomerular responses to four ant alarm pheromones. Alarm pheromones robustly activated [≤]6 glomeruli, and activity maps for the three pheromones inducing panic-alarm in our study species converged on a single glomerulus. These results demonstrate that, rather than using broadly tuned combinatorial encoding, ants employ precise, narrowly tuned, and stereotyped representation of alarm pheromone cues. The identification of a central sensory hub glomerulus for alarm behavior suggests that a simple neural architecture is sufficient to translate pheromone perception into behavioral outputs.

neuroscience↗

bioRxiv2022

PICKLES v3: the updated database of pooled in vitro CRISPR knockout library essentiality screens

PICKLES (https://pickles.hart-lab.org) is an updated web interface to a freely available database of genome-scale CRISPR knockout fitness screens in human cell lines. Using a completely rewritten interface, researchers can explore gene knockout fitness phenotypes across cell lines and tissue types and compare fitness profiles with fitness, expression, or mutation profiles of other genes. The database has been updated to include data from three CRISPR libraries (Avana, Score, and TKOv3), and includes information from 1,162 whole-genome screens probing the knockout fitness phenotype of 18,959 genes. Source code for the interface and the integrated database are available for download.

bioinformatics↗

bioRxiv2022

Optimal construction of a functional interaction network from pooled library CRISPR fitness screens

Functional interaction networks, where edges connect genes likely to operate in the same biological process or pathway, can be inferred from CRISPR knockout screens in cancer cell lines. Genes with similar knockout fitness profiles across a sufficiently diverse set of cell line screens are likely to be co-functional, and these "coessentiality" networks are increasingly powerful predictors of gene function and biological modularity. While several such networks have been published, most use different algorithms for each step of the network construction process. In this study, we identify an optimal measure of functional interaction and test all combinations of options at each step - essentiality scoring, sample variance and covariance normalization, and similarity measurement - to identify best practices for generating a functional interaction network from CRISPR knockout data. We show that Bayes Factor and Ceres scores give the best results, that Ceres outperforms the newer Chronos scoring scheme, and that covariance normalization is a critical step in network construction. We further show that Pearson correlation, mathematically identical to ordinary least squares after covariance normalization, can be extended by using partial correlation to detect and amplify signals from "moonlighting" proteins which show context-dependent interaction with different partners.

systems biology↗

bioRxiv2022

Integration of metabolism and regulation reveals rapid adaptability to growth on non-native substrates.

Engineering synthetic heterotrophy (i.e., growth on non-native substrates) is key to the efficient bio-based valorization of renewable and waste substrates. Among these, engineering hemicellulosic pentose utilization has been well-explored in Saccharomyces cerevisiae (yeast) over several decades - yet the answer to what makes their utilization inherently recalcitrant remains elusive. Through implementation of a semi-synthetic regulon, we find that harmonizing cellular and engineering objectives are key to obtaining highest growth rates and yields with minimal metabolic engineering effort. Concurrently, results indicate that "extrinsic" factors - specifically, upstream genes that direct flux of pentoses into central carbon metabolism - are rate-limiting. We also reveal that yeast metabolism is innately highly adaptable to rapid growth on non-native substrates and that systems metabolic engineering (i.e., flux balancing, directed evolution, functional genomics, and network modeling) is largely unnecessary. We posit that the need for extensive engineering espoused by prior works is a consequence of unfortunate (albeit avoidable) antagonism between engineering and cellular objectives. We also found that deletion of endogenous genes to promote growth demonstrate inconsistent outcomes that are genetic-context- and condition-dependent. For the most part, these knockouts also lead to deleterious pleiotropic effects that decrease the robustness of strains against inhibitors and stressors associated with bioprocessing. Thus, at best, perturbation of "intrinsic" factors (e.g., native metabolic, regulatory genes) results in incremental and inconsistent benefits. At worst, they are detrimental. Overall, this work provides insight into the limitations and pitfalls to realizing efficient synthetic heterotrophy using traditional/systems metabolic engineering approaches, demonstrates the innate adaptability of yeast for metabolism of non-native substrates, and provides an alternate, novel, holistic (and yet minimalistic) approach based on integrating non-native metabolic genes with a native regulon system.

synthetic biology↗

bioRxiv2022

Recovering false negatives in CRISPR fitness screens with JLOE

It is widely accepted that pooled library CRISPR knockout screens offer greater sensitivity and specificity than prior technologies in detecting genes whose disruption leads to fitness defects, a critical step in identifying candidate cancer targets. However, the assumption that CRISPR screens are saturating has been largely untested. Through integrated analysis of screen data in cancer cell lines generated by the Cancer Dependency Map, we show that a typical CRISPR screen has a [~]20% false negative rate, in addition to library-specific false negatives. Replicability falls sharply as gene expression decreases, while cancer subtype-specific genes within a tissue show distinct profiles compared to false negatives. Cumulative analyses across tissues improves our understanding of core essential genes and suggest only a small number of lineage-specific essential genes, enriched for transcription factors that define pathways of tissue differentiation. To recover false negatives, we introduce a method, Joint Log Odds of Essentiality (JLOE), which builds on our prior work with BAGEL to selectively rescue the false negatives without an increased false discovery rate.

bioinformatics↗

bioRxiv2021

A general deep learning model for bird detection in high resolution airborne imagery

Advances in artificial intelligence for computer vision hold great promise for increasing the scales at which ecological systems can be studied. The distribution and behavior of individuals is central to ecology, and computer vision using deep neural networks can learn to detect individual objects in imagery. However, developing supervised models for ecological monitoring is challenging because it needs large amounts of human-labeled training data, requires advanced technical expertise and computational infrastructure, and is prone to overfitting. This limits application across space and time. One solution is developing generalized models that can be applied across species and ecosystems. Using over 250,000 annotations from 13 projects from around the world, we develop a general bird detection model that achieves over 65% recall and 50% precision on novel aerial data without any local training despite differences in species, habitat, and imaging methodology. Fine-tuning this model with only 1000 local annotations increase these values to an average of 84% recall and 69% precision by building on the general features learned from other data sources. Retraining from the general model improves local predictions even when moderately large annotation sets are available and makes model training faster and more stable. Our results demonstrate that general models for detecting broad classes of organisms using airborne imagery are achievable. These models can reduce the effort, expertise, and computational resources necessary for automating the detection of individual organisms across large scales, helping to transform the scale of data collection in ecology and the questions that can be addressed.

ecology↗

bioRxiv2021

Dynamic rewiring of biological activity across genotype and lineage revealed by context-dependent functional interactions

Coessentiality networks derived from CRISPR screens in cell lines provide a powerful framework for identifying functional modules in the cell and for inferring the role of uncharacterized genes. However, these networks integrate signal across all underlying data, and can mask strong interactions that occur in only a subset of the cell lines analyzed. Here we decipher dynamic functional interactions by identifying significant cellular contexts, primarily by oncogenic mutation, lineage, and tumor type, and discovering coessentiality relationships that depend on these contexts. We recapitulate well-known gene-context interactions such as oncogene-mutation, paralog buffering, and tissue-specific essential genes, show how mutation rewires known signal transduction pathways, including RAS/RAF and IGF1R-PIK3CA, and illustrate the implications for drug targeting. We further demonstrate how context-dependent functional interactions can elucidate lineage-specific gene function, as illustrated by the maturation of proreceptors IGF1R and MET by proteases FURIN and CPD. This approach advances our understanding of context-dependent interactions and how they can be gleaned from these data. We provide an online resource to explore these context-dependent interactions at diffnet.hart-lab.org.

systems biology↗

bioRxiv2021

Imprinting on time-structured acoustic stimuli in ducklings

Filial imprinting is a dedicated learning process that lacks explicit reinforcement. The phenomenon itself is narrowly heritably canalized, but its content, the representation of the parental object, reflects the circumstances of the newborn. Imprinting has recently been shown to be even more subtle and complex than previously envisaged, since ducklings and chicks are now known to select and represent for later generalization abstract conceptual properties of the objects they perceive as neonates, including movement pattern, heterogeneity, and inter-component relationships of same or different. Here we investigate whether day-old Mallard ducklings (Anas platyrhynchos) imprint on the temporal separation between duos of brief acoustic stimuli, and whether they generalize such timing information to novel sound types. Subjects did discriminate temporal structure when imprinted and tested on natural duck calls, but not when using white noise for imprinting or testing. Our data confirm that imprinting includes the establishment of neural representations of both primary percepts and abstract properties of candidate objects, meshing together genetically transmitted prior knowledge with selected perceptual input.

animal behavior and cognition↗

bioRxiv2020

Discovery of novel tumor suppressors from CRISPR screens reveals lipid-sensitive subtype of AML

CRISPR knockout screens in hundreds of cancer cell lines have revealed a substantial number of context-specific essential genes that, when associated with a biomarker such as lineage or oncogenic mutation, offer candidate tumor-specific vulnerabilities for targeted therapies or novel drug development. Data-driven analysis of knockout fitness screens also yields many other functionally coherent modules that show emergent essentiality or, in rarer cases, the opposite phenotype of faster proliferation. We develop a systematic approach to classify these suppressors of proliferation, which are highly enriched for tumor suppressor genes, and define a network of 145 genes in 22 discrete modules. One surprising module contains several elements of the glycerolipid biosynthesis pathway and operates exclusively in a subset of AML lines, which we call Fatty Acid Synthesis/Tumor Suppressor (FASTS) cells. The proliferation suppressor activity of genes involved in the synthesis of saturated fatty acids, coupled with a more severe fitness phenotype for the desaturation pathway, suggests that these cells operate at the limit of their carrying capacity for saturated fatty acids, which we confirmed biochemically. Overexpression of genes in this module is associated with a survival advantage in an age-matched cohort of AML patients, suggesting the gene cluster driving an in vitro phenotype may be associated with a novel, clinically relevant subtype.

genetics↗

bioRxiv2020

Diversity Across the Pancreatic Ductal Adenocarcinoma Disease Spectrum Revealed by Network-Anchored Functional Genomics

Cancers are highly complex ecosystems composed of molecularly distinct sub-populations of tumor cells, each exhibiting a unique spectrum of genetic features and phenotypes, and embedded within a complex organ context. To substantially improve clinical outcomes, there is a need to comprehensively define inter- and intra-tumor phenotypic diversity, as well as to understand the genetic dependencies that underlie discrete molecular subpopulations. To this end, we integrated CRISPR-based co-dependency annotations with a tissue-specific co-expression network developed from patient-derived models to establish CoDEX, a framework to quantitatively associate gene-cluster patterns with genetic vulnerabilities in pancreatic ductal adenocarcinoma (PDAC). Using CoDEX, we defined multiple prominent anticorrelated gene-cluster signatures and specific pathway dependencies, both across genetically distinct PDAC models and intratumorally at the single-cell level. Of these, one differential signature recapitulated the characteristics of classical and basal-like PDAC molecular subtypes on a continuous scale. Anchoring genetic dependencies identified through functional genomics within the gene-cluster signature defined fundamental vulnerabilities associated with transcriptomic signatures of PDAC subtypes. Subtype-associated dependencies were validated by feature-barcoded CRISPR knockout of prioritized basal-like-associated genetic vulnerabilities (SMAD4, ILK, and ZEB1) followed by scRNAseq in multiple PDAC models. Silencing of these genes resulted in a significant and directional clonal shift toward the classical-like signature of more indolent tumors. These results validate CoDEX as a novel, quantitative approach to identify specific genetic dependencies within defined molecular contexts that may guide clinical positioning of targeted therapeutics.

cancer biology↗

bioRxiv2020

Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier

Identifying essential genes in genome-wide loss of function screens is a critical step in functional genomics and cancer target finding. We previously described the Bayesian Analysis of Gene Essentiality (BAGEL) algorithm for accurate classification of gene essentiality from short hairpin RNA and CRISPR/Cas9 genome wide genetic screens. Here, we introduce an updated version, BAGEL2, which employs an improved model that offers greater dynamic range of Bayes Factors, enabling detection of tumor suppressor genes, and a multi-target correction that reduces false positives from off-target CRISPR guide RNA. We also suggest a metric for screen quality at the replicate level and demonstrate how different algorithms handle lower-quality data in substantially different ways. BAGEL2 is written in Python 3 and source code, along with all supporting files, are available on github (https://github.com/hart-lab/bagel).

bioinformatics↗

bioRxiv2020

Multiplex enCas12a screens show functional buffering by paralogs is systematically absent from genome-wide CRISPR/Cas9 knockout screens

Major efforts on pooled library CRISPR knockout screening across hundreds of cell lines have identified genes whose disruption leads to fitness defects, a critical step in identifying candidate cancer targets. However, the number of essential genes detected from these monogenic knockout screens are very low compared to the number of constitutively expressed genes in a cell, raising the question of why there are so few essential genes. Through a systematic analysis of screen data in cancer cell lines generated by the Cancer Dependency Map, we observed that half of all constitutively-expressed genes are never hits in any CRISPR screen, and that these never-essentials are highly enriched for paralogs. We investigated paralog buffering through systematic dual-gene CRISPR knockout screening by testing algorithmically defined ~400 candidate paralog pairs with the enCas12a multiplex knockout system in three cell lines. We observed 24 synthetic lethal paralog pairs which have escaped detection by monogenic knockout screens at stringent thresholds. Nineteen of 24 (79%) synthetic lethal interactions were present in at least two out of three cell lines and 14 of 24 (58%) were present in all three cell lines tested, including alternate subunits of stable protein complexes as well as functionally redundant enzymes. Together these observations strongly suggest that paralogs represent a targetable set of genetic dependencies that are systematically under-represented among cell-essential genes due to genetic buffering in monogenic CRISPR-based mammalian functional genomics approaches.

systems biology↗

bioRxiv2020

Biases and Blind-Spots in Genome-Wide CRISPR Knockout Screens

It is widely accepted that pooled library CRISPR knockout screens offer greater sensitivity and specificity than prior technologies in detecting genes whose disruption leads to fitness defects, a critical step in identifying candidate cancer targets. However, the assumption that CRISPR screens are saturating has been largely untested. Through integrated analysis of screen data in cancer cell lines generated by the Cancer Dependency Map, we show that a typical CRISPR screen has a [~]20% false negative rate, beyond library-specific false negatives previously described. Replicability falls sharply as gene expression decreases, while cancer subtype-specific genes within a tissue show distinct profiles compared to false negatives. Cumulative analyses across tissues suggest only a small number of lineage-specific essential genes and that these genes are highly enriched for transcription factors that define pathways of tissue differentiation. In addition, we show that half of all constitutively-expressed genes are never hits in any CRISPR screen, and that these never-essentials are highly enriched for paralogs. Together these observations strongly suggest that functional buffering masks single knockout phenotypes for a substantial number of genes, describing a major blind spot in CRISPR-based mammalian functional genomics approaches.

systems biology↗

bioRxiv2019

Environmental DNA for indigenous-led monitoring of a culturally important fish

Although environmental DNA shed from an organism is now widely used for species detection in a wide variety of contexts, mobilizing environmental DNA for management requires estimation of population size and trends rather than simply assessing presence or absence. However, the efficacy of environmental-DNA-based indices of abundance for long-term population monitoring have not yet been assessed. Here we report on the relationship between six years of mark-recapture population estimates for eulachon (Thaleichthys pacificus) and eDNA rates, which are calculated from the product of stream flow and DNA concentration. Eulachon are a culturally and biologically important anadromous fish that have significantly declined in the southern part of their range but were historically rendered into oil and traded. Both the peak eDNA rate and the area under the curve of the daily eDNA rate were highly predictive of the mark-recapture population estimate, explaining 84.96% and 92.53% of the deviance respectively. Even in the absence of flow correction, the peak of the daily eDNA concentration explained an astonishing 89.53% while the area under the curve explained 90.74% of the deviance. These results support the use of eDNA to monitor eulachon population trends and represent a >80% cost savings over mark-recapture, which could be further increased with automated water sampling, reduced replication, and focused temporal sampling. Due to its logistical ease and affordability, eDNA sampling can facilitate monitoring a larger number of rivers and in remote locations where mark-recapture is infeasible.

ecology↗

bioRxiv2019

The genome of the endangered dryas monkey provides new insights into the evolutionary history of the vervets

Genomic data can be a powerful tool for inferring ecology, behaviour and conservation needs of highly elusive species, particularly when other sources of information are hard to come by. Here we focus on the Dryas monkey (Cercopithecus dryas), an endangered primate endemic to the Congo Basin with cryptic behaviour and possibly less than 250 remaining individuals. Using whole genome sequencing data we show that the Dryas monkey represents a sister lineage to the vervets (Chlorocebus sp.) and has diverged from them around 1.4 million years ago with additional bi-directional gene flow [~]750,000 - [~]500,000 years ago that has likely involved the crossing of the Congo River. Together with evidence of gene flow across the Congo River in bonobos and okapis, our results suggest that the fluvial topology of the Congo River might have been more dynamic than previously recognised. Despite the presence of several homozygous loss-of-function mutations in genes associated with sperm mobility and immunity, we find high genetic diversity and low levels of inbreeding and genetic load in the studied Dryas monkey individual. This suggests that the current population carries sufficient genetic variability for long-term survival and might be larger than currently recognised. We thus provide an example of how genomic data can directly improve our understanding of highly elusive species.

genomics↗

bioRxiv2019

A consensus set of genetic vulnerabilities to ATR inhibition

The response to DNA replication stress in eukaryotes is under the control of the ataxia-telangiectasia and Rad3-related (ATR) kinase. ATR responds to single-stranded (ss) DNA to stabilize distressed DNA replication forks, modulate DNA replication firing and prevent cells with damaged DNA or incomplete DNA replication from entering into mitosis. Furthermore, inhibitors of ATR are currently in clinical development either as monotherapies or in combination with agents that perturb DNA replication. To gain a genetic view of the cellular pathways requiring ATR kinase function, we mapped genes whose mutation causes hypersensitivity to ATR inhibitors with genome-scale CRISPR/Cas9 screens. We delineate a consensus set of 117 genes enriched in DNA replication, DNA repair and cell cycle regulators that promote survival when ATR kinase activity is suppressed. We validate 14 genes from this set and report genes not previously described to modulate response to ATR inhibitors. In particular we found that the loss of the POLE3/POLE4 proteins, which are DNA polymerase e accessory subunits, results in marked hypersensitivity to ATR inhibition. We anticipate that this 117-gene set will be useful for the identification of genes involved in the regulation of genome integrity, the characterization of new biological processes involving ATR, and may reveal biomarkers of ATR inhibitor response in the clinic.

molecular biology↗

bioRxiv2018

Multiplexed pooled library screening with Cpf1

RNA interference and CRISPR/Cas9-based pooled library screens have revolutionized the field of functional genomics. However, currently available pooled library screens face a trade-off between library effectiveness and library complexity. We developed a multiplexed, high-throughput screening strategy based on an optimized AsCpf1 nuclease that minimizes library size without sacrificing gene targeting efficiency. Our AsCpf1-based multiplexed library performed similarly well compared to currently available CRISPR/Cas9 libraries, but with a single polycistronic crRNA clone targeting each gene. With this strategy, we constructed the smallest whole-genome knock-out library available, \"Mini-human\" for the human genome, which is one-fourth the size of the smallest CRISPR library currently available.

systems biology↗

bioRxiv2018

Mobility in Osteogenesis Imperfecta: A Multicenter North American Study

BackgroundOsteogenesis imperfecta (OI) is a genetic connective tissue disorder characterized by increased bone fragility and recurrent fractures. The phenotypic severity of OI has a significant influence on the ability to walk but little is known about the ambulatory characteristics, strength, or functional abilities in individuals with OI, especially in the more severe forms. To advance clinical research in OI, the Linked Clinical Research Centers, network of clinical centers in North America with significant experience in treating patients with OI, was established in 2009. The purpose of this work was to characterize mobility in OI using standard clinical assessment tools. and determine if any patient characteristics could be used to predict mobility outcomes.\n\nMethodsData were collected at five clinical sites and included age, gender, ethnicity, height, weight, use of assistive device, and bisphosphonate use and mobility metrics (age at first walk, Gillette Functional Assessment Questionnaire, Functional Mobility Scale, and distance walked in the 6 minute walk test). Linear mixed models were developed to explore the relationships between subject demographics and mobility metrics.\n\nResultsThe study identified 491 individuals age 3 and older. In general, the results showed minor limitations in the type I group while the more severe types showed more significant limitations in all mobility metrics analyzed. Height and weight were shown to be the most significant predictors of mobility metrics. Relationships with mobility and bisphosphonates varied with OI type and whether oral or IV was used.\n\nConclusionThis paper is the most comprehensive report of mobility in individuals with OI to date. These results are vital to understanding the mobility limitations of specific types of OI and beneficial when developing rehabilitation protocols for this population. It is important for physicians, patients, and caregivers to gain insight into severity and classification of the disease and the influence of disease-related characteristics on the prognosis for mobility.

bioengineering↗