scholarly journals Identification of functional residues using machine learning provides insights into the evolution of odorant receptor gene families in solitary and social insects

2021 ◽  
Author(s):  
Pablo Mier ◽  
Jean-Fred Fontaine ◽  
Marah Stoldt ◽  
Romain Libbrecht ◽  
Carlotta Martelli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Insects ◽  
Odorant Receptor ◽  
Receptor Gene ◽  
Functional Characterization ◽  
Gene Families ◽  
Volatile Chemicals ◽  
Large Gene ◽  
Novel Approach ◽  
Mosquito Anopheles Gambiae

The gene family of insect odorant receptors (ORs) has greatly expanded in the course of evolution. ORs allow insects to detect volatile chemicals and therefore play an important role in social interactions, the detection of enemies and preys, and during foraging. The sequences of several thousand ORs are known, but their specific function or ligands have been identified only for very few of them. To advance the functional characterization of ORs, we compiled, curated and aligned the sequences of 3,902 ORs from 21 insect species. We identified the amino acid positions that best predict the response to ligands using machine learning on sets of functionally characterized proteins from the fly Drosophila melanogaster, the mosquito Anopheles gambiae and the ant Harpegnathos saltator. We studied the conservation of these predicted relevant residues across all OR subfamilies and show that the subfamilies that expanded strongly in social insects exhibit high levels of conservation in their binding sites. This indicates that ORs of social insect families are typically finely tuned and exhibit a sensitivity to very similar odorants. Our novel approach provides a powerful tool to use functional information from a limited number of genes to investigate the functional evolution of large gene families.

scholarly journals Population differences in olfaction accompany host shift in Drosophila mojavensis

2016 ◽  
Vol 283 (1837) ◽  
pp. 20161562 ◽  
Author(s):  
Amber Crowley-Gall ◽  
Priya Date ◽  
Clair Han ◽  
Nicole Rhodes ◽  
Peter Andolfatto ◽  
...  
Keyword(s):  
Host Plant ◽  
Odorant Receptor ◽  
Receptor Gene ◽  
Host Shift ◽  
Olfactory Receptor Neuron ◽  
Drosophila Mojavensis ◽  
Ecological Specialization ◽  
Volatile Chemicals ◽  
Specificity And Sensitivity ◽  
And Function

Evolutionary shifts in plant–herbivore interactions provide a model for understanding the link among the evolution of behaviour, ecological specialization and incipient speciation. Drosophila mojavensis uses different host cacti across its range, and volatile chemicals emitted by the host are the primary cue for host plant identification. In this study, we show that changes in host plant use between distinct D. mojavensis populations are accompanied by changes in the olfactory system. Specifically, we observe differences in olfactory receptor neuron specificity and sensitivity, as well as changes in sensillar subtype abundance, between populations. Additionally, RNA-seq analyses reveal differential gene expression between populations for members of the odorant receptor gene family. Hence, alterations in host preference are associated with changes in development, regulation and function at the olfactory periphery.

scholarly journals A purely bioinformatic pipeline for the prediction of mammalian odorant receptor gene enhancers

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Andrea Degl’Innocenti ◽  
Gabriella Meloni ◽  
Barbara Mazzolai ◽  
Gianni Ciofani
Keyword(s):  
Odorant Receptor ◽  
Receptor Gene ◽  
Gene Families ◽  
Or Gene ◽  
Comprehensive Classification ◽  
Genomic Clusters ◽  
Or Genes ◽  
Multiple Receptor

Abstract Background In most mammals, a vast array of genes coding for chemosensory receptors mediates olfaction. Odorant receptor (OR) genes generally constitute the largest multifamily (> 1100 intact members in the mouse). From the whole pool, each olfactory neuron expresses a single OR allele following poorly characterized mechanisms termed OR gene choice. OR genes are found in genomic aggregations known as clusters. Nearby enhancers, named elements, are crucial regulators of OR gene choice. Despite their importance, searching for new elements is burdensome. Other chemosensory receptor genes responsible for smell adhere to expression modalities resembling OR gene choice, and are arranged in genomic clusters — often with chromosomal linkage to OR genes. Still, no elements are known for them. Results Here we present an inexpensive framework aimed at predicting elements. We redefine cluster identity by focusing on multiple receptor gene families at once, and exemplify thirty — not necessarily OR-exclusive — novel candidate enhancers. Conclusions The pipeline we introduce could guide future in vivo work aimed at discovering/validating new elements. In addition, our study provides an updated and comprehensive classification of all genomic loci responsible for the transduction of olfactory signals in mammals.

scholarly journals Giant lungfish genome elucidates the conquest of land by vertebrates

Nature ◽  
2021 ◽  
Vol 590 (7845) ◽  
pp. 284-289 ◽  
Author(s):  
Axel Meyer ◽  
Siegfried Schloissnig ◽  
Paolo Franchini ◽  
Kang Du ◽  
Joost M. Woltering ◽  
...  
Keyword(s):  
Odorant Receptor ◽  
Receptor Gene ◽  
Gene Families ◽  
Lung Surfactants ◽  
Repeat Content ◽  
Rates Of Evolution ◽  
Major Transition ◽  
Intergenic Regions ◽  
Long Interspersed Nuclear Elements ◽  
Phylogenomic Analyses

AbstractLungfishes belong to lobe-fined fish (Sarcopterygii) that, in the Devonian period, ‘conquered’ the land and ultimately gave rise to all land vertebrates, including humans1–3. Here we determine the chromosome-quality genome of the Australian lungfish (Neoceratodus forsteri), which is known to have the largest genome of any animal. The vast size of this genome, which is about 14× larger than that of humans, is attributable mostly to huge intergenic regions and introns with high repeat content (around 90%), the components of which resemble those of tetrapods (comprising mainly long interspersed nuclear elements) more than they do those of ray-finned fish. The lungfish genome continues to expand independently (its transposable elements are still active), through mechanisms different to those of the enormous genomes of salamanders. The 17 fully assembled lungfish macrochromosomes maintain synteny to other vertebrate chromosomes, and all microchromosomes maintain conserved ancient homology with the ancestral vertebrate karyotype. Our phylogenomic analyses confirm previous reports that lungfish occupy a key evolutionary position as the closest living relatives to tetrapods4,5, underscoring the importance of lungfish for understanding innovations associated with terrestrialization. Lungfish preadaptations to living on land include the gain of limb-like expression in developmental genes such as hoxc13 and sall1 in their lobed fins. Increased rates of evolution and the duplication of genes associated with obligate air-breathing, such as lung surfactants and the expansion of odorant receptor gene families (which encode proteins involved in detecting airborne odours), contribute to the tetrapod-like biology of lungfishes. These findings advance our understanding of this major transition during vertebrate evolution.

scholarly journals Expansion and accelerated evolution of 9-exon odorant receptors in Polistes paper wasps

2021 ◽  
Author(s):  
Andrew W Legan ◽  
Christopher M Jernigan ◽  
Sara E Miller ◽  
Matthieu F Fuchs ◽  
Michael J Sheehan
Keyword(s):  
Gene Family ◽  
Odorant Receptor ◽  
Receptor Gene ◽  
Gene Tree ◽  
Gene Family Evolution ◽  
Paper Wasp ◽  
Gene Copy ◽  
Evolutionary Divergence ◽  
Wasp Species ◽  
Or Gene

Abstract Independent origins of sociality in bees and ants are associated with independent expansions of particular odorant receptor (OR) gene subfamilies. In ants, one clade within the OR gene family, the 9-exon subfamily, has dramatically expanded. These receptors detect cuticular hydrocarbons (CHCs), key social signaling molecules in insects. It is unclear to what extent 9-exon OR subfamily expansion is associated with the independent evolution of sociality across Hymenoptera, warranting studies of taxa with independently derived social behavior. Here we describe odorant receptor gene family evolution in the northern paper wasp, Polistes fuscatus, and compare it to four additional paper wasp species spanning ∼40 million years of evolutionary divergence. We find 200 putatively functional OR genes in P. fuscatus, matching predictions from neuroanatomy, and more than half of these are in the 9-exon subfamily. Most OR gene expansions are tandemly arrayed at orthologous loci in Polistes genomes, and microsynteny analysis shows species-specific gain and loss of 9-exon ORs within tandem arrays. There is evidence of episodic positive diversifying selection shaping ORs in expanded subfamilies. Values of omega (d  N/dS) are higher among 9-exon ORs compared to other OR subfamilies. Within the Polistes OR gene tree, branches in the 9-exon OR clade experience relaxed negative (purifying) selection relative to other branches in the tree. Patterns of OR evolution within Polistes are consistent with 9-exon OR function in CHC perception by combinatorial coding, with both natural selection and neutral drift contributing to interspecies differences in gene copy number and sequence.

A novel approach for phishing URLs detection using lexical based machine learning in a real-time environment

2021 ◽  
Author(s):  
Brij B. Gupta ◽  
Krishna Yadav ◽  
Imran Razzak ◽  
Konstantinos Psannis ◽  
Arcangelo Castiglione ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Novel Approach

scholarly journals Functional prediction of environmental variables using metabolic networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Adèle Weber Zendrera ◽  
Nataliya Sokolovska ◽  
Hédi A. Soula
Keyword(s):  
Machine Learning ◽  
Growth Temperature ◽  
Environmental Variables ◽  
Metabolic Networks ◽  
Machine Learning Techniques ◽  
Underlying Structure ◽  
Glutathione Biosynthesis ◽  
Additional Information ◽  
Cold Environments ◽  
Novel Approach

AbstractIn this manuscript, we propose a novel approach to assess relationships between environment and metabolic networks. We used a comprehensive dataset of more than 5000 prokaryotic species from which we derived the metabolic networks. We compute the scope from the reconstructed graphs, which is the set of all metabolites and reactions that can potentially be synthesized when provided with external metabolites. We show using machine learning techniques that the scope is an excellent predictor of taxonomic and environmental variables, namely growth temperature, oxygen tolerance, and habitat. In the literature, metabolites and pathways are rarely used to discriminate species. We make use of the scope underlying structure—metabolites and pathways—to construct the predictive models, giving additional information on the important metabolic pathways needed to discriminate the species, which is often absent in other metabolic network properties. For example, in the particular case of growth temperature, glutathione biosynthesis pathways are specific to species growing in cold environments, whereas tungsten metabolism is specific to species in warm environments, as was hinted in current literature. From a machine learning perspective, the scope is able to reduce the dimension of our data, and can thus be considered as an interpretable graph embedding.

scholarly journals Machine Learning Solutions for Bridge Scour Forecast Based on Monitoring Data

2021 ◽  
pp. 036119812110126
Author(s):  
Negin Yousefpour ◽  
Steve Downie ◽  
Steve Walker ◽  
Nathan Perkins ◽  
Hristo Dikanski
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Monitoring Data ◽  
Sensor Data ◽  
Monitoring Systems ◽  
Bridge Scour ◽  
Scour Monitoring ◽  
Novel Approach ◽  
Bayesian Inference Method ◽  
Water Level Variations

Bridge scour is a challenge throughout the U.S.A. and other countries. Despite the scale of the issue, there is still a substantial lack of robust methods for scour prediction to support reliable, risk-based management and decision making. Throughout the past decade, the use of real-time scour monitoring systems has gained increasing interest among state departments of transportation across the U.S.A. This paper introduces three distinct methodologies for scour prediction using advanced artificial intelligence (AI)/machine learning (ML) techniques based on real-time scour monitoring data. Scour monitoring data included the riverbed and river stage elevation time series at bridge piers gathered from various sources. Deep learning algorithms showed promising in prediction of bed elevation and water level variations as early as a week in advance. Ensemble neural networks proved successful in the predicting the maximum upcoming scour depth, using the observed sensor data at the onset of a scour episode, and based on bridge pier, flow and riverbed characteristics. In addition, two of the common empirical scour models were calibrated based on the observed sensor data using the Bayesian inference method, showing significant improvement in prediction accuracy. Overall, this paper introduces a novel approach for scour risk management by integrating emerging AI/ML algorithms with real-time monitoring systems for early scour forecast.

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