Nutritional Status, Sex, and Ambient Temperature Modulate the Wingbeat Frequency of the Diamondback Moth Plutella xylostella.

The diamondback moth, Plutella xylostella L. (Lepidoptera: Plutellidae), is a cosmopolitan horticultural pest that is undergoing a fast, climate-driven range expansion. Its wide geographic distribution, pest status, and high incidence of insecticide resistance are directly tied to long-distance migration. Wingbeat frequency (WBF) is a key aspect of P. xylostella migratory behavior, but has received limited scientific attention. Here, we investigated the effects of environmental parameters, age, adult nutrition, and sex on P. xylostella WBF. Across experimental regimes, WBF ranged from 31.39 Hz to 78.87 Hz. Over a 10-35 °C range, the WBF of both male and female moths increased with temperature up to 62.96 Hz. Though male WBF was unaffected by humidity, females exhibited the highest WBF at 15% relative humidity (RH). WBF was unaffected by adult age, but adult nutrition exerted important impacts. Specifically, the WBF of moths fed honey water (54.66 Hz) was higher than that of water-fed individuals (49.42 Hz). Lastly, males consistently exhibited a higher WBF than females. By uncovering the biological and (nutritional) ecological determinants of diamondback moth flight, our work provides invaluable guidance to radar-based monitoring, migration forecasting, and the targeted deployment of preventative mitigation tactics.

Influence of seasonal migration on evolution of insecticide resistance in Plutella xylostella.

The diamondback moth, Plutella xylostella (L.), is one of the most destructive migratory pest species of cruciferous vegetables worldwide and has developed resistance to most of the insecticides used for its control. The migration regularity, migratory behavior, and relationship between flight and reproduction of P. xylostella have been widely reported. However, the effect of migration on insecticide resistance in this pest is still unclear. In this study, the effect of migration on P. xylostella resistance to seven insecticides was investigated using populations across the Bohai Sea that were collected in the early and late seasons during 2017-2019. The bioassay results showed that the early season populations of P. xylostella from south China possessed much higher resistance to insecticides because of intensive insecticide application; alternatively, the late season populations migrated from northeast China, where the insecticides were only used occasionally, showed much lower insecticide resistance. The genome re-sequencing results revealed that, among the eight mutations involved in insecticide resistance, the frequencies of two acetylcholinesterase mutations (A298S and G324A) responsible for organophosphorus insecticide resistance were significantly decreased in the late season populations. The results indicated that P. xylostella migration between tropical and temperate regions significantly delayed the development of insecticide resistance. These findings illustrated the effect of regional migration on the evolution of insecticide resistance in P. xylostella, and provided foundational information for further research on the relationship between migration and insecticide resistance development in other insects.

The de Rham-Hodge Analysis and Modeling of Biomolecules.

Biological macromolecules have intricate structures that underpin their biological functions. Understanding their structure-function relationships remains a challenge due to their structural complexity and functional variability. Although de Rham-Hodge theory, a landmark of twentieth-century mathematics, has had a tremendous impact on mathematics and physics, it has not been devised for macromolecular modeling and analysis. In this work, we introduce de Rham-Hodge theory as a unified paradigm for analyzing the geometry, topology, flexibility, and Hodge mode analysis of biological macromolecules. Geometric characteristics and topological invariants are obtained either from the Helmholtz-Hodge decomposition of the scalar, vector, and/or tensor fields of a macromolecule or from the spectral analysis of various Laplace-de Rham operators defined on the molecular manifolds. We propose Laplace-de Rham spectral-based models for predicting macromolecular flexibility. We further construct a Laplace-de Rham-Helfrich operator for revealing cryo-EM natural frequencies. Extensive experiments are carried out to demonstrate that the proposed de Rham-Hodge paradigm is one of the most versatile tools for the multiscale modeling and analysis of biological macromolecules and subcellular organelles. Accurate, reliable, and topological structure-preserving algorithms for implementing discrete exterior calculus (DEC) have been developed to facilitate the aforementioned modeling and analysis of biological macromolecules. The proposed de Rham-Hodge paradigm has potential applications to subcellular organelles and the structure construction from medium- or low-resolution cryo-EM maps, and functional predictions from massive biomolecular datasets.

Mutations Strengthened SARS-CoV-2 Infectivity.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infectivity is a major concern in coronavirus disease 2019 (COVID-19) prevention and economic reopening. However, rigorous determination of SARS-CoV-2 infectivity is very difficult owing to its continuous evolution with over 10,000 single nucleotide polymorphisms (SNP) variants in many subtypes. We employ an algebraic topology-based machine learning model to quantitatively evaluate the binding free energy changes of SARS-CoV-2 spike glycoprotein (S protein) and host angiotensin-converting enzyme 2 receptor following mutations. We reveal that the SARS-CoV-2 virus becomes more infectious. Three out of six SARS-CoV-2 subtypes have become slightly more infectious, while the other three subtypes have significantly strengthened their infectivity. We also find that SARS-CoV-2 is slightly more infectious than SARS-CoV according to computed S protein-angiotensin-converting enzyme 2 binding free energy changes. Based on a systematic evaluation of all possible 3686 future mutations on the S protein receptor-binding domain, we show that most likely future mutations will make SARS-CoV-2 more infectious. Combining sequence alignment, probability analysis, and binding free energy calculation, we predict that a few residues on the receptor-binding motif, i.e., 452, 489, 500, 501, and 505, have high chances to mutate into significantly more infectious COVID-19 strains.

Boosting Tree-Assisted Multitask Deep Learning for Small Scientific Datasets.

Machine learning approaches have had tremendous success in various disciplines. However, such success highly depends on the size and quality of datasets. Scientific datasets are often small and difficult to collect. Currently, improving machine learning performance for small scientific datasets remains a major challenge in many academic fields, such as bioinformatics or medical science. Gradient boosting decision tree (GBDT) is typically optimal for small datasets, while deep learning often performs better for large datasets. This work reports a boosting tree-assisted multitask deep learning (BTAMDL) architecture that integrates GBDT and multitask deep learning (MDL) to achieve near-optimal predictions for small datasets when there exists a large dataset that is well correlated to the small datasets. Two BTAMDL models are constructed, one utilizing purely MDL output as GBDT input while the other admitting additional features in GBDT input. The proposed BTAMDL models are validated on four categories of datasets, including toxicity, partition coefficient, solubility, and solvation. It is found that the proposed BTAMDL models outperform the current state-of-the-art methods in various applications involving small datasets.

Flight Performance of Mamestra brassicae (Lepidoptera: Noctuidae) Under Different Biotic and Abiotic Conditions.

Mamestra brassicae L. is an important, regionally migratory pest of vegetable crops in Europe and Asia. Its migratory activity contributes significantly to population outbreaks, causing severe crop yield losses. Because an in-depth understanding of flight performance is key to revealing migratory patterns, here we used a computer-linked flight mill and stroboscope to study the flight ability and wingbeat frequency (WBF) of M. brassicae in relation to sex, age, temperature, and relative humidity (RH). The results showed that age significantly affected the flight ability and WBF of M. brassicae, and 3-d-old individuals performed the strongest performance (total flight distance: 45.6 ± 2.5 km; total flight duration: 9.3 ± 0.3 h; WBF: 44.0 ± 0.5 Hz at 24°C and 75% RH). The age for optimal flight was considered to be 2-3 d old. Temperature and RH also significantly affected flight ability and WBF; flight was optimal from 23°C to 25°C and 64-75% RH. Because M. brassicae thus has great potential to undertake long-distance migration, better knowledge of its flight behavior and migration will help establish a pest forecasting and early-warning system.

MathDL: mathematical deep learning for D3R Grand Challenge 4.

We present the performances of our mathematical deep learning (MathDL) models for D3R Grand Challenge 4 (GC4). This challenge involves pose prediction, affinity ranking, and free energy estimation for beta secretase 1 (BACE) as well as affinity ranking and free energy estimation for Cathepsin S (CatS). We have developed advanced mathematics, namely differential geometry, algebraic graph, and/or algebraic topology, to accurately and efficiently encode high dimensional physical/chemical interactions into scalable low-dimensional rotational and translational invariant representations. These representations are integrated with deep learning models, such as generative adversarial networks (GAN) and convolutional neural networks (CNN) for pose prediction and energy evaluation, respectively. Overall, our MathDL models achieved the top place in pose prediction for BACE ligands in Stage 1a. Moreover, our submissions obtained the highest Spearman correlation coefficient on the affinity ranking of 460 CatS compounds, and the smallest centered root mean square error on the free energy set of 39 CatS molecules. It is worthy to mention that our method on docking pose predictions has significantly improved from our previous ones.

A topology-based network tree for the prediction of protein-protein binding affinity changes following mutation.

The ability to predict protein-protein interactions is crucial to our understanding of a wide range of biological activities and functions in the human body, and for guiding drug discovery. Despite considerable efforts to develop suitable computational methods, predicting protein-protein interaction binding affinity changes following mutation (ΔΔG) remains a severe challenge. Algebraic topology, a champion in recent worldwide competitions for protein-ligand binding affinity predictions, is a promising approach to simplifying the complexity of biological structures. Here we introduce element- and site-specific persistent homology (a new branch of algebraic topology) to simplify the structural complexity of protein-protein complexes and embed crucial biological information into topological invariants. We also propose a new deep learning algorithm called NetTree to take advantage of convolutional neural networks and gradient-boosting trees. A topology-based network tree is constructed by integrating the topological representation and NetTree for predicting protein-protein interaction ΔΔG. Tests on major benchmark datasets indicate that the proposed topology-based network tree is an important improvement over the current state of the art in predicting ΔΔG.

Floral Visitation Can Enhance Fitness of Helicoverpa armigera (Lepidoptera: Noctuidae) Long-Distance Migrants.

Numerous insect species engage in seasonal, trans-latitudinal migration, in response to varying resource availability, climatic conditions and associated opportunities, to maximize fitness and reproductive success. For certain species, the interaction between migrant adults and individual host plants is well-studied under laboratory conditions, but scant knowledge exists on the nutritional ecology of wild (i.e., field-caught) moths. During 2017-2018, we trapped adults of the cotton bollworm Helicoverpa armigera (Hübner) along its migration pathway in northeastern China and used pollen grain analysis to assess its visitation of particular host plants. Next, we assessed life history effects of adult feeding on carbohydrate-rich resources, for migrant individuals. Pollen grain analysis revealed H. armigera visitation of 32 species from 28 families, with the largest carrier ratio for northward migrants. Evening primrose (Oenothera spp.) accounted for 48% of pollen grains, indicating a marked H. armigera feeding preference. Furthermore, feeding on sugar-rich foods benefited adult fitness, enhanced fecundity by 65-82% and increased flight distance by 38-55% as compared to unfed individuals. Also, the degree of enhancement of reproduction and flight performance following sugar feeding varied between different migratory cohorts. Our work combines (polymerase chain reaction [PCR]-assisted) palynology and laboratory-based life history trials to generate novel perspectives on the nutritional ecology of long-distance migratory insects. These findings can aid the development of population monitoring and 'area-wide' management strategies for a globally-important agricultural pest.

Molecular Characterization and Expression Profiles of Cryptochrome Genes in a Long-Distance Migrant, Agrotis segetum (Lepidoptera: Noctuidae).

Cryptochromes act as photoreceptors or integral components of the circadian clock that involved in the regulation of circadian clock and regulation of migratory activity in many animals, and they may also act as magnetoreceptors that sensed the direction of the Earth's magnetic field for the purpose of navigation during animals' migration. Light is a major environmental signal for insect circadian rhythms, and it is also necessary for magnetic orientation. We identified the full-length cDNA encoding As-CRY1 and As-CRY2 in Agrotis segetum Denis and Schiffermaller (turnip moth (Lepidoptera: Noctuidae)). The DNA photolyase domain and flavin adenine dinucleotide-binding domain were found in both cry genes, and multiple alignments showed that those domains that are important for the circadian clock and magnetosensing were highly conserved among different animals. Quantitative polymerase chain reaction showed that cry genes were expressed in all examined body parts, with higher expression in adults during the developmental stages of the moths. Under a 14:10 (L:D) h cycle, the expression of cry genes showed a daily biological rhythm, and light can affect the expression levels of As-cry genes. The expression levels of cry genes were higher in the migratory population than in the reared population and higher in the emigration population than in the immigration population. These findings suggest that the two cryptochrome genes characterized in the turnip moth might be associated with the circadian clock and magnetosensing. Their functions deserve further study, especially for potential control of the turnip moth.

Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges.

Advanced mathematics, such as multiscale weighted colored subgraph and element specific persistent homology, and machine learning including deep neural networks were integrated to construct mathematical deep learning models for pose and binding affinity prediction and ranking in the last two D3R Grand Challenges in computer-aided drug design and discovery. D3R Grand Challenge 2 focused on the pose prediction, binding affinity ranking and free energy prediction for Farnesoid X receptor ligands. Our models obtained the top place in absolute free energy prediction for free energy set 1 in stage 2. The latest competition, D3R Grand Challenge 3 (GC3), is considered as the most difficult challenge so far. It has five subchallenges involving Cathepsin S and five other kinase targets, namely VEGFR2, JAK2, p38-α, TIE2, and ABL1. There is a total of 26 official competitive tasks for GC3. Our predictions were ranked 1st in 10 out of these 26 tasks.

Divide-and-conquer strategy for large-scale Eulerian solvent excluded surface.

MOTIVATION: Surface generation and visualization are some of the most important tasks in biomolecular modeling and computation. Eulerian solvent excluded surface (ESES) software provides analytical solvent excluded surface (SES) in the Cartesian grid, which is necessary for simulating many biomolecular electrostatic and ion channel models. However, large biomolecules and/or fine grid resolutions give rise to excessively large memory requirements in ESES construction. We introduce an out-of-core and parallel algorithm to improve the ESES software. RESULTS: The present approach drastically improves the spatial and temporal efficiency of ESES. The memory footprint and time complexity are analyzed and empirically verified through extensive tests with a large collection of biomolecule examples. Our results show that our algorithm can successfully reduce memory footprint through a straightforward divide-and-conquer strategy to perform the calculation of arbitrarily large proteins on a typical commodity personal computer. On multi-core computers or clusters, our algorithm can reduce the execution time by parallelizing most of the calculation as disjoint subproblems. Various comparisons with the state-of-the-art Cartesian grid based SES calculation were done to validate the present method and show the improved efficiency. This approach makes ESES a robust software for the construction of analytical solvent excluded surfaces. AVAILABILITY AND IMPLEMENTATION: http://weilab.math.msu.edu/ESES.

Rigidity Strengthening: A Mechanism for Protein-Ligand Binding.

Protein-ligand binding is essential to almost all life processes. The understanding of protein-ligand interactions is fundamentally important to rational drug and protein design. Based on large scale data sets, we show that protein rigidity strengthening or flexibility reduction is a mechanism in protein-ligand binding. Our approach based solely on rigidity is able to unveil a surprisingly apparently long-range contribution of apparently four residue layers to protein-ligand binding, which has ramifications for drug and protein design. Additionally, the present work reveals that among various pairwise interactions, the short-range ones within the distance of the van der Waals diameter are most important. It is found that the present approach outperforms all other state-of-the-art scoring functions for protein-ligand binding affinity predictions of two benchmark test sets.