Biological Problems Research Articles

Dogs and their genes: what ever will they think of next?

The Edward Novitski Prize recognizes creativity and intellectual ingenuity in the solution of problems in genetics research. The prize honors scientific experimental work-either a single experimental accomplishment or a body of work. Ostrander is recognized for work developing the domestic dog as an experimental system for solving fundamental biological problems and identifying genetic sequences of relevance to human health and disease. Including work on disease and behavioral health, Ostrander has shown a dedication to creative methods for understanding canine genetics and the value of translating research organisms to human genetics.

Deep-learning-enabled antibiotic discovery through molecular de-extinction.

Molecular de-extinction aims at resurrecting molecules to solve antibiotic resistance and other present-day biological and biomedical problems. Here we show that deep learning can be used to mine the proteomes of all available extinct organisms for the discovery of antibiotic peptides. We trained ensembles of deep-learning models consisting of a peptide-sequence encoder coupled with neural networks for the prediction of antimicrobial activity and used it to mine 10,311,899 peptides. The models predicted 37,176 sequences with broad-spectrum antimicrobial activity, 11,035 of which were not found in extant organisms. We synthesized 69 peptides and experimentally confirmed their activity against bacterial pathogens. Most peptides killed bacteria by depolarizing their cytoplasmic membrane, contrary to known antimicrobial peptides, which tend to target the outer membrane. Notably, lead compounds (including mammuthusin-2 from the woolly mammoth, elephasin-2 from the straight-tusked elephant, hydrodamin-1 from the ancient sea cow, mylodonin-2 from the giant sloth and megalocerin-1 from the extinct giant elk) showed anti-infective activity in mice with skin abscess or thigh infections. Molecular de-extinction aided by deep learning may accelerate the discovery of therapeutic molecules.

Full-length 16S rRNA gene sequencing reveals the operating mode and chlorination-aggravated SWRO biofouling at a nuclear power plant.

Reverse osmosis (RO) membrane fouling and biological contamination problems faced by seawater desalination systems are microbiologically related. We used full-length 16S rRNA gene sequencing to assess the bacterial community structure and chlorine-resistant bacteria (CRB) associated with biofilm growth in different treatment processes under the winter mode of a chlorinated seawater desalination system in China. At the outset of the winter mode, certain CRB, such as Acinetobacter, Pseudomonas, and Bacillus held sway over the bacterial community structure, playing a pivotal role in biofouling. At the mode's end, Deinococcus and Paracoccus predominated, with Pseudomonas and Roseovarius following suit, while certain CRB genera still maintained their dominance. RO and chlorination are pivotal factors in shaping the bacterial community structure and diversity, and increases in total heterotrophic bacterial counts and community diversity in safety filters may adversely affect the effectiveness of subsequent RO systems. Besides, the bacterial diversity and culturable biomass in the water produced by the RO system remain high, and some conditionally pathogenic CRBs pose a certain microbial risk as a source of drinking water. Targeted removal of these CRBs will be an important area of research for advancing control over membrane clogging and ensuring water quality safety in the future.

Insights into AlphaFold's breakthrough in neurodegenerative diseases.

Neurodegenerative diseases (ND) are disorders of the central nervous system (CNS) characterized by impairment in neurons' functions, and complete loss, leading to memory loss, and difficulty in learning, language, and movement processes. The most common among these NDs are Alzheimer's disease (AD) and Parkinson's disease (PD), although several other disorders also exist. These are frontotemporal dementia (FTD), amyotrophic lateral syndrome (ALS), Huntington's disease (HD), and others; the major pathological hallmark of NDs is the proteinopathies, either of amyloid-β (Aβ), tauopathies, or synucleinopathies. Aggregation of proteins that do not undergo normal configuration, either due to mutations or through some disturbance in cellular pathway contributes to the diseases. Artificial Intelligence (AI) and deep learning (DL) have proven to be successful in the diagnosis and treatment of various congenital diseases. DL approaches like AlphaFold (AF) are a major leap towards success in CNS disorders. This 3D protein geometry modeling algorithm developed by DeepMind has the potential to revolutionize biology. AF has the potential to predict 3D-protein confirmation at an accuracy level comparable to experimentally predicted one, with the additional advantage of precisely estimating protein interactions. This breakthrough will be beneficial to identify diseases' advancement and the disturbance of signaling pathways stimulating impaired functions of proteins. Though AlphaFold has solved a major problem in structural biology, it cannot predict membrane proteins-a beneficial approach for drug designing.

Phylogenetic network-assisted rooting of unrooted gene trees

Gene trees inferred from molecular sequence alignments are typically unrooted, and determining the most credible rooting edge is a classical problem in computational biology. One approach to solve this problem is unrooted reconciliation, where the rooting edge is postulated based on the split of the root from a given species tree. In this paper, we propose a novel variant of the gene tree rooting problem, where the gene tree root is inferred using a phylogenetic network of the species present in the gene tree. To obtain the best rooting, unrooted reconciliation can be applied, where the unrooted gene tree is jointly reconciled with a set of splits inferred from the network. However, the exponential size of the set induced by display trees of the network makes this approach computationally prohibitive. To address this, we propose a broader and easier-to-control set of splits based on the structural properties of the network. We then derive exact mathematical formulas for the rooting problem and propose two general rooting algorithms to handle cases where the input network does not meet the initial requirements. Our experimental study based on simulated gene trees and networks demonstrates that our algorithms infer gene tree rootings correctly or with a small error in most cases.

Multivariate, heteroscedastic empirical Bayes via nonparametric maximum likelihood

Abstract Multivariate, heteroscedastic errors complicate statistical inference in many large-scale denoizing problems. Empirical Bayes is attractive in such settings, but standard parametric approaches rest on assumptions about the form of the prior distribution which can be hard to justify and which introduce unnecessary tuning parameters. We extend the nonparametric maximum-likelihood estimator (NPMLE) for Gaussian location mixture densities to allow for multivariate, heteroscedastic errors. NPMLEs estimate an arbitrary prior by solving an infinite-dimensional, convex optimization problem; we show that this convex optimization problem can be tractably approximated by a finite-dimensional version. The empirical Bayes posterior means based on an NPMLE have low regret, meaning they closely target the oracle posterior means one would compute with the true prior in hand. We prove an oracle inequality implying that the empirical Bayes estimator performs at nearly the optimal level (up to logarithmic factors) for denoizing without prior knowledge. We provide finite-sample bounds on the average Hellinger accuracy of an NPMLE for estimating the marginal densities of the observations. We also demonstrate the adaptive and nearly optimal properties of NPMLEs for deconvolution. We apply our method to two denoizing problems in astronomy and to two hierarchical linear modelling problems in social science and biology.

DSSGNN-PPI: A Protein–Protein Interactions prediction model based on Double Structure and Sequence graph neural networks

The process of experimentally confirming complex interaction networks among proteins is time-consuming and laborious. This study aims to address Protein–Protein Interactions (PPIs) prediction based on graph neural networks (GNN). A novel multilevel prediction model for PPIs named DSSGNN-PPI (Double Structure and Sequence GNN for PPIs) is designed. Initially, a distance graph between amino acid residues is constructed. Subsequently, the distance graph is fed into an underlying graph attention network module. This enables us to efficiently learn vector representations that encode the three-dimensional structure of proteins and simultaneously aggregate key local patterns and overall topological information to obtain graph embedding that adequately represent local and global structural features. In addition, the embedding representations that reflect sequence properties are obtained. Two features are fused to construct high-level protein complex networks, which are fed into the designed gated graph attention network to extract complex topological patterns. By combining heterogeneous multi-source information from downstream structure graph and upstream sequence models, the understanding of PPIs is comprehensively enhanced. A series of evaluation results validate the remarkable effectiveness of DSSGNN-PPI framework in enhancing the prediction of multi-type interactions among proteins. The multilevel representation learning and information fusion strategies provide a new effective solution paradigm for structural biology problems. The source code for DSSGNN-PPI has been hosted on GitHub and is available at https://github.com/cstudy1/DSSGNN-PPI.

A structured iterative division approach for non-sparse regression models and applications in biological data analysis.

In this paper, we focus on the modeling problem of estimating data with non-sparse structures, specifically focusing on biological data that exhibit a high degree of relevant features. Various fields, such as biology and finance, face the challenge of non-sparse estimation. We address the problems using the proposed method, called structured iterative division. Structured iterative division effectively divides data into non-sparse and sparse structures and eliminates numerous irrelevant variables, significantly reducing the error while maintaining computational efficiency. Numerical and theoretical results demonstrate the competitive advantage of the proposed method on a wide range of problems, and the proposed method exhibits excellent statistical performance in numerical comparisons with several existing methods. We apply the proposed algorithm to two biology problems, gene microarray datasets, and chimeric protein datasets, to the prognostic risk of distant metastasis in breast cancer and Alzheimer's disease, respectively. Structured iterative division provides insights into gene identification and selection, and we also provide meaningful results in anticipating cancer risk and identifying key factors.

WSM-MIL: a weakly supervised segmentation method with multiple instance learning for C elegans image

Recently, image analysis techniques have been introduced to automate nematode information assessment. In image analysis-based nematode information assessment, the initial step involves detecting and segmenting C. elegans from microscopic images and network-based methods have been investigated. However, training a network for C. elegans image segmentation is typically associated with the labor-intensive process of pixel-level mask labeling. To address this challenge, we introduced a weakly supervised segmentation method using multiple instance learning (WSM-MIL). The proposed multi-instance weakly supervised segmentation method comprises three key components: a backbone network, a detection branch, and a segmentation branch. In contrast to fully supervised pixel-level annotation, we opted for weakly supervised bounding box-level annotation. This approach reduces the labour cost of annotation to some extent. The approach offers several advantages, such as simplicity, an end-to-end architecture, and good scalability. We conducted experiments comparing the proposed network with benchmark methods, and the results showed that the network exhibits competitive performance in the image segmentation task of C. elegans. The results of this study provide an effective method in the field of biological image analysis, as well as new ideas for solving complex segmentation tasks. The method is not only applicable to the study of C. elegans but also has wide applicability in biological image segmentation problems in other fields.

Psychosocial factors related to dropout in substance use disorder inpatient treatment: a longitudinal study

Background: Substance use disorder (SUD) is associated with psychological, biological, and social problems. To date, existing treatments have a high dropout rate. Objective: This study aimed to investigate the psychosocial factors related to dropout from prolonged inpatient treatment due to SUD. Methods: A nine-week longitudinal follow-up study of 148 adult men inpatient for treatment for SUD. Psychosocial measures, aspects related to treatment, substance use, and sociodemographic data were collected using self-completed questionnaires. The main outcome was treatment dropout nine weeks after initial contact. Binomial logistic regression was used to evaluate the association of variables with dropout. Results: Prolonged inpatient treatment dropout was related to anxiety, depression, stress, self-judgment, expression of anger, positive and negative affect, negative urgency, lack of perseverance, and impulsivity, but not to sociodemographic characteristics or characteristics related to the substances consumed and the history of use. Conclusion: Psychological factors, such as negative emotional states and impulsivity, were the main predictors of dropout. These results highlight the relevance of emotional management skills to a male population in preventing the treatment dropout associated with SUD.

ADAPTATION MECHANISMS ARE USED BY THE BODY OF STUDENTS FOR RECOVERY AFTER PHYSICAL EXERTION

The study of the morphofunctional features of the body's adaptation to constantly changing living conditions is considered one of the most important problems of biology and medicine today. The effectiveness of adaptive processes in the body is the primary basis of good health and high productivity. Therefore, the resolution of the unexplained issues of this problem is important for the prevention and treatment of diseases, the search for effective rehabilitation measures. Knowledge of the laws of adaptation of the human body (athlete) to physical exertion is an objective prerequisite for the effective use of physical exercises in the rationalization of physical training aimed at preserving and strengthening people's health, increasing their work capacity, and implementing a genetically programmed longevity program. The defining function of muscle activity is the function of active adaptation of the body to constantly changing environmental conditions. The ultimate goal of this active adjustment is to maintain internal constancy environment, expanding the homeostatic limits of individual physiological constants, ensuring highly productive activity. The most effective means of adaptive change of a person's own nature is systematic physical training. High perfection of mechanisms of neurohumoral regulation functions; optimization of inter-system and intra-system connections; high development of self-regulation in the activity of functional systems. There is a direct relationship between the power of work and the perfection of mechanisms of regulation of functions: the more intensively the body functions under conditions of physical exertion, the greater the tension of regulatory systems; the more perfectly function regulation systems function, the body will be able to develop greater work capacity [1].

Probing the energy barriers and stages of membrane protein unfolding using solid-state NMR spectroscopy.

Understanding how the amino acid sequence dictates protein structure and defines its stability is a fundamental problem in molecular biology. It is especially challenging for membrane proteins that reside in the complex environment of a lipid bilayer. Here, we obtain an atomic-level picture of the thermally induced unfolding of a membrane-embedded α-helical protein, human aquaporin 1, using solid-state nuclear magnetic resonance spectroscopy. Our data reveal the hierarchical two-step pathway that begins with unfolding of a structured extracellular loop and proceeds to an intermediate state with a native-like helical packing. In the second step, the transmembrane domain unravels as a single unit, resulting in a heterogeneous misfolded state with high helical content but with nonnative helical packing. Our results show the importance of loops for the kinetic stabilization of the whole membrane protein structure and support the three-stage membrane protein folding model.

Large Flux Supply of NAD(H) under Aerobic Conditions by Candida glycerinogenes.

NAD is a redox coenzyme and is the center of energy metabolism. In metabolic engineering modifications, an insufficient NAD(H) supply often limits the accumulation of target products. In this study, Candida glycerinogenes was found to be able to supply NAD(H) in large fluxes, up to 7.6 times more than Saccharomyces cerevisiae in aerobic fermentation. Aerobic fermentation in a medium without amino nitrogen sources demonstrated that C. glycerinogenes NAD synthesis was not dependent on NAD precursors in the medium. Inhibition by antisense RNA and the detection of transcript levels indicated that the main NAD supply pathway is the de novo biosynthesis pathway. It was further demonstrated that NAD(H) supply was unaffected by changes in metabolic flow through C. glycerinogenes ΔGPD aerobic fermentation (80 g/L ethanol). In conclusion, the ability of C. glycerinogenes to supply NAD(H) in large fluxes provides a new approach to solving the NAD(H) supply problem in synthetic biology.

The Way to Construct Innovative Methods for Solving Initial-Value Problem of the Volterra Integro-Differential Equation

Mathematical models for many problems in the natural sciences are often simplified to solving initial-value problems (IVPs) for the Volterra integro-differential equations (VIDE). Numerical methods of a multistep type are typically used to solve these problems. It is known that in some cases, the multi-step method (MSM) is applied to solving the IVPs of both ordinary differential equations (ODEs) and VIDE encountered in solving some problems in mathematical biology. Here, to solve such problems by combining different methods, some modifications of established methods were developed, and it was demonstrated that these methods outperform the existing ones. As is known, one of the main issues in solving the aforementioned problems is determining the reliability of calculating values using the known mathematicalstatistical models (MSMs). In this regard, some experts utilize the predictor-corrector method. Having highlighted the disadvantages of this method, the proposal is to develop an innovative approach and assess the errors that may arise when applying this method to solve various problems. Here, the IVPs for the VIDE of the first order are primarily investigated. To illustrate the benefits of the innovative methods proposed here, we discuss the use of simple numerical methods to solve some common examples.

The role of neutral and adaptive evolutionary processes on patterns of genetic diversity across small cave-dwelling populations of Icelandic Arctic charr (Salvelinus alpinus).

Understanding the adaptability of small populations in the face of environmental change is a central problem in evolutionary biology. Solving this problem is challenging because neutral evolutionary processes that operate on historical and contemporary timescales can override the effects of selection in small populations. We assessed the effects of isolation by colonization (IBC), isolation by dispersal limitation (IBDL) as reflected by a pattern of isolation by distance (IBD), and isolation by adaptation (IBA) and the roles of genetic drift and gene flow on patterns of genetic differentiation among 19 cave-dwelling populations of Icelandic Arctic charr (Salvelinus alpinus). We detected evidence of IBC based on the genetic affinity of nearby cave populations and the genetic relationships between the cave populations and the presumed ancestral population in the lake. A pattern of IBD was evident regardless of whether high-level genetic structuring (IBC) was taken into account. Genetic signatures of bottlenecks and lower genetic diversity in smaller populations indicate the effect of drift. Estimates of gene flow and fish movement suggest that gene flow is limited to nearby populations. In contrast, we found little evidence of IBA as patterns of local ecological and phenotypic variation showed little association with genetic differentiation among populations. Thus, patterns of genetic variation in these small populations likely reflect localized gene flow and genetic drift superimposed onto a larger-scale structure that is largely a result of colonization history. Our simultaneous assessment of the effects of neutral and adaptive processes in a tractable and replicated system has yielded novel insights into the evolution of small populations on both historical and contemporary timescales and over a smaller spatial scale than is typically studied.

More Is Faster: Why Population Size Matters in Biological Search.

Many biological scenarios have multiple cooperating searchers, and the timing of the initial first contact between any one of those searchers and its target is critically important. However, we are unaware of biological models that predict how long it takes for the first of many searchers to discover a target. We present a novel mathematical model that predicts initial first contact times between searchers and targets distributed at random in a volume. We compare this model with the extreme first passage time approach in physics that assumes an infinite number of searchers all initially positioned at the same location. We explore how the number of searchers, the distribution of searchers and targets, and the initial distances between searchers and targets affect initial first contact times. Given a constant density of uniformly distributed searchers and targets, the initial first contact time decreases linearly with both search volume and the number of searchers. However, given only a single target and searchers placed at the same starting location, the relationship between the initial first contact time and the number of searchers shifts from a linear decrease to a logarithmic decrease as the number of searchers grows very large. More generally, we show that initial first contact times can be dramatically faster than the average first contact times and that the initial first contact times decrease with the number of searchers, while the average search times are independent of the number of searchers. We suggest that this is an underappreciated phenomenon in biology and other collective search problems.

Physiology Core Concepts Guide Successful Mechanistic Reasoning

Mechanistic reasoning is a powerful strategy used to organize one’s thinking by leveraging the properties and behaviors of underlying entities to explain how and why phenomena occur. Modell identified seven “general models” (i.e., principles) in physiology that could serve as a cognitive framework for students to use to reason mechanistically about a variety of seemingly-distinct processes. Many of these principles are also Physiological Core Concepts. Though there are frameworks to analyze students’ use of mechanistic reasoning, there is currently no framework to analyze how physiology students use principles or Core Concepts to support their use of mechanistic reasoning to solve complex problems. In this study we ask two research questions about students use of the principle of Flux (i.e., the Core Concept of Flow Down Gradients) 1) How do students use the Flux Relationship to guide their mechanistic reasoning? 2) Are students who use the Flux Relationship to guide their mechanistic reasoning more successful when solving complex organismal biological problems? We recorded and transcribed in-class discussions of 14 introductory biology students in 4 small groups as they worked across a term to solve complex problems related to the principle of Flux (rate of movement ∝ gradient/resistance). To address RQ1, we combined emergent codes with established frameworks for mechanistic reasoning and Flux reasoning. We iteratively applied our codes to a subset of transcripts, compared interpretations and coding, revised the framework and re-coded. In this way we developed a framework to characterize how students use the Flux Relationship to guide their mechanistic reasoning. To address RQ2, we scored each discussion for accuracy and whether or not the students used the Flux relationship to guide their mechanistic reasoning. We then used a chi-square test to determine if more students who used Flux to guide their reasoning were accurate. RQ1 results: Our principle-based mechanistic reasoning framework for Flux includes two established mechanistic reasoning categories: identification (introduction of components of a system) and chaining (using the relationships between components to reason mechanistically). The framework then identifies how students use flux, or don’t, while discussing a complex physiological problem. For example, students may 1) identify or chain without using Flux, 2) identify an aspect of the Flux Relationship without using it to chain, 3) use the Flux relationship to chain. RQ2 results: We found that students who used the Flux Relationship to chain were significantly more likely to be accurate in their discussion. Our study adds to the current literature by providing researchers and instructors with a more meaningful analysis of how students use physiological principles/Core Concepts when mechanistically reasoning to solve complex problems. Additionally, our results support the claim that supporting students' use of principles/Core concepts can lead to higher accuracy in their reasoning. Finally, our framework can be used to measure the impact of instructional interventions designed to increase the use of students' principle-based mechanistic reasoning. Work funded by NSF. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Au-Based Nanoparticles Enhance Low Temperature Tolerance in Wheat by Regulating Some Physiological Parameters and Gene Expression.

One of the key problems of biology is how plants adapt to unfavorable conditions, such as low temperatures. A special focus is placed on finding ways to increase tolerance in important agricultural crops like wheat. Au-based nanoparticles (Au-NPs) have been employed extensively in this area in recent years. Au-NPs can be produced fast and easily using low-cost chemical reagents. When employed in microdoses, Au-NPs are often non-toxic to plants, animals, and people. In addition, Au-NPs mainly have favorable impacts on plants. In this study, we investigated the effect of Au-NP seed nanopriming (diameter 15.3 nm, Au concentration 5-50 µg mL-1) on cold tolerance, as well as some physiological, biochemical and molecular parameters, of cold-sustainable wheat (Triticum aestivum L.) genotype Zlata. The treatment with Au-NPs improved tolerance to low temperatures in control conditions and after cold hardening. Au-NPs treatment boosted the intensity of growth processes, the quantity of photosynthetic pigments, sucrose in leaves, and the expressions of encoded RuBisCo and Wcor15 genes. The potential mechanisms of Au-NPs' influence on the cold tolerance of wheat varieties were considered.

How a Plant-Based Diet (PBD) Reduces In-Stent Restenosis (ISR) and Stent Thrombosis (ST)

Despite the advances in stent design, coating drugs, polymers, the use of intravascular imaging, special devices (such as intravascular lithotripsy, ultra-high-pressure non-compliant balloons, and drug-coated balloons), and improved operator skills and techniques, the problem of in-stent restenosis (ISR) and stent thrombosis (ST) remains a challenge for interventional cardiologists. Atherosclerotic lesions, ISR, and ST, are systemic metabolic chronic inflammatory illnesses with a local manifestation in the coronary vessels. Therefore, we propose managing these systemic illnesses systemically through plant-based diet (PBD) intervention. We explain how PBD intervention can help mitigate atherosclerosis, ISR, and ST. Thus, PBD intervention can overcome biological and epigenetic problems in managing ISR and ST similarly to reversing atherosclerosis. Our team is pioneering the implementation of PBD for cardiology patients who have undergone coronary intervention. So far, the results have been very promising. We hope that in the future, many research studies will be performed to investigate further the role of PBD in decreasing the incidence of ISR and ST

Phosphate and Nitrate Electrochemical Sensor Based on a Bifunctional Boron-Doped Diamond Electrode.

Phosphorus and nitrogen are important elements in both environmental cycles and biological growth, and their imbalance can lead to serious environmental and biological problems. It is important to be able to monitor the concentration of nitrate and phosphate in the water online. In this paper, a bifunctional boron-doped diamond (BDD) electrode with repeatable electrochemical renewal and modification ability has been developed and used as a shared working electrode for the determination of nitrate and phosphate. First, phosphate can be detected directly with a bare BDD electrode. After a thin copper (Cu) layer was electrodeposited on the BDD electrode, nitrate could be determined. The copper layer is then removed under a positive voltage, and the BDD electrode is renewed and can be used again for phosphate detection. This method enables the detection of both phosphate and nitrate while also improving the stability and repeatability through the renewal of the electrode surface. The segmented linear ranges for phosphate were 0.02-0.4 and 0.4-3 mg/L with a detection limit of 0.004 mg/L. The sensor detected nitrate in a wide concentration range, with segmented linear relationships in the ranges of 0.07-3 and 3-100 mg/L, with a detection limit of 0.065 mg/L. The electrochemical sensor based on the BDD electrode has a good reproducibility for phosphate and nitrate detection. The relative standard deviation (RSD) values of the current responses were 2.98, 2.79, 1.66, 1.81, and 1.23%, respectively, for 35 consecutive tests in 0.05, 0.2, 0.5, 1, and 1.5 mg/L phosphate solution. The RSD values of the current responses were 2.00, 0.97, and 1.03%, respectively, for 25 consecutive tests in 5, 7, and 10 mg/L nitrate solution.