Essential Biological Information Research Articles

A Current Development of Energy Harvesting Systems for Energy-Independent Bioimplantable Biosensors.

Biosensors have emerged as vital tools for the detection and monitoring of essential biological information. However, their efficiency is often constrained by limitations in the power supply. To address this challenge, energy harvesting systems have gained prominence. These off-grid, independent systems harness energy from the surrounding environment, providing a sustainable solution for powering biosensors autonomously. This continuous power source overcomes critical constraints, ensuring uninterrupted operation and seamless data collection. In this article, a comprehensive review of recent literature on energy harvesting-based biosensors is presented. Various techniques and technologies are critically examined, including optical, mechanical, thermal, and wireless power transfer, focusing on their applications and optimization. Furthermore, the immense potential of these energy harvesting-driven biosensors is highlighted across diverse fields, such as medicine, environmental surveillance, and biosignal analysis. By exploring the integration of energy harvesting systems, this review underscores their pivotal role in advancing biosensor technology. These innovations promise improved efficiency, reduced environmental impact, and broader applicability, marking significant progress in the field of biosensors.

Protein sequence analysis in the context of drug repurposing

MotivationDrug repurposing speeds up the development of new treatments, being less costly, risky, and time consuming than de novo drug discovery. There are numerous biological elements that contribute to the development of diseases and, as a result, to the repurposing of drugs.MethodsIn this article, we analysed the potential role of protein sequences in drug repurposing scenarios. For this purpose, we embedded the protein sequences by performing four state of the art methods and validated their capacity to encapsulate essential biological information through visualization. Then, we compared the differences in sequence distance between protein-drug target pairs of drug repurposing and non - drug repurposing data. Thus, we were able to uncover patterns that define protein sequences in repurposing cases.ResultsWe found statistically significant sequence distance differences between protein pairs in the repurposing data and the rest of protein pairs in non-repurposing data. In this manner, we verified the potential of using numerical representations of sequences to generate repurposing hypotheses in the future.

Screening plasma metabolites as potential biomarkers for type B aortic dissection.

Aortic dissection (AD) is a serious aortic disease. Although current imaging methods can provide accurate diagnosis for AD, they do not include essential biological information. The aim of this study is to identify plasma metabolites for the risk and severity of type B AD (TBAD). In this cross-sectional study, we enrolled 16 hypertensive patients with TBAD and 7 hypertensive patients without TBAD in Jieyang People's Hospital between December 2021 and April 2022. After plasma metabolomics analysis, a metabolites risk score (MRS) model was conducted through logistic regression and least absolute shrinkage and selection operator (LASSO) regression to predict the risk of TBAD. Subsequently, TBAD group was divided into uncomplicated and complicated TBAD subgroups for further screening for metabolites related to the severity of TBAD. Three metabolites, including 1,5-anhydro-D-glucitol, D-(+)-sucrose and PC(O-16:0/0:0) were related to the risk of TBAD. Compared to hypertensive patients without TBAD, the abundance of 1,5-anhydro-D-glucitol and D-(+)-sucrose were significantly increased while PC(O-16:0/0:0) was significantly reduced in hypertensive patients with TBAD (P<0.001). We subsequently built an MRS model based on these three metabolites. Furthermore, we found that hydrocinnamic acid (r=0.741, P<0.001) was independently correlated with the TBAD severity, while glycine deoxycholic acid (r=-0.538, P=0.008) and glycochenodeoxycholic acid (r=-0.538, P=0.008) were inversely correlated with the TBAD severity independently. The present study screened out three plasma metabolites associated with the risk of TBAD, constructed an MRS model, and identified three metabolites that were independently associated with the severity of TBAD. These findings may serve to identify more TBAD-related biomarkers and shed light on exploring potential mechanisms of TBAD.

Initial refinement of data from video-based single-cell tracking.

Video recording of cells offers a straightforward way to gain valuable information from their response to treatments. An indispensable step in obtaining such information involves tracking individual cells from the recorded data. A subsequent step is reducing such data to represent essential biological information. This can help to compare various single-cell tracking data yielding a novel source of information. The vast array of potential data sources highlights the significance of methodologies prioritizing simplicity, robustness, transparency, affordability, sensor independence, and freedom from reliance on specific software or online services. The provided data presents single-cell tracking of clonal (A549) cells as they grow in two-dimensional (2D) monolayers over 94 hours, spanning several cell cycles. The cells are exposed to three different concentrations of yessotoxin (YTX). The data treatments showcase the parametrization of population growth curves, as well as other statistical descriptions. These include the temporal development of cell speed in family trees with and without cell death, correlations between sister cells, single-cell average displacements, and the study of clustering tendencies. Various statistics obtained from single-cell tracking reveal patterns suitable for data compression and parametrization. These statistics encompass essential aspects such as cell division, movements, and mutual information between sister cells. This work presents practical examples that highlight the abundant potential information within large sets of single-cell tracking data. Data reduction is crucial in the process of acquiring such information which can be relevant for phenotypic drug discovery and therapeutics, extending beyond standardized procedures. Conducting meaningful big data analysis typically necessitates a substantial amount of data, which can stem from standalone case studies as an initial foundation.

All-in-One Nanowire Assay System for Capture and Analysis of Extracellular Vesicles from an ex Vivo Brain Tumor Model.

Extracellular vesicles (EVs) have promising potential as biomarkers for early cancer diagnosis. The EVs have been widely studied as biological cargo containing essential biological information not only from inside vesicles such as nucleic acids and proteins but also from outside vesicles such as membrane proteins and glycolipids. Although various methods have been developed to isolate EVs with high yields such as captures based on density, size, and immunoaffinity, different measurement systems are needed to analyze EVs after isolation, and a platform that enables all-in-one analysis of EVs from capture to detection in multiple samples is desired. Since a nanowire-based approach has shown an effective capability for capturing EVs via surface charge interaction compared to other conventional methods, here, we upgraded the conventional well plate assay to an all-in-one nanowire-integrated well plate assay system (i.e., a nanowire assay system) that enables charge-based EV capture and EV analysis of membrane proteins. We applied the nanowire assay system to analyze EVs from brain tumor organoids in which tumor environments, including vascular formations, were reconstructed, and we found that the membrane protein expression ratio of CD31/CD63 was 1.42-fold higher in the tumor organoid-derived EVs with a p-value less than 0.05. Furthermore, this ratio for urine samples from glioblastoma patients was 2.25-fold higher than that from noncancer subjects with a p-value less than 0.05 as well. Our results demonstrated that the conventional well plate method integrated with the nanowire-based EV capture approach allows users not only to capture EVs effectively but also to analyze them in one assay system. We anticipate that the all-in-one nanowire assay system will be a powerful tool for elucidating EV-mediated tumor-microenvironment crosstalk.

Novel lineages of single-stranded DNA phages that coevolved with the symbiotic bacteria Rhizobium.

This study describes novel single-stranded DNA phages isolated from common bean agriculture soils by infection of the nitrogen-fixing symbiotic bacteria Rhizobium etli and R. phaseoli. A total of 29 phages analyzed have 4.3–6 kb genomes in size and GC 59–60%. They belong to different clades unrelated to other Microviridae subfamilies. Three-dimensional models of the major capsid protein (MCP) showed a conserved β-barrel structural “jelly-roll” fold. A variable-length loop in the MCPs distinguished three Rhizobium microvirus groups. Microviridae subfamilies were consistent with viral clusters determined by the protein-sharing network. All viral clusters, except for Bullavirinae, included mostly microviruses identified in metagenomes from distinct ecosystems. Two Rhizobium microvirus clusters, chaparroviruses, and chicoviruses, were included within large viral unknown clusters with microvirus genomes identified in diverse metagenomes. A third Rhizobium microvirus cluster belonged to the subfamily Amoyvirinae. Phylogenetic analysis of the MCP confirms the divergence of the Rhizobium microviruses into separate clades. The phylogeny of the bacterial hosts matches the microvirus MCP phylogeny, suggesting a coevolutionary history between the phages and their bacterial host. This study provided essential biological information on cultivated microvirus for understanding the evolution and ecological diversification of the Microviridae family in diverse microbial ecosystems.

Random Forest Modelling of High-Dimensional Mixed-Type Data for Breast Cancer Classification.

Simple SummaryBreast cancer is a complex disease, and the identification of its underlying molecular mechanisms is critical for the development of treatment strategies. The purpose of this study was to implement a computational framework that is capable of combining many types of data into a meaningful classification. While our approach can be used on many types of data and in many diseases, we applied this framework to breast cancer data and identified six triple-negative breast cancer subtypes with distinct underlying molecular mechanisms. The relevance of our approach is highlighted by the clinical outcome analysis in which a group of patients responding poorly to standard-of-care adjuvant chemotherapy was identified. This study serves as a starting point for our computational framework, which can be extended to different types of data from different diseases.Advances in high-throughput technologies encourage the generation of large amounts of multiomics data to investigate complex diseases, including breast cancer. Given that the aetiologies of such diseases extend beyond a single biological entity, and that essential biological information can be carried by all data regardless of data type, integrative analyses are needed to identify clinically relevant patterns. To facilitate such analyses, we present a permutation-based framework for random forest methods which simultaneously allows the unbiased integration of mixed-type data and assessment of relative feature importance. Through simulation studies and machine learning datasets, the performance of the approach was evaluated. The results showed minimal multicollinearity and limited overfitting. To further assess the performance, the permutation-based framework was applied to high-dimensional mixed-type data from two independent breast cancer cohorts. Reproducibility and robustness of our approach was demonstrated by the concordance in relative feature importance between the cohorts, along with consistencies in clustering profiles. One of the identified clusters was shown to be prognostic for clinical outcome after standard-of-care adjuvant chemotherapy and outperformed current intrinsic molecular breast cancer classifications.

Demographic and harvest analysis for blue shark (Prionace glauca) in the Indian Ocean

An estimate of the population growth rate (γ) is an essential biological information that can be input into stock assessment models to estimate management quantities for commercial fisheries. Demographic analysis conducted using the Leslie matrix method was used to estimate γ for Indian Ocean blue shark (Prionace glauca), with Monte Carlo simulation used to quantify uncertainty. A harvest analysis was conducted with various selectivity patterns. The results indicated that the productivity of the blue shark was high, with γ=0.26–0.32 y−1 under the assumption of a one-year reproductive cycle. The steepness of the Beverton–Holt stock-recruitment model was estimated to be 0.72 (0.24–0.87) when the Castro and Mejuto fecundity formular was assumed, and 0.80 (0.65–0.88) when the Fujinami et al. fecundity formular was assumed. Harvest analysis with tuna longline selectivity led to lower sustainable harvest rates, but a higher proportion of biomass removed compared to the selectivity patterns for other fisheries. This study provides crucial prior information on the population growth rate and steepness parameters, which can be incorporated into stock assessments for blue shark. Besides, harvest analysis could be a supplement for data-poor stock assessment and risk analysis, to evaluate a tradeoff among different fisheries when considering a management strategy.

CODA-ML: context-specific biological knowledge representation for systemic physiology analysis

BackgroundComputational analysis of complex diseases involving multiple organs requires the integration of multiple different models into a unified model. Different models are often constructed in heterogeneous formats. Thus, the integration of the models requires a standard language format that can effectively represent essential biological information. However, the previously introduced formats have limitations that prevent from adequately representing essential biological information, particularly specifications of bio-molecules and biological contexts.ResultsWe defined an XML-based markup language called context-oriented directed association markup language (CODA-ML), which better represents essential biological information. The CODA-ML has two major strengths in designating molecular specifications and biological contexts. It can cover heterogeneous entity types involved in biological events (e.g. gene/protein, compound, cellular function, disease). Molecular types of entities can have molecular specifications which include detailed information of a molecule from isoforms to modifications, enabling high-resolution representation of molecules. In addition, it can distinguish biological events that vary depending on different biological contexts such as cell types or disease conditions. Especially representation of inter-cellular events as well as intra-cellular events is available. These two major strengths can resolve contradictory associations when different models are integrated into one unified model, which improves the accuracy of the model.ConclusionsWith the CODA-ML, diverse models such as signaling pathways, metabolic pathways, and gene regulatory pathways can be represented in a unified language format. Heterogeneous entity types can be covered by the CODA-ML, thus it enables detailed description for the mechanisms of diseases or drugs from multiple perspectives (e.g., molecule, function or disease). The CODA-ML is expected to help integrate different models into one systemic model in an efficient and effective. The unified model can be used to perform computational analysis not only for cancer but also for other complex diseases involving multiple organs beyond a single cell.

Structural Bioinformatics: Computational Software and Databases for the Evaluation of Protein Structure

Databases are the computerized platform where information is stored and can be retrieved easily by public users. Biological databases are the repositories of biological data. These biological data libraries contain facts and figures related to various disciplines of research including genomics, proteomics, microarray technology, metabolomics and phylogenetics. By using biological databases, a broad collection of essential biological information can be exploited ranging from function, structure and localization of gene, clinical consequences of mutation to similarity index among biological sequences and structures. Nowadays, different kinds of biological databases are available on the web. The present write up focuses on biological databases and bioinformatics tools for protein structure analysis. This review also aims to elaborate the searching schemes, available in different structural databases. The wide variety of different levels and types of information content related to 3D protein structures are available on web-based databases. Regarding the biological functions and 3D structures of various proteins, these databases provide a huge range of useful links, schematic diagrams as well as strategies for detailed analysis of proteins and other macromolecules structures. 3D structural illustration of proteins stored in structural databases is determined and visualized by X-ray crystallography, electron microscopy and NMR spectroscopy. On regular basis, a large number of protein structures are submitted by structural biologists, updated and curated by subject experts. Most familiar biological databases that store 3D protein and other macromolecules structures include, PDB, 3D Genomics, CATH, &amp; SCOP. These databases contain valuable information of overall protein structures, domains and motif structures, protein-protein complex systems and complex of protein with other biomolecules.

Paradigm shifts in community ecology: Open versus closed units, challenges and limits of connectivity studies

AbstractDue to the presence of the complex life cycles involving a benthic adult and a pelagic larval phase, the study of benthic community dynamics cannot ignore investigations of the processes occurring in the water column. Current investigations focus mainly on larval dispersal from an evolutionary and a biogeographic perspective, taking into account also population connectivity, conservation planning and coastal management. In the present paper we underline the need to improve knowledge of the main traits of marine invertebrate life cycles, highlighting the limits and challenges of current approaches. Firstly, we summarized the changing approaches within community studies, following the paradigm shifts found in recent marine ecological research, from supply‐side ecology to connectivity, and involving the concepts of open and closed populations. Secondly, we analysed the main larval traits influencing dispersal, paying particular attention to pelagic larval duration in light of the few available data for connectivity studies. The difficulty in estimating many of the main traits of larval ecology make numerical simulation fundamental for a better understanding of the relationship between propagule dispersal and seawater dynamics, both being highly variable. We conclude that some essential biological information is still lacking for the proper integration of the modeling approaches. Thus it is necessary to further investigate the life‐cycle traits and physiological and ecological characteristics of each species, an approach known as autecology or natural history. All too frequently modern ecologists ignore such reductionist approaches, although they are essential for a full understanding of processes, such as connectivity and metapopulation dynamics.

Analysis and prediction of protein folding energy changes upon mutation by element specific persistent homology.

Site directed mutagenesis is widely used to understand the structure and function of biomolecules. Computational prediction of mutation impacts on protein stability offers a fast, economical and potentially accurate alternative to laboratory mutagenesis. Most existing methods rely on geometric descriptions, this work introduces a topology based approach to provide an entirely new representation of mutation induced protein stability changes that could not be obtained from conventional techniques. Topology based mutation predictor (T-MP) is introduced to dramatically reduce the geometric complexity and number of degrees of freedom of proteins, while element specific persistent homology is proposed to retain essential biological information. The present approach is found to outperform other existing methods in the predictions of globular protein stability changes upon mutation. A Pearson correlation coefficient of 0.82 with an RMSE of 0.92 kcal/mol is obtained on a test set of 350 mutation samples. For the prediction of membrane protein stability changes upon mutation, the proposed topological approach has a 84% higher Pearson correlation coefficient than the current state-of-the-art empirical methods, achieving a Pearson correlation of 0.57 and an RMSE of 1.09 kcal/mol in a 5-fold cross validation on a set of 223 membrane protein mutation samples. http://weilab.math.msu.edu/TML/TML-MP/. wei@math.msu.edu. Supplementary data are available at Bioinformatics online.

Improving the forecast for biodiversity under climate change.

New biological models are incorporating the realistic processes underlying biological responses to climate change and other human-caused disturbances. However, these more realistic models require detailed information, which is lacking for most species on Earth. Current monitoring efforts mainly document changes in biodiversity, rather than collecting the mechanistic data needed to predict future changes. We describe and prioritize the biological information needed to inform more realistic projections of species' responses to climate change. We also highlight how trait-based approaches and adaptive modeling can leverage sparse data to make broader predictions. We outline a global effort to collect the data necessary to better understand, anticipate, and reduce the damaging effects of climate change on biodiversity.

IncaRNAfbinv: a web server for the fragment-based design of RNA sequences.

In recent years, new methods for computational RNA design have been developed and applied to various problems in synthetic biology and nanotechnology. Lately, there is considerable interest in incorporating essential biological information when solving the inverse RNA folding problem. Correspondingly, RNAfbinv aims at including biologically meaningful constraints and is the only program to-date that performs a fragment-based design of RNA sequences. In doing so it allows the design of sequences that do not necessarily exactly fold into the target, as long as the overall coarse-grained tree graph shape is preserved. Augmented by the weighted sampling algorithm of incaRNAtion, our web server called incaRNAfbinv implements the method devised in RNAfbinv and offers an interactive environment for the inverse folding of RNA using a fragment-based design approach. It takes as input: a target RNA secondary structure; optional sequence and motif constraints; optional target minimum free energy, neutrality and GC content. In addition to the design of synthetic regulatory sequences, it can be used as a pre-processing step for the detection of novel natural occurring RNAs. The two complementary methodologies RNAfbinv and incaRNAtion are merged together and fully implemented in our web server incaRNAfbinv, available at http://www.cs.bgu.ac.il/incaRNAfbinv.

멸종위기식물의 IUCN 적색목록 보전지위 평가 -금강초롱꽃에 대하여-

ABSTRACT : The conservation status of an endemic perennial herb, Hanabusaya asiatica (Nakai) Nakai (Cam-panulaceae) was determined by applying the IUCN risk assessment criteria from our field study and availablespecimen data. Also, the GIS technology was used to develop a species distribution map to calculate the extentof occurrence (EOO) and area of occupancy (AOO) for the taxon. After two years of continuous field studies,269 mature individuals were found in four localities in 2011, while 216 mature individuals were confirmed inthree localities in 2012. Based on the following data, such as EOO (2,742 km 2 ), AOO (76 km 2 ) and estimatedpopulation size of mature individuals, the taxon, which is known as 20 localities in Korean peninsula, is eval-uated as the category of Endangered (EN). A major difficulty in application of IUCN criteria to Korean rareplants were the lack of essential biological information and understanding the correct knowledge of the IUCNcriteria in previous Korean studies. Sound conclusions regarding the conservation status of individual speciesrequire more intensive population studies, observations, and applying IUCN assessment procedures correctly.

Sexual body size and body shape dimorphism of Testudo hermanni in central and eastern Serbia

AbstractHermann's tortoise (Testudo hermanni) is widely distributed in western and southern Europe. Most populations in the western part of the distribution range (e.g. Spain, France, Italy) are severely reduced, and relatively well studied, whilst the species is still abundant in eastern areas (i.e. the Balkans). However, essential biological information (e.g. main morphological, ecological, and behavioural characteristics) for the Balkans are still extremely limited. As reptiles exhibit strong geographic variation in most morphological, as well as life history traits, gathering data from distant areas is important. We present data from two populations of T. hermanni in Serbia, focusing on sexual dimorphism in body size and body shape. We found that almost all of the 43 morphological traits analysed were significantly different between sexes and that sexual size and sexual shape dimorphisms were not expressed in similar ways. Notably, sexual size dimorphism (SSD) was more pronounced than sexual shape dimorphism (SShD). Our analyses suggested that SShD is more stable than SSD, and that the scale of the focus (i.e. whole body proportions versus morphological details) is a key factor to test this notion. When general measurements were considered, the expected consistency of SShD between populations was verified; nevertheless, when more specific morphological attributes were considered, substantial variations were observed. These results provide a baseline for comparisons between populations to further examine geographic variation of sexual dimorphism.

Conserved patterns of protein interaction in multiple species

To elucidate cellular machinery on a global scale, we performed a multiple comparison of the recently available protein-protein interaction networks of Caenorhabditis elegans, Drosophila melanogaster, and Saccharomyces cerevisiae. This comparison integrated protein interaction and sequence information to reveal 71 network regions that were conserved across all three species and many exclusive to the metazoans. We used this conservation, and found statistically significant support for 4,645 previously undescribed protein functions and 2,609 previously undescribed protein interactions. We tested 60 interaction predictions for yeast by two-hybrid analysis, confirming approximately half of these. Significantly, many of the predicted functions and interactions would not have been identified from sequence similarity alone, demonstrating that network comparisons provide essential biological information beyond what is gleaned from the genome.

Development of a Phenology Model for Predicting Western Cherry Fruit Fly, Rhagoletis indifferens Curran (Diptera: Tephritidae), Emergence in the Mid Columbia Area of the Western United States

The western cherry fruit fly (CFF), Rhagoletis indifferens Curran, is a major pest of cherries. Because of a ‘zero tolerance' for damage, detecting CFF emergence is critical for successful control. To improve predictions of CFF emergence, historical observations on first emergence, rainfall, and temperature were analyzed. The amount of precipitation in March found to accelerate the first CFF emergence. In average, CFF emerges 9 days earlier in The Dalles than in Hood River, however, the heat unit (degree days, DD) were required 71DD more in The Dalles then in Hood River. A CFF phenology model was developed based on the time varying distributed delay concepts. The model can simulate the whole phenology system including post-diapause pupal development, adult emergence, egg-laying, and larval development in user friendly menu driven system. The model also has the capability to simulate the effect of control measures on the CFF population. The model predicts field population when compared to the trap catch records last few years, however, some essential biological information was needed for inclusion in this model to make it more biologically meaningful and reliable. From the simulation, a simple heat requirement values (DD) for the first emergence were estimated as 480DD in The Dalles and 550DD in Hood River for the site-specific prediction.

Application of IUCN criteria and Red List categories to species of five Anacardiaceae genera in Madagascar

The conservation status of five genera of Anacardiaceae in Madagascarwas determined by applying IUCN risk assessment criteria to recent taxonomicrevisions and available specimen data. A major problem in establishing protocolsfor efficiently protecting and conserving Madagascar's biodiversity is the lackof essential biological information. In light of this, primary occurrence dataappears to be an invaluable tool for assessing both current and historic speciesdistributions. GIS technology was used to create species distribution maps forhistoric vs. recent occurrence data to analyze the change in conservationstatus, extent of occurrence, and area of occupancy for each of the targetspecies. A GAP analysis reveals that 14 (100%) of the target species for whichdata were available are considered threatened by IUCN standards. Furthermore, 11(79%) species showed a decrease both in the number of subpopulations and theirextent of occurrence when compared to historic distributions. This studyhighlights the importance of two factors necessary to address modernconservation questions. Sound conclusions regarding the conservation status ofindividual species requires a strong taxonomic framework and good collectiondata for a species distribution. However, because specimen data are very oftenincomplete and biased both geographically and taxonomically, reaching soundconclusions requires field knowledge of individual species to compensate forthese limitations.