Field Of Biology Research Articles

Deciphering cellular heterogeneity in Spodoptera frugiperda midgut cell line through single cell RNA sequencing

Using the 10x Genomics Chromium single-cell RNA sequencing (scRNA-seq) platform, we discovered unexpected heterogeneity in an established cell line developed from the midgut of the Fall armyworm, Spodoptera frugiperda, a major global pest. We analyzed the sequences of 18,794 cells and identified ten unique cellular clusters, including stem cells, enteroblasts, enterocytes and enteroendocrine cells, characterized by the expression of specific marker genes. Additionally, these studies addressed an important knowledge gap by investigating the expression of genes coding for respiratory and midgut membrane insecticide targets classified by the Insecticide Resistance Action Committee. Dual-fluorescence tagging method, fluorescence microscopy and fluorescence-activated cell sorting confirmed the expression of midgut cell type-specific genes. Stem cells were isolated from the heterogeneous population of SfMG-0617 cells. Our results, validated by KEGG and Gene Ontology analyses and supported by Monocle 3.0, advance the fields of midgut cellular biology and establish standards for scRNA-seq studies in non-model organisms.

“Top-down” overexpression optimization of butelase-1 in Escherichia coli and its application in anti-tumor peptides

Butelase-1, the fastest known Asn/Asp-specific peptide ligase capable of catalyzing peptide ligation and cyclization, holds promising application prospects in the fields of food and biology. However, limited research exists on its recombinant expression and potential applications in peptide drugs. In this study, the activity of recombinantly-produced butelase-1 was enhanced by co-expressing it with a molecular chaperone in the SHuffle T7 strain. By introducing single or multiple synonymous rare codons at the beginning of the coding regions of beta-strand or alpha-helix, in combination with ribosomal binding site engineering, the activity of butelase-1 could be further improved. Consequently, the butelase-1 with a specific activity of 386.93 U/mg and a catalytic efficiency of 11,048 M−1 s−1 was successfully prepared in E. coli, resulting in a total activity of 8183.54 U/L and a yield of about 100 mg/L. This optimized butelase-1 was then used to efficiently cyclize the redesigned anti-cancer peptide lunasin, leading to enhanced bioavailability and anti-cancer effects. Overall, this study not only provided valuable biotechnology strategies for improving the recombinant expression of butelase-1 but also demonstrated a successful application for enhancing the biological efficacy of anti-cancer peptides.

A primer for junior trainees: Recognition of RNA modifications by RNA-binding proteins.

The complexity of RNA cannot be fully expressed with the canonical A, C, G, and U alphabet. To date, over 170 distinct chemical modifications to RNA have been discovered in living systems. RNA modifications can profoundly impact the cellular outcomes of messenger RNAs (mRNAs), transfer and ribosomal RNAs, and noncoding RNAs. Additionally, aberrant RNA modifications are associated with human disease. RNA modifications are a rising topic within the fields of biochemistry and molecular biology. The role of RNA modifications in gene regulation, disease pathogenesis, and therapeutic applications increasingly captures the attention of the scientific community. This review aims to provide undergraduates, junior trainees, and educators with an appreciation for the significance of RNA modifications in eukaryotic organisms, alongside the skills required to identify and analyze fundamental RNA-protein interactions. The pumilio RNA-binding protein and YT521-B homology (YTH) family of modified RNA-binding proteins serve as examples to highlight the fundamental biochemical interactions that underlie the specific recognition of both unmodified and modified ribonucleotides, respectively. By instilling these foundational, textbook concepts through practical examples, this review contributes an analytical toolkit that facilitates engagement with RNA modifications research at large.

Impact of contextual factors on the evaluation outcomes of national virtual simulation experimental teaching projects in biology and medicine

The number of applications for National Virtual Simulation Experimental Teaching Projects (NVSETPs) in China has seen a significant increase. Consequently, the influence of contextual factors and their potential personal connections on the evaluation results, whether for national or non-national NVSETPs, has become a prominent concern. In this study, we employed a modified back-chaining method using logistic regression to examine whether contextual factors in NVSETP applications could explain the evaluation outcomes. Our analysis was based on data available on the open platform of China's Ministry of Education (MOE). We identified several significant influencing factors, including the score on a five-point rating system, the number of clicks on the application page, school quality, school region, and the gender, title, and position of the applicants. Our results shed light on the impact of contextual factors on the evaluation results of NVSETPs in the fields of biology and medicine, using a modified back-chaining method. We conclude that enhancing the transparency of the assessment process and implementing standardized, detailed scoring guidelines for NVSETPs would mitigate the negative influence of contextual factors.

Citation network analysis of retractions in molecular biology field

Citation network analysis of retractions in molecular biology field

Simultaneous imaging and element differentiation by energy-resolved x-ray absorption ghost imaging.

Based on the x-ray absorption edges of different elements, we simultaneously image and distinguish the composition of three differently shaped components of an object by using energy-resolved x-ray absorption ghost imaging (GI). The initial x-ray beam is spatially modulated by a series of Hadamard matrix masks, and the object is composed of three pieces of Mo, Ag, and Sn foil in the shape of a triangle, square, and circle, respectively. The transmitted x-ray intensity is measured by an energy-resolved single-pixel detector with a spectral resolution better than 0.8 keV. Through correlation of the transmission spectra with the corresponding Hadamard patterns, the spectral image of the sample is reconstructed, with a spatial resolution of 108 µm. Our experiment demonstrates a practical application of spectral ghost imaging, which has important potential for the noninvasive analysis of material composition and distribution in biology, medical science, and many other fields.

Expansion-assisted selective plane illumination microscopy for nanoscale imaging of centimeter-scale tissues.

Recent advances in tissue processing, labeling, and fluorescence microscopy are providing unprecedented views of the structure of cells and tissues at sub-diffraction resolutions and near single molecule sensitivity, driving discoveries in diverse fields of biology, including neuroscience. Biological tissue is organized over scales of nanometers to centimeters. Harnessing molecular imaging across intact, three-dimensional samples on this scale requires new types of microscopes with larger fields of view and working distance, as well as higher throughput. We present a new expansion-assisted selective plane illumination microscope (ExA-SPIM) with aberration-free 1×1×3 μm optical resolution over a large field of view (10.6×8.0 mm 2 ) and working distance (35 mm) at speeds up to 946 megavoxels/sec. Combined with new tissue clearing and expansion methods, the microscope allows imaging centimeter-scale samples with 250×250×750 nm optical resolution (4× expansion), including entire mouse brains, with high contrast and without sectioning. We illustrate ExA-SPIM by reconstructing individual neurons across the mouse brain, imaging cortico-spinal neurons in the macaque motor cortex, and visualizing axons in human white matter.

Multi-condition adaptive detail characterization model of magnetorheological dampers and experimental verification

The theoretical model for predicting the damping characteristics of magnetorheological dampers (MRDs) is significant for enhancing the design efficiency of the control algorithm. However, some existing theoretical models face limitations in characterizing MRD damping characteristics simultaneously in terms of nonlinear detail characterization and adaptability to variable working conditions. Therefore, this paper proposed the Composite Double-Boltzmann (CDB) model combining the Double-Boltzmann (DB) function widely used in the field of biology and chemistry for its strong nonlinear characterization capability. Utilizing this model to fit the sinusoidal vibration testing data of the MRD prototype under variable combination working conditions, obtaining quantitative relationships between the undetermined parameters in the CDB model and the excitation current, vibration frequency, and amplitude to enable the model to address both the nonlinear details characterization of MRDs and adaptability to variable working conditions. Subsequently, the validity of the quantitative relationships were verified by comparing the calculated parameter values using the quantitative relationships with the original accurate parameter values. In order to verify the validity of the CDB model, extensive unknown working condition vibration tests were conducted on the MRD prototype under variable excitation currents, vibration frequencies, amplitudes and random excitation working conditions, employing the CDB and Tanh models to predict the damping characteristics, to compare to demonstrate the CDB model’s capability of adapting to variable working conditions while accurately characterizing the nonlinear details of MRD damping characteristics.

Understanding Species Boundaries that Arise from Complex Histories: Gene Flow Across the Speciation Continuum in the Spotted Whiptail Lizards.

Gene flow between diverging lineages challenges the resolution of species boundaries and the understanding of evolutionary history in recent radiations. Here, we integrate phylogenetic and coalescent tools to resolve reticulate patterns of diversification and use a perspective focused on evolutionary mechanisms to distinguish interspecific and intraspecific taxonomic variation. We use this approach to resolve the systematics for one of the most intensively studied but difficult to understand groups of reptiles: the spotted whiptail lizards of the genus Aspidoscelis (A. gularis complex). Whiptails contain the largest number of unisexual species known within any vertebrate group and the spotted whiptail complex has played a key role in the generation of this diversity through hybrid speciation. Understanding lineage boundaries and the evolutionary history of divergence and reticulation within this group is therefore key to understanding the generation of unisexual diversity in whiptails. Despite this importance, long-standing confusion about their systematics has impeded understanding of which gonochoristic species have contributed to the formation of unisexual lineages. Using reduced representation genomic data, we resolve patterns of divergence and gene flow within the spotted whiptails and clarify patterns of hybrid speciation. We find evidence that biogeographically structured ecological and environmental variation has been important in morphological and genetic diversification, as well as the maintenance of species boundaries in this system. Our study elucidates how gene flow among lineages and the continuous nature of speciation can bias the practice of species delimitation and lead taxonomists operating under different frameworks to different conclusions (here we propose that a two species arrangement best reflects our current understanding). In doing so, this study provides conceptual and methodological insights into approaches to resolving diversification patterns and species boundaries in rapid radiations with complex histories, as well as long-standing taxonomic challenges in the field of systematic biology.

Array tomography: trails to discovery.

Tissue slicing is at the core of many approaches to studying biological structures. Among the modern volume electron microscopy (vEM) methods, array tomography (AT) is based on serial ultramicrotomy, section collection onto solid support, imaging via light and/or scanning electron microscopy, and re-assembly of the serial images into a volume for analysis. While AT largely uses standard EM equipment, it provides several advantages, including long-term preservation of the sample and compatibility with multi-scale and multi-modal imaging. Furthermore, the collection of serial ultrathin sections improves axial resolution and provides access for molecular labeling, which is beneficial for light microscopy and immunolabeling, and facilitates correlation with EM. Despite these benefits, AT techniques are underrepresented in imaging facilities and labs, due to their perceived difficulty and lack of training opportunities. Here we point towards novel developments in serial sectioning and image analysis that facilitate the AT pipeline, and solutions to overcome constraints. Because no single vEM technique can serve all needs regarding field of view and resolution, we sketch a decision tree to aid researchers in navigating the plethora of options available. Lastly, we elaborate on the unexplored potential of AT approaches to add valuable insight in diverse biological fields.

Monoclonal antibody techniques. 50 years of development

Monoclonal antibodies are widely used in all fields of biology and medicine. The emergence and development of the technology for their production revolutionized immunology and allowed the creation of not only new diagnostic methods, but also many effective drugs. The purpose of our review is to analyze and summarize relevant data concerning the technology of obtaining monoclonal antibodies. We have analyzed information from 70 modern literary sources devoted to various methods of obtaining them. In this review, we tried to cover the entire range of methods used to obtain monoclonal antibodies today.

PeakQC: A Software Tool for Omics-Agnostic Automated Quality Control of Mass Spectrometry Data.

Mass spectrometry is broadly employed to study complex molecular mechanisms in various biological and environmental fields, enabling 'omics' research such as proteomics, metabolomics, and lipidomics. As study cohorts grow larger and more complex with dozens to hundreds of samples, the need for robust quality control (QC) measures through automated software tools becomes paramount to ensure the integrity, high quality, and validity of scientific conclusions from downstream analyses and minimize the waste of resources. Since existing QC tools are mostly dedicated to proteomics, automated solutions supporting metabolomics are needed. To address this need, we developed the software PeakQC, a tool for automated QC of MS data that is independent of omics molecular types (i.e., omics-agnostic). It allows automated extraction and inspection of peak metrics of precursor ions (e.g., errors in mass, retention time, arrival time) and supports various instrumentations and acquisition types, from infusion experiments or using liquid chromatography and/or ion mobility spectrometry front-end separations and with/without fragmentation spectra from data-dependent or independent acquisition analyses. Diagnostic plots for fragmentation spectra are also generated. Here, we describe and illustrate PeakQC's functionalities using different representative data sets, demonstrating its utility as a valuable tool for enhancing the quality and reliability of omics mass spectrometry analyses.

Physico-chemical characteristics of Ca/P ratio on the composition and structure of oxygenated apatite

Phosphocalcic apatites have osteoconductive and bioactive properties that make them suitable for bone reconstruction. But, they are inactive against pathogenic microorganisms that can infect bone tissue. To overcome this limitation, we synthesized oxygen-doped phosphocalcic apatites that can release molecular oxygen as a bioactive molecule. We investigated how the calcium-to-phosphorus ratio (Ca/P) gave impacts on the chemical and structural composition of the oxygen-doped phosphocalcic apatites. We here used the double decomposition method, which involved mixing calcium nitrate and diammonium phosphate solutions in an ammonia buffer. We then characterized the products using several analysis, including infrared absorption spectroscopy, X-ray diffraction, thermal analysis, nitrogen adsorption-desorption, and elemental chemical analysis. It was found that the oxygen-doped phosphocalcic apatites were calcium-deficient and had a well-defined crystallinity at room temperature. After calcination at 900°C, the crystallinity improved further. The thermal analysis showed two mass losses: one at 50°C due to water adsorption and another at 450°C due to CO2 release. The specific surface area was about 100 ± 2 m2/g without any change with the Ca/P ratio. The quantity of molecular oxygen increased with the Ca/P ratio and reached an optimal value of the order of 3.6 ×10-4 mol for Ca/P=1/65 with the chemical formula of Ca9.9(PO4)6(OH)1.25(O2)0.74(CO3)0.01. It is important to make further analysis to know more about the properties of oxygenated apatite, and to combine this apatite with polymers purposely to have biomedical composites. It then can be concluded that the oxygen-doped phosphocalcic apatites could be a promising biomaterial for bone infection prevention and treatment. This research highlights an oxygenation treatment of phosphocalcic apatite and brings new ideas and possibilities for future research and development to get better understanding of the behavior of these biomaterials to be more effective, especially in the biological field. As a perspective, improving the biological properties in these biomaterials needs to be further explored, including experimental parameters for the obtainment of more conclusive results.

Tumor-associated genetic amplifications impact extracellular vesicle miRNA cargo and their recruitment of nerves in head and neck cancer.

Cancer neuroscience is an emerging field of cancer biology focused on defining the interactions and relationships between the nervous system, developing malignancies, and their environments. Our previous work demonstrates that small extracellular vesicles (sEVs) released by head and neck squamous cell carcinomas (HNSCCs) recruit loco-regional nerves to the tumor. sEVs contain a diverse collection of biological cargo, including microRNAs (miRNAs). Here, we asked whether two genes commonly amplified in HNSCC, CCND1, and PIK3CA, impact the sEV miRNA cargo and, subsequently, sEV-mediated tumor innervation. To test this, we individually overexpressed these genes in a syngeneic murine HNSCC cell line, purified their sEVs, and tested their neurite outgrowth activity on dorsal root ganglia (DRG) neurons invitro. sEVs purified from Ccnd1-overexpressing cells significantly increased neurite outgrowth of DRG compared to sEVs from parental or Pik3ca over-expressing cells. When implanted into C57BL/6 mice, Ccnd1 over-expressing tumor cells promoted significantly more tumor innervation invivo. qPCR analysis of sEVs shows that increased expression of Ccnd1 altered the packaging of miRNAs (miR-15-5p, miR-17-5p, and miR-21-5p), many of which target transcripts important in regulating axonogenesis. These data indicate that genetic amplifications harbored by malignancies impose changes in sEV miRNA cargo, which can influence tumorc innervation.

Extraordinary model systems for regeneration.

Regeneration is the remarkable phenomenon through which an organism can regrow lost or damaged parts with fully functional replacements, including complex anatomical structures, such as limbs. In 2019, Development launched its 'Model systems for regeneration' collection, a series of articles introducing some of the most popular model organisms for studying regeneration in vivo. To expand this topic further, this Perspective conveys the voices of five expert biologists from the field of regenerative biology, each of whom showcases some less well-known, but equally extraordinary, species for studying regeneration.

Cancer Diagnosis by Gene-Environment Interactions via Combination of SMOTE-Tomek and Overlapped Group Screening Approaches with Application to Imbalanced TCGA Clinical and Genomic Data

The complexity of cancer development involves intricate interactions among multiple biomarkers, such as gene-environment interactions. Utilizing microarray gene expression profile data for cancer classification is anticipated to be effective, thus drawing considerable interest in the fields of bioinformatics and computational biology. Due to the characteristics of genomic data, problems of high-dimensional interactions and noise interference do exist during the analysis process. When building cancer diagnosis models, we often face the dilemma of model adaptation errors due to an imbalance of data types. To mitigate the issues, we apply the SMOTE-Tomek procedure to rectify the imbalance problem. Following this, we utilize the overlapping group screening method alongside a binary logistic regression model to integrate gene pathway information, facilitating the identification of significant biomarkers associated with clinically imbalanced cancer or normal outcomes. Simulation studies across different imbalanced rates and gene structures validate our proposed method’s effectiveness, surpassing common machine learning techniques in terms of classification prediction accuracy. We also demonstrate that prediction performance improves with SMOTE-Tomek treatment compared to no imbalance treatment and SMOTE treatment across various imbalance rates. In the real-world application, we integrate clinical and gene expression data with prior pathway information. We employ SMOTE-Tomek and our proposed methods to identify critical biomarkers and gene-environment interactions linked to the imbalanced binary outcomes (cancer or normal) in patients from the Cancer Genome Atlas datasets of lung adenocarcinoma and breast invasive carcinoma. Our proposed method consistently achieves satisfactory classification accuracy. Additionally, we have identified biomarkers indicative of gene-environment interactions relevant to cancer and have provided corresponding estimates of odds ratios. Moreover, in high-dimensional imbalanced data, for achieving good prediction results, we recommend considering the order of balancing processing and feature screening.

Dynamic Second Harmonic Imaging of Proton Translocation Through Water Needles in Lipid Membranes.

Proton translocation through lipid membranes is a fundamental process in the field of biology. Several theoretical models have been developed and presented over the years to explain the phenomenon, yet the exact mechanism is still not well understood. Here, we show that proton translocation is directly related to membrane potential fluctuations. Using high-throughput wide-field second harmonic (SH) microscopy, we report apparently universal transmembrane potential fluctuations in lipid membrane systems. Molecular simulations and free energy calculations suggest that H+ permeation proceeds predominantly across a thin, membrane-spanning water needle and that the transient transmembrane potential drives H+ ions across the water needle. This mechanism differs from the transport of other cations that require completely open pores for transport and follows naturally from the well-known Grotthuss mechanism for proton transport in bulk water. Furthermore, SH imaging and conductivity measurements reveal that the rate of proton transport depends on the structure of the hydrophobic core of bilayer membranes.

Stability analysis of Reiner–Philippoff nanofluid flow due to sinusoidal waves past a nonuniform channel with Brownian and thermophoretic diffusions

The need for understanding peristaltic flow (PF) is growing frequently because of its applicability in several scientific and technical domains, particularly in biological and medical field. The primary objective of this analysis is to examine the PF in tapering network considering Reiner–Philippoff nanofluid (RPF-NF) under the influence of pseudoplastic fluid (PF) and dilatant fluid (DF) performance. In order to examine the heat and mass transportation inquiry, Buongiorno’s nanofluid model (BNFM) is taken into consideration. The suitable alterations are taken to transform the channel from static reference to stirring frame and, then used the dimensionless variables to transform the moving frame to dimensionless form. Since the Reynolds number (RN) is small and the wavelength is long, the underlying equations are simplified. The statistical consequences are gained through bvp4c technique. The motivation of the physical features on the liquefied crescendos is particularized using graphs as well as tables. For the immovability exploration, eigenvalues are figured. The lowest positive eigenvalues suggest the reliable resolutions; however, the negative eigenvalues denote the unreliable solution. It has been shown that altering the RPF parameter causes the velocity of fluid to switch from a dilatant liquid to a Newtonian fluid and from Newtonian to Pseudoplastic. The results show that the temperature curves ascend with increasing Brownian motion and thermophoretic factors and decrease with increasing Prandtl number. Additionally, a brief mathematical and graphical investigation of the effects of each key parameter on the flow characteristics is conducted.

Are We There Yet? Assessing the Readiness of Single-Cell Proteomics to Answer Biological Hypotheses.

Single-cell analysis is an active area of research in many fields of biology. Measurements at single-cell resolution allow researchers to study diverse populations without losing biologically meaningful information to sample averages. Many technologies have been used to study single cells, including mass spectrometry-based single-cell proteomics (SCP). SCP has seen a lot of growth over the past couple of years through improvements in data acquisition and analysis, leading to greater proteomic depth. Because method development has been the main focus in SCP, biological applications have been sprinkled in only as proof-of-concept. However, SCP methods now provide significant coverage of the proteome and have been implemented in many laboratories. Thus, a primary question to address in our community is whether the current state of technology is ready for widespread adoption for biological inquiry. In this Perspective, we examine the potential for SCP in three thematic areas of biological investigation: cell annotation, developmental trajectories, and spatial mapping. We identify that the primary limitation of SCP is sample throughput. As proteome depth has been the primary target for method development to date, we advocate for a change in focus to facilitate measuring tens of thousands of single-cell proteomes to enable biological applications beyond proof-of-concept.

Directed Evolution of Acoustic Reporter Genes Using High-Throughput Acoustic Screening.

A major challenge in the fields of biological imaging and synthetic biology is noninvasively visualizing the functions of natural and engineered cells inside opaque samples such as living animals. One promising technology that addresses this limitation is ultrasound (US), with its penetration depth of several cm and spatial resolution on the order of 100 μm. Within the past decade, reporter genes for US have been introduced and engineered to link cellular functions to US signals via heterologous expression in commensal bacteria and mammalian cells. These acoustic reporter genes (ARGs) represent a novel class of genetically encoded US contrast agent, and are based on air-filled protein nanostructures called gas vesicles (GVs). Just as the discovery of fluorescent proteins was followed by the improvement and diversification of their optical properties through directed evolution, here we describe the evolution of GVs as acoustic reporters. To accomplish this task, we establish high-throughput, semiautomated acoustic screening of ARGs in bacterial cultures and use it to screen mutant libraries for variants with increased nonlinear US scattering. Starting with scanning site saturation libraries for two homologues of the primary GV structural protein, GvpA/B, two rounds of evolution resulted in GV variants with 5- and 14-fold stronger acoustic signals than the parent proteins. We anticipate that this and similar approaches will help high-throughput protein engineering play as large a role in the development of acoustic biomolecules as it has for their fluorescent counterparts.