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Lectins are proteins which can bind to the surfaces of animal and human cells. They are widespread in the plant and animal kingdom. Though many lectins have been isolated and characterized, and though some are extensively used as cell surface "tools", their biological role is only subject to hypotheses.
A systematic and bibliometric review, combined with a meta-analysis, was used to adjust an equation for estimating the physiological responses of Santa Inês sheep subjected to different thermal challenges. The systematic review compiled data on physiological responses and the thermal environment, which were then used in the meta-analysis to adjust regression models. The bibliometric analysis mapped the relationships among studies, highlighting their usefulness in interpreting research findings and biases. Addressing prior methodological critiques, the core of this study involves replacing the linear approach with a non-linear segmented regression model to accurately define the Thermal Neutral Zone (TNZ). The Segmented Regression Model was crucial, establishing the upper limit of the Thermal Neutral Zone (TNZ) at an air temperature (tair) of 34.64 °C, where trectal begins to increase abruptly. The model, while identifying a biologically significant breakpoint, exhibited a moderate Multiple R-squared of 0.3529, highlighting the high heterogeneity and methodological variability in the current Santa Inês literature. This non-linear approach offers a biologically superior tool for identifying the onset of thermal distress.
Interest in nucleic acid hybridization stems mainly from its great power as a tool in biological research. It is used in several quite distinct ways. Because of the high degree of specificity that they show, hybridization techniques can be used to measure the amount of one specific sequence within a very heterogeneous mixture of sequences. Measurements of 1/10(6)-10(7) have been recorded. In extension of this, various properties of a specific sequence can often be studied. Secondly, because the kinetics of nucleic acid hybridization are quite well understood, it can be used to characterize both a pure sequence and a very complex mixture of sequences, like the genome of a vertebrate. Thirdly, again because of its specificity, it can be used to measure homologies between different populations of nucleic acids. Lastly, in conjunction with other techniques, it can be used as a basis for the fractionation of nucleic acid populations and the purification of specific sequences. Specific examples of these applications are given, with special reference to the organization of the genome in higher eukaryotes.
Understanding how biological sequences give rise to observable traits, that is, how genotype maps to phenotype, is a central goal in biology. Yet our knowledge of genotype-phenotype maps in natural systems is limited due to the high dimensionality of sequence space and the context-dependent effects of mutations. The emergence of Multiplex assays of variant effect (MAVEs), along with large collections of natural sequences, offer new opportunities to empirically characterize these maps at an unprecedented scale. However, tools for statistical and exploratory analysis of these high-dimensional data are still needed. To address this gap, we developed gpmap-tools (https://github.com/cmarti/gpmap-tools), a python library that integrates a series of models for inference, phenotypic imputation, and error estimation from MAVE data or collections of natural sequences in the presence of genetic interactions of every possible order. gpmap-tools also provides methods for summarizing patterns of epistasis and visualization of genotype-phenotype maps containing up to millions of genotypes. To demonstrate its utility, we used gpmap-tools to infer genotype-phenotype maps containing 262,144 variants of the Shine-Dalgarno sequence from both genomic 5'UTR sequences and experimental MAVE data. Visualization of the inferred landscapes consistently revealed high-fitness ridges that link core motifs at different distances from the start codon. In summary, gpmap-tools provides a flexible, interpretable framework for studying complex genotype-phenotype maps, opening new avenues for understanding the architecture of genetic interactions and their evolutionary consequences.
Human biology and diseases are the result of constantly evolving processes within an intricately complex molecular network of interactions, such as epigenetic regulation. Epigenetics refers to heritable changes in gene expression that occur without alterations to the underlying DNA sequence. These changes, driven by mechanisms such as DNA methylation, histone modifications, and noncoding RNAs, play critical roles in regulating chromatin structure and gene activity. Epigenetic regulation offers valuable insights into biological systems, and when integrated with sophisticated analyses, it enables us to gain insights into gene regulation and cellular behavior. Here, we describe an artificial intelligence (AI)-based model that is capable of generating 3-dimensional (3D) heterogeneous network by integrating multimodal data for the functional prediction of epigenetic mechanisms, emphasizing its applications in medicine, developmental biology, and personalized therapeutics. Heterogeneous networks in biology are powerful tools for understanding the complex interactions and interdependencies within biological systems. Key advancements in AI and multiomics data integration have propelled this field, offering new insights into disease mechanisms, biomarker discovery, and therapeutic interventions.
Modern livestock breeding has mastered genotyping. Genome-wide association studies, genomic selection, and SNP arrays enable genetic merit prediction at lower cost. However, phenotyping remains the bottleneck, as manual measurement is slow, expensive, subjective, and unable to capture spatial or temporal trait organization. Digital phenotyping via artificial intelligence could resolve this, but deep learning requires thousands of labelled examples, impractical when phenotyping cost itself limits datasets to hundreds of individuals. This creates a paradox: AI could accelerate phenotyping but requires large numbers of samples to train the models. Here, we demonstrate that integrating computer vision with machine learning offers sample-efficient digital phenotyping using eggshell colour as a model system. Rather than learning features from scratch (deep learning), we engineer physically motivated features via Wavelet transforms that decompose images into multi-scale spatial components. Wavelet features captured 14.2 percentage points more variance (R2 = 0.976 vs. 0.834, p < 0.001) than standard colorimetry, with 50% better sample efficiency (achieving at n = 60 what colorimetry required n = 120). Variance decomposition revealed 77% of discriminative capacity derives from spatial patterns (bands, spots, gradients) invisible to scalar averages. Additionally, we identified "cryptic phenotypes" (3.3%) where spatial patterns contradicted average colour, cases where colorimeters failed but Wavelets succeeded. The underlying principle-that spatial decomposition can recover organizational information lost by scalar averaging-may be applicable to other traits with spatial or temporal structure, such as marbling, dermatitis, or pigmentation rhythms, although whether comparable performance gains would be observed remains to be tested empirically. Hence, for breeding programs implementing genomic selection, computer vision-based digital phenotyping captures complex trait variation without massive training datasets, addressing the bottleneck that increasingly limits genetic progress as genotyping becomes trivial.
The toxicity of some antibiotics used today necessitates rapid, specific and accurate determinations of antibiotic concentrations in biological fluids. Microbiological assays do not fulfill these needs and new procedures have recently been developed, for instance, fluorimetric assays, radioimmunoassays and radioenzymatic assays. However, these techniques have not been generally applicable to various antimicrobial agents. - The technique for high-pressure liquid chromatography (HPLC) has recently been applied to determination of antibiotics in biological fluids. The methods involve extraction of drug from the biological samples, separation by HPLC and detection by ultraviolet spectrophotometry of fluorimetry. Results obtained with tetracycline, amphotericin B, and cephalothin show that this procedure meets the demands for rapid, specific and accurate monitoring of antibiotic concentrations in body fluids. Because of its versatility, the HPLC technique seems to be applicable to the determination of a variety of antibiotics and other drugs in clinical and experimental medicine.
Metabolomics, a critical tool for analyzing small-molecule metabolites, integrates with genomics, transcriptomics, and proteomics to provide a systems-level understanding of fungal biology. By mapping metabolic networks, it elucidates regulatory mechanisms driving physiological and ecological adaptations. In fungal pathogenesis, metabolomics reveals host-pathogen dynamics, identifying virulence factors like gliotoxin in Aspergillus fumigatus and metabolic shifts, such as glyoxylate cycle upregulation in Candida albicans. Ecologically, it highlights fungal responses to abiotic stressors, including osmolyte production like trehalose, enhancing survival in extreme environments. These insights highlight metabolomics' role in decoding fungal persistence and niche colonization. In drug discovery, it aids target identification by profiling biosynthetic pathways, supporting novel antifungal and nanostructured therapy development. Combined with multi-omics, metabolomics advances insights into fungal pathogenesis, ecological interactions, and therapeutic innovation, offering translational potential for addressing antifungal resistance and improving treatment outcomes for fungal infections. Its progress shed light on complex fungal molecular profiles, advancing discovery and innovation in fungal biology.
A series of triesters of adenosine cyclic 3',5'-phosphate was synthesized by treatment of the free acid with various diazoalkanes (R=H, CH3, C6H5,0-NO2C6H4, p-NO2C6H4, p-CH3C6H4). The resulting diastereomeric mixtures were separated into their axial and equatorial components. Hydrolysis of the compounds was examined as well as photolysis of the photolabile o-nitrobenzyl ester. All compounds were then tested for their ability to activate the cAMP-dependent protein kinase and for their ability to serve as a substrate for the cAMP phosphodiesterase showing almost no effect on either enzyme. In a biological assay the benzyl triesters were able to penetrate into C 6 rat glioma cells and to induce the typical morphological alteration of the cell shape known for high cellular levels of cAMP. It was concluded that the benzyl triesters of cAMP are useful derivatives which can be efficiently and specifically converted to the parent nucleotide. Benzyl derivatives of biologically active phosphodiesters may provide a useful tool for study in biology and pharmacology.
Bacterial genome engineering has evolved to provide increasingly precise, robust and rapid tools, driving the development and optimization of bacterial production of numerous compounds. The field has progressed from early random mutagenesis methods, labour-intensive and inefficient, to rational and multiplexed strategies enabled by advances in genomics and synthetic biology. Among these tools, CRISPR/Cas has stood out for its versatility and its ability to achieve precision levels ranging from 50% to 90%, compared to the 10-40% obtained with earlier techniques, thereby enabling remarkable improvements in bacterial productivity. Nevertheless, like its predecessors, it still demands continuous refinement to reach full maturity. In this context, the present review addresses the lack of a unified overview by summarizing historical milestones and practical applications of genomic engineering tools in bacteria. It integrates diverse approaches to provide a comprehensive perspective on the evolution and prospects of these fundamental biotechnological tools.
The article summarizes the data obtained within the scope of the "Revertase" project organized in 1972 under the guidance of Scientific Councel on Molecular Biology. The scientific program included four main topics: synthesis of complementary DNA via reverse transcription on the various RNA templates and their use as tools in molecular biology, enzymatic synthesis of structural genes in double DNA-polymerase system and their propagation by means of genetic engineering, enzymology of reverse transcription, problems of molecular oncornavirology. It is stressed that the success in realization of this wide and intensive research program was achieved due to cooperative efforts and division of labour between scientists of different specialities due to international cooperation within the frame of the project. Appearance of more than 80 publications between 1973 and 1977 clearly manifested that the program was fullfilled and led to creation of a new area of research absent in the given countries before functioning of the "Revertase" project.
Commercial sulfonic styrene-divinylbenzene ion-exchange resins are activated with aqueous H2O2 to generate metal-free decontamination systems that combine strong Brønsted acidity with immobilized oxidizing capability. Among five tested materials, Amberlyst 15 dry showed the best performance in terms of oxidant immobilization capacity and promoting the oxidative degradation of the sulfur mustard simulant (2-chloroethyl)ethyl sulfide, CEES, and the organophosphorus pesticide malathion under very mild conditions. Control experiments with K2CO3-exchanged resin demonstrate that efficient decontamination requires the synergy between surface acidity and peroxide functionality. The activated resins also display rapid biocidal activity, strongly reducing viable Escherichia coli and Staphylococcus aureus and completely suppressing the infectivity of HSV-1 and SARS-CoV-2 within min. These findings identify peroxide-activated sulfonic resins as simple, sustainable, regenerable, and versatile tools for efficient combined hazardous chemical and biological decontamination.
The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results. OME is designed to support high-content cell-based screening as well as traditional image analysis applications. The OME Data Model, expressed in Extensible Markup Language (XML) and realized in a traditional database, is both extensible and self-describing, allowing it to meet emerging imaging and analysis needs.
The well-established inaccuracy of purely computational methods for annotating genome sequences necessitates an interactive tool to allow biological experts to refine these approximations by viewing and independently evaluating the data supporting each annotation. Apollo was developed to meet this need, enabling curators to inspect genome annotations closely and edit them. FlyBase biologists successfully used Apollo to annotate the Drosophila melanogaster genome and it is increasingly being used as a starting point for the development of customized annotation editing tools for other genome projects.
In this review we have attempted to highlight each of the major areas of interest in liposome-cell interactions: the purely physical chemical, the cell biological, and the medical. Liposomes can be generated in a number of ways and are classified as small unilamellar, large unilamellar, and multilamellar vesicles. Although liposomes are easy to prepare, it is important to consider the effects of impurities, and also the possible changes in liposome properties with time (particularly at or below the phase transition temperature). Intelligent application of liposomes to cell biological and clinical problems requires an understanding of their mechanisms of interaction with cells. The mechanisms thus far delineated, largely by studies in vitro, are fusion, endocytosis, lipid transfer, and stable adsorption. In practice, demonstrating the occurrence of a given mechanism in an actual system is difficult because these are not mutually exclusive. Cell type, conditions of incubation, and liposome properties (charge, fluidity, size) are important in determining mechanism and appear to organize the literature effectively. However, this may be an oversimplification resulting from the sketchiness of current information. Liposomes have been used in cell biology to alter the phospholipid and cholesterol composition of cells, to bypass the membrane permeability barrier to normally impermeant solutes, and to promote cell-cell fusion. Perhaps the most fruitful of these applications has been the alteration of cholesterol, which can result in changes in cell permeability and morphology. On the other hand, delivery into cells of liposome-entrapped, water-soluble materials has not yet proved an effective tool in cell biology; delivery, and consequent physiological changes, have been demonstrated, but generally to answer questions about liposome-cell interactions, not to answer questions about the cells. Much of the current interest in liposomes derives from their potential applications in vivo. Liposomes are envisioned as pharmacological capsules for delivery of therapeutic agents in treatment of such conditions as diabetes, enzyme deficiencies, heavy metal poisoning, and neoplasms. Although much of the literature to date has been concerned with the end applications, it seems clear that a more systematic approach to the pharmacokinetics of liposomes will be necessary. In particular, such aspects as their leakage rates and their ability to cross cell and anatomical barriers require further study. Targeting of liposomes to particular cells or tissues will be essential for many applications. Finally, it must be remembered that all of these in vivo applications of liposomes are future tense; as with other technologies, passage from demonstration of the phenomenon to practical application is likely to be arduous.
MOTIVATION: Single-strand DNA-specific Hi-C (ssHi-C) is a recently developed technique enabling the capture of chromatin interactions involving single-stranded DNA (ssDNA), an intermediate of various DNA metabolic processes. ssHi-C entails the restoration of restriction sites in ssDNA regions of interest upon introduction of designer, internally barcoded "annealing oligonucleotides" prior to the restriction digestion step of Hi-C. The design of these "annealing oligonucleotides," as well as the analysis of the resulting ssHi-C data presents specific challenges, such as (i) differentiating ssDNA from dsDNA-derived contacts, (ii) tracking probe-specific interactions, and (iii) calibrating the amount of ssDNA contacts across biological samples. Dedicated computational tools are therefore needed to facilitate the design of, and extract biological information from, ssHi-C experiments. RESULTS: We present ssHiCstuff, a Rust- and Python-based package for the design of key reagents for ssHi-C experiments and for the analysis of ssHi-C data. ssHiCstuff provides (i) an automated annealing oligonucleotides design module, (ii) an end-to-end analyses pipeline, and (iii) a graphical user interface. ssHiCstuff simplifies the high-resolution analysis of ssDNA interactions at genome-wide scale. A graphical user interface (GUI) implemented in Python is also available for biologists without coding skills. AVAILABILITY: ssHiCstuff is freely available at https://github.com/Piazzalab/ssHiCstuff and https://zenodo.org/records/19677479 (https://doi.org/10.5281/zenodo.19677479) under the GPL 3.0 license. The annealing oligonucleotides design and the visualization modules are additionally freely available on a web browser at https://bioshiny.ens-lyon.fr/public/app/sshicstuff. A test dataset is available at https://zenodo.org/records/20035366 (https://doi.org/10.5281/zenodo.20035366).
Spatial omics technologies are providing new opportunities for plant biology by enabling molecular profiling within structurally intact tissues, revealing spatially organised cell states, developmental gradients, and regulatory interactions. While spatial transcriptomics has driven early advances, the field is rapidly expanding toward integrated spatial multi-omics by combining single-cell and spatial transcriptomic, epigenomic, proteomic, and metabolomic data. These approaches offer new opportunities to study development, physiology, and plant biotic and abiotic interactions in spatially preserved cellular contexts. However, despite rapid adoption, the field remains constrained by plant-specific challenges when applying technologies largely developed for animal systems. Compared with animal systems, plant tissues pose additional challenges due to rigid cell walls, and diverse chemistries, complicating sample preparation, cell and subcellular segmentation, signal detection, and data integration. As a result, many studies rely on bespoke protocols and analysis pipelines that are often difficult to reproduce or generalise. Here, we provide a practical, solution-oriented synthesis of current bottlenecks across experimental and computational pipelines, highlight emerging strategies to overcome these limitations, and propose a roadmap for community-driven protocol sharing, benchmarking, and integration across spatial and multi-omics modalities. Addressing these challenges will be essential to establish spatial omics as a routine and scalable tool for plant biology.
Data on serum protein levels in Binga Pygmies are presented, and these are compared with the levels in healthy Italians and in Italians suffering from liver diseases. Principal Component Analysis carried out on the three groups points out similarities in protein levels between the Pygmies and the Italian Hepatopaths on one hand and between healthy and liver diseased Italians on the other. Discriminant Analyses reveal the important differences between the populations. It is suggested that such analyses of protein levels could serve as tools in population biology.