biologically relevant
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Olufunso O. Abosede

Abstract: In the recent past, the pharmaceutical modification of drug molecules by complexation with biologically relevant metals to improve their properties such as stability, dissolution rate, absorption and bioavailability has been extensively studied. In order to achieve better and enhanced medicinal activity, vanadyl complexes of the widely used lincomycin (Lin-van) and neomycin (Neo-van) have been synthesized and their physico-chemical properties examined. The UV-Vis absorption properties of these complexes were determined and their antimicrobial activities were tested against some pathogenic organisms viz: Proteus vulgaris, Klebsiella pneumonae, Escherichia coli and Staphylococcus aureus. In all cases, Neo-van showed better antimicrobial activity than Lin-van while both complexes showed better activity than the antibiotic lincomycin and the previously reported Cu-Lin. Keywords: lincomycin, neomycin, UV-Vis spectroscopy, Physico-chemical, Oxovanadyl, synthesis

2022 ◽  
Joanna von Berg ◽  
Michelle ten Dam ◽  
Sander W. van der Laan ◽  
Jeroen de Ridder

Pleiotropic SNPs are associated with multiple traits. Such SNPs can help pinpoint biological processes with an effect on multiple traits or point to a shared etiology between traits. We present PolarMorphism, a new method for the identification of pleiotropic SNPs from GWAS summary statistics. PolarMorphism can be readily applied to more than two traits or whole trait domains. PolarMorphism makes use of the fact that trait-specific SNP effect sizes can be seen as Cartesian coordinates and can thus be converted to polar coordinates r (distance from the origin) and theta (angle with the Cartesian x-axis). r describes the overall effect of a SNP, while theta describes the extent to which a SNP is shared. r and theta are used to determine the significance of SNP sharedness, resulting in a p-value per SNP that can be used for further analysis. We apply PolarMorphism to a large collection of publicly available GWAS summary statistics enabling the construction of a pleiotropy network that shows the extent to which traits share SNPs. This network shows how PolarMorphism can be used to gain insight into relationships between traits and trait domains. Furthermore, pathway analysis of the newly discovered pleiotropic SNPs demonstrates that analysis of more than two traits simultaneously yields more biologically relevant results than the combined results of pairwise analysis of the same traits. Finally, we show that PolarMorphism is more efficient and more powerful than previously published methods.

2022 ◽  
Gustave Ronteix ◽  
Valentin Bonnet ◽  
Sebastien Sart ◽  
Jeremie Sobel ◽  
Elric Esposito ◽  

Microscopy techniques and image segmentation algorithms have improved dramatically this decade, leading to an ever increasing amount of biological images and a greater reliance on imaging to investigate biological questions. This has created a need for methods to extract the relevant information on the behaviors of cells and their interactions, while reducing the amount of computing power required to organize this information. This task can be performed by using a network representation in which the cells and their properties are encoded in the nodes, while the neighborhood interactions are encoded by the links. Here we introduce Griottes, an open-source tool to build the "network twin" of 2D and 3D tissues from segmented microscopy images. We show how the library can provide a wide range of biologically relevant metrics on individual cells and their neighborhoods, with the objective of providing multi-scale biological insights. The library's capacities are demonstrated on different image and data types. This library is provided as an open-source tool that can be integrated into common image analysis workflows to increase their capacities.

2022 ◽  
Vol 8 (1) ◽  
pp. 9
Jin Zhang ◽  
Abdallah M. Eteleeb ◽  
Emily B. Rozycki ◽  
Matthew J. Inkman ◽  
Amy Ly ◽  

Existing small noncoding RNA analysis tools are optimized for processing short sequencing reads (17–35 nucleotides) to monitor microRNA expression. However, these strategies under-represent many biologically relevant classes of small noncoding RNAs in the 36–200 nucleotides length range (tRNAs, snoRNAs, etc.). To address this, we developed DANSR, a tool for the detection of annotated and novel small RNAs using sequencing reads with variable lengths (ranging from 17–200 nt). While DANSR is broadly applicable to any small RNA dataset, we applied it to a cohort of matched normal, primary, and distant metastatic colorectal cancer specimens to demonstrate its ability to quantify annotated small RNAs, discover novel genes, and calculate differential expression. DANSR is available as an open source tool.

2022 ◽  
Vol 1 ◽  
M. Deepa Maheshvare ◽  
Soumyendu Raha ◽  
Debnath Pal

Trillions of chemical reactions occur in the human body every second, where the generated products are not only consumed locally but also transported to various locations in a systematic manner to sustain homeostasis. Current solutions to model these biological phenomena are restricted in computability and scalability due to the use of continuum approaches in which it is practically impossible to encapsulate the complexity of the physiological processes occurring at diverse scales. Here, we present a discrete modeling framework defined on an interacting graph that offers the flexibility to model multiscale systems by translating the physical space into a metamodel. We discretize the graph-based metamodel into functional units composed of well-mixed volumes with vascular and cellular subdomains; the operators defined over these volumes define the transport dynamics. We predict glucose drift governed by advective–dispersive transport in the vascular subdomains of an islet vasculature and cross-validate the flow and concentration fields with finite-element–based COMSOL simulations. Vascular and cellular subdomains are coupled to model the nutrient exchange occurring in response to the gradient arising out of reaction and perfusion dynamics. The application of our framework for modeling biologically relevant test systems shows how our approach can assimilate both multi-omics data from in vitro–in vivo studies and vascular topology from imaging studies for examining the structure–function relationship of complex vasculatures. The framework can advance simulation of whole-body networks at user-defined levels and is expected to find major use in personalized medicine and drug discovery.

2022 ◽  
Vol 12 (1) ◽  
Nikita Potemkin ◽  
Sophie M. F. Cawood ◽  
Jackson Treece ◽  
Diane Guévremont ◽  
Christy J. Rand ◽  

AbstractRNA sequencing offers unprecedented access to the transcriptome. Key to this is the identification and quantification of many different species of RNA from the same sample at the same time. In this study we describe a novel protocol for simultaneous detection of coding and non-coding transcripts using modifications to the Ion Total RNA-Seq kit v2 protocol, with integration of QIASeq FastSelect rRNA removal kit. We report highly consistent sequencing libraries can be produced from both frozen high integrity mouse hippocampal tissue and the more challenging post-mortem human tissue. Removal of rRNA using FastSelect was extremely efficient, resulting in less than 1.5% rRNA content in the final library. We identified > 30,000 unique transcripts from all samples, including protein-coding genes and many species of non-coding RNA, in biologically-relevant proportions. Furthermore, the normalized sequencing read count for select genes significantly negatively correlated with Ct values from qRT-PCR analysis from the same samples. These results indicate that this protocol accurately and consistently identifies and quantifies a wide variety of transcripts simultaneously. The highly efficient rRNA depletion, coupled with minimized sample handling and without complicated and high-loss size selection protocols, makes this protocol useful to researchers wishing to investigate whole transcriptomes.

2022 ◽  
Vol 12 (1) ◽  
Ida Wang Henriksen ◽  
Josue Leonardo Castro Mejia ◽  
Caroline Martha Junker Mentzel ◽  
Frederikke Lindenberg ◽  
Axel Kornerup Hansen

AbstractSeveral mammalian species are vaccinated in early life, but little is known about the effect of diet on vaccine response. Oligosaccharides are increasingly proposed as dietary supplement for young individuals due to their anti-inflammatory potential elicited through modulation of gut microbiota (GM). Also, diet, e.g. the size of the fat fraction, is known to modulate the GM. We tested if an oligosaccharide diet (Immulix) and/or increased dietary fat content affected antibody titers to a tetanus vaccine in 48 BALB/cJTac mice through GM modulation. Female mice had significantly higher IgG titers with higher variation compared to male mice. The effects of Immulix and/or increased fat content were minor. Immulix negatively affected IgG titers in male mice four weeks after secondary vaccination but upregulated Il1b gene expression in the spleen. Immulix had a downregulating effect on expression of Cd4 and Foxp3 in ileum only if the mice were fed the diet with increased fat. The diet with increased dietary fat increased Il1b but decreased Cd8a gene expression in the spleen. Immulix and diet affected GM composition significantly. Increased dietary fat content upregulated Lactobacillus animalis but downregulated an unclassified Prevotella spp. Immulix decreased Lactobacillales, Streptococcaceae and Prevotellaceae but increased Bacteroides. It is concluded that in spite of some minor influences on immune cell markers, cytokines and IgG titers Immulix feeding or increased dietary fat content did not have any biologically relevant effects on tetanus vaccine responses in this experiment in mice.

Michal Avital-Shmilovici ◽  
Xiaohe Liu ◽  
Thomas Shaler ◽  
Andrew Lowenthal ◽  
Pauline Bourbon ◽  

Mackenzie Postel ◽  
Julie O. Culver ◽  
Charité Ricker ◽  
David Craig

The vast volume of data that has been generated as a result of the next-generation sequencing revolution is overwhelming to sift through and interpret. Parsing functional vs. non-functional and benign vs. pathogenic variants continues to be a challenge. Out of three billion bases, the genomes of two given individuals will only differ by about 3 million variants (0.1%). Furthermore, only a small fraction of these are biologically-relevant and, of those that are functional, only a handful actually drive disease pathology. While whole genome and exome sequencing have transformed our collective understanding of the role that genetics plays in disease pathogenesis, there are certain conditions and populations for whom DNA-level data has failed to produce a molecular diagnosis. Patients of non-White race/non-European ancestry are disproportionately affected by “variants of unknown/uncertain significance” (VUS). This limits the scope of precision medicine for minority patients and perpetuates health disparities. VUS often include deep intronic and splicing variants which are difficult to interpret in DNA alone. RNA analysis is capable of illuminating the consequences of VUS thereby allowing for their reclassification as pathogenic vs. benign. Here we review the critical role, going forward, of transcriptome analysis for clarifying VUS in both neoplastic and non-neoplastic diseases.

mBio ◽  
2022 ◽  
Taylor Van Gundy ◽  
Edward Martin ◽  
Jeremy Bono ◽  
Olivia Hatton ◽  
Meghan C. Lybecker

Next-generation RNA sequencing of numerous organisms has revealed that transcription is widespread across the genome, termed pervasive transcription, and does not adhere to annotated gene boundaries. The function of pervasive transcription is enigmatic and has generated considerable controversy as to whether it is transcriptional noise or biologically relevant.

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