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Results for “molecular biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2019

Discovery of new mitochondrial DNA segregation machinery components in Trypanosoma brucei: a comparison of three approaches

Trypanosoma brucei is a single celled eukaryotic parasite and the causative agent of human African sleeping sickness and Nagana in cattle. Aside from its medical relevance T. brucei has also been key to the discovery of several general biological principles including GPI-anchoring, RNA-editing and trans-splicing. The parasite contains a single mitochondrial organelle with a singular genome. Recent studies have identified several molecular components of the mitochondrial genome segregation machinery (tripartite attachment complex, TAC), which connects the basal body of the flagellum to the mitochondrial DNA of T. brucei. The TAC component in closest proximity to the mitochondrial DNA is TAC102. Here we apply and compare three different approaches (proximity labeling, Immunoprecipitation and yeast two-hybrid) to identify novel interactors of TAC102 and subsequently verify their localisation. Furthermore, we establish the direct interaction of TAC102 and p166 in the unilateral filaments of the TAC. Subject areabiochemistry, molecular biology, cellular biology

cell biology↗

bioRxiv2020

Protein Structural Alignments From Sequence

Computing sequence similarity is a fundamental task in biology, with alignment forming the basis for the annotation of genes and genomes and providing the core data structures for evolutionary analysis. Standard approaches are a mainstay of modern molecular biology and rely on variations of edit distance to obtain explicit alignments between pairs of biological sequences. However, sequence alignment algorithms struggle with remote homology tasks and cannot identify similarities between many pairs of proteins with similar structures and likely homology. Recent work suggests that using machine learning language models can improve remote homology detection. To this end, we introduce DeepBLAST, that obtains explicit alignments from residue embeddings learned from a protein language model integrated into an end-to-end differentiable alignment framework. This approach can be accelerated on the GPU architectures and outperforms conventional sequence alignment techniques in terms of both speed and accuracy when identifying structurally similar proteins.

bioinformatics↗

bioRxiv2023

scGeneRythm: Using Neural Networks and Fourier Transformation to Cluster Genes by Time-Frequency Patterns in Single-Cell Data

Clustering genes in single-cell RNA sequencing plays a pivotal role in unraveling a plethora of biological processes, from cell differentiation to disease progression and metabolic pathways. Traditional time-domain methods are instrumental in certain analyses, yet they may overlook intricate relationships. For instance, genes that appear distinct in the time domain might exhibit striking similarities in the frequency domain. Recognizing this, we present scGeneRhythm, an innovative deep learning technique that employs Fourier transformation. This approach captures the rich tapestry of gene expression from both the time and frequency domains. When evaluated across a spectrum of single-cell datasets, scGeneRhythm consistently outperforms conventional approaches. The gene clusters it identifies not only demonstrate heightened statistical significance in enriched pathways but also bring to light underlying gene relationships previously obscured. Through integrating frequency-domain data, scGeneRhythm not only refines gene grouping but also uncovers pivotal biological insights, such as nuanced gene rhythmicity. By deploying scGeneRhythm, we foster a richer, multi-dimensional understanding of gene expression dynamics, enriching the potential avenues of cellular and molecular biology research.

bioinformatics↗

bioRxiv2021

Genetic diversity assessment of neem (Azadirachta indica A. Juss) in northern Nigeria

Neem is a tropical tree that can adapt to a wide range of places and particularly to semi-arid conditions. As at present, it is grown in many Asian countries and also in the tropical regions of the western hemisphere. Genetic variability and diversity are a major requirement needed for both immediate results and the one ones thereafter adaptation of plant types in their original domain. The evaluation of genetic diversity of any species is extremely crucial for their sustainability, continuity, survival and gene manipulation. Major breakthroughs in the field of molecular biology was able to develop several tools for the investigation of genetic diversity at the genome level to determine phylogenetic relationships among inter or intra-species. The advent of molecular markers for the detection and exploitation of DNA polymorphism is one of the major breakthroughs in the world of molecular genetics. The importance of genetic diversity in plant germplasm conservation, especially in economically important species such as Azadirachtaindica, is enormous, particularly in Nigeria. The question is whether A. indica from different Agro-ecological zones have genetic variations or similarities. This was the bane of the current study, which used RAPD to look atgenetic diversity of 27 randomly selected neem trees within the agro-ecological zones in Northern Nigeria. A total of 9 primers were employed out of which only 5 were responsive (OPA-02, OPA-03, OPA-15 and OPA-19). These primers showed dissimilarities in the visible DNA bands among the various tree samples. There was evidence of genetic dissimilarities among the trees sampled. Differences in percentage polymorphism was reported, where it was reportedly highest among the Borno State tree samples (97.44%), compared to those in Yobe State with no polymorphism.

plant biology↗

bioRxiv2021

Single cell label-free probing of chromatin dynamics during B lymphocyte maturation

Large-scale intracellular signalling during developmental growth or in response to environmental alterations are largely orchestrated by chromatin within the cell nuclei. Chemical and conformational modifications of the chromatin architecture are critical steps in the regulation of differential gene expression and ultimately cell fate determination. Therefore, establishing chemical properties of the nucleus could provide key markers for phenotypic characterisation of cellular processes on a scale of individual cells. Raman microscopy is a sensitive technique that is capable of probing single cell chemical composition - and sub-cellular regions - in a label-free optical manner. As such, it has great potential in both clinical and basic research. However, perceived limitations of Raman spectroscopy such as low signal intensity and the difficulty in linking alterations in vibrational signals directly with ensuing biological effects have hampered advances in the field. Here we use immune B lymphocyte development as a model to assess chromatin and transcriptional changes using confocal Raman microscopy in combination with microfluidic devices and correlative transcriptomics, thereby linking changes in chemical and structural properties to biological outcomes. Live B lymphocytes were assessed before and after maturation. Multivariate analysis was applied to distinguish cellular components within each cell. The spectral differences between non-activated and activated B lymphocytes were then identified, and their correlation with known intracellular biological changes were assessed in comparison to conventional RNA-seq analysis. Our data shows that spectral analysis provides a powerful tool to study gene activation that can complement conventional molecular biology techniques and opens the way for mapping the dynamics in the biochemical makeup of individual cells.

biophysics↗

bioRxiv2024

PDBe tools for an in-depth analysis of small molecules in the Protein Data Bank

The Protein Data Bank (PDB) is the primary global repository for experimentally determined 3D structures of biological macromolecules and their complexes with ligands, proteins, and nucleic acids. PDB contains over 47,000 unique small molecules bound to the macromolecules. Despite the extensive data available, the complexity of small molecule data in the PDB necessitates specialised tools for effective analysis and visualisation. PDBe has developed a number of tools, including PDBe CCDUtils (https://github.com/PDBeurope/ccdutils) for accessing and enriching ligand data, PDBe Arpeggio (https://github.com/PDBeurope/arpeggio) for analysing interactions between ligands and macromolecules, and PDBe RelLig (https://github.com/PDBeurope/rellig) for identifying the functional roles of ligands (such as reactants, cofactors, or drug-like molecules) within protein-ligand complexes. Furthermore, the enhanced ligand annotations and data generated by these tools are presented in a comprehensive view on the novel PDBe-KB ligand pages, providing a holistic view of small molecules that enables the establishment of their biological contexts (Example page for Imatinib: https://wwwdev.ebi.ac.uk/pdbe-srv/pdbechem/chemicalCompound/show/STI). By improving the standardisation of ligand identification, adding various annotations, and offering advanced visualisation capabilities, these tools help researchers navigate the complexities of small molecules and their roles in biological systems, facilitating mechanistic understanding of biological functions. The ongoing enhancements to these resources are designed to support the scientific community in gaining valuable insights into ligands and their applications across various fields, including drug discovery, molecular biology, systems biology, structural biology, and pharmacology.

bioinformatics↗

bioRxiv2025

White adipose tissue undergoes pathological dysfunction in the TDP-43A315T mouse model of amyotrophic lateral sclerosis (ALS)

White adipose tissue (WAT) has a crucial role in maintaining systemic energy homeostasis. Numerous biological pathway studies have highlighted the importance of adipokines in regulating metabolic pathways and contributing to metabolic dysfunction in animal models and patients with ALS. Despite these associations, the specific molecular mechanisms remain poorly understood. Moreover, the direct contribution of WAT to the energy metabolism abnormalities observed in ALS has yet to be clearly defined. The current study sought to identify perturbances in WAT, main source of leptin, during the clinical course of the disease in TDP-43A315T mice using histological, proteomic, and molecular biological techniques. We present the first evidence of a significant histological alteration in WAT prior to the symptomatic stage of the disease in TDP-43A315T mice, providing novel insights into pathological features earlier in the onset of symptoms, and showing WAT as a target organ for ALS. In human ALS cases, we found that circulating leptin levels at the time of diagnosis were lower in the plasma of men with ALS who were overweight or obese and had rapidly progressive ALS, emphasizing the importance of considering sex-specific approaches when analysing adipokines essential for body weight control.

neuroscience↗

bioRxiv2019

Pan-cancer image-based detection of clinically actionable genetic alterations

Precision treatment of cancer relies on genetic alterations which are diagnosed by molecular biology assays.1 These tests can be a bottleneck in oncology workflows because of high turnaround time, tissue usage and costs.2 Here, we show that deep learning can predict point mutations, molecular tumor subtypes and immune-related gene expression signatures3,4 directly from routine histological images of tumor tissue. We developed and systematically optimized a one-stop-shop workflow and applied it to more than 4000 patients with breast5, colon and rectal6, head and neck7, lung8,9, pancreatic10, prostate11 cancer, melanoma12 and gastric13 cancer. Together, our findings show that a single deep learning algorithm can predict clinically actionable alterations from routine histology data. Our method can be implemented on mobile hardware14, potentially enabling point-of-care diagnostics for personalized cancer treatment in individual patients.

genetics↗

bioRxiv2019

Binary integer programming for Multiple Sequence Alignment

Molecular biology advances in the past few decades have contributed to the rapid increase in genome sequencing of various organisms; sequence alignment is usually considered as the first step in understanding the molecular function of a sequence. An optimal alignment adjusts two or more sequences in a way that it could compare the maximum number of identical or similar residues. The two sequence alignments types are: Pairwise Sequence Alignment (PSA) and Multiple Sequence Alignment (MSA). While dynamic programming (DP) technique is used in PSA to provide the optimal method, it will lead to more complexity if used in MSA. So, the MSA mainly uses heuristic and approximation methods. This paper presents a mathematical model for MSA that can be used as a basis for optimal solution in different ways. In order to obtain the results, the model is implemented using Genetic Algorithm method on the web.

bioinformatics↗

bioRxiv2021

Systematic exploration of Escherichia coli phage-host interactions with the BASEL phage collection

Bacteriophages, the viruses infecting bacteria, hold great potential for the treatment of multidrug-resistant bacterial infections and other applications due to their unparalleled diversity and recent breakthroughs in their genetic engineering. However, fundamental knowledge of molecular mechanisms underlying phage-host interactions is mostly confined to a few traditional model systems and did not keep pace with the recent massive expansion of the field. The true potential of molecular biology encoded by these viruses has therefore remained largely untapped, and phages for therapy or other applications are often still selected empirically. We therefore sought to promote a systematic exploration of phage-host interactions by composing a well-assorted library of 66 newly isolated phages infecting the model organism Escherichia coli that we share with the community as the BASEL collection (BActeriophage SElection for your Laboratory). This collection is largely representative of natural E. coli phage diversity and was intensively characterized phenotypically and genomically alongside ten well-studied traditional model phages. We experimentally determined essential host receptors of all phages, quantified their sensitivity to eleven defense systems across different layers of bacterial immunity, and matched these results to the phages host range across a panel of pathogenic enterobacterial strains. Our results reveal clear patterns in the distribution of phage phenotypes and genomic features that highlight systematic differences in the potency of different immunity systems and point towards the molecular basis of receptor specificity in several phage groups. Strong trade-offs were detected between fitness traits like broad host recognition and resistance to bacterial immunity that might drive the divergent adaptation of different phage groups to specific niches. We envision that the BASEL collection will inspire future work exploring the biology of bacteriophages and their hosts by facilitating the discovery of underlying molecular mechanisms as the basis for an effective translation into biotechnology or therapeutic applications.

microbiology↗

bioRxiv2019

The Five-Primer Challenge: An inquiry-based laboratory module for synthetic biology

New technologies in DNA synthesis and assembly give genetic engineers complete freedom in genetic design, where virtually any plasmid DNA sequence can be created efficiently and economically. Learning how to design, construct, and test new DNA sequences is a critical skill for researchers in molecular biology and biotechnology. Here we present a student-centered, inquiry-based module in which students learn how to control bacterial gene expression by appplying various DNA assembly techniques. The central activity in this learning module is termed the Five-Primer Challenge. Each student is allowed to order up to five 60-mer oligonucleotide primers to then modify a GFP expression plasmid with the goal of increasing GFP expression as much as possible. This module was developed and implemented at the 2016 Cold Spring Harbor Laboratory Synthetic Biology Course, and was effective at engaging students in critical thinking and in promoting student learning.

scientific communication and education↗

bioRxiv2017

A plasmid-based Escherichia coli gene expression system with cell-to-cell variation below the extrinsic noise limit

Experiments in synthetic biology and microbiology can benefit from protein expression systems with low cell-to-cell variability (noise) and expression levels precisely tunable across a useful dynamic range. Despite advances in understanding the molecular biology of microbial gene regulation, many experiments employ protein-expression systems exhibiting high noise and nearly all-or-none responses to induction. I present an expression system that incorporates elements known to reduce gene expression noise: negative autoregulation and bicistronic transcription. I show by stochastic simulation that while negative autoregulation can produce a more gradual response to induction, bicistronic expression of a repressor and gene of interest can be necessary to reduce noise below the extrinsic limit. I synthesized a plasmid-based system incorporating these principles and studied its properties in Escherichia coli cells, using flow cytometry and fluorescence microscopy to characterize induction dose-response, induction/repression kinetics and gene expression noise. By varying ribosome binding site strengths, expression levels from 55-- 10,740 molecules/cell were achieved with noise below the extrinsic limit. Individual strains are inducible across a dynamic range greater than 20-fold. Experimental comparison of different regulatory networks confirmed that bicistronic autoregulation reduces noise, and revealed unexpectedly high noise for a conventional expression system with a constitutively expressed transcriptional repressor. I suggest a hybrid, low-noise expression system to increase the dynamic range.

synthetic biology↗

bioRxiv2023

SYNTHETIC CIRCUITS BASED ON SPLIT CAS9 TO DETECT CELLULAR EVENTS

Synthetic biology involves the generation of logic circuits to create or control biological functions and behaviors by engineering interconnected genetic elements such as promoters, repressors, and transcriptional activators. CRISPR discovery, and its adaptations to mammalian cells, has made it the tool of choice in molecular biology and revolutionized genome engineering in biomedical sciences. Here, we describe an adaptation of a split Cas9 to generate synthetic logic gates to sense biological events. As proof-of-concept, the complementing halves of split Cas9 were placed under different promoters, one unique to cancer cells of epithelial origin (phCEA) and one universal promoter (pCMV). We used self-assembling inteins to reunite the halves when co-expressed. Only cancer cells with epithelial origin expressed both halves and activated a reporter becoming green fluorescent. We then investigated whether we could apply this system to the detection of biological processes such as epithelial to mesenchymal transition (EMT). We designed another logic gate where one halve is expressed only by cancer cells of epithelial origin, while the other is activated during EMT - under the control of TWIST1. Indeed, cells undergoing EMT were detected by the activation of the reporter. Finally, the split-Cas9 logic gate was applied as a sensor to detect cell-cell fusion events in multiple cell lines. Each cell type expressed only one halve of split Cas9, and only induction of fusion resulted in the appearance of multinucleated syncytia and the expression of the reporter system. The simplicity and flexibility of the split Cas9 system reported here can be integrated to many other cellular processes, not only as a sensor but as an actuator. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=88 SRC="FIGDIR/small/533022v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@1f63cf0org.highwire.dtl.DTLVardef@11ab298org.highwire.dtl.DTLVardef@1c3270eorg.highwire.dtl.DTLVardef@1394488_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2023

Quantifying the impact of sample, instrument, and data processing on biological signatures detected with Raman spectroscopy

Raman spectroscopy is a popular tool for characterizing complex biological materials and their geological remains1-10. Ordination methods, such as Principal Component Analysis (PCA), rely on spectral variance to create a compositional space1, the ChemoSpace, grouping samples based on spectroscopic manifestations that reflect different biological properties or geological processes1-7. PCA allows to reduce the dimensionality of complex spectroscopic data and facilitates the extraction of relevant informative features into data formats suitable for downstream statistical analyses, thus representing an essential first step in the development of diagnostic biosignatures. However, there is presently no systematic survey of the impact of sample, instrument, and spectral processing on the occupation of the ChemoSpace. Here the influence of sample count, signal-to-noise ratios, spectrometer decalibration, baseline subtraction routines, and spectral normalization on ChemoSpace grouping is investigated using synthetic spectra. Increase in sample size improves the dissociation of sample groups in the ChemoSpace, however, a stable pattern in occupation can be achieved with less than 10 samples per group. Systemic noise of different amplitude and frequency, features that can be introduced by instrument or sample11,12, are eliminated by PCA even when spectra of differing signal-to-noise ratios are compared. Routine offsets ({+/-} 1 cm-1) in spectrometer calibration contribute to less than 0.1% of the total spectral variance captured in the ChemoSpace, and do not obscure biological information. Standard adaptive baselining, together with normalization, increase spectral comparability and facilitate the extraction of informative features. The ChemoSpace approach to biosignatures represents a powerful tool for exploring, denoising, and integrating molecular biological information from modern and ancient organismal samples.

evolutionary biology↗

bioRxiv2021

Hierarchical Computational Anatomy: Unifying the Molecular to Tissue Continuum via Measure Representations of the Brain

ObjectiveThe objective of this research is to unify the molecular representations of spatial transcriptomics and cellular scale histology with the tissue scales of Computational Anatomy for brain mapping. Impact statementWe present a unified representation theory for brain mapping based on geometric measures of the micro-scale phenotypes of molecular disease simultaneously with the connectomic scales of complex interacting brain circuits. IntroductionMapping across coordinate systems in computational anatomy allows us to understand structural and functional properties of the brain at the millimeter scale. New measurement technologies in digital pathology and spatial transcriptomics allow us to measure the brain molecule by molecule and cell by cell based on protein and transcriptomic identity. We currently have no mathematical representations for integrating consistently the tissue limits with the molecular particle descriptions. The formalism derived here demonstrates the methodology for transitioning consistently from the molecular scale of quantized particles - as first introduced by Dirac as the class of generalized functions - to the continuum and fluid mechanics scales appropriate for tissue. MethodsWe introduce two methods based on notions of generalized function geometric measures and statistical mechanics. We use generalized functions expanded to include functional geometric descriptions - electrophysiology, transcriptomic, molecular histology - to represent the molecular biology scale integrated with a Boltzman like procedure to pass from the sparse particles to empirical probability laws on the functional state of the tissue. ResultsWe demonstrate a unified mapping methodology for transferring molecular information in the transcriptome and histological scales to the human atlas scales for understanding Alzheimers disease. ConclusionsWe demonstrate a unified brain mapping theory for molecular and tissue scales based on geometric measure representations.

neuroscience↗

bioRxiv2023

Schisandrin B suppresses colon cancer growth by inducing cell cycle arrest and apoptosis via the CHOP signalling pathway

Colon cancer is among the most lethal and prevalent malignant tumours in the world, and the lack of effective therapies highlights the need for novel therapeutic approaches. Schisandrin B (Sch B), a lignan extracted from the fruit Schisandra chinensis, has been reported for its anti-cancer properties. However, no studies to date have been done to characterise the exact molecular mechanisms regarding the anti-tumorigenic effect of Sch B in colon cancer. A comprehensive analysis of the molecular mechanism for the anti-tumorigenic effect of Sch B on human colon cancer cells was performed using combination of Raman spectroscopy, RNA-seq, computational docking and molecular biological experiments. The in vivo efficacy was evaluated by a mouse xenograft model. Sch B reduced cell proliferation and triggered apoptosis in human colon cancer cell lines. Raman spectroscopy, computational, RNA-seq, molecular and cellular studies revealed that Sch B activated unfolded protein responses by interacting with CHOP and upregulating CHOP, which thereby induced apoptosis. CHOP knockdown alleviated the Sch B-induced reduction in cell viability and apoptosis. Sch B reduced colon tumour growth in vivo. Our findings provide essential background for clinical trials examining the effects of Sch B in patients with colon cancer.

pharmacology and toxicology↗

bioRxiv2018

Rapid detection of cytochrome cd1-containing nitrite reductase encoding gene nirS with loop-mediated isothermal amplification assay

The cytochrome cd1-containing nitrite reductase, nirS, plays an important role in biological denitrification. Consequently, investigating the presence and abundance of nirS is a commonly used approach to understand the distribution and potential activity of denitrifying bacteria, in addition to denitrifier communities. Herein, a new molecular biology technique termed loop-mediated isothermal amplification (LAMP) was developed to rapidly detect nirS gene using those of Pseudomonas aeruginosa to optimize the assay. The LAMP assay relied on a set of four primers that were designed to recognize six target sequence sites, resulting in high target specificity. The specificity of the assay was confirmed by the lack of amplification when using DNA from 15 other bacterial species lacking nirS gene. The limit of detection for the LAMP assay under optimized conditions was 1.87 pg/reaction of genomic DNA, which was an order of magnitude lower than that required by conventional PCR assays. Moreover, a cell-template based LAMP assay was also developed for detecting nirS gene that directly used bacterial cells as template rather than genomic DNA. Only 1 h was needed from the addition of bacterial cells to the reaction to the verification of amplification success, and bulky and sophisticated equipment were not needed. Further, the nirS gene of P. aeruginosa in spiked seawater samples could be detected with both the DNA-template based LAMP assay and the cell-template based LAMP assay, thereby demonstrating the practicality of in-field use of them. In summary, the LAMP assays described here represent a rapid, user-friendly, and cost-effective alternative to conventional PCR.

microbiology↗

bioRxiv2020

Epigenome Prediction of Gene Expression using a Dynamical System Approach

Gene regulation is an important fundamental biological process. The regulation of gene expression is managed through a variety of methods including epigentic processes (e.g., DNA methylation). Understanding the role of epigenetic changes in gene expression is a fundamental question of molecular biology. Predictions of gene expression values from epigenetic data have tremendous research and clinical potential. Despite active research, studies to date have focused on using statistical models to predict gene expression from methylation data. In contrast, dynamical systems can be used to generate a model to predict gene expression using epigenetic data and a gene regulatory network (GRN) which can also serve as a mechanistic hypothesis. Here we present a novel stochastic dynamical systems model that predicts gene expression levels from methylation data of genes in a given GRN. Software for dataset preparation, model parameter fitting and prediction generation, and reporting are available at https://github.com/kordk/stoch_epi_lib.

systems biology↗