bioRxiv Science⌕ Search

SEARCH · bioRxiv Science

Results for “biochemistry”

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 667 records · Page 37Linked to original sources

Abundant clock proteins point to missing molecular regulation in the plant circadian clock

Understanding the biochemistry behind whole-organism traits such as flowering time is a longstanding challenge, where mathematical models are critical. Very few models of plant gene circuits use the absolute units required for comparison to biochemical data. We refactor two detailed models of the plant circadian clock from relative to absolute units. Using absolute RNA quantification, a simple model predicted abundant clock protein levels in Arabidopsis thaliana, up to 100,000 proteins per cell. NanoLUC reporter protein fusions validated the predicted levels of clock proteins in vivo. Recalibrating the detailed models to these protein levels estimated their DNA-binding dissociation constants (Kd). We estimate the same Kd from multiple results in vitro, extending the method to any promoter sequence. The detailed models simulated the Kd range estimated from LUX DNA-binding in vitro but departed from the data for CCA1 binding, pointing to further circadian mechanisms. Our analytical and experimental methods should transfer to understand other plant gene regulatory networks, potentially including the natural sequence variation that contributes to evolutionary adaptation.

systems biology↗

Climate History and Metabolic Trade-offs shape the Bacterial Response to Drought

Soil drying challenges microbial viability and survival, with bacteria employing various mechanisms to respond to shifts in osmolarity, including dormancy or metabolic upregulation of osmoprotectants. However, the extent to which these responses are shaped by an organisms phylogeny or the climate history of a given environment is poorly understood. This study examines the responses of phylogenetically similar bacteria from semi-arid and humid tropical forest soils to osmotic and matric stress using synchrotron radiation-based Fourier Transform Infrared spectromicroscopy. This non-destructive approach depicts the biochemical phenotype for whole cells under control and stress conditions. We observed that, under osmotic stress, bacteria upregulated cell-signaling pathways, rapidly turned over lipid-storage compounds, and increased osmolyte production. In contrast, matric stress induced a more muted response, typically elevating the production of carbohydrate stress compounds, such as glycine betaine and trehalose. While phylogenetically similar bacteria showed comparable biochemistry under control conditions, climate history played an important role in regulating responses to stress, whereby a stronger metabolic response was observed from semi-arid relative to tropical forest isolates. We conclude that bacterial stress response to drought can be more diverse than previously observed, and regulated by both phylogeny and climate history.

microbiology↗

Satellite glial cells modulate proprioceptive neuron activity in dorsal root ganglia

Proprioception, the sense of body and limb position, is mediated by proprioceptors and is crucial for important motor functions such as standing and walking. Proprioceptor cell bodies reside within the peripheral dorsal root ganglia (DRG) and are tightly enveloped by satellite glial cells (SGCs). SCGs express a number of Gq protein-coupled receptors (Gq GPCRs), but their functional consequences on proprioceptor activity is unknown. Using a combination of chemogenetics, genetics, Ca2+ imaging, pharmacology, immunohistochemistry, and biochemistry, we provide evidence that SGC Gq GPCR signaling is sufficient to drive purinergic receptor-mediated Ca2+ responses in proprioceptor cell bodies. Our findings suggest a potential role for SGC Gq GPCR signaling in shaping proprioceptor information processing. Furthermore, this demonstration of SGC-induced proprioceptor activation has profound implications with SGC Gq GPCR signaling and purinergic receptors representing potential therapeutic targets for alleviating some proprioceptor and sensorimotor impairments associated with spinal muscular atrophy or Friedreichs ataxia.

neuroscience↗

3D Molecular Pretraining via Localized Geometric Generation

Self-supervised learning on 3D molecular structures is gaining importance in data-driven scientific research and applications due to the high costs of annotating bio-chemical data. However, the strategic selection of semantic units for modeling 3D molecular structures remains underexplored, despite its crucial role in effective pre-training--a concept well-established in language processing and computer vision. We introduce Localized Geometric Generation (LEGO), a novel approach that treats tetrahedrons within 3D molecular structures as fundamental building blocks, leveraging their geometric simplicity and widespread presence across chemical functional patterns. Inspired by masked modeling, LEGO perturbs tetrahedral local structures and learns to reconstruct them in a self-supervised manner. Experimental results demonstrate LEGO consistently enhances molecular representations across biochemistry and quantum property prediction benchmarks. Additionally, the tetrahedral modeling and pretraining generalize from small molecules to larger molecular systems, validating by protein-ligand affinity prediction. Our results highlight the potential of selecting semantic units to build more expressive and interpretable neural networks for scientific AI applications.

bioinformatics↗

SusC/D-like proteins in Gammaproteobacteria that utilize fructans

Fructans are ubiquitous in terrestrial ecosystems, however, these glycans are unexplored in the marine environment. We have discovered that the Antarctic gammaproteobacterium Pseudoalteromonas distincta is highly adapted to the degradation of fructose-containing substrates. This is enabled by proteins encoded in several genomic regions, including a fructan polysaccharide utilization locus (PUL). In addition to a glycoside hydrolase from family 32 (GH32), the fructan PUL encodes two proteins that have been described as specific for Bacteroidota and were previously unknown for Gammaproteobacteria: a glycan-binding SusD-like protein and a SusC-like TonB-dependent transporter (TBDT), which work as a complex in glycan import. Proteome analyses and biochemistry results suggest that the SusC/D-like proteins of P. distincta shuttle small-sized inulin-type fructans directly into the cell, where they are degraded by a periplasmic exo-active GH32. A SusD-like protein could provide a competitive adavantage in the absence of extracelluar endo-active inulinases. Comparative genomics identified further SusC/D-like proteins in Gammaproteobacteria, most of which are co-encoded with GH32s, indicative of fructan PULs, and are frequently associated with the marine habitat. Our study thus shows the first known exception to the paradigm that only Bacteroidota use SusC/D-like proteins. It further suggests that fructans contribute to the marine glycan pool.

molecular biology↗

Accurate and Rapid Prediction of Protein pKa: Protein Language Models Reveal the Sequence-pKa Relationship

Protein pKa prediction is a key challenge in computational biology. In this study, we present pKALM, a novel deep learning-based method for high-throughput protein pKa prediction. pKALM uses a protein language model (PLM) to capture the complex sequence-structure relationship of proteins. While traditionally considered a structure-based problem, our results show that a PLM pre-trained on large-scale protein sequence databases can effectively learn this relationship and achieve state-of-the-art performance. pKALM accurately predicts the pKa values of six residues (Asp, Glu, His, Lys, Cys, and Tyr) and two termini with high precision and efficiency. It excels at predicting both exposed and buried residues, which often deviate from standard pKa values measured in solvent. We demonstrate a novel finding that predicted protein isoelectric points (pI) can be used to improve the accuracy of pKa prediction. High-throughput pKa prediction of the human proteome using pKALM achieves a speed of 4,965 pKa predictions per second, which is several orders of magnitude faster than existing state-of-the-art methods. The case studies illustrate the efficacy of pKALM in estimating pKa values and the constraints of the method. pKALM will thus be a valuable tool for researchers in the fields of biochemistry, biophysics, and drug design.

bioinformatics↗

Structure-guided assembly of an influenza spike nanobicelle vaccine provides pan H1 intranasal protection

Development of intranasal vaccines for respiratory viruses has gained popularity. However, currently only a live-attenuated influenza vaccine is FDA-approved for intranasal administration. Here, we focused on influenza virus as it circulates seasonally, has pandemic potential, and has vaccine formulations that present hemagglutinin (HA) in different structural arrangements. These display differences have not been correlated with induction of pan-H1 antibodies or shown to provide intranasal protection. Using electron microscopy, biochemistry and animal studies, we identified HA complexes arranged as lipid discs with multiple trimeric HAs displayed along the perimeter, termed spike nanobicelles (SNB). We utilized a structure-guided approach to synthesize in vitro assembled spiked nanobicelles (IA-SNB) from a classical 1934 H1N1 influenza virus. IA-SNBs elicited pan-H1 antibodies and provided protection against antigenically divergent H1N1 viruses via intranasal immunizations. Viral glycoprotein spikes displayed as SNBs could aid in combating antigenic variation and provide innovative intranasal vaccines to aid universal influenza vaccine development.

microbiology↗

A regulatory module driving the recurrent evolution of irreducible molecular complexes.

To sustain life, molecular complexes require the concerted action of multiple proteins, each relying on one another to perform intricate tasks. However, how such interdependent protein interactions evolve in the first place is poorly understood. To address this, we investigated the origins of a group of fast-evolving genetic parasites--toxin-antidote elements--which boil down this dilemma to a simple question: what came first, the toxin or the antidote? By integrating quantitative genetics, biochemistry, and evolutionary genomics, we discovered that toxins and antidotes can arise simultaneously through the duplication of a regulatory module comprising an F-box protein in linkage to its substrate. Our findings provide one solution to the recurrent emergence of mutual dependence in protein complexes and illustrate in detail how complexity can swiftly arise from simplicity.

genomics↗

Chromatin remodeler BRG1 recruits huntingtin to repair DNA double-strand breaks in neurons

Persistent DNA double-strand breaks (DSBs) are enigmatically implicated in neurodegenerative diseases including Huntingtons disease (HD), the inherited late-onset disorder caused by CAG repeat elongations in Huntingtin (HTT). Here we combine biochemistry, computation and molecular cell biology to unveil a mechanism whereby HTT coordinates a Transcription-Coupled Non-Homologous End-Joining (TC-NHEJ) complex. HTT joins TC-NHEJ proteins PNKP, Ku70/80, and XRCC4 with chromatin remodeler Brahma-related Gene 1 (BRG1) to resolve transcription-associated DSBs in brain. HTT recruitment to DSBs in transcriptionally active gene- rich regions is BRG1-dependent while efficient TC-NHEJ protein recruitment is HTT-dependent. Notably, mHTT compromises TC-NHEJ interactions and repair activity, promoting DSB accumulation in HD tissues. Importantly, HTT or PNKP overexpression restores TC-NHEJ in a Drosophila HD model dramatically improving genome integrity, motor defects, and lifespan. Collective results uncover HTT stimulation of DSB repair by organizing a TC-NHEJ complex that is impaired by mHTT thereby implicating dysregulation of transcription-coupled DSB repair in mHTT pathophysiology. Highlights* BRG1 recruits HTT and NHEJ components to transcriptionally active DSBs. * HTT joins BRG1 and PNKP to efficiently repair transcription related DSBs in brain. * Mutant HTT impairs the functional integrity of TC-NHEJ complex for DSB repair. * HTT expression improves DSB repair, genome integrity and phenotypes in HD flies.

neuroscience↗

Understanding the regulation of chronic wounds by tissue inhibitors of matrix metalloproteinases through mathematical modelling

Understanding the biochemistry and pharmacodynamics of chronic wounds is of key importance, due to the millions of people in the UK affected and the significant cost to the NHS. Chronic wounds are characterised by elevated concentrations of matrix metalloproteinases (MMPs) that destroy the surrounding extracellular matrix (ECM). However, fibroblasts can produce tissue inhibitors of MMPs (TIMPs) in order to regulate wound healing. Therefore, the role of TIMPs in both acute and chronic wounds needs to be properly understood in order to develop therapeutic treatments. In this work, we propose a reaction-diffusion system of four partial differential equations that describe the interaction of the ECM, fibroblasts, MMPs, and TIMPs in a wound. We observe that, subject to biologically realistic parameter values, this mathematical model gives rise to travelling wave solutions. Using bifurcation analysis, we demonstrate that deregulated apoptosis in the ECM results in the emergence of chronic wounds, and the reversal of these chronic wounds is prohibited for lower TIMP production values. These results are replicated within a simplified model obtained via a parameter sensitivity analysis. This model is further extended to more realistic spatial domains where we demonstrate the effectiveness of a therapeutic hydrogel containing TIMPs as a treatment for chronic wounds.

cell biology↗

Shift from visceral to subcutaneous fat in Cyp17a1 knockout rats prevent the progression of metabolic syndrome

In this study, we investigated the effects of Cyp17a1 gene knockout (KO) on obesity and metabolic syndrome. Cyp17a1 KO in rats using CRISPR-Cas9 resulted in sex dimorphism and obesity, and interestingly, idiopathic accumulation was found in subcutaneous adipose tissue. Surprisingly, an insulin tolerance test and oral glucose tolerance test did not show any issues with insulin sensitivity and secretion despite hyperglycemia. In addition, Cyp17a1 KO rats showed normal plasma insulin and free fatty acid levels compared to wild-type rats, and blood biochemistry analysis revealed normal triglyceride, total cholesterol, high-density lipoprotein, and low-density lipoprotein levels. Cyp17a1 KO adipose-tissue-derived stem cells from subcutaneous fat showed increased expression of KLF5, an early adipogenesis marker, which implies enhanced adipogenic potential in subcutaneous adipose tissue. When gene expression associated with lipid, glucose, and insulin metabolism as well as inflammation in adipose tissue was examined, a metabolic shift to subcutaneous adipose tissue was discovered in the Cyp17a1 KO group. In conclusion, in the Cyp17a1 KO rat models we generated for the first time, the phenotype promoted by obesity reflected health obesity hypothesis, but this did not result in metabolic syndrome due to enhanced metabolism in the subcutaneous fats.

physiology↗

Nucleoprotein phase-separation affinities revealed via atomistic simulations of short peptide and RNA fragments

Liquid-liquid phase separation of proteins and nucleic acids into condensate phases is a versatile mechanism for ensuring compartmentalization of cellular biochemistry. RNA molecules play critical roles in these condensates, particularly in transcriptional regulation and stress responses, exhibiting a wide range of thermodynamic and dynamic behaviors. However, deciphering the molecular grammar that governs the stability and dynamics of protein-RNA condensates remains challenging due to the multicomponent and heterogeneous nature of these biomolecular mixtures. In this study, we employ atomistic simulations of twenty distinct mixtures containing minimal RNA and peptide fragments to dissect the phase-separating affinities of all twenty amino acids in the presence of RNA. Our findings elucidate chemically specific interactions, hydration profiles, and ionic effects that synergistically promote or suppress protein-RNA phase separation. We map a ternary phase diagram of interactions, identifying four distinct groups of residues that promote, maintain, suppress, or disrupt protein-RNA clusters.

biophysics↗

novoStoic2.0: An integrated framework for pathway synthesis, thermodynamic evaluation, and enzyme selection

Computational pathway design and retro-biosynthetic approaches can facilitate the development of innovative biochemical production routes, biodegradation strategies, and the funneling of multiple precursors into a single bioproduct. However, effective pathway design necessitates a comprehensive understanding of biochemistries, enzyme activities, and thermodynamic feasibility. Herein, we introduce novoStoic2.0, an integrated platform that combines tools for estimating overall stoichiometry, designing de novo synthesis pathways, assessing thermodynamic feasibility, and selecting enzymes. novoStoic2.0 offers a unified web-based interface as a part of the AlphaSynthesis platform (http://novostoic.platform.moleculemaker.org/) tailored for the synthesis of thermodynamically viable pathways as well as the selection of enzymes for re-engineering required for novel reaction steps. We exemplify the utility of the platform to identify novel pathways for hydroxytyrosol synthesis, which are shorter than the known pathways and require reduced cofactor usage. In summary, novoStoic2.0 aims to streamline the process of pathway design contributing to the development of sustainable biotechnological solutions.

synthetic biology↗

Cannabis Expression Atlas: a comprehensive resource for integrative analysis of Cannabis sativa L. gene expression

Cannabis sativa L., a plant originating from Central Asia, is a versatile crop with applications spanning textiles, construction, pharmaceuticals, and food products. This study aimed to compile and analyze publicly available Cannabis RNA-Seq data and develop an integrated database tool to help advance Cannabis research in various topics such as fiber production, cannabinoid biosynthesis, sex determination, and plant development. We identified 515 publicly available RNA-Seq samples that, after stringent quality control, resulted in a high-quality dataset of 394 samples. Utilizing the Jamaican Lion genome as reference, we constructed a comprehensive database and developed the Cannabis Expression Atlas (https://cannatlas.venanciogroup.uenf.br/), a web application for visualization of gene expression, annotation, and functional classification. Key findings include the quantification of 27,640 Cannabis genes and their classification into seven expression categories: not-expressed, low-expressed, housekeeping, tissue-specific, group-enriched, mixed, and expressed-in-all tissues. The study revealed substantial variability and coherence in gene expression across different tissues and chemotypes. We found 2,382 tissue-specific genes, including 177 transcription factors.. The Cannabis Expression Atlas constitutes a valuable tool for exploring gene expression patterns and offers insights into Cannabis biology, supporting research in plant breeding, genetic engineering, biochemistry, and functional genomics.

plant biology↗

Characterization of the cellular and subcellular distribution of fms-like tyrosine kinase 3 (FLT3) and other neuronal proteins using an alkaline phosphatase (AP) immunolabeling method

Precisely localizing the spatial distribution of proteins within various cell types in the brain, and in sub-cellular compartments such as the synapses, is critical for generating and testing hypothesis to elucidating the function of target proteins in the brain. The fms-like tyrosine kinase-3 (FLT3) has been studied extensively in the context of blood cell development and leukemia pathogenesis, but little is known about its role in the brain. Previous work characterizing FLT3 protein expression in the brain have not yielded convincing results, mainly limited by the low expression level of FLT3 and poor sensitivity of standard immunolabeling method using fluorescent secondary antibody. In this study, we report the systematic characterization of FLT3 protein localization during brain development using a highly sensitive immunolabeling methodology based on alkaline phosphatase (AP) polymer biochemistry. Our results reveal a previously unknown neuron-selective FLT3 expression pattern in both mouse and human cerebellum tissue samples, and demonstrate a gradual increase in the total FLT3 protein level and a cytosolic-to-dendritic change in subcellular FLT3 distribution during mouse cerebellum development. Through combining the AP immunolabeling of FLT3 with standard immunostaining of various cell type markers to achieve hybrid co-labeling of multiple antigens in tissue sections, we demonstrate that the main cell type that express FLT3 in the cerebellum is PV+, Calbindin+ Purkinje cells. To expand the use case of AP immunolabeling method in labeling neuronal proteins, we show robust labeling of Kir2.1, a potassium channel protein that is expressed at low level in neurons, in brain tissue sections collected from mouse, pig, and human brains. We further established that the AP immunolabeling method can be used in human stem cell-derived neurons to detect postsynaptic density scaffold protein PSD95 within fine subcellular structures such as dendritic spines and synapses, in cultured primary mouse cortical neurons and human stem cell-derived neurons. To our knowledge, our work represents the first report of applying AP immunolabeling to detect neuronal protein expression in brain tissue and cell samples. Moreover, our work reveals a previously unknown neuron-specific pattern of FLT3 expression in the brain, providing the foundation for further mechanistic studies to uncover its role in brain development and functioning.

neuroscience↗

Bioinformatics Analysis Suggests That SE_1780 protein from Staphylococcus epidermidis may be a member of the Fph Family of Lipases

The Protein Databank entry for protein SE_1780 from Staphylococcus epidermidis lists the function as unknown. We leveraged the framework outlined in the Biochemistry Authentic Scientific Inquiry Laboratory and used bioinformatics tools to ascertain the function of the protein. Based on our analysis we posit that SE_1780 is a lipase of the /{beta} hydrolase family with a proposed active site catalytic triad composed of Ser144, Asp235, and His269. Further we identified the lipase FphD as having significant sequence identity to protein SE 1780, and suggest that the protein is a member of the Fph family of lipases from S. epidermidis.

bioinformatics↗

The dinucleotide structure of NAD enables specific reduction on mineral surfaces

Nucleotide-derived cofactors could function as a missing link between the informational and the metabolic part at lifes emergence. One well-known example is nicotinamide dinucleotide (NAD), one of the evolutionarily most conserved redox cofactors found in metabolism. Here, we propose that the role of these cofactors could even extend to missing links between geo- and biochemistry. We show NAD+ can be reduced under close-to nature conditions with nickel-iron-alloys found in water-rock-interaction settings rich in hydrogen (serpentinizing systems) and that nicotinamide mononucleotide (NMN), a precursor molecule to NAD, has different properties regarding reduction specificity and sensitivity than NAD. The additional adenosine monophosphate (AMP) "tail" of the dinucleotide, a shared trait between many organic cofactors, seems to play a crucial mechanistic role in preventing overreduction of the nicotinamide-bearing nucleotide. This specificity is also connected to the used transition metals. While the combination of nickel and iron promotes the reduction of NAD+ to 1,4-NADH most efficiently, in the case of NMN, the presence of nickel leads to the accumulation of overreduction products. Testing the reducing abilities of both NADH and NMNH under abiotic conditions showed that both molecules act as equally effective, soluble hydride donors in non-enzymatic, proto-metabolic stages of lifes emergence.

evolutionary biology↗

Active RNA synthesis patterns nuclear condensates

Biomolecular condensates are membraneless compartments that organize biochemical processes in cells. In contrast to well-understood mechanisms describing how condensates form and dissolve, the principles underlying condensate patterning - including their size, number and spacing in the cell - remain largely unknown. We hypothesized that RNA, a key regulator of condensate formation and dissolution, influences condensate patterning. Using nucleolar fibrillar centers (FCs) as a model condensate, we found that inhibiting ribosomal RNA synthesis significantly alters the patterning of FCs. Physical theory and experimental observations support a model whereby active RNA synthesis generates a non-equilibrium state that arrests condensate coarsening and thus contributes to condensate patterning. Altering FC condensate patterning by expression of the FC component TCOF1 impairs ribosomal RNA processing, linking condensate patterning to biological function. These results reveal how non-equilibrium states driven by active chemical processes regulate condensate patterning, which is important for cellular biochemistry and function.

biophysics↗