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bioRxiv2025

Chiral inversion mutagenesis identifies geometrically constrained residues within self-associating low-complexity domains

Many protein low-complexity domains (LCDs) reversibly self-associate to enable cellular function, yet fundamental questions remain regarding how polypeptide chemical and structural features beyond side chain identity contribute to LCD-LCD interactions. For instance, the folds adopted by globular proteins emerge from constraints enforced by homo-chirality of genetically-encoded polypeptides. However, it remains unclear to what extent similar geometric constraints apply to LCD self-association. Herein, we use protein total- and semi-synthesis to probe the contribution of C stereochemistry to LCD self-association with synthetic Chiral Inversion Mutagenesis (ChIM). By introducing targeted L-to-D amino acid inversions, ChIM identifies C stereocenters under geometric constraint without modification of side-chain functionalities. We apply ChIM to the LCDs of inner nuclear lamina protein Emerin and neurofilament light chain and find that chiral inversion produces strongly position-dependent effects upon LCD self-association. Our study describes essential structural features that enable LCD self-association and chemical strategies to interrogate LCD biochemistry. Significance StatementThis study identifies a critical role for amino acid side chain chirality in low-complexity domain (LCD) self-association. Using synthetic protein chemistry, we introduced targeted L-to-D amino acid inversions in LCDs without altering side chain functionalities. Chiral inversion mutagenesis identified geometrically constrained residues in the LCDs of Emerin and neurofilament light chain that localize to self-association hotspots within these sequences. Our work demonstrates that, like globular proteins, LCDs exploit polypeptide homochirality for biochemical function. The position-dependent effects of chiral inversion indicate the formation of labile structural elements that mediate oligomerization. By employing synthetic protein chemistry to probe LCD biochemistry with high positional resolution, this work provides a powerful approach to dissect chemical principles and polypeptide structural features that govern LCD-LCD interactions.

biochemistry↗

bioRxiv2026

Scaffold Affinity Tunes Biomolecular Condensate Function

Biomolecular condensates (BMCs) organize cellular biochemistry by concentrating selected molecules into dynamic membrane-free compartments. Yet the molecular parameters that determine not only whether condensates form, but also how they behave and what they do, remain poorly defined. Here we show that scaffold binding affinity (Kd) is a quantitative determinant of condensate phase behavior, internal dynamics and biochemical output. Using a modular SUMO-SIM system in which scaffold valency was held constant while binding affinity was systematically varied, we found that affinity governs the phase boundary, resistance to chemical perturbation, and molecular mobility of condensates in vitro and in human cells. In multicomponent mixtures, the highest-affinity scaffold dominated dense-phase composition and dynamics, revealing a hierarchical rule for condensate organization. Finally, affinity-dependent changes in condensate dynamics translated into tunable enzyme activity, establishing binding energetics as an engineerable parameter for programming condensate biochemistry.

biochemistry↗

bioRxiv2014

Modelling reactions catalysed by carbohydrate-active enzymes

Carbohydrate polymers are ubiquitous in biological systems and their roles are highly diverse, ranging from energy storage over mechanical stabilisation to mediating cell-cell or cell-protein interactions. The functional diversity is mirrored by a chemical diversity that results from the high flexibility of how different sugar monomers can be arranged into linear, branched or cyclic polymeric structures. Mathematical models describing biochemical processes on polymers are faced with various difficulties. First, polymer-active enzymes are often specific to some local configuration within the polymer but are indifferent to other features. That is they are potentially active on a large variety of different chemical compounds, meaning that polymers of different size and structure simultaneously compete for enzymes. Second, especially large polymers interact with each other and form water-insoluble phases that restrict or exclude the formation of enzyme-substrate complexes. This heterogeneity of the reaction system has to be taken into account by explicitly considering processes at the, often complex, surface of the polymer matrix. We review recent approaches to theoretically describe polymer biochemical systems. All attempts address a particular challenge, which we discuss in more detail. We emphasise a recent attempt which draws novel analogies between polymer biochemistry and statistical thermodynamics and illustrate how this parallel leads to novel insights about non-uniform polymer reactant mixtures. Finally, we discuss the future challenges of the young and growing field of theoretical polymer biochemistry.

Systems Biology↗

bioRxiv2020

Growth of the Fucus embryo: insights into wall-mediated cell expansion through mechanics and transcriptomics

Morphogenesis in walled organisms represents a highly controlled process that involves cell proliferation and expansion; cell growth is regulated through changes in the structure and mechanics of the cells walls. Despite taking different evolutionary paths, land plants and some brown algae exhibit developmental and morphological similarities; however, the role of the algal cell wall in morphogenesis remains heavily underexplored. Cell expansion in plants is hypothesized to involve modifications of hemicellulose linkages and pectin gelation in the cell wall. Little is known about the wall-based control of cell expansion in brown algae; however, the algal analog to pectin, alginate, exhibits different gelation depending on its biochemistry. Here we show that cell wall mechanics and alginate biochemistry are correlated with cell expansion versus proliferation in the developing Fucus serratus embryo. In the elongating cells of the embryo rhizoid, we found a reduced cell wall stiffness and lower amounts of stiffer alginate epitopes. In comparison, the early embryo thallus was shown to undergo cleavage-type cell proliferation, without expansion, and this was correlated with higher amounts of stiff alginate epitopes and increased wall stiffness. An embryo development RNAseq dataset was generated to characterize differential gene expression during development. This data set allowed for identification of many enriched GO functions through developmental time. In addition, the transcriptome allowed for the identification of cell-wall related genes whose differential expression may underlie our observed growth phenotypes. We propose that differential gene expression of genes involved in alginate stiffness are strong candidates underlying differential wall stiffness and cell elongation in the developing Fucus embryo. Our results show that wall-driven cellular expansion mechanisms in brown algae are similar to those observed in plants. In addition, our data show that cleavage-type cell proliferation exists in brown algae similar to that seen in plant and animal systems indicating a possible conserved developmental phenomenon across the branches of multicellular life.

developmental biology↗

bioRxiv2020

Using computational modeling to teach metabolism as a dynamic system improves student performance

Understanding metabolic function requires knowledge of the dynamics, interdependence, and regulation of biochemical networks. However, current approaches are not optimal to develop the needed mechanistic understanding, and misconceptions about biological processes persist even after graduation. To address these issues, we developed a computational modeling and simulation approach that employs scaffolded learning to teach biochemistry students about the regulation of metabolism. The power of the approach lies in students abilities to alter any component or connection in a modeled system and instantly observe the effects of their changes. We find that students who use our approach perform better on biochemistry metabolism questions compared to students in a course that did not use this approach. We also investigated performance by gender and found that our modules may have the potential to increase equity in education. We noted that students are generally positive about the approach and appreciate its benefits. Our modules provide life science instructors with a dynamic and systems-driven approach to teach metabolic regulation and control that improves learning and also equips students with important technical skills.

scientific communication and education↗

bioRxiv2020

Cargo crowding drives sorting stringency in COPII vesicles

Accurate maintenance of organelle identity in the secretory pathway relies on retention and retrieval of resident proteins. In the endoplasmic reticulum (ER), secretory proteins are packaged into COPII vesicles that largely exclude ER residents and misfolded proteins by mechanisms that remain unresolved. Here we combined biochemistry and genetics with correlative light and electron microscopy (CLEM) to explore how selectivity is achieved. Our data suggest that vesicle occupancy dictates ER retention: in the absence of abundant cargo, non-specific bulk flow increases. We demonstrate that ER leakage is influenced by vesicle size and cargo occupancy: overexpressing an inert cargo protein, or reducing vesicle size restores sorting stringency. We propose that cargo recruitment into vesicles creates lumenal steric pressure that drives selectivity. Sorting stringency is thus an emergent property of the biophysical process of cargo enrichment into a constrained spherical membrane-bound carrier. SummaryCombining correlative light and electron microscopy with yeast genetics and biochemistry, Gomez-Navarro, Melero and colleagues show that cargo recruitment into a constrained COPII vesicle restricts bulk flow, thereby contributing to sorting stringency and ER quality control.

cell biology↗

bioRxiv2020

Multi-omics phenotyping of the gut-liver axis allows health risk predictability from in vivo subchronic toxicity tests of a low-dose pesticide mixture

Human health effects from chronic exposure to mixtures of pesticide residues are little investigated. We compared standard histopathology and serum biochemistry measures and multi-omics analyses in an in vivo subchronic toxicity test of a mixture of six pesticide active ingredients frequently detected in foodstuffs (azoxystrobin, boscalid, chlorpyrifos, glyphosate, imidacloprid and thiabendazole). Sprague-Dawley rats were administered with the pesticide mixture with each ingredient at its regulatory permitted acceptable daily intake. Analysis of water and feed consumption, body weight, histopathology and serum biochemistry showed little or no physiological effects from exposure to the pesticide mixture. In marked contrast, analysis of the host-gut microbiome axis using serum and caecum metabolomics revealed that nicotinamide and tryptophan metabolism were affected, which suggested the initiation of a cell danger response, including adaptation to oxidative stress. Only limited effects were detected on the caecum microbiota by shotgun metagenomics. Further analyses of in vitro bacterial cultures showed that growth of Lactobacillus rhamnosus and Escherichia coli strains was negatively impacted by the pesticide mixture at concentrations that were not inhibitory when exposure was to a single agent. Transcriptomics of the liver showed that 257 genes had their expression changed. Gene functions affected included those involved in the regulation of response to hormones and correlated with previously reported transcriptome changes following administration of nicotinamide. Genome-wide DNA methylation analysis of the same liver samples showed that 4255 CpG sites were differentially methylated (> 10% difference). Overall, we demonstrated that unlike standard blood biochemical and organ histological analysis, in-depth molecular profiling using a combination of high-throughput -omics methods in laboratory animals exposed to low concentrations of pesticides reveals metabolic effects on the gut-liver axis, which can potentially be used as biomarkers for the prediction of future negative health outcomes. Our data suggest that adoption of multi-omics as part of regulatory risk assessment procedures will result in more accurate outcome measures, with positive public health implications.

pharmacology and toxicology↗

bioRxiv2020

Scarcity of scale-free topology is universal across biochemical networks

Biochemical reactions underlie all living processes. Like many biological and technological systems, their complex web of interactions is difficult to fully capture and quantify with simple mathematical objects. Nonetheless, a huge volume of research has suggested many real-world biological and technological systems - including biochemical systems - can be described rather simply as scale-free networks, characterized by a power-law degree distribution. More recently, rigorous statistical analyses across a variety of systems have upended this view, suggesting truly scale-free networks may be rare. We provide a first application of these newer methods across two distinct levels of biological organization: analyzing a large ensemble of biochemical networks generated from the reactions encoded in 785 ecosystem-level metagenomes and 1082 individual-level genomes (representing all three domains of life). Our results confirm only a few percent of individual and ecosystem-level biochemical networks meet the criteria necessary to be anything more than super-weakly scale-free. Leveraging the simultaneous analysis of the multiple coarse-grained projections of biochemistry, we perform distinguishability tests across properties of individual and ecosystem-level biochemical networks to determine whether or not they share common structure, indicative of common generative mechanisms across levels. Our results indicate there is no sharp transition in the organization of biochemistry across distinct levels of the biological hierarchy - a result that holds across different network projections. This suggests the existence of common organizing principles operating across different levels of organization in biochemical networks, independent of the project chosen. Author SummaryFully characterizing living systems requires rigorous analysis of the complex webs of interactions governing living processes. Here we apply statistical approaches to analyze a large data set of biochemical networks across two levels of organization: individuals and ecosystems. We find that independent of level of organization, the standard scale-free model is not a good description of the data. Interestingly, there is no sharp transition in the shape of degree distributions for biochemical networks when comparing those of individuals to ecosystems. This suggests the existence of common organizing principles operating across different levels of biochemical organization that are revealed across different network projections.

systems biology↗

bioRxiv2020

delta-Melt: Nucleic acid conformational penalties from melting experiments

Thermodynamic preferences to form non-native conformations are crucial for understanding how nucleic acids fold and function. However, they are difficult to measure experimentally because this requires accurately determining the population of minor low-abundance (<10%) conformations in a sea of other conformations. Here we show that melting experiments enable facile measurements of thermodynamic preferences to adopt non-native conformations in DNA and RNA. The key to this delta-melt approach is to use chemical modifications to render specific minor non-native conformations the major state. The validity and robustness of delta-melt is established for four different non-native conformations under various physiological conditions and sequence contexts through independent measurements of thermodynamic preferences using NMR. delta-melt is fast, simple, cost-effective, and enables thermodynamic preferences to be measured for exceptionally low-populated conformations. Using delta-melt, we obtained rare insights into conformational cooperativity, obtaining evidence for significant cooperativity (1.0-2.5 kcal/mol) when simultaneously forming two adjacent Hoogsteen base pairs. We also measured the thermodynamic preferences to form G-C+ and A-T Hoogsteen and A-T base open states for nearly all sixteen trinucleotide sequence contexts and found distinct sequence-specific variations on the order of 2-3 kcal/mol. This rich landscape of sequence-specific non-native minor conformations in the DNA double helix may help shape the sequence-specificity of DNA biochemistry. Thus, melting experiments can now be used to access thermodynamic information regarding regions of the free energy landscape of biomolecules beyond the native folded and unfolded conformations. Significance StatementThermodynamic preferences of nucleic acids to adopt non-native conformations are crucial for understanding how they function but prove difficult to measure experimentally. As a result, little is known about how these thermodynamic preferences vary with sequence and structural contexts, physiological conditions, and chemical modifications. Here, we show that modifications stabilizing non-native conformations and rendering them the major state, in conjunction with melting experiments, enable facile measurements of thermodynamic preferences to form various non-native conformations in DNA and RNA. delta-melt provided rare insights into the cooperativity of forming tandem Hoogsteen base pairs and revealed large and distinct sequence-specific preferences to form G-C+ and A-T Hoogsteen and A-T base open conformations in DNA, which may contribute to sequence-specific DNA biochemistry.

biophysics↗

bioRxiv2021

Do size and shape matter? Exploring the interactions and the metabolome of the soil isolate Hylemonella gracilis

Microbial community analysis of aquatic environments showed that an important component of its microbial diversity consists of bacteria with cell sizes of ~0.1 m. Such small bacteria can show genomic reductions and metabolic dependencies with other bacteria. However, so far no study investigated if such bacteria exist in terrestrial environments like e.g. soil. Here, we isolated soil bacteria that passed through a 0.1 m filter, by applying a novel isolation and culturing approach. The complete genome of one of the isolates was sequenced and the bacterium was identified as Hylemonella gracilis. A set of co-culture assays with phylogenetically distant soil bacteria with different cell and genome sizes was performed. The co-culture assays revealed that H. gracilis grows better when interacting with other soil bacteria like Paenibacillus sp. AD87 and Serratia plymuthica. Transcriptomics and metabolomics showed that H. gracilis was able to change gene expression, behavior, and biochemistry of the interacting bacteria without direct cell-cell contact. Our study indicates that bacteria are present in the soil that can pass through a 0.1 m filter. These bacteria may have been overlooked in previous research on soil microbial communities. Such small bacteria, exemplified here by H. gracilis, are able to induce transcriptional and metabolomic changes in other bacteria upon their interactions in soil. In vitro, the studied interspecific interactions allowed utilization of growth substrates that could not be utilized by monocultures, suggesting that biochemical interactions between substantially different sized soil bacteria may contribute to the symbiosis of soil bacterial communities. ImportanceAnalysis of aquatic microbial communities revealed that parts of its diversity consist of bacteria with cell sizes of ~0.1 m. Such bacteria can show genomic reductions and metabolic dependencies with other bacteria. So far, no study investigated if such bacteria exist in terrestrial environments e.g. soil. By applying a novel isolation method, we show that such bacteria also exist in soil. The isolated bacteria was identified as Hylemonella gracilis. Co-culture assays with phylogenetically different soil bacteria revealed that H. gracilis grows better when co-cultured with other soil bacteria. Transcriptomics and metabolomics showed that H. gracilis was able to change gene expression, behavior, and biochemistry of the interacting bacteria without direct contact. Our study revealed that bacteria are present in soil that can pass through 0.1 m filters. Such bacteria may have been overlooked in previous research on soil microbial communities and may contribute to the symbiosis of soil bacterial communities.

microbiology↗

bioRxiv2021

Analysing and simulating energy-based models in biology using BondGraphTools

Like all physical systems, biological systems are constrained by the laws of physics. However, mathematical models of biochemistry frequently neglect the conservation of energy, leading to unrealistic behaviour. Energy-based models that are consistent with conservation of mass, charge and energy have the potential to aid the understanding of complex interactions between biological components, and are becoming easier to develop with recent advances in experimental measurements and databases. In this paper, we motivate the use of bond graphs (a modelling tool from engineering) for energy-based modelling and introduce, BondGraphTools, a Python library for constructing and analysing bond graph models. We use examples from biochemistry to illustrate how BondGraphTools can be used to automate model construction in systems biology while maintaining consistency with the laws of physics.

systems biology↗

bioRxiv2021

A shuttle-vector system allows heterologous gene expression in the thermophilic methanogen Methanothermobacter thermautotrophicus ΔH

Thermophilic Methanothermobacter spp. are used as model microbes to study the physiology and biochemistry of the conversion of hydrogen and carbon dioxide into methane (i.e., hydrogenotrophic methanogenesis), because of their short doubling times and robust growth with high growth yields. Yet, a genetic system for these model microbes was missing despite intense work for four decades. Here, we report the establishment of tools for genetic modification of M. thermautotrophicus. We developed the modular Methanothermobacter vector system, which provided shuttle-vector plasmids (pMVS) with exchangeable selectable markers and replicons for both Escherichia coli and M. thermautotrophicus. For M. thermautotrophicus, a thermostable neomycin-resistance cassette served as the selectable marker for positive selection with neomycin, and the cryptic plasmid pME2001 from Methanothermobacter marburgensis served as the replicon. The pMVS-plasmid DNA was transferred from E. coli into M. thermautotrophicus via interdomain conjugation. After the successful validation of DNA transfer and positive selection in M. thermautotrophicus, we demonstrated heterologous gene expression of a thermostable {beta}-galactosidase-encoding gene (bgaB) from Geobacillus stearothermophilus under the expression control of four distinct synthetic and native promoters. In quantitative in-vitro enzyme activity assays, we found significantly different {beta}-galactosidase activity with these distinct promoters. With a formate dehydrogenase operon-encoding shuttle vector, we allowed growth of M. thermautotrophicus on formate as the sole growth substrate, while this was not possible for the empty vector control. These genetic tools provide the basis to investigate hypotheses from four decades of research on the physiology and biochemistry of Methanothermobacter spp. on a genetic level. Significance StatementThe world economies are facing permanently increasing energy demands. At the same time, carbon emissions from fossil sources need to be circumvented to minimize harmful effects from climate change. The power-to-gas platform is utilized to store renewable electric power and decarbonize the natural gas grid. The microbe Methanothermobacter thermautotrophicus is already applied as the industrial biocatalyst for the biological methanation step in large-scale power-to-gas processes. To improve the biocatalyst in a targeted fashion, genetic engineering is required. With our shuttle-vector system for heterologous gene expression in M. thermautotrophicus, we set the cornerstone to engineer the microbe for optimized methane production, but also for production of high-value platform chemicals in power-to-x processes.

microbiology↗

bioRxiv2021

The terminal heme synthetic enzyme, Coproheme Decarboxylase, coordinates heme synthesis and uptake in response to iron in Mycobacteria

Heme is both an essential cofactor and an abundant source of nutritional iron for the human pathogen Mycobacterium tuberculosis (Mtb). While heme is required for Mtb survival and virulence, it is also potentially cytotoxic. Since Mtb has the ability to both make and uptake heme, the de novo synthesis of heme and its acquisition from the host must be balanced in order to mitigate heme toxicity. However, the mechanisms employed by Mtb to regulate heme uptake, synthesis, and bioavailability are poorly understood. By integrating ratiometric heme sensors with mycobacterial genetics, cell biology, and biochemistry, we determined that the terminal heme biosynthetic enzyme, coproheme decarboxylase (ChdC), plays a role in regulating both heme bioavailability and uptake in Mtb. Moreover, we found that Mtb has a preference for scavenging reduced ferrous heme and exhibits a cell surface heme reductase activity that is regulated by ChdC. In Mtb, ChdC expression is down-regulated when iron is limiting, which in-turn increases both heme import and bioavailability. Such a mechanism may serve to protect cells from heme toxicity while trying to meet the nutritional demand for iron. Our results demonstrate that heme synthesis and uptake are tightly integrated in mycobacteria and represent the first example of a heme synthetic enzyme playing a role in controlling heme uptake. Significance StatementHeme is an essential but potentially cytotoxic cofactor and iron source for the pathogen, Mycobacterium tuberculosis (Mtb). To understand how Mtb coordinates heme uptake and synthesis to mitigate heme toxicity, we integrated heme sensors with mycobacterial genetics and biochemical approaches to probe the interplay between heme synthesis and scavenging. We discovered that the terminal heme synthetic enzyme, coproheme decarboxylase (ChdC), negatively regulates heme uptake and utilization in response to iron availability through a mechanism involving control of a ferric heme reductase. During iron limitation, ChdC is downregulated, thereby enhancing exogenous heme reduction, uptake and utilization while simultaneously suppressing heme synthesis, which allows Mtb to avoid heme toxicity. Our results highlight the close coordination between heme synthesis and uptake in mycobacteria. ClassificationBiological sciences : Biochemistry

microbiology↗

bioRxiv2021

Generation, quality control, and analysis of the first genomically humanised knock-in mice for the ALS/FTD genes SOD1, TARDBP (TDP-43), and FUS

Amyotrophic lateral sclerosis - frontotemporal dementia spectrum disorder (ALS/FTD) is a complex neurodegenerative disease; up to 10% of cases are familial, usually arising from single dominant mutations in >30 causative genes. Transgenic mouse models that overexpress human ALS/FTD causative genes have been the preferred organism for in vivo modelling. However, while conferring human protein biochemistry, these overexpression models are not ideal for dosage-sensitive proteins such as TDP-43 or FUS. We have created three next-generation genomically humanised knock-in mouse models for ALS/FTD research, by replacing the entire mouse coding region of Sod1, Tardbp (TDP-43) and Fus, with their human orthologues to preserve human protein biochemistry, with exons and introns intact to enable future modelling of coding or non-coding mutations and variants and to preserve human splice variants. In generating these mice, we have established a new-standard of quality control: we demonstrate the utility of indirect capture for enrichment of a region of interest followed by Oxford Nanopore sequencing for robustly characterising large knock-in alleles. This approach confirmed that targeting occurred at the correct locus and to map homologous recombination events. Furthermore, extensive expression data from the three lines shows that homozygous humanised animals only express human protein, at endogenous levels. Characterisation of humanised FUS animals showed that they are phenotypically normal compared to wildtype littermates throughout their lifespan. These humanised mouse strains are critically needed for preclinical assessment of interventions, such as antisense oligonucleotides (ASOs), to modulate expression levels in patients, and will serve as templates for the addition of human ALS/FTD mutations to dissect disease pathomechanisms.

neuroscience↗

bioRxiv2021

Pfam domain adaptation profiles reflect plant species' evolutionary history

The increase in global temperatures predicted by climate change models presents a serious problem for agriculture because high temperatures reduce crop yields. Protein biochemistry is at the core of plant heat stress response, and understanding the interactions between protein biochemistry and temperature will be key to developing heat-tolerant crop varieties. Current experimental studies of proteome-wide plant thermostability are limited by the complexity of plant proteomes: evaluating function for thousands of proteins across a variety of temperatures is simply not feasible with existing technologies. In this paper, we use homologous prokaryote sequences to predict plant Pfam temperature adaptation and gain insights into how thermostability varies across the proteome for three species: maize, Arabidopsis, and poplar. We find that patterns of Pfam domain adaptation across organelles are consistent and highly significant between species, with cytosolic proteins having the largest range of predicted Pfam stabilities and a long tail of highly-stable ribosomal proteins. Pfam adaptation in leaf and root organs varies between species, and maize root proteins have more low-temperature Pfam domains than do Arabidopsis or poplar root proteins. Both poplar and maize populations have an excess of low-temperature mutations in Pfam domains, but only the mutations identified in poplar accessions have a negative effect on Pfam temperature adaptation overall. These Pfam domain adaptation profiles provide insight into how different plant structures adapt to their surrounding environment and can help inform breeding or protein editing strategies to produce heat-tolerant crops.

plant biology↗

bioRxiv2021

Tissue-wide metabolomics reveals wide impact of gut microbiota on mice metabolite composition

The essential role of gut microbiota in health and disease is well-recognized, but the biochemical details underling beneficial impact remain largely undefined. Dysbiosis of gut bacteria results in the alteration of certain microbial and host metabolites, and identifying these markers could enhance the early detection of certain diseases. We report LC-MS based non-targeted metabolic profiling to demonstrate a large effect of gut microbiota on mammalian tissue metabolites. It was hypothesized that gut microbiota influences the overall biochemistry of the host metabolome and this effect is tissue-specific. Thirteen different tissues from germ-free and conventional mice were selected and their metabolic differences were analyzed. Our study demonstrated a large effect of the microbiome on mammalian biochemistry at different tissue levels and resulted in significant modulation of metabolites from multiple metabolic pathway (p [≤] 0.05). A vast metabolic response of host to metabolites generated by the microbiota was observed, Hundreds of molecular features were detected exclusively in one mouse group, with the majority of these being unique to specific tissue, suggesting direct impact gut microbiota on host metabolism.

microbiology↗

bioRxiv2021

Mevastatin in colon cancer by spectroscopic and microscopic methods - Raman imaging and AFM studies

One of the most important areas of medical science is oncology, which is responsible for both the diagnostics and treatment of cancer diseases. Simultaneously one of the main challenges of oncology is the development of modern drugs effective in the fight against cancer. Statins are a group of biologically active compounds with the activity of 3-hydroxy-3-methyl glutaryl-CoA reductase inhibitors, an enzyme catalyzing the reduction of 3-hydroxy-3-methyl-glutaryl-CoA (HMG-CoA) to mevalonic acid. By acting on this enzyme, statins inhibit the endogenous cholesterol synthesis which in turn causes the reduction of its systemic concentrations. However, in vitro and in vivo studies confirm also the cytostatic and cytotoxic effects of statins against various types of cancer cells including colon cancer. In the presented studies the influence of mevastatin on cancerous colon cells CaCo-2 by Raman spectroscopy and imaging is discussed and compared with biochemistry characteristic for normal colon cells CCD-18Co. Based on vibrational features of colon cells: normal cells CCD-18Co, cancerous cells CaCo-2 and cancerous cells CaCo-2 treated by mevastatin in different concentrations and incubation times we have confirm the influence of this statin on biochemistry composition of cancerous human colon cells. Moreover, the spectroscopic results for colon normal cells and cancerous cells based on data typical for nucleic acids, proteins, lipids have been compared. The cytotoxisity of mevastatin was determined by using XTT tests.

cancer biology↗

bioRxiv2022

Integrative analysis of metabolite GWAS illuminates the molecular basis of pleiotropy and genetic correlation

Pleiotropy and genetic correlation are widespread features in GWAS, but they are often difficult to interpret at the molecular level. Here, we perform GWAS of 16 metabolites clustered at the intersection of amino acid catabolism, glycolysis, and ketone body metabolism in a subset of UK Biobank. We utilize the well-documented biochemistry jointly impacting these metabolites to analyze pleiotropic effects in the context of their pathways. Among the 213 lead GWAS hits, we find a strong enrichment for genes encoding pathway-relevant enzymes and transporters. We demonstrate that the effect directions of variants acting on biology between metabolite pairs often contrast with those of upstream or downstream variants as well as the polygenic background. Thus, we find that these outlier variants often reflect biology local to the traits. Finally, we explore the implications for interpreting disease GWAS, underscoring the potential of unifying biochemistry with dense metabolomics data to understand the molecular basis of pleiotropy in complex traits and diseases.

genomics↗