Biology subjects

Nielsen, J.

Publications and source records attributed to Nielsen, J..

At least 55 records · Page 3Linked to original sources

bioRxiv2022

Drug repurposing screens identifies compounds that inhibit α-synuclein oligomers' membrane disruption and block antibody interactions

Small soluble oligomers of the protein -synuclein (SO) have been linked to disruptions in neuronal homeostasis, contributing to the development of Parkinsons Disease (PD). While this makes SO an obvious drug target, the development of effective therapeutics against SO are challenged by its low abundance and structural and morphological complexity. Here we employ two different approaches to neutralize toxic interactions made by SOs with different cellular components. Firstly, we use available data to identify four neuronal proteins as likely candidates for SO interactions, namely Cfl1, Uchl1, Sirt2 and SerRS. However, despite promising results when immobilized, all 4 proteins only bind weakly to SO in solution in microfluidic assays, making them inappropriate for screening. In contrast, the formation of stable contacts formed between SO and vesicles consisting of anionic lipids not only mimics a likely biological role of SO but also provided a platform to screen two small molecule libraries for disruptors of these contacts. Of the 11 leads obtained in this way, 2 significantly impaired SO contacts with other proteins in a sandwich ELISA assay using SO-binding monoclonal antibodies and nanobodies. In addition, 5 of these leads suppressed -synuclein amyloid formation. Thus a repurposing screening that directly targets a key culprit in PD pathogenesis shows therapeutic potential. HighlightsO_LIThe toxic oligomer formed by -synuclein (SO) is an important drug target. C_LIO_LINeuronal proteins found by pull-down assays do not bind SOs in solution. C_LIO_LILiposome assay identifies 7 approved drugs reducing SO membrane disruption. C_LIO_LIWe identify different inhibitory mechanisms used by different compounds. C_LIO_LITwo top drug hits disrupt SO binding to oligomer-specific antibodies. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=123 SRC="FIGDIR/small/511078v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@184611aorg.highwire.dtl.DTLVardef@33049org.highwire.dtl.DTLVardef@1573ae4org.highwire.dtl.DTLVardef@1db6f38_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2022

Preponderant Myosin Super-Relaxed State In Skeletal Muscle From Endurance Athletes

It has recently been established that myosin, the molecular motor protein, is able to exist in two conformations in relaxed skeletal muscle. These conformations are known as super-relaxed (SRX) and disordered-relaxed (DRX) states and are finely balanced to optimize skeletal muscle metabo-lism. Indeed, SRX myosins are thought to have a 10-fold reduction in ATP turnover compared to DRX myosins. Here, we investigated whether chronic physical activity in humans would be associated with changes in the proportions of SRX and DRX skeletal myosins. For that, we isolated mus-cle fibres from various athletic and sedentary populations and ran a loaded Mant-ATP chase proto-col. We observed that, in endurance-trained athletes, the amounts of myosin molecules in the SRX state was significantly greater than in age-matched sedentary individuals or than in strength athletes. To further assess whether this change would have an impact on the potency of a SRX-inducing pharmacological compound, Mavacamten, we performed similar analyses as above with and without the drug in muscle fibres from endurance athletes. Surprisingly, we found that 0.3 M of Mava-camten had only marginal effects. Altogether, our results indicate that chronic endurance training-status influences resting skeletal myosin conformations, and Mavacamten potency. Our findings also emphasize that environmental stimuli such as exercise can re-wire the molecular metabolism of human skeletal muscle through myosin. SummaryLewis et al., investigate how training-status influences myosin conformations involved in the resting metabolism of skeletal muscle. They find that, in endurance-trained athletes, skeletal myosin preferentially adopts an energy-saving conformation known as super-relaxed state, lowering the metabolic rate and affecting the potency of a super-relaxed state-inducing drug, Mavacamten.

physiology↗

bioRxiv2022

Penetratin inhibits α-synuclein fibrillation and improves locomotor functions in mice model of Parkinsons disease

Parkinsons disease (PD) is the second most common neurodegenerative disease. The presence of lewy bodies, primarily consisting of -synuclein (-syn) aggregates is one of the common features seen in the substantia nigra region of the brain in PD patients. The disease remains incurable and only symptomatic relief is available. We screened various cell-penetrating peptides and reveal that penetratin is a potent inhibitor of -syn aggregation in-vitro, and significantly improved locomotor coordination in mice models of PD in-vivo. The peptide inhibits -syn aggregation in vitro as well as in yeast, and C.elegans models. We further made a cyclic derivative of penetratin by disulfide coupling of N- and C-terminal cysteine residues. Both penetratin and its cyclized derivative interact with -syn. NMR studies show that both linear as well as cyclic derivative interact at the acidic C-terminal tail of the protein. Similar to penetratin, its cyclic derivative inhibited -syn aggregation in the C.elegans model of Parkinsons disease, and also improved worm motility. Molecular Dynamics studies show that penetratin interacts with -synuclein and prevents its conformational transition from disordered into {beta}-sheet rich structure. The therapeutic efficacy of penetratin was further confirmed in a transgenic mice model of the disease, wherein penetratin treatment over a period of 90 days improved locomotor coordination, and halted disease progression. Overall, the present work provides a potent therapeutic agent that could be further explored in the management of PD.

neuroscience↗

bioRxiv2022

Fine-tuning of coumaric acid synthesis to increase naringenin production in yeast

(2S)-Naringenin is a key precursor for biosynthesis of various high-value flavonoids and possesses a variety of nutritional and pharmaceutical properties on human health. Systematic optimization approaches have been employed to improve (2S)-naringenin production in different microbial hosts. However, very few studies have focused on the spatiotemporal distribution of (2S)-naringenin and related pathway intermediate p-coumaric acid, which is an important factor for efficient production. Here, we show that fine-turning of p-coumaric acid synthesis enables alleviated cell burden and improved (2S)-naringenin production in yeast. First, we systematically optimized the (2S)-naringenin biosynthetic pathway by alleviating the bottleneck downstream of p-coumaric acid and increasing malonyl-CoA supply, which improved (2S)-naringenin production but significant amounts of p-coumaric acid still accumulated outside the cell. We further established a dual dynamic control system through combing a malonyl-CoA biosensor regulator and an RNAi strategy, to autonomously control the synthesis of p-coumaric acid and downregulate a pathway competing for malonyl-CoA. The optimized strains remarkably decreased extracellular accumulation of p-coumaric acid and simultaneously improved (2S)- naringenin production. Finally, production of 933 mg/L of (2S)-naringenin could be achieved by using minimal medium with negligible accumulation of p-coumaric acid. Our work highlights the importance of systematic control of pathway intermediates for efficient microbial production of plant natural products.

synthetic biology↗

bioRxiv2022

Systems analysis of metabolic responses to a mixed meal test in an obese cohort reveals links between tissue metabolism and the gut microbiota

Individuals with prediabetes and type 2 diabetes mellitus (T2DM) have poor ability to adapt to diet-triggered perturbations. We investigated global metabolic responses to a mixed meal test (MMT) in morbidly obese individuals with different diabetic status by performing plasma metabolomic profiling. Abnormal metabolism of carbohydrates, (branched-chain) amino acids, fatty acids and acylcholines in individuals with (pre)diabetes was observed. Moreover, differences in metabolic responses were associated with altered fecal metagenomics and transcriptomes of liver, jejunum and adipose tissues, which revealed a modified gut microbiome and multi-tissue metabolism in individuals having insulin resistance. Finally, using integrative machine learning models, we built a predictive model based on metabolomics data after 2h MMT, and identified possible new biomarkers for glycemic control including N-acetylaspartate and phenylalanine-derived metabolites that may be useful for diagnosis, intervention and prevention of T2DM.

systems biology↗

bioRxiv2022

Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data

Single-cell RNA sequencing has the potential to unravel the differences in metabolism across cell types and cell states in both the healthy and diseased human body. The use of existing knowledge in the form of genome-scale metabolic models (GEMs) holds promise to strengthen such analyses, but the combined use of these two methods requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the tINIT algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile, emphasizing the need to study them separately rather than to build models from bulk RNA-Seq data. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.

systems biology↗

bioRxiv2022

Learning deep representations of enzyme thermal adaptation

Temperature is a fundamental environmental factor that shapes the evolution of organisms. Learning thermal determinants of protein sequences in evolution thus has profound significance for basic biology, drug discovery, and protein engineering. Here, we use a dataset of over 3 million enzymes labeled with optimal growth temperatures (OGT) of their source organisms to train a deep neural network model (DeepET). The protein-temperature representations learned by DeepET provide a temperature-related statistical summary of protein sequences and capture structural properties that affect thermal stability. For prediction of enzyme optimal catalytic temperatures and protein melting temperatures via a transfer learning approach, our DeepET model outperforms classical regression models trained on rationally designed features and other recent deep-learning-based representations. DeepET thus holds promise for understanding enzyme thermal adaptation and guiding the engineering of thermostable enzymes.

bioinformatics↗

bioRxiv2022

Discovery of therapeutic agents targeting PKLR for NAFLD using drug repositioning

Background & AimsNon-alcoholic fatty liver disease (NAFLD) encompasses a wide spectrum of liver pathologies. However, not medical treatment has been approved for the treatment of the disease. In our previous study, we found PKLR could be a potential target for treatment of NALFD. Here, the aim is to investigate the effect of PKLR in in vivo model and perform drug repositioning to identify a drug candidate for treatment of NAFLD. MethodsBiopsies from liver, muscle, white adipose tissue and heart were obtained from control and PKLR knockout mice fed with chow and high sucrose diets. Lipidomics as well as transcriptmics analyses were conducted using these tissue samples. In addition, a computational drug repositioning analysis was performed and drug candidates were identified. The drug candidates were finally tested in both in vitro and in vivo models to evaluated their toxicity and efficacy. ResultsThe Pklr KO reversed the increased hepatic triglyceride level in mice fed with high sucrose diet and partly recovered the transcriptomic changes in liver as well as other three tissues. Both liver and white adipose tissues exhibited dysregulated circadian transcriptomic profiles, and these dysregulations were reversed by hepatic knockout of Pklr. In addition, 10 small molecule drugs were identified as potential inhibitor of PKLR by the drug repositioning pipeline, and two of them significantly inhibited both the PKLR expression and triglyceride level in in vitro model. Finally, the two selected small molecule drugs were evaluated in in vivo rat models and it was demonstrated that these drugs attenuated hepatic steatosis without side effect on other tissues. ConclusionIn conclusion, our study provided biological insights about the critical role of PKLR in NAFLD progression and proposed a treatment strategy for NAFLD patients, which has been validated in preclinical experiment.

systems biology↗

bioRxiv2022

Cellular limitation of enzymatic capacity explains glutamine addiction in cancers

The tumor microenvironment is comprised of a complex mixture of different cell types interacting under conditions of nutrient deprivation, but the metabolism therein is not fully understood due to difficulties in measuring metabolic fluxes and exchange of metabolites between different cell types in vivo. Genome-scale metabolic modeling enables estimation of such exchange fluxes as well as an opportunity to gain insight into the metabolic behavior of individual cell types. Here, we estimated the availability of nutrients and oxygen within the tumor microenvironment using concentration measurements from blood together with a metabolite diffusion model. In addition, we developed an approach to efficiently apply enzyme usage constraints in a comprehensive metabolic model of human cells. The combined modeling reproduced severe hypoxic conditions and the Warburg effect, and we found that limitations in enzymatic capacity contribute to cancer cells preferential use of glutamine as a substrate to the citric acid cycle. Furthermore, we investigated the common hypothesis that some stromal cells are exploited by cancer cells to produce metabolites useful for the cancer cells. We identified over 200 potential metabolites that could support collaboration between cancer cells and cancer associated fibroblasts, but when limiting to metabolites previously identified to participate in such collaboration, no growth advantage was observed. Our work highlights the importance of enzymatic capacity limitations for cell behaviors and exemplifies the utility of enzyme constrained models for accurate prediction of metabolism in cells and tumor microenvironments.

cancer biology↗

bioRxiv2021

Polarized α-synuclein trafficking and transcytosis across Brain Endothelial Cells via Rab7-decorated carriers

Parkinsons disease is mainly caused by aggregation of -synuclein (-syn) in the brain. Exchange of -syn between the brain and peripheral tissues could have important pathophysiological and therapeutic implications, but the trafficking mechanism of -syn across the blood brain barrier (BBB) remains unclear. In this study, we therefore investigated uptake and transport mechanisms of -syn monomers and oligomers across an in vitro BBB model system. Both -syn monomers and oligomers were internalized by primary brain endothelial cells, with increased restriction of oligomeric over monomeric transport. To enlighten the trafficking route of monomeric -syn in brain endothelial cells, we investigated co-localization of -syn and intracellular markers of vesicular transport. Here, we observed the highest colocalization with clathrin, Rab7 and VPS35, suggesting a clathrin-dependent internalization, preferentially followed by a late endosome retromer-connected trafficking pathway. Furthermore, STED microscopy revealed monomeric -syn trafficking via Rab7-decorated carriers. Knockdown of Caveolin1, VPS35, and Rab7 using siRNA did not affect monomeric -syn uptake into endothelial cells. However, it significantly reduced transcytosis of monomeric -syn in the luminal-abluminal direction, suggesting a polarized regulation of monomeric -syn vesicular transport. Our findings suggest a direct role for Rab7 in polarized trafficking of monomeric -syn across BBB endothelium, and the potential of Rab7 directed trafficking to constitute a target pathway for new therapeutic strategies against Parkinsons disease and related synucleinopathies. Significance StatementIn the submitted manuscript, we describe the use of a state-of-the-art porcine blood-brain barrier model based on primary cells to get information about this important issue. We identify several hitherto undescribed cellular pathways to mediate polarized transport of alpha-synuclein. One of these paths we find regulated by Rab7 and can be inhibited by targeting several intracellular proteins such as VPS35, Caveolin1 and Rab7. New knowledge describing brain endothelial intracellular transport systems are highly warranted for identifying new target to alleviate Parkinsons disease. We believe that our findings could be the seed to establish new therapeutic strategies against Parkinsons disease and related synucleinopathies.

cell biology↗

bioRxiv2021

Non-random organization of flux control mechanisms in yeast central metabolic pathways

Metabolic flux can be regulated by a variety of different mechanisms, but the organization of these mechanisms within the metabolic network has remained unknown. Here we test the hypothesis that flux control mechanisms are not distributed randomly in the metabolic network, but rather organized according to pathway. Combining proteomics, phosphoproteomics, and metabolic modeling, we report the largest collection of flux-enzyme-phosphoenzyme relationships to date in Saccharomyces cerevisiae. In support of the hypothesis, we show that (i) amino acid metabolic pathways are predominantly regulated by enzyme abundance stemming from transcriptional regulation; (ii) upper glycolysis and associated pathways, by inactivating enzyme phosphorylation; (iii) lower glycolysis and associated pathways, by activating enzyme phosphorylation; and (iv) glycolipid/glycophospholipid pathways, by a combination of enzyme phosphorylation and metabolic compartmentalization. We delineate the evolutionary history for the observed organization of flux control mechanisms in yeast central metabolic pathways, furthering our understanding of the regulation of metabolism and its evolution.

systems biology↗

bioRxiv2021

Cerebellar and subcortical atrophy contribute to psychiatric symptoms in frontotemporal dementia

Recent studies have suggested that cerebellar and subcortical structures are impacted early in the disease progression of genetic frontotemporal dementia (FTD) due to microtubule-associated protein tau (MAPT), progranulin (GRN) and chromosome 9 open reading frame 72 (C9orf72). However, the clinical contribution of the structures involved in the cerebello-subcortical circuitry has been understudied in FTD given their potentially central role in cognition and behaviour processes. The present study aims to investigate whether there is an association between the atrophy of the cerebellar and subcortical structures, and neuropsychiatric symptoms (using the revised version of the Cambridge Behavioral Inventory, CBI-R) across genetic mutations and whether this association starts during the preclinical phase of the disease. Our study included 983 participants from the Genetic Frontotemporal dementia Initiative (GENFI) including mutation carriers (n=608) and non-carrier first-degree relatives of known symptomatic carriers (n= 375). Voxel-wise analysis of the thalamus, striatum, globus pallidus, amygdala, and the cerebellum was performed using deformation based morphometry (DBM) and partial least squares analyses (PLS) were used to link morphometry and behavioural symptoms. Our univariate results suggest that in this group of primarily presymptomatic subjects, volume loss in subcortical and cerebellar structure was primarily a function of aging, with only the C9orf72 group showing more pronounced volume loss in the thalamus compared to the non-carrier individuals. PLS analyses demonstrated that the cerebello-subcortical circuitry is related to all neuropsychiatric symptoms from the CBI-R, with significant overlap in brain/behaviour patterns, but also specificity for each genetic group. The biggest differences were in the extent of the cerebellar involvement (larger extent in C9orf72 group) and more prominent amygdalar contribution in the MAPT group. Finally, our findings demonstrated that C9orf72 and MAPT brain scores were related to estimated years before the age of symptom onset (EYO) in a second order relationship highlighting a steeper brain score decline 20 years before expected symptom onset, while GRN brain scores were related to age and not EYO. Overall, these results demonstrated the important role of the subcortical structures and especially of the cerebellum in genetic FTD symptom expression.

neuroscience↗

bioRxiv2021

Specific ATPases drive compartmentalized glycogen utilization in rat skeletal muscle

Glycogen is a key energy substrate in excitable tissue and especially in skeletal muscle fibers it contributes with a substantial, but also local energy production. A heterogenic subcellular distribution of three distinct glycogen pools in skeletal muscle is proved by transmission electron microscopy (TEM), which is thought to represent the requirements for local energy stores at the subcellular level. Here, we show that the three main energy-consuming ATPases in skeletal muscles (Ca2+-, Na+,K+-, and myosin ATPases) utilize different local pools of glycogen. These results clearly demonstrate compartmentalized glycogen metabolism and emphasize that spatially distinct pools of glycogen particles act as energy substrate for separated energy requiring processes, suggesting a new paradigm for understanding glycogen metabolism in working muscles, muscle fatigue and metabolic disorders.

physiology↗

bioRxiv2021

Genome scale modeling of the protein secretory pathway reveals novel targets for improved recombinant protein production in yeast

Eukaryal cells are used for the production of many recombinant pharmaceutical proteins, including several of the current top-selling products. The protein secretory pathway in eukaryal cells is complex and involves many different processes such as post-translational modifications, translocation, and folding. Furthermore, recombinant protein production competes with native secretory proteins for the limited energy and proteome resources allocated to the protein secretory pathway. Due to the complexity of this pathway, improvement through metabolic engineering has traditionally been relatively ad-hoc; and considering the industrial importance of this pathway, there is a need for more systematic approaches for novel design principles. Here, we present the first proteome-constrained genome-scale protein secretory model of a eukaryal cell, namely for the yeast Saccharomyces cerevisiae (pcSecYeast). The model contains all key processes of this pathway, i.e., protein translation, modification, and degradation coupled with metabolism. The model can capture delicate phenotypic changes such as the switch in the use of specific glucose transporters in response to changing extracellular glucose concentration. Furthermore, the model can also simulate the effects of protein misfolding on cellular growth, suggesting that retro-translocation of misfolded proteins contributes to protein retention in the Endoplasmic reticulum (ER). We used pcSecYeast to simulate various recombinant proteins production and identified overexpression targets for different recombinant proteins overproduction. We experimentally validated many of the predicted targets for -amylase production in this study, and the results show that the secretory pathways have more limited capacity than metabolism in terms of protein secretion.

systems biology↗

bioRxiv2021

Deep learning based kcat prediction enables improved enzyme constrained model reconstruction

Enzyme turnover numbers (kcat values) are key parameters to understand cell metabolism, proteome allocation and physiological diversity, but experimentally measured kcat data are sparse and noisy. Here we provide a deep learning approach to predict kcat values for metabolic enzymes in a high-throughput manner with the input of substrate structures and protein sequences. Our approach can capture kcat changes for mutated enzymes and identify amino acid residues with great impact on kcat values. Furthermore, we applied the approach to predict genome scale kcat values for over 300 yeast species, demonstrating that the predicted kcat values are consistent with current evolutional understanding. Additionally, we designed an automatic pipeline using the predicted kcat values to parameterize enzyme-constrained genome scale metabolic models (ecGEMs) facilitated by a Bayesian approach, which outperformed the default ecGEMs in predicting phenotypes and proteomes and enabled to explain phenotype differences among yeast species. The deep learning kcat prediction approach and automatic ecGEM construction pipeline would thus be a valuable tool to uncover the global trend of enzyme kinetics and physiological diversity, and to further elucidate cell metabolism on a large scale.

systems biology↗

bioRxiv2021

A network-based approach reveals the dysregulated transcriptional regulation in non-alcohol fatty liver disease

Non-alcohol-related fatty liver disease (NAFLD) is a leading cause of chronic liver disease worldwide. We performed network analysis to investigate the dysregulated biological processes in the disease progression and revealed the molecular mechanism underlying NAFLD. Based on network analysis, we identified a highly conserved disease-associated gene module across three different NAFLD cohorts and highlighted the predominant role of key transcriptional regulators associated with lipid and cholesterol metabolism. In addition, we revealed the detailed metabolic differences between heterogenous NAFLD patients through integrative systems analysis of transcriptomic data and liver-specific genome-scale metabolic model. Furthermore, we identified transcription factors (TFs), including SREBF2, HNF4A, SREBF1, YY1 and KLF13, showing regulation of hepatic expression of genes in the NAFLD-associated modules and validated the TFs using data generated from a mouse NAFLD model. In conclusion, our integrative analysis facilitated our understanding of the regulatory mechanism of these perturbed TFs and associated biological processes.

systems biology↗

bioRxiv2021

Supervised generative design of regulatory DNA forgene expression control

In order to control gene expression, regulatory DNA variants are commonly designed using random synthetic approaches with mutagenesis and screening. This however limits the size of the designed DNA to span merely a part of a single regulatory region, whereas the whole gene regulatory structure including the coding and adjacent non-coding regions is involved in controlling gene expression. Here, we prototype a deep neural network strategy that models whole gene regulatory structures and generates de novo functional regulatory DNA with prespecified expression levels. By learning directly from natural genomic data, without the need for large synthetic DNA libraries, our ExpressionGAN can traverse the whole sequence-expression landscape to produce sequence variants with target mRNA levels as well as natural-like properties, including over 30% dissimilarity to any natural sequence. We experimentally demonstrate that this generative strategy is more efficient than a mutational one when using purely natural genomic data, as 57% of the newly-generated highly-expressed sequences surpass the expression levels of natural controls. We foresee this as a lucrative strategy to expand our knowledge of gene expression regulation as well as increase expression control in any desired organism for synthetic biology and metabolic engineering applications.

synthetic biology↗

bioRxiv2021

Amino acid and nucleotide metabolism shape the selection of trophic levels in animals

What an animal eats determines its trophic level (TL) in the food web. The diet of high-TL animals is thought to contain more energy because it contains higher levels of lipids. This however has not been systematically examined in the context of comprehensive metabolic networks of different animals. Here, we reconstruct species-specific genome-scale metabolic models (GEMs) of 32 animals, and calculate the maximum ATP production per unit of food for each animal. Surprisingly, we find that ATP production is closely associated with metabolic flux through central carbon metabolism and amino acid metabolism, while correlation with lipid metabolism is low. Further, metabolism of specific amino acids and nucleotides underlie maximum ATP production from food. Our analyses indicate that amino acid and nucleotide metabolism, rather than lipid metabolism, are major contributors to the selection of animal trophic levels, demonstrating that species-level metabolic flux plays key roles in trophic interactions and evolution.

ecology↗