Biology subjects

Jiang, Y.

Publications and source records attributed to Jiang, Y..

At least 217 records · Page 12Linked to original sources

bioRxiv2024

A Wearable Mindfulness Brain-Computer Interface for Alleviating Car Sickness

Car sickness, an enormous vehicular travel challenge, affects a significant proportion of the population. Pharmacological interventions are limited by adverse side effects, and effective nonpharmacological alternatives remain to be identified. Here, we introduce a novel attention shifting method based on a closed-loop, artificial intelligence (AI)-driven, wearable mindfulness brain-computer interface (BCI) to alleviate car sickness. As the user performs an attentional task, i.e., focusing on breathing as in mindfulness, with a wearable headband, the BCI collects and analyses electroencephalography (EEG) data via a convolutional neural network to assess the users mindfulness state and provide real-time audiovisual feedback. This approach might sustainedly shift the users attention from physiological discomfort towards the BCI-based mindfulness practices, thereby mitigating car sickness symptoms. The efficacy of the proposed method was rigorously evaluated in two real-world experiments, namely, short and long car rides, with a large cohort of more than 100 participants susceptible to car sickness. Remarkably, over 83% of the participants rated the BCI-based attention shifting as effective, with significant reductions in car sickness severity, particularly in individuals with severe symptoms. Furthermore, EEG data analysis revealed a neurobiological signature of car sickness, which provided mechanistic insights into the efficacy of the BCI-based attention shifting for alleviating car sickness. This study proposed the first large-scale validated, nonpharmacological and wearable intervention method and system for car sickness, with the potential to transform the travel experiences of hundreds of millions of people suffering from car sickness, which also represents a new application of BCI technology.

bioengineering↗

bioRxiv2024

A Novel Engineered U7 Small Nuclear RNA Scaffold Greatly Increases in vitro and in vivo ADAR-Mediated Programmable RNA Base Editing

Custom RNA base editing using the endogenous human Adenosine Deaminase Acting on RNA (ADAR) enzyme presents a promising approach for precision therapeutics, alleviating concerns of permanent DNA damage or immunogenicity from1 foreign bacterial proteins such as CRISPR/Cas. ADAR can be directed to act on therapeutic RNA targets by antisense guide RNAs (gRNAs) that create a substrate for ADARs adenosine-to-inosine (effectively A-to-G) deamination activity. Delivery of gRNAs via a DNA expression construct provided by Adeno-Associated Virus (AAV) might allow life-long duration of the therapy. However, a major challenge for RNA editing using gene-encoded gRNAs and endogenous levels of ADAR is achieving sufficient gRNA activity inside cells, especially in therapeutic situations where AAV delivery may provide as low as one viral genome per cell. Here we show that embedding antisense gRNAs into a U7 small nuclear RNA (snRNA) framework and adding hnRNP A1 binding domains greatly increases the efficiency of custom RNA editing. This increased editing efficiency allows for detectable RNA editing from a single genomic insertion of gRNA construct per cell, which enabled a pooled library screen of 750+ gRNA variations to further improve the SmOPT U7 hairpin system. The screen revealed critical residues responsible for RNA editing and generated new SmOPT and U7 hairpin variants that further boosted RNA editing. The final design, combined with an improved synthetic U7 promoter, resulted in up to 76% targeted editing with a single integrated copy of construct per cell, representing a 10- to 100-fold increase over existing circular gRNA approaches. Using systemic in vivo AAV delivery, we achieved an unprecedented 75% RNA editing in the total brain of a mouse model of Hurler syndrome. Our novel SmOPT U7 system also improved published antisense oligos for DMD exon skipping, currently in clinical trials, by up to 25-fold in differentiated myoblasts, and therefore represents a universal scaffold for ADAR-based RNA editing as well as other antisense RNA therapies.

bioengineering↗

bioRxiv2024

scGNN+: Adapting ChatGPT for Seamless Tutorial and Code Optimization

Foundation models have transformed AI by leveraging large-scale data to efficiently perform diverse tasks, and their applications in bioinformatics are primarily focused on data-centric tasks like cell type annotation and gene expression analysis. However, their potential extends beyond data analysis, offering significant opportunities in software development and optimization, such as code refinement, tutorial generation, and advanced visualization. For example, models like OpenAI Codex can suggest optimized code snippets and generate well-documented, reproducible workflows, enhancing accessibility and reducing computational complexity. Despite these advantages, the use of foundation models for improving computational tool engineering in single-cell research remains underutilized. To address this gap, we developed scGNN+, a web-based platform that combines the power of graph neural networks with the capabilities of ChatGPT to enhance reproducibility, code optimization, and visualization. scGNN+ further simplifies the process for users by generating standardized, well-annotated code, making complex procedures more accessible to non-programmers. Additionally, ChatGPT integration allows users to create high-quality, customizable visualizations through natural language prompts, improving data interpretation and presentation. Ultimately, scGNN+ offers a user-friendly, reproducible, and optimized solution for single-cell research, leveraging the full potential of foundation models in bioinformatics. scGNN+ is publicly available at https://bmblx.bmi.osumc.edu/scgnn+.

bioinformatics↗

bioRxiv2024

Evaluation of scalp-based targeting methods of DLPFC for TMS therapy: EEG cap F3, Beam F3, CPC F3, and adjusted Beam F3

BackgroundRepetitive transcranial magnetic stimulation targeting the left dorsolateral prefrontal cortex (DLPFC) offers a promising approach for treating depression. Scalp-based targeting methods, including EEG Cap, Beam F3, CPC F3, and adjusted Beam F3, have evolved to improve clinical application. ObejectivesTo evaluate and compare the accuracy, reliability, speed, and training efficacy of these four methods. MethodsThree trained technicians conducted repeated DLPFC targeting measurements on 10 3D-printed head models. Accuracy was assessed by calculating targeting error and electric field strength at the subgenual anterior cingulate cortex anti-correlated peak. Reliability was evaluated through inter- and intra-rater variability, and training efficacy was examined by comparing targeting errors between trained technicians and novices. ResultsCPC F3, Beam F3, and adjusted Beam F3 demonstrated comparable accuracy in terms of electric field strength, but CPC F3 had the lowest targeting error (4.2 mm). CPC F3 also exhibited the highest reliability (inter-rater variability at 3.3 mm, intra-rater variability at 2.3 mm). The EEG cap was the fastest method (80 seconds), while CPC F3 was faster than Beam F3 but slower than EEG Cap. No significant differences in targeting error between trained technicians and novices were noted with CPC F3 and adjusted Beam F3. ConclusionThe comparative analysis of four scalp-based targeting methods provides insights that can enhance clinical decision-making and the practical application. While accuracy among CPC F3, Beam F3, and adjusted Beam F3 was comparable, CPC F3 emerges as highly practical due to its significantly better reliability, training efficacy, moderate speed, and straightforward implementation.

neuroscience↗

bioRxiv2024

Generative machine learning of ADAR substrates for precise and efficient RNA editing

Adenosine Deaminase Acting on RNA (ADAR) converts adenosine to inosine within certain double-stranded RNA structures. However, ADARs promiscuous editing and poorly understood specificity hinder therapeutic applications. We present an integrated approach combining high-throughput screening (HTS) with generative deep learning to rapidly engineer efficient and specific guide RNAs (gRNAs) to direct ADARs activity to any target. Our HTS quantified ADAR-mediated editing across millions of unique gRNA sequences and structures, identifying key determinants of editing outcomes. We leveraged these data to develop DeepREAD (Deep learning for RNA Editing by ADAR Design), a diffusion-based model that elucidates complex design rules to generate novel gRNAs outperforming existing design heuristics. DeepREADs gRNAs achieve highly efficient and specific editing, including challenging multi-site edits. We demonstrate DeepREADs therapeutic potential by designing gRNAs targeting the MECP2R168X mutation associated with Rett syndrome, achieving both allelic specificity and species cross-reactivity. This approach significantly accelerates the development of ADAR-based RNA therapeutics for diverse genetic diseases.

bioengineering↗

bioRxiv2024

Water use efficiency of Cupressus gigantea is higher in mountain slope habitats compared to riverside habitats

Cupressus gigantea is an endemic species of the Yarlung Zangbo River in Tibet, China, and is designated as a national key protected wild plant. It is only distributed in the narrow area along Yarlung Zangbo River from Jiacha County to Danniang village Milin City in various habitats such as the waterline along the Yarlung Zangbo River, terraces, and mountain slopes. In this study, we utilized stable isotope technology ({delta}D, {delta}18O, and {delta}13C), combined with the IsoSource model, to quantitatively determined the sources of water absorption and the water use efficiency of C. gigantea in various habitats, including riverside area, terraces and mountain slopes. The results showed that the content of soil total nitrogen, soil nitrate nitrogen, soil ammonium nitrogen, and soil organic carbon are significantly higher on mountain slope than in the other two habitats, while the total carbon content, water content, and total nitrogen content of C. gigantea leaves are significantly lower on mountain slope than in the other two habitats (P<0.05). The water use efficiency (WUE) of C. gigantea in the mountain slope habitats is 107.8 mol mol-1, which is significantly higher than that of other habitats (P<0.05), with the WUE of C. gigantea in the riverside habitat being the lowest at 35.7 mol mol-1. C. gigantea distributed on the mountain slope and terrace uses soil water as the main water source. During the normal rainy season, the proportion of soil water utilization is the highest, accounting for more than 60%; in the dry season, the utilization of river water and groundwater increases, while soil water utilization decreases. C. gigantea distributed along the riverside mainly absorbs water from the Yarlung Zangbo River, about 63%. In August when the rainfall increases in the rainy season, the use of soil moisture increases, and in the dry season, river water becomes the main water source. This indicates that with the alternation of rainy and dry seasons, the water absorption sources and strategies of C.gigantea adjusted accordingly. The research results provide an important scientific basis for the protection and management measures of the C. gigantea population.

ecology↗

bioRxiv2024

Complex genetic variation in nearly complete human genomes

Diverse sets of complete human genomes are required to construct a pangenome reference and to understand the extent of complex structural variation. Here, we sequence 65 diverse human genomes and build 130 haplotype-resolved assemblies (130 Mbp median continuity), closing 92% of all previous assembly gaps1,2 and reaching telomere-to-telomere (T2T) status for 39% of the chromosomes. We highlight complete sequence continuity of complex loci, including the major histocompatibility complex (MHC), SMN1/SMN2, NBPF8, and AMY1/AMY2, and fully resolve 1,852 complex structural variants (SVs). In addition, we completely assemble and validate 1,246 human centromeres. We find up to 30-fold variation in -satellite high-order repeat (HOR) array length and characterize the pattern of mobile element insertions into -satellite HOR arrays. While most centromeres predict a single site of kinetochore attachment, epigenetic analysis suggests the presence of two hypomethylated regions for 7% of centromeres. Combining our data with the draft pangenome reference1 significantly enhances genotyping accuracy from short-read data, enabling whole-genome inference3 to a median quality value (QV) of 45. Using this approach, 26,115 SVs per sample are detected, substantially increasing the number of SVs now amenable to downstream disease association studies.

genomics↗

bioRxiv2024

A Machine Learning-Based Investigation of Integrin Expression Patterns in Cancer and Metastasis

BackgroundIntegrins, a family of transmembrane receptor proteins, play complex roles in cancer development and metastasis. These roles could be better delineated through machine learning of transcriptomic data to reveal relationships between integrin expression patterns and cancer. MethodsWe collected publicly available RNA-Seq integrin expression from 8 healthy tissues and their corresponding tumors, along with data from metastatic breast cancer. We then used machine learning methods, including t-SNE visualization and Random Forest classification, to investigate changes in integrin expression patterns. ResultsIntegrin expression varied across tissues and cancers, and between healthy and cancer samples from the same tissue, enabling the creation of models that classify samples by tissue or disease status. The integrins whose expression was important to these classifiers were identified. For example, ITGA7 was key to classification of breast samples by disease status. Analysis in breast tissue revealed that cancer rewires co-expression for most integrins, but the co-expression relationships of some integrins remain unchanged in healthy and cancer samples. Integrin expression in primary breast tumors differed from their metastases, with liver metastasis notably having reduced expression. ConclusionsIntegrin expression patterns vary widely across tissues and are greatly impacted by cancer. Machine learning of these patterns can effectively distinguish samples by tissue or disease status.

cancer biology↗

bioRxiv2024

Mitochondrial Transplantation: Adaptive Bio-enhancement

Mitochondria, often referred to the powerhouse of the cell, are essential for cellular energy production, and their dysfunction can profoundly affect various organs. Transplantation of healthy mitochondria can restore the bioenergetics in diseased cells and address multiple conditions, but more potentials of this approach remain unclear. In this study, I demonstrated that the source of transplanted mitochondria is not limited by species, as exhibit no significant responses to mitochondria derived from different germlines. Moreover, I identified that metabolic compatibility between the recipient and exogenous mitochondria as a crucial factor in mitochondrial transplantation, which confers unique metabolic properties to recipient cells, enabling them to combat diseases. Additionally, my findings indicated competitive interactions among mitochondria with varying functions, with more bioenergetic-active mitochondria yielded superior therapeutic benefits. Notably, no upper limit for the bio-enhancement provided by exogenous mitochondria has been identified. Based on these insights, I proposes a novel therapeutic approach--adaptive bio-enhancement through mitochondrial transplantation.

cell biology↗

bioRxiv2024

Protrusion force and cell-cell adhesion play a critical role in collective tumor cluster migration

Collective migration refers to the coordinated movement of cells as a single unit during migration. While collective migration enhances invasive and metastatic potential in cancer, the mechanisms driving this behavior and regulating tumor migration plasticity remain poorly understood. This study provides a mechanistic model explaining the emergence of different modes of collective migration under hypoxia-induced secretome. We focus on the interplay between cellular protrusion force and cell-cell adhesion using collectively migrating three-dimensional microtumors as models with well-defined microenvironments. Large microtumors show directional migration due to intrinsic hypoxia, while small microtumors exhibit radial migration when exposed to hypoxic secretome. Here, we developed an in silico multi-scale microtumor model (MSMM) based on the cellular Potts model and implemented in CompuCell3D to elucidate underlying mechanisms. We identified distinct migration modes within specific regions of protrusion force and cell-cell adhesion parameter space and studied these modes using in vitro experimental microtumor models. We show that sufficient cellular protrusion force is crucial for radial and directional collective microtumor migration. Radial migration emerges when sufficient cellular protrusion force is generated, driving neighboring cells to move collectively in diverse directions. Within migrating tumors, strong cell-cell adhesion enhances the alignment of cell polarity, breaking the symmetric angular distribution of protrusion forces and leading to directional microtumor migration. The integrated results from the experimental and computational models provide fundamental insights into collective migration in response to different microenvironmental stimuli. Our computational and experimental models can adapt to various scenarios, providing valuable insights into cancer migration mechanisms. Statement of SignificanceWhile single-cell metastasis is well-studied, mechanisms of collective cluster migration are less understood. Significant challenges include the lack of a fundamental physics perspective on collective cluster migration mechanisms and suitable physiologically relevant three-dimensional (3D) in vitro models that can recapitulate collective cluster migration. In this article, we developed a computational model depicting microtumor migration behaviors. Collective cell migration, with varying correlation lengths, exhibits different migratory modes such as directional and radial migration. These modes are predicted by in silico models and confirmed using experimental microtumor models. Machine learning methods were exploited to identify migratory modes. Our computational and experimental models are flexible in various circumstances, offering insights into cancer migration mechanisms.

cancer biology↗

bioRxiv2024

Listening to music modulates visual perception of paintings during binocular rivalry

Music, as a hierarchically organized art form, profoundly influences perception and mood through its rich information and intense emotion. While previous research has demonstrated that music can shape visual perception, its influences on visual awareness in aesthetic scenes remain largely unclear. In a series of five experiments involving 138 participants, we used the binocular rivalry paradigm to investigate how sad or lively Chinese folk music influences the perception of traditional Chinese paintings with varying aesthetic emotion (bleak vs. vivid). Paintings were presented in upright or inverted orientations or replaced by patches. Results revealed that sad music significantly increased both the average dominance duration and the predominance score of bleak paintings. Conversely, lively music with a faster tempo accelerated the switch rate of both paintings and patches. These effects were interdependent, exclusively for meaningful (upright) paintings, and correlated with individual sensitivity to musical reward. Our findings support the hierarchical predictive coding model of binocular rivalry, demonstrating that musical aesthetic emotion exerts top-down modulation, while musical tempo provides bottom-up entrainment, jointly shaping visual awareness of paintings.

systems biology↗

bioRxiv2024

Novel regulator Fpr2 controls immune homeostasis via CCL4-dependent neutrophils

Polymorphonuclear leukocytes (PMNs) in Fpr2-deficient mice have significantly impaired bactericidal activity, yet the precise mechanism remains elusive. Our research unveiled the cellular heterogeneity of peripheral blood PMNs through single-cell RNA-sequencing (scRNA-seq) and identified two new neutrophil clusters related to bacterial infection, including a sterilization cluster named Neu_CSTA and a regulation cluster named Neu_CCL4. The lack of Fpr2 leads to an immune regulation imbalance of Neu_CCL4 on other clusters in uninfected conditions, resulting in persistent instability of PMNs in peripheral blood, elevated inflammatory factors, and increased NOX2 expression. Furthermore, our findings corroborate that the regulatory function of Neu_CCL4 on PMNs immune balance is closely tied to its high expression of the anti-inflammatory ligand Anxa1. Regulon analysis inferred by scRNA-seq and ChIP-seq data suggested that a specific transcription factor Elf4, could modulate the expression of Anxa1, thereby facilitating its regulatory impact on other clusters. In the infection state, there was a noted increase in intercellular interactions across different clusters, leading to a general improvement in the bactericidal activity of PMNs. In summary, our research revealed distinct functional cellular sub-populations of PMNs, discovered the potential regulatory mechanisms, and elucidated the essential roles of Fpr2 in their ability to resist infection.

immunology↗

bioRxiv2024

Increased neuronal expression of the early endosomal adaptor APPL1 leads to endosomal and synaptic dysfunction with cholinergic neurodegeneration

Endosomal system dysfunction within neurons is a prominent early feature of Alzheimers disease (AD) pathology. Multiple AD risk factors are regulators of endocytosis and are known to cause hyper-activity of the early-endosome small GTPase rab5, resulting in neuronal endosomal pathway disruption and cholinergic neurodegeneration. Adaptor protein containing Pleckstrin homology domain, Phosphotyrosine binding domain, Leucine zipper motif (APPL1), an important rab5 effector protein and signaling molecule, has been shown in vitro to interface between endosomal and neuronal dysfunction through a rab5-activating interaction with the BACE1-generated C-terminal fragment of amyloid precursor protein (APP-{beta}CTF), a pathogenic APP fragment generated within endosomal compartments. To understand the contribution of APPL1 to AD-related endosomal dysfunction in vivo, we generated a transgenic mouse model over-expressing human APPL1 within neurons (Thy1-APPL1 mice). Strongly supporting the important endosomal regulatory roles of APPL1 and their relevance to AD etiology, Thy1-APPL1 mice develop enlarged neuronal early endosomes and increased synaptic endocytosis due to increased rab5 activation. We demonstrated pathophysiological consequences of APPL1 overexpression, including functional changes in hippocampal long-term potentiation (LTP) and long-term depression (LTD), degeneration of large projection cholinergic neurons of the basal forebrain, and impaired hippocampal-dependent memory. Our evidence shows that neuronal APPL1 elevation modeling its functional increase in the AD brain induces a cascade of AD-related pathological effects within neurons, including early endosome anomalies, synaptic dysfunction, and selective neurodegeneration. Our in vivo model highlights the contributions of APPL1 to the pathobiology and neuronal consequences of early endosomal pathway disruption and its potential value as a therapeutic target. Significance StatementNeuronal endosome dysfunction appears early in Alzheimers disease (AD) and is linked to memory loss. Genes and risk factors associated with AD often increase rab5 activity, a protein that disrupts endosomal signalling when hyperactivated. APPL1, a key rab5 partner, worsens this dysfunction via its interaction with APP-{beta}CTF, a protein fragment associated with AD. To explore APPL1s role, we created a genetically modified mouse that overexpresses APPL1 in neurons. This model provides the first in vivo evidence that APPL1 overexpression triggers key AD-like effects: rab5 hyperactivation, enlarged early endosomes, loss of cholinergic neurons, reduced synaptic plasticity in memory-related brain regions, and memory deficits. These findings highlight APPL1s role in AD pathogenesis and its potential as a therapeutic target.

neuroscience↗

bioRxiv2024

Monkeys Predict US Elections

How people vote often defies rational explanation. Physical traits sometimes sway voters more than policies do-but why? Here we show that rhesus macaques, who have no knowledge about political candidates or their policies, implicitly predict the outcomes of U.S. gubernatorial and senatorial elections based solely on visual features. Given a pair of candidate photos, monkeys spent more time looking at the loser than the winner, and this gaze bias predicted not only binary election outcomes but also the candidates vote share. Analysis of facial features revealed candidates with more masculine faces were more likely to win an election, and vote share was a linear function of jaw prominence. Our findings endorse the idea that voters spontaneously respond to evolutionarily conserved visual cues to physical prowess and that voting behavior is shaped, in part, by ancestral adaptations shared with nonhuman primates. Significance StatementWe report that monkeys and humans alike respond spontaneously to evolutionarily conserved facial masculinity cues in political candidates, and this innate sensitivity partly shapes voting behavior, highlighting the imperative for voters to overcome this ancient heuristic by becoming more informed on candidates and their policies.

animal behavior and cognition↗

bioRxiv2024

Decoding Specificity of Cyanobacterial MysDs in Mycosporine-Like Amino Acid Biosynthesis through Heterologous Expression in Saccharomyces cerevisiae

Mycosporine-like amino acids (MAAs) are potent natural UV-protectants, but their industrial production is hindered by efficiency and sustainability issues of large-scale extraction of their native hosts. Heterologous expression of MAA biosynthesis pathway genes in chassis organisms provides a promising alternative route, though the substrate promiscuity of the ATP-grasp ligase MysD complicates the biosynthesis of specific MAAs. In this study, we developed a Saccharomyces cerevisiae strain with enhanced capacity of producing mycosporine-glycine (MG), through genomic expression of biosynthesis pathway genes and knockout of competing pathway genes. This strain serves as an efficient MysD expression platform, which converts MG into shinorine and porphyra-334. Through structural modelling, site-directed mutagenesis and mutant characterization, we identified two residues on the omega-loop of MysD involved in determining product specificity. We further characterized the product specificity of 20 MysDs from diverse cyanobacterial lineages and confirmed the residue pattern-product specificity correlation. Our findings provide guidance for screening, selecting, and designing novel MysDs for industrial-scale MAA production through heterologous expression.

synthetic biology↗

bioRxiv2024

Molecular mechanisms of uric acid transport by the native human URAT1 and its inhibition by anti-gout drugs

Gout, a common and painful disease, stems from hyperuricemia, where elevated blood uric acid levels lead to urate crystal formation in joints and kidneys. The human urate transporter 1 (hURAT1) plays a critical role in urate homeostasis by facilitating urate reabsorption in the renal proximal tubule, making it a key target for gout therapy. Pharmacological inhibition of hURAT1 with drugs such as dotinurad, benzbromarone, lesinurad, and verinurad promotes uric acid excretion and alleviates gout symptoms. Here we present cryo-electron microscopy structures of native hURAT1 bound with these anti-gout drugs in the inward-open state, and with uric acid in inward-open, outward-open, and occluded states. Complemented by mutagenesis and cell-based assays, these structures reveal the mechanisms of uric acid reabsorption and hURAT1 inhibition. Our findings elucidate the molecular basis of uric acid transport and anti-gout medication action, and provide a structural framework for the rational design of next-generation therapies for hyperuricemia and gout.

biochemistry↗

bioRxiv2024

Selective Agonism of Liver and Gut FXR Prevents Cholestasis and Intestinal Atrophy in Parenterally Fed Neonatal Pigs

BACKGROUND & AIMSWe aimed to investigate the relative efficacy of feeding different bile acids in preventing PNALD in neonatal pigs. METHODSNewborn pigs given total parenteral nutrition (TPN) combined with minimal enteral feeding of chenodeoxycholic acid (CDCA), or increasing doses of obeticholic acid (OCA) for 19 days. RESULTSEnteral OCA (5 and 15 mg/kg), but not CDCA (30 mg/kg) reduced blood cholestasis markers compared to TPN controls and increased bile acids in the gallbladder and intestine. Major bile acids in the liver and distal intestine were CDCA, HCA, HDCA and OCA, and their relative proportions were increased by the type of bile acid (CDCA or OCA) given enterally. High doses of OCA increased the total NR1H4-agonistic bile acid profile in the liver and intestine above 50% total bile acids. Both CDCA and OCA treatments suppressed hepatic cyp7a1 expression, but only OCA increased hepatobiliary transporters, ABCB11, ABCC$ and ABCB1. Plasma phytosterol levels were reduced and biliary levels were increased by CDCA and OCA and hepatic sterol transporters, abcg5/8, expression were increased by OCA. Both CDCA and OCA increased plasma FGF19 and OCA increased intestinal FGF19, FABP6, and SLC51A. Both CDCA and OCA increased intestinal mucosal growth, whereas CDCA increased the plasma GLP-2, GLP-1 and GIP. CONCLUSIONSEnteral OCA prevented cholestasis and phytosterolemia by increased hepatic bile acid and sterol transport via induction of hepatobiliary transporter FXR target genes and not by suppression of bile acid synthesis genes. We also showed an intestinal trophic action of OCA that demonstrates a dual clinical benefit of FXR agonism in the prevention of PNALD in piglets.

physiology↗

bioRxiv2024

Yeast-MetaTwin for Systematically Exploring Yeast Metabolism through Retrobiosynthesis and Deep Learning

Underground metabolism plays a crucial role in understanding enzyme promiscuity, cellular metabolism, and biological evolution, yet experimental exploration of underground metabolism is often sparse. Even though yeast genome-scale metabolic models have been reconstructed and curated for over 20 years, more than 90% of the yeast metabolome is still not covered by these models. To address this gap, we have developed a workflow based on retrobiosynthesis and deep learning methods to comprehensively explore yeast underground metabolism. We integrated the predicted underground network into the yeast consensus genome-scale model, Yeast8, to reconstruct the yeast metabolic twin model, Yeast-MetaTwin, covering 16,244 metabolites (92% of the total yeast metabolome), 2,057 metabolic genes and 59,914 reactions. We revealed that Km parameters differ between the known and underground network, identified hub molecules connecting the underground network and pinpointed the underground percentages for yeast metabolic pathways. Moreover, the Yeast-MetaTwin can predict the by-products of chemicals produced in yeast, offering valuable insights to guide metabolic engineering designs.

synthetic biology↗