Rational Design of High-Efficiency Synthetic Small Regulatory RNAs and Their Application in Robust Genetic Circuit Performance Through Tight Control of Leaky Gene Expression.

Synthetic sRNAs show promise as tools for targeted and programmable gene expression manipulation. However, the design of high-efficiency synthetic sRNAs is a challenging task that necessitates careful consideration of multiple factors. Therefore, this study aims to investigate rational design strategies that significantly and robustly enhance the efficiency of synthetic sRNAs. This is achieved by optimizing the following parameters: the sRNA scaffold, mRNA binding affinity, Hfq protein expression level, and mRNA secondary structure. By utilizing optimized synthetic sRNAs within a positive feedback circuit, we effectively addressed the issue of gene expression leakage─an enduring challenge in synthetic biology that undermines the reliability of genetic circuits in bacteria. Our designed synthetic sRNAs successfully prevented gene expression leakage, thus averting unintended circuit activation caused by initial expression noise, even in the absence of signal molecules. This result shows that high-efficiency synthetic sRNAs not only enable precise gene knockdown for metabolic engineering but also ensure the robust performance of synthetic circuits. The strategies developed here hold significant promise for broad applications across diverse biotechnological fields, establishing synthetic sRNAs as pivotal tools in advancing synthetic biology and gene regulation.

Autoinhibited Protein Database: a curated database of autoinhibitory domains and their autoinhibition mechanisms.

Autoinhibition, a crucial allosteric self-regulation mechanism in cell signaling, ensures signal propagation exclusively in the presence of specific molecular inputs. The heightened focus on autoinhibited proteins stems from their implication in human diseases, positioning them as potential causal factors or therapeutic targets. However, the absence of a comprehensive knowledgebase impedes a thorough understanding of their roles and applications in drug discovery. Addressing this gap, we introduce Autoinhibited Protein Database (AiPD), a curated database standardizing information on autoinhibited proteins. AiPD encompasses details on autoinhibitory domains (AIDs), their targets, regulatory mechanisms, experimental validation methods, and implications in diseases, including associated mutations and post-translational modifications. AiPD comprises 698 AIDs from 532 experimentally characterized autoinhibited proteins and 2695 AIDs from their 2096 homologs, which were retrieved from 864 published articles. AiPD also includes 42 520 AIDs of computationally predicted autoinhibited proteins. In addition, AiPD facilitates users in investigating potential AIDs within a query sequence through comparisons with documented autoinhibited proteins. As the inaugural autoinhibited protein repository, AiPD significantly aids researchers studying autoinhibition mechanisms and their alterations in human diseases. It is equally valuable for developing computational models, analyzing allosteric protein regulation, predicting new drug targets, and understanding intervention mechanisms AiPD serves as a valuable resource for diverse researchers, contributing to the understanding and manipulation of autoinhibition in cellular processes. Database URL: http://ssbio.cau.ac.kr/databases/AiPD.

A computational clinical decision-supporting system to suggest effective anti-epileptic drugs for pediatric epilepsy patients based on deep learning models using patient's medical history.

BACKGROUND: Epilepsy, a chronic brain disorder characterized by abnormal brain activity that causes seizures and other symptoms, is typically treated using anti-epileptic drugs (AEDs) as the first-line therapy. However, due to the variations in their modes of action, identification of effective AEDs often relies on ad hoc trials, which is particularly challenging for pediatric patients. Thus, there is significant value in computational methods capable of assisting in the selection of AEDs, aiming to minimize unnecessary medication and improve treatment efficacy. RESULTS: In this study, we collected 7,507 medical records from 1,000 pediatric epilepsy patients and developed a computational clinical decision-supporting system for AED selection. This system leverages three multi-channel convolutional neural network (CNN) models tailored to three specific AEDs (vigabatrin, prednisolone, and clobazam). Each CNN model predicts whether a respective AED is effective on a given patient or not. The CNN models showed AUROCs of 0.90, 0.80, and 0.92 in 10-fold cross-validation, respectively. Evaluation on a hold-out test dataset further revealed positive predictive values (PPVs) of 0.92, 0.97, and 0.91 for the three respective CNN models, representing that suggested AEDs by our models would be effective in controlling epilepsy with a high accuracy and thereby reducing unnecessary medications for pediatric patients. CONCLUSION: Our CNN models in the system demonstrated high PPVs for the three AEDs, which signifies the potential of our approach to support the clinical decision-making by assisting doctors in recommending effective AEDs within the three AEDs for patients based on their medical history. This would result in a reduction in the number of unnecessary ad hoc attempts to find an effective AED for pediatric epilepsy patients.

Corydalis ternata Nakai Alleviates Cognitive Decline in Alzheimer's Disease by Reducing ß-Amyloid and Neuroinflammation.

Recently, natural herbs have gained increasing attention owing to their comparatively low toxicity levels and the abundance of historical medical documentation regarding their use. Nevertheless, owing to a lack of knowledge regarding these herbs and their compounds, attempts to find those that could be beneficial for treating diseases have often been ad hoc; thus, there is now a growing demand for an in silico method to identify beneficial herbs. In this study, we present a computational approach for identifying natural herbs specifically effective in treating cognitive decline in Alzheimer's disease (AD) sufferers, which analyzes the similarities between herbal compounds and known drugs targeting AD-related proteins. Our in silico method suggests that Corydalis ternata can improve cognitive decline in AD sufferers. Behavioral tests with an AD mouse model for the confirmation of the in silico prediction reveals that C. ternata significantly alleviated the cognitive decline (memory and motor functions) caused by neurodegeneration. Further pathology analyses reveal that C. ternata decreases the level of Aß plaques, reduces neuroinflammation, and promotes autophagy flux, and thus C. ternata can be clinically effective for preventing mild cognitive impairment during the early stages of AD. These findings highlight the potential utility of our in silico method and the potential clinical application of the identified natural herb in treating and preventing AD.

Widespread alteration of protein autoinhibition in human cancers.

Autoinhibition is a prevalent allosteric regulatory mechanism in signaling proteins. Reduced autoinhibition underlies the tumorigenic effect of some known cancer drivers, but whether autoinhibition is altered generally in cancer remains elusive. Here, we demonstrate that cancer-associated missense mutations, in-frame insertions/deletions, and fusion breakpoints are enriched within inhibitory allosteric switches (IASs) across all cancer types. Selection for IASs that are recurrently mutated in cancers identifies established and unknown cancer drivers. Recurrent missense mutations in IASs of these drivers are associated with distinct, cancer-specific changes in molecular signaling. For the specific case of PPP3CA, the catalytic subunit of calcineurin, we provide insights into the molecular mechanisms of altered autoinhibition by cancer mutations using biomolecular simulations, and demonstrate that such mutations are associated with transcriptome changes consistent with increased calcineurin signaling. Our integrative study shows that autoinhibition-modulating genetic alterations are positively selected for by cancer cells.

A multi-layered network model identifies Akt1 as a common modulator of neurodegeneration.

The accumulation of misfolded and aggregated proteins is a hallmark of neurodegenerative proteinopathies. Although multiple genetic loci have been associated with specific neurodegenerative diseases (NDs), molecular mechanisms that may have a broader relevance for most or all proteinopathies remain poorly resolved. In this study, we developed a multi-layered network expansion (MLnet) model to predict protein modifiers that are common to a group of diseases and, therefore, may have broader pathophysiological relevance for that group. When applied to the four NDs Alzheimer's disease (AD), Huntington's disease, and spinocerebellar ataxia types 1 and 3, we predicted multiple members of the insulin pathway, including PDK1, Akt1, InR, and sgg (GSK-3ß), as common modifiers. We validated these modifiers with the help of four Drosophila ND models. Further evaluation of Akt1 in human cell-based ND models revealed that activation of Akt1 signaling by the small molecule SC79 increased cell viability in all models. Moreover, treatment of AD model mice with SC79 enhanced their long-term memory and ameliorated dysregulated anxiety levels, which are commonly affected in AD patients. These findings validate MLnet as a valuable tool to uncover molecular pathways and proteins involved in the pathophysiology of entire disease groups and identify potential therapeutic targets that have relevance across disease boundaries. MLnet can be used for any group of diseases and is available as a web tool at http://ssbio.cau.ac.kr/software/mlnet.

A machine learning-based quantitative model (LogBB_Pred) to predict the blood-brain barrier permeability (logBB value) of drug compounds.

MOTIVATION: Efficient assessment of the blood-brain barrier (BBB) penetration ability of a drug compound is one of the major hurdles in central nervous system drug discovery since experimental methods are costly and time-consuming. To advance and elevate the success rate of neurotherapeutic drug discovery, it is essential to develop an accurate computational quantitative model to determine the absolute logBB value (a logarithmic ratio of the concentration of a drug in the brain to its concentration in the blood) of a drug candidate. RESULTS: Here, we developed a quantitative model (LogBB_Pred) capable of predicting a logBB value of a query compound. The model achieved an R2 of 0.61 on an independent test dataset and outperformed other publicly available quantitative models. When compared with the available qualitative (classification) models that only classified whether a compound is BBB-permeable or not, our model achieved the same accuracy (0.85) with the best qualitative model and far-outperformed other qualitative models (accuracies between 0.64 and 0.70). For further evaluation, our model, quantitative models, and the qualitative models were evaluated on a real-world central nervous system drug screening library. Our model showed an accuracy of 0.97 while the other models showed an accuracy in the range of 0.29-0.83. Consequently, our model can accurately classify BBB-permeable compounds as well as predict the absolute logBB values of drug candidates. AVAILABILITY AND IMPLEMENTATION: Web server is freely available on the web at http://ssbio.cau.ac.kr/software/logbb_pred/. The data used in this study are available to download at http://ssbio.cau.ac.kr/software/logbb_pred/dataset.zip.

Functional small peptides for enhanced protein delivery, solubility, and secretion in microbial biotechnology.

In microbial biotechnology, there is a constant demand for functional peptides to give new functionality to engineered proteins to address problems such as direct delivery of functional proteins into bacterial cells, enhanced protein solubility during the expression of recombinant proteins, and efficient protein secretion from bacteria. To tackle these critical issues, we selected three types of functional small peptides: cell-penetrating peptides (CPPs) enable the delivery of diverse cargoes into bacterial cytoplasm for a variety of purposes, protein-solubilizing peptide tags demonstrate remarkable efficiency in solubilizing recombinant proteins without folding interference, and signal peptides play a key role in enabling the secretion of recombinant proteins from bacterial cells. In this review, we introduced these three functional small peptides that offer effective solutions to address emerging problems in microbial biotechnology. Additionally, we summarized various engineering efforts aimed at enhancing the activity and performance of these peptides, thereby providing valuable insights into their potential for further applications.

Genotypes and phenotypes of DNM1 encephalopathy.

BACKGROUND: Variants in the dynamin-1 (DNM1) gene typically cause synaptopathy, leading to developmental and epileptic encephalopathy (DEE). We aimed to determine the genotypic and phenotypic spectrum of DNM1 encephalopathy beyond DEE. METHODS: Electroclinical phenotyping and genotyping of patients with a DNM1 variant were conducted for patients undergoing next-generation sequencing at our centre, followed by a systematic review. RESULTS: Six patients with heterozygous DNM1 variants were identified in our cohort. Three had a typical DEE phenotype characterised by epileptic spasms, tonic seizures and severe-to-profound intellectual disability with pathogenic variants located in the GTPase or middle domain. The other three patients had atypical phenotypes of milder cognitive impairment and focal epilepsy. Genotypically, two patients with atypical phenotypes had variants located in the GTPase domain, while the third patient had a novel variant (p.M648R) in the linker region between pleckstrin homology and GTPase effector domains. The third patient with an atypical phenotype showed normal development until he developed febrile status epilepticus. Our systematic review on 55 reported cases revealed that those with GTPase or middle domain variants had more severe intellectual disability (p<0.001) and lower functional levels of ambulation (p=0.001) or speech and language (p<0.001) than the rest. CONCLUSION: DNM1-related phenotypes encompass a wide spectrum of epilepsy and neurodevelopmental disorders, with specific variants underlying different phenotypes.

Synthetic bacteria for the detection and bioremediation of heavy metals.

Toxic heavy metal accumulation is one of anthropogenic environmental pollutions, which poses risks to human health and ecological systems. Conventional heavy metal remediation approaches rely on expensive chemical and physical processes leading to the formation and release of other toxic waste products. Instead, microbial bioremediation has gained interest as a promising and cost-effective alternative to conventional methods, but the genetic complexity of microorganisms and the lack of appropriate genetic engineering technologies have impeded the development of bioremediating microorganisms. Recently, the emerging synthetic biology opened a new avenue for microbial bioremediation research and development by addressing the challenges and providing novel tools for constructing bacteria with enhanced capabilities: rapid detection and degradation of heavy metals while enhanced tolerance to toxic heavy metals. Moreover, synthetic biology also offers new technologies to meet biosafety regulations since genetically modified microorganisms may disrupt natural ecosystems. In this review, we introduce the use of microorganisms developed based on synthetic biology technologies for the detection and detoxification of heavy metals. Additionally, this review explores the technical strategies developed to overcome the biosafety requirements associated with the use of genetically modified microorganisms.

Evaluation of Various Escherichia coli Strains for Enhanced Lycopene Production.

Lycopene is a carotenoid widely used as a food and feed supplement due to its antioxidant, anti-inflammatory, and anti-cancer functions. Various metabolic engineering strategies have been implemented for high lycopene production in Escherichia coli, and for this purpose it was essential to select and develop an E. coli strain with the highest potency. In this study, we evaluated 16 E. coli strains to determine the best lycopene production host by introducing a lycopene biosynthetic pathway (crtE, crtB, and crtI genes cloned from Deinococcus wulumuqiensis R12 and dxs, dxr, ispA, and idi genes cloned from E. coli). The 16 lycopene strain titers diverged from 0 to 0.141 g/l, with MG1655 demonstrating the highest titer (0.141 g/l), while the SURE and W strains expressed the lowest (0 g/l) in an LB medium. When a 2 × YTg medium replaced the MG1655 culture medium, the titer further escalated to 1.595 g/l. These results substantiate that strain selection is vital in metabolic engineering, and further, that MG1655 is a potent host for producing lycopene and other carotenoids with the same lycopene biosynthetic pathway.

Differentiation of astrocytes with characteristics of ventral midbrain from human embryonic stem cells.

Molecular and functional diversity among region-specific astrocytes is of great interest in basic neuroscience and the study of neurological diseases. In this study, we present the generation and characterization of astrocytes from human embryonic stem cells with the characteristics of the ventral midbrain (VM). Fine modulation of WNT and SHH signaling during neural differentiation induced neural precursor cells (NPCs) with high expression of EN1 and NKX6.1, but less expression of FOXA2. Overexpression of nuclear factor IB in NPCs induced astrocytes, thereby maintaining the expression of region-specific genes acquired in the NPC stage. When cocultured with dopaminergic (DA) precursors or DA neurons, astrocytes with VM characteristics (VM-iASTs) promoted the differentiation and survival of DA neurons better than those that were not regionally specified. Transcriptomic analysis showed that VM-iASTs were more closely related to human primary midbrain astrocytes than to cortical astrocytes, and revealed the upregulation of WNT1 and WNT5A, which supports their VM identity and explains their superior activity in DA neurons. Taken together, we hope that VM-iASTs can serve to improve ongoing DA precursor transplantation for Parkinson's disease, and that their transcriptomic data provide a valuable resource for investigating regional diversity in human astrocyte populations.

A machine learning model for classifying G-protein-coupled receptors as agonists or antagonists.

BACKGROUND: G-protein coupled receptors (GPCRs) sense and transmit extracellular signals into the intracellular machinery by regulating G proteins. GPCR malfunctions are associated with a variety of signaling-related diseases, including cancer and diabetes; at least a third of the marketed drugs target GPCRs. Thus, characterization of their signaling and regulatory mechanisms is crucial for the development of effective drugs. RESULTS: In this study, we developed a machine learning model to identify GPCR agonists and antagonists. We designed two-step prediction models: the first model identified the ligands binding to GPCRs and the second model classified the ligands as agonists or antagonists. Using 990 selected subset features from 5270 molecular descriptors calculated from 4590 ligands deposited in two drug databases, our model classified non-ligands, agonists, and antagonists of GPCRs, and achieved an area under the ROC curve (AUC) of 0.795, sensitivity of 0.716, specificity of 0.744, and accuracy of 0.733. In addition, we verified that 70% (44 out of 63) of FDA-approved GPCR-targeting drugs were correctly classified into their respective groups. CONCLUSIONS: Studies of ligand-GPCR interaction recognition are important for the characterization of drug action mechanisms. Our GPCR-ligand interaction prediction model can be employed in the pharmaceutical sciences for the efficient virtual screening of putative GPCR-binding agonists and antagonists.

Enhancement of the solubility of recombinant proteins by fusion with a short-disordered peptide.

In protein biotechnology, large soluble fusion partners are widely utilized for increased yield and solubility of recombinant proteins. However, the production of additional large fusion partners poses an additional burden to the host, leading to a decreased protein yield. In this study, we identified two highly disordered short peptides that were able to increase the solubility of an artificially engineered aggregation-prone protein, GFP-GFIL4, from 0.6% to 61% (D3-DP00592) and 46% (D4-DP01038) selected from DisProt database. For further confirmation, the peptides were applied to two insoluble E. coli proteins (YagA and YdiU). The peptides also enhanced solubility from 52% to 90% (YagA) and from 27% to 93% (YdiU). Their ability to solubilize recombinant proteins was comparable with strong solubilizing tags, maltose-binding protein (40 kDa) and TrxA (12 kDa), but much smaller (< 7 kDa) in size. For practical application, the two peptides were fused with a restriction enzyme, I-SceI, and they increased I-SceI solubility from 24% up to 75%. The highly disordered peptides did not affect the activity of I-SceI while I-SceI fused with MBP or TrxA displayed no restriction activity. Despite the small size, the highly disordered peptides were able to solubilize recombinant proteins as efficiently as conventional fusion tags and did not interfere with the function of recombinant proteins. Consequently, the identified two highly disordered peptides would have practical utility in protein biotechnology and industry.

Computational Design of a Multi-Epitope Vaccine Against Porphyromonas gingivalis.

Porphyromonas gingivalis is a Gram-negative pathogenic bacterium associated with chronic periodontitis. The development of a chimeric peptide-based vaccine targeting this pathogen could be highly beneficial in preventing oral bone loss as well as other severe gum diseases. We applied a computational framework to design a multi-epitope-based vaccine candidate against P. gingivalis. The vaccine comprises epitopes from subunit proteins prioritized from the P. gingivalis reference strain (P. gingivalis ATCC 33277) using several reported vaccine properties. Protein-based subunit vaccines were prioritized through genomics techniques. Epitope prediction was performed using immunoinformatic servers and tools. Molecular modeling approaches were used to build a putative three-dimensional structure of the vaccine to understand its interactions with host immune cells through biophysical techniques such as molecular docking simulation studies and binding free energy methods. Genome subtraction identified 18 vaccine targets: six outer-membrane, nine cytoplasmic membrane-, one periplasmic, and two extracellular proteins. These proteins passed different vaccine checks required for the successful development of a vaccine candidate. The shortlisted proteins were subjected to immunoinformatic analysis to map B-cell derived T-cell epitopes, and antigenic, water-soluble, non-toxic, and good binders of DRB1*0101 were selected. The epitopes were then modeled into a multi-epitope peptide vaccine construct (linked epitopes plus adjuvant) to enhance immunogenicity and effectively engage both innate and adaptive immunity. Further, the molecular docking approach was used to determine the binding conformation of the vaccine to TLR2 innate immune receptor. Molecular dynamics simulations and binding free energy calculations of the vaccine-TLR2 complex were performed to highlight key intermolecular binding energies. Findings of this study will be useful for vaccine developers to design an effective vaccine for chronic periodontitis pathogens, specifically P. gingivalis.

Analysis methods for the gut microbiome in neuropsychiatric and neurodegenerative disorders.

For a long time, the central nervous system was believed to be the only regulator of cognitive functions. However, accumulating evidence suggests that the composition of the microbiome is strongly associated with brain functions and diseases. Indeed, the gut microbiome is involved in neuropsychiatric diseases (e.g., depression, autism spectrum disorder, and anxiety) and neurodegenerative diseases (e.g., Parkinson's disease and Alzheimer's disease). In this review, we provide an overview of the link between the gut microbiome and neuropsychiatric or neurodegenerative disorders. We also introduce analytical methods used to assess the connection between the gut microbiome and the brain. The limitations of the methods used at present are also discussed. The accurate translation of the microbiome information to brain disorder could promote better understanding of neuronal diseases and aid in finding alternative and novel therapies.

Advanced biotechnology using methyltransferase and its applications in bacteria: a mini review.

Since prokaryotic restriction-modification (RM) systems protect the host by cleaving foreign DNA by restriction endonucleases, it is difficult to introduce engineered plasmid DNAs into newly isolated microorganisms whose RM system is not discovered. The prokaryotes also possess methyltransferases to protect their own DNA from the endonucleases. As those methyltransferases can be utilized to methylate engineered plasmid DNAs before transformation and to enhance the stability within the cells, the study on methyltransferases in newly isolated bacteria is essential for genetic engineering. Here, we introduce the mechanism of the RM system, specifically the methyltransferases and their biotechnological applications. These biotechnological strategies could facilitate plasmid DNA-based genetic engineering in bacteria strains that strongly defend against foreign DNA.

A Novel Vitronectin Peptide Facilitates Differentiation of Oligodendrocytes from Human Pluripotent Stem Cells (Synthetic ECM for Oligodendrocyte Differentiation).

Differentiation of oligodendrocytes (ODs) presents a challenge in regenerative medicine due to their role in various neurological diseases associated with dysmyelination and demyelination. Here, we designed a peptide derived from vitronectin (VN) using in silico docking simulation and examined its use as a synthetic substrate to support the differentiation of ODs derived from human pluripotent stem cells. The designed peptide, named VNP2, promoted OD differentiation induced by the overexpression of SOX10 in OD precursor cells compared with Matrigel and full-length VN. ODs differentiated on VNP2 exhibited greater contact with axon-mimicking nanofibers than those differentiated on Matrigel. Transcriptomic analysis revealed that the genes associated with morphogenesis, cytoskeleton remodeling, and OD differentiation were upregulated in cells grown on VNP2 compared with cells grown on Matrigel. This new synthetic VN-derived peptide can be used to develop a culture environment for efficient OD differentiation.

Trophoblast glycoprotein is a new candidate gene for Parkinson's disease.

Parkinson's disease (PD) is a movement disorder caused by progressive degeneration of the midbrain dopaminergic (mDA) neurons in the substantia nigra pars compacta (SNc). Despite intense research efforts over the past decades, the etiology of PD remains largely unknown. Here, we discovered the involvement of trophoblast glycoprotein (Tpbg) in the development of PD-like phenotypes in mice. Tpbg expression was detected in the ventral midbrain during embryonic development and in mDA neurons in adulthood. Genetic ablation of Tpbg resulted in mild degeneration of mDA neurons in aged mice (12-14 months) with behavioral deficits reminiscent of PD symptoms. Through in silico analysis, we predicted potential TPBG-interacting partners whose functions were relevant to PD pathogenesis; this result was substantiated by transcriptomic analysis of the SNc of aged Tpbg knockout mice. These findings suggest that Tpbg is a new candidate gene associated with PD and provide a new insight into PD pathogenesis.

Construction of a tunable promoter library to optimize gene expression in Methylomonas sp. DH-1, a methanotroph, and its application to cadaverine production.

BACKGROUND: As methane is 84 times more potent than carbon dioxide in exacerbating the greenhouse effect, there is an increasing interest in the utilization of methanotrophic bacteria that can convert harmful methane into various value-added compounds. A recently isolated methanotroph, Methylomonas sp. DH-1, is a promising biofactory platform because of its relatively fast growth. However, the lack of genetic engineering tools hampers its wide use in the bioindustry. RESULTS: Through three different approaches, we constructed a tunable promoter library comprising 33 promoters that can be used for the metabolic engineering of Methylomonas sp. DH-1. The library had an expression level of 0.24-410% when compared with the strength of the lac promoter. For practical application of the promoter library, we fine-tuned the expressions of cadA and cadB genes, required for cadaverine synthesis and export, respectively. The strain with PrpmB-cadA and PDnaA-cadB produced the highest cadaverine titre (18.12 ± 1.06 mg/L) in Methylomonas sp. DH-1, which was up to 2.8-fold higher than that obtained from a non-optimized strain. In addition, cell growth and lysine (a precursor of cadaverine) production assays suggested that gene expression optimization through transcription tuning can afford a balance between the growth and precursor supply. CONCLUSIONS: The tunable promoter library provides standard and tunable components for gene expression, thereby facilitating the use of methanotrophs, specifically Methylomonas sp. DH-1, as a sustainable cell factory.