Novel Umami Peptides from Mushroom (Agaricus bisporus) and Their Umami Enhancing Effect via Virtual Screening and Molecular Simulation.

This study aimed to identify novel umami peptides in Agaricus bisporus and investigate their umami enhancing effect. We virtually screened 155 potential umami peptides from the ultrasound-assisted A. bisporus hydrolysate according to Q values, iUmami-SCM, Umami_YYDS, and Tastepeptides_DM models, and molecular docking. Five peptides (AGKNTNGSQF, DEAVARGATF, REESDFQSSF, SEETTTGVHH, and WNNDAFQSSTN) were synthesized for sensory evaluation and kinetic analysis. The result showed that the umami thresholds of the five peptides were in the range of 0.21-0.40 mmol/L. Notably, REESDFQSSF, SEETTTGVHH, and WNNDAFQSSTN had low dissociation constant (KD) values and high affinity for the T1R1-VFT receptor. The enhancing effect of the three peptides with MSG or IMP was investigated by sensory evaluation, kinetic analysis, and molecular dynamics simulations. In stable complexes, ARG_277 in T1R1 played a major role in umami peptide binding to T1R1-VFT. These results provide a theoretical basis for future screening of umami peptides and improving the umami taste of food containing mushrooms.

A ß-Primeverosidase-like Enzyme in Soybean [Glycine max (L.) Merr] Hypocotyls: Specificity toward 1-Octen-3-yl and 3-Octanyl ß-Primeverosides.

A novel ß-primeverosidase-like enzyme, originating from the hypocotyl of soybeans, was isolated and characterized. This enzyme, with an estimated molecular weight of 44 kDa, was identified as a monomer and exhibited peak activity at 55 °C and pH 5.5. It demonstrated a specific and efficient hydrolysis of 1-octen-3-yl ß-primeveroside (1-octen-3-yl prim) and 3-octanyl ß-primeveroside (3-octanyl prim) but did not act on glucopyranosides. Mn2+ significantly enhanced its activity, while Zn2+, Cu2+, and Hg2+ exerted inhibitory effects. Kinetic analysis revealed a higher hydrolytic capacity toward 1-octen-3-yl prim. Partial amino acid sequences were determined and the N-terminal amino acid sequence was determined to be AIVAYAL ALSKRAIAAQ. The binding energy and binding free energy between the ß-primeverosidase enzyme and its substrates were observed to be higher than that of ß-glucosidase, thus validating its superior hydrolysis efficiency. Hydrogen bonds and hydrophobic interactions were the main types of interactions between ß-primeverosidase enzyme and 1-octen-3-yl prim and 3-octanyl prim, involving amino acid residues such as GLU-470, TRP-463, GLU-416, TRP-471, GLN-53, and GLN-477 (hydrogen bonds) and PHE-389, TYR-345, LEU-216, and TYR-275 (hydrophobic interactions). This study contributes to the application of a ß-primeverosidase-like enzyme in improving the release efficiency of glycosidically conjugated flavor substances.

From Molecular Dynamics to Taste Sensory Perception: A Comprehensive Study on the Interaction of Umami Peptides with the T1R1/T1R3-VFT Receptor.

The research on the umami receptor-ligand interaction is crucial for understanding umami perception. This study integrated molecular simulations, sensory evaluation, and biosensor technology to analyze the interaction between umami peptides and the umami receptor T1R1/T1R3-VFT. Molecular dynamics simulations were used to investigate the dissociation process of seven umami peptides with the umami receptor T1R1/T1R3-VFT, and by calculating the potential mean force curve using the Jarzynski equation, it was found that the binding free energy of umami peptide is between -58.80 and -12.17 kcal/mol, which had a strong correlation with the umami intensity obtained by time intensity sensory evaluation. Through correlation analysis, the dissociation rate constants (0.0126-0.394 1/s) of umami peptides were found to have a great impact on umami perception. The faster the dissociation rate of umami peptides from receptors, the stronger the perceived intensity of the umami taste. This research aims to elucidate the relationship between the umami peptide-receptor interaction and umami perception, providing theoretical support for the exploration of umami perception mechanisms.

Genome-scale transcriptional activation by non-homologous end joining-mediated integration in Yarrowia lipolytica.

BACKGROUND: Genome-scale screening can be applied to efficiently mine for unknown genes with phenotypes of interest or special functions. It is also useful to identify new targets for engineering desirable properties of cell factories. RESULTS: Here, we designed a new approach for genome-scale transcription activation using non-homologous end joining (NHEJ)-mediated integration in Yarrowia lipolytica. We utilized this approach to screen for genes that, upon activation, confer phenotypes including improved acetic acid tolerance and xylose metabolism. The candidates were validated using gene overexpression, and functional changes including improved growth performance under multiple stressors and activated pentose metabolism were identified. CONCLUSIONS: This study provides a simple and effective approach to randomly activate endogenous genes and mine for key targets associated with phenotypes of interest. The specific gene targets identified here will be useful for cell factory construction and biorefining lignocellulose.

Noteworthy Consensus Effects of D/E Residues in Umami Peptides Used for Designing the Novel Umami Peptides.

Aspartic acid (D) and glutamic acid (E) play vital roles in the umami peptides. To understand their exact mechanism of action, umami peptides were collected and cut into 1/2/3/4 fragments. Connecting D/E to the N/C-termini of the fragments formed D/E consensus effect groups (DEEGs), and all fragments containing DEEG were summarized according to the ratio and ranking obtained in the above four situations. The interaction patterns between peptides in DEEG and T1R1/T1R3-VFD were compared by statistical analysis and molecular docking, and the most conservative contacts were found to be HdB_277_ARG and HdB_148_SER. The molecular docking score of the effector peptides significantly dropped compared to that of their original peptides (-1.076 ± 0.658 kcal/mol, p value < 0.05). Six types of consensus fingerprints were set according to the Top7 contacts. The exponential of relative umami was linearly correlated with ΔGbind (R2 = 0.961). Under the D/E consensus effect, the electrostatic effect of the umami peptide was improved, and the energy gap between the highest occupied molecular orbital-the least unoccupied molecular orbital (HOMO-LUMO) was decreased. The shortest path map showed that the peptides had similar T1R1-T1R3 recognition pathways. This study helps to reveal umami perception rules and provides support for the efficient screening of umami peptides based on the material richness in D/E sequences.

Reconfiguration of the reductive TCA cycle enables high-level succinic acid production by Yarrowia lipolytica.

Succinic acid (SA) is an important C4-dicarboxylic acid. Microbial production of SA at low pH results in low purification costs and hence good overall process economics. However, redox imbalances limited SA biosynthesis from glucose via the reductive tricarboxylic acid (TCA) cycle in yeast. Here, we engineer the strictly aerobic yeast Yarrowia lipolytica for efficient SA production without pH control. Introduction of the reductive TCA cycle into the cytosol of a succinate dehydrogenase-disrupted yeast strain causes arrested cell growth. Although adaptive laboratory evolution restores cell growth, limited NADH supply restricts SA production. Reconfiguration of the reductive SA biosynthesis pathway in the mitochondria through coupling the oxidative and reductive TCA cycle for NADH regeneration results in improved SA production. In pilot-scale fermentation, the engineered strain produces 111.9 g/L SA with a yield of 0.79 g/g glucose within 62 h. This study paves the way for industrial production of biobased SA.

[Clinical Features and Prognostic Factors of Patients with Primary Cutaneous Lymphoma].

OBJECTIVE: To retrospectively analyze the clinical characteristics and prognostic factors of patients with primary cutaneous lymphoma. METHODS: The clinical data of 22 patients with primary cutaneous lymphoma admitted to Xinjiang Hotan District People's Hospital, Heji Hospital affiliated to Changzhi Medical College and the Fifth Medical Center of PLA General Hospital from January 2013 to June 2021 were retrospectively analyzed. RESULTS: The incidence of primary cutaneous T cell and NK/T cell lymphoma was about 91.9/100 000, and the incidence of primary cutaneous B cell lymphoma was about 14.5/100 000. The overall survival (OS) of patients aged ≥65 years was significantly shorter than that of patients younger than 65 years (P <0.05). Patients with elevated ß2-microglobulin (ß2-MG) had shorter OS and progression-free survival (PFS) (both P <0.05). Patients who achieved complete/partial response after initial treatment had longer OS than those with stable or progressive disease (P <0.05). There were significant differences in OS and PFS among patients with different pathological types of primary cutaneous lymphoma that originated from T and NK/T cells, the OS and PFS of patients with mycosis fungoides were longer than those of patients with other pathological types (both P <0.05). In addition, disease stage might also affect the PFS of the patients (P =0.056). CONCLUSION: The age, disease stage, ß2-MG level, pathological type and remission state after treatment of the patients were related to the clinical prognosis.

TastePeptides-EEG: An Ensemble Model for Umami Taste Evaluation Based on Electroencephalogram and Machine Learning.

In the field of food, the sensory evaluation of food still relies on the results of manual sensory evaluation, but the results of human sensory evaluation are not universal, and there is a problem of speech fraud. This work proposed an electroencephalography (EEG)-based analysis method that effectively enables the identification of umami/non-umami substances. First, the key features were extracted using percentage conversion, standardization, and significance screening, and based on these features, the top four models were selected from 19 common binary classification algorithms as submodels. Then, the support vector machine (SVM) algorithm was used to fit the outputs of these four submodels to establish TastePeptides-EEG. The validation set of the model achieved a judgment accuracy of 90.2%, and the test set achieved a judgment accuracy of 77.8%. This study discovered the frequency change of α wave in umami taste perception and found the frequency response delay phenomenon of the F/RT/C area under umami taste stimulation for the first time. The model is published at www.tastepeptides-meta.com/TastePeptides-EEG, which is convenient for relevant researchers to speed up the analysis of umami perception and provide help for the development of the next generation of brain-computer interfaces for flavor perception.

Identifying Umami Peptides Specific to the T1R1/T1R3 Receptor via Phage Display.

Umami peptides are small molecular weight oligopeptides that play a role in umami taste attributes. However, the identification of umami peptides is easily limited by environmental conditions, and the abundant source and high chromatographic separation efficiency remain difficult. Herein, we report a robust strategy based on a phage random linear heptapeptide library that targets the T1R1-Venus flytrap domain (T1R1-VFT). Two candidate peptides (MTLERPW and MNLHLSF) were readily identified with high affinity for T1R1-VFT binding (KD of MW-7 and MF-7 were 790 and 630 nM, respectively). The two peptides exhibited umami taste and significantly enhanced the umami intensity when added to the monosodium glutamate solution. Overall, this strategy shows that umami peptides could be developed via phage display technology for the first time. The phage display platform has a promising application to discover other taste peptides with affinity for taste receptors of interest and has more room for improvement in the future.

Bimetallic bionic taste sensor for perception of the synergistic effect of umami substances.

Synergistic effect is one of the main properties of umami substances, elucidating the synergistic effect of umami is of great significance in the food industry. In this study, a bimetallic bionic taste sensor was developed to evaluate the synergistic effect of umami substances based on the perceptual mechanism of the human taste system. The Venus flytrap domain of T1R1 which is in charge of recognizing umami ligands was employed as the sensing element and self-assembled on the bimetallic nanomaterial (MoS2-PtPd) by Au-S bonding, the binding of receptors and ligands is characterized by changes of electrical signals. The sensor had good linearity (R2 > 0.99) and wide detection range in the detection of different kinds of umami substances (amino acids, nucleotides, organic acids, umami peptides) with detection limits as low as 0.03 pM. Comparing with electronic tongues, the sensor owned multiple characteristics of human taste system and could recognize the presence of synergistic effect of umami substances in a variety of real samples. Moreover, the differences in synergistic effect at different concentrations and ratios were also explored, the findings showed that the synergistic effect was more obvious at lower concentrations and balanced ratios of multiple umami substances added. The strategy would afford a promising platform for in-depth research on the mechanism of synergistic effect and multifunctional industrial applications.

Conserved Sites and Recognition Mechanisms of T1R1 and T2R14 Receptors Revealed by Ensemble Docking and Molecular Descriptors and Fingerprints Combined with Machine Learning.

Taste peptides, as an important component of protein-rich foodstuffs, potentiate the nutrition and taste of food. Thereinto, umami- and bitter-taste peptides have been ex tensively reported, while their taste mechanisms remain unclear. Meanwhile, the identification of taste peptides is still a time-consuming and costly task. In this study, 489 peptides with umami/bitter taste from TPDB (http://tastepeptides-meta.com/) were collected and used to train the classification models based on docking analysis, molecular descriptors (MDs), and molecular fingerprints (FPs). A consensus model, taste peptide docking machine (TPDM), was generated based on five learning algorithms (linear regression, random forest, gaussian naive bayes, gradient boosting tree, and stochastic gradient descent) and four molecular representation schemes. Model interpretive analysis showed that MDs (VSA_EState, MinEstateIndex, MolLogP) and FPs (598, 322, 952) had the greatest impact on the umami/bitter prediction of peptides. Based on the consensus docking results, we obtained the key recognition modes of umami/bitter receptors (T1Rs/T2Rs): (1) residues 107S-109S, 148S-154T, 247F-249A mainly form hydrogen bonding contacts and (2) residues 153A-158L, 163L, 181Q, 218D, 247F-249A in T1R1 and 56D, 106P, 107V, 152V-156F, 173K-180F in T2R14 constituted their hydrogen bond pockets. The model is available at http://www.tastepeptides-meta.com/yyds.

FAPD: An Astringency Threshold and Astringency Type Prediction Database for Flavonoid Compounds Based on Machine Learning.

Astringency is a puckering or velvety sensation mainly derived from flavonoid compounds in food. The traditional experimental approach for astringent compound discovery was labor-intensive and cost-consuming, while machine learning (ML) can greatly accelerate this procedure. Herein, we propose the Flavonoid Astringency Prediction Database (FAPD) based on ML. First, the Molecular Fingerprint Similarities (MFSs) and thresholds of flavonoid compounds were hierarchically clustering analyzed. For the astringency threshold prediction, four regressions models (i.e., Gaussian Process Regression (GPR), Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosted Decision Tree (GBDT)) were established, and the best model was RF which was interpreted by the SHapley Additive exPlanations (SHAP) approach. For the astringency type prediction, six classification models (i.e., RF, GBDT, Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Stochastic Gradient Descent (SGD)) were established, and the best model was SGD. Furthermore, over 1200 natural flavonoid compounds were discovered and built into the customized FAPD. In FAPD, the astringency thresholds were achieved by RF; the astringency types were distinguished by SGD, and the real and predicted astringency types were verified by t-Distributed Stochastic Neighbor Embedding (t-SNE). Therefore, ML models can be used to predict the astringency threshold and astringency type of flavonoid compounds, which provides a new paradigm to research the molecular structure-flavor property relationship of food components.

A TastePeptides-Meta system including an umami/bitter classification model Umami_YYDS, a TastePeptidesDB database and an open-source package Auto_Taste_ML.

Taste peptides with umami/bitterness play a role in food attributes. However, the taste mechanisms of peptides are not fully understood, and the identification of these peptides is time-consuming. Here, we created a taste peptide database by collecting the reported taste peptide information. Eight key molecular descriptors from di/tri-peptides were selected and obtained by modeling screening. A gradient boosting decision tree model named Umami_YYDS (89.6% accuracy) was established by data enhancement, comparison algorithm and model optimization. Our model showed a great prediction performance compared to other models, and its outstanding ability was verified by sensory experiments. To provide a convenient approach, we deployed a prediction website based on Umami_YYDS and uploaded the Auto_Taste_ML machine learning package. In summary, we established the system TastePeptides-Meta, containing a taste peptide database TastePeptidesDB an umami/bitter taste prediction model Umami_YYDS and an open-source machine learning package Auto_Taste_ML, which were helpful for rapid screening of umami peptides.

Engineering Yarrowia lipolytica for the sustainable production of ß-farnesene from waste oil feedstock.

BACKGROUND: ß-Farnesene is a sesquiterpene with versatile industrial applications. The production of ß-farnesene from waste lipid feedstock is an attractive method for sustainable production and recycling waste oil. Yarrowia lipolytica is an unconventional oleaginous yeast, which can use lipid feedstock and has great potential to synthesize acetyl-CoA-derived chemicals. RESULTS: In this study, we engineered Y. lipolytica to produce ß-farnesene from lipid feedstock. To direct the flux of acetyl-CoA, which is generated from lipid ß-oxidation, to ß-farnesene synthesis, the mevalonate synthesis pathway was compartmentalized into peroxisomes. ß-Farnesene production was then engineered by the protein engineering of ß-farnesene synthase and pathway engineering. The regulation of lipid metabolism by enhancing ß-oxidation and eliminating intracellular lipid synthesis was further performed to improve the ß-farnesene synthesis. As a result, the final ß-farnesene production with bio-engineering reached 35.2 g/L and 31.9 g/L using oleic acid and waste cooking oil, respectively, which are the highest ß-farnesene titers reported in Y. lipolytica. CONCLUSIONS: This study demonstrates that engineered Y. lipolytica could realize the sustainable production of value-added acetyl-CoA-derived chemicals from waste lipid feedstock.

Typical Umami Ligand-Induced Binding Interaction and Conformational Change of T1R1-VFT.

Umami taste receptor type 1 member 1/3 (T1R1/T1R3) heterodimer has multiple ligand-binding sites, most of which are located in T1R1-Venus flytrap domain (T1R1-VFT). However, the critical binding process of T1R1-VFT/umami ligands remains largely unknown. Herein, T1R1-VFT was prepared with a sufficient amount and functional activity, and its binding characteristics with typical umami molecules (monosodium l-glutamate, disodium succinate, beefy meaty peptide, and inosine-5'-monophosphate) were explored via multispectroscopic techniques and molecular dynamics simulation. The results showed that, driven mainly by hydrogen bond, van der Waals forces, and electrostatic interactions, T1R1-VFT bound to umami compound at 1:1 (stoichiometric interaction) and formed T1R1-VFT/ligand complex (static fluorescence quenching) with a weak binding affinity (Ka values: 252 ± 19 to 1169 ± 112 M-1). The binding process was spontaneous and exothermic (ΔG, -17.72 to -14.26 kJ mol-1; ΔH, -23.86 to -12.11 kJ mol-1) and induced conformational changes of T1R1-VFT, which was mainly reflected in slight unfolding of α-helix (Δα-helix < 0) and polypeptide chain backbone structure. Meanwhile, the binding of the four ligands stabilized the active conformation of the T1R1-VFT pocket. This work provides insight into the binding interaction between T1R1-VFT/umami ligands and improves understanding of how umami receptor recognizes specific ligand molecules.

Quercetin targets VCAM1 to prevent diabetic cerebrovascular endothelial cell injury.

Introduction: Endothelial cells play important roles in neurodegenerative diseases caused by diabetes, therefore, we aimed at investigating the mechanisms through which endothelial cells are involved in diabetes development. Methods: Single cell analysis was performed to identify the major endothelial cell subtypes in cardiovascular tissues that are involved in diabetes development. A cell-cell communication approach was then used to identify ligand-receptor interaction pairs between these cell types. Differential expression analysis between the two experimental groups [standard chow diet group and diabetogenic diet with cholesterol (DDC) group] was used to identify diabetes-related differentially expressed genes (DEGs). The upregulated genes were used to identify candidate ligands or receptors, as well as the corresponding cell types. Cell trajectory inference was performed to identify the stage of cell development and changes in expression of candidate ligands or receptors during cell development. Gene set enrichment analysis (GSEA) was conducted to investigate the biological functions of genes of purpose. Finally, molecular dynamics simulations (MDSs) were used to predict potential drugs with the ability to target the proteins of purpose. Results: Seven cell types, including five endothelial cell subtypes (EC_1, EC_2, EC_3, EC_4, and EC_EndMT), were identified from endothelial cell-enriched single cell samples from the heart and aorta of mice. Cell-cell communication analysis revealed the potential ligand-receptor interactions between these cell types while five important ligand-receptor-associated genes, including Fn1, Vcam1, Fbn1, Col4a1, and Col4a2, were established by differential expression analysis. Among them, Vcam1 is mainly expressed in EC_EndMT and is involved in interactions between EC_EndMT and other cells. Cell trajectory extrapolation analysis revealed a shift from EC_2/EC_4 to EC_EndMT and a shift from EC_EndMT to EC_3/EC_1 during the progression of diabetes. GSEA analysis revealed that upregulation of VCAM1 may have inhibitory effects on cell growth and energy metabolism. Conclusion: EC_EndMT subtypes have a complex role in neurodegenerative diseases caused by diabetes. Through mechanisms involved in cell-cell communication, Vcam1 may play an important role in dysregulation of biological functions of EC_ EndMT. Molecular docking results of the quercetin-VCAM1 complex suggest that quercetin may be an effective drug for targeting this protein.

Cancer incidence and spectrum among Uygurs in Hotan District in China.

Cancer is the leading cause of death in China and a significant public health problem with increasing incidence and fatality rates. The Han nationality is the main ethnic group in China, and many reports on the epidemiology of cancers in Han nationality are published. However no studies report the cancer spectrum of Uygurs which are one of the minority nationalities in China. Hence, we present incidence and mortality numbers of different cancers for the Uygur patients for the period 2018-2020 in Hotan District where Uygur population accounts for 99 %. During the 3-year study period, 2509 new Uygur cancer cases were registered, comprising 774 men and 1735 women. Cervical cancer was the most common, followed by esophageal, breast, gastric and colorectal cancers. The most common cancers in women and men were cervical cancer and esophageal cancer, respectively. In conclusion, the cancer spectrum in Hotan is different from other regions of China and our research revealed the cancer incidence in Hotan, which could help us to take appropriate measures to reduce the incidence rate.

Absence of causal association between Vitamin D and bone mineral density across the lifespan: a Mendelian randomization study.

Vitamin D deficiency is a candidate risk factor for osteoporosis, characterized by decreased bone mineral density (BMD). We performed this two-sample Mendelian randomization (MR) analysis to investigate the causal effect of vitamin D on BMD. We extracted 143 single-nucleotide polymorphisms from a recent GWAS on 417,580 participants of European ancestry as instrumental variables, and used summary statistics for BMD at forearm (n = 10,805), femoral neck (n = 49,988), lumbar spine (n = 44,731) and total-body of different age-stages (< 15, 15-30, 30-45, 45-60, > 60) (n = 67,358). We explored the direct effect of vitamin D on BMD with an adjusted body mass index (BMI) in a multivariable MR analysis. We found no support for causality of 25-hydroxyvitamin D on BMD at forearm, femoral neck, lumbar spine, and total-body BMD across the lifespan. There was no obvious difference between the total and direct effect of vitamin D on BMD after adjusting for BMI. Our MR analysis provided evidence that genetically determined vitamin D was not causally associated with BMD in the general population. Large-scale randomized controlled trials are warranted to investigate the role of vitamin D supplementation in preventing osteoporosis in the high-risk population.

Characterizing car-following behaviors of human drivers when following automated vehicles using the real-world dataset.

As the market penetration rate of automated vehicles (AVs) increases, there will be a transition period when the traffic stream is composed of both AVs and human-driven vehicles (MVs) in the near future. However, the interactions between MVs and AVs, especially whether MVs will behave differently when following AVs compared to following MVs, have not been fully understood. Previous studies in this field mainly conducted traffic/numerical simulations or field experiments to investigate human drivers' behavior changes, but these approaches all have critical drawbacks such as simplified driving environments and limited sample sizes. To fill in the knowledge gap, this study uses the high-resolution (10 Hz) Waymo Open Dataset to reveal differences in car-following behaviors between MV-following-AV and MV-following-MV cases. Driving volatility measures, time headways and time-to-collision (TTC) are adopted to quantify and compare MV-following-AV and MV-following-MV interactions. The principal component analysis (PCA) is applied on the high-dimensional feature space, followed by the hierarchical clustering on the dimension-reduced feature set to categorize MV driving styles when following AVs. The comparison results indicate that MV-following-AV events have lower driving volatility in terms of velocity and acceleration/deceleration, smaller time headways and higher TTC values. Furthermore, the clustering results reveal that human drivers when following AVs exhibit four different car-following styles: high-velocity-non-aggressive, high-velocity-aggressive, low-velocity-non-aggressive, and low-velocity-aggressive. These findings highlight the vital importance of taking into account the heterogeneity of MV-following-AV behaviors when designing mixed traffic control algorithms and can be beneficial for AV fleet operators to improve their algorithms.

Identification of genome integration sites for developing a CRISPR-based gene expression toolkit in Yarrowia lipolytica.

With the rapid development of synthetic biology, the oleaginous yeast Yarrowia lipolytica has become an attractive microorganism for chemical production. To better optimize and reroute metabolic pathways, we have expanded the CRISPR-based gene expression toolkit of Y. lipolytica. By sorting the integration sites associated with high expression, new neutral integration sites associated with high expression and high integration efficiency were identified. Diverse genetic components, including promoters and terminators, were also characterized to expand the expression range. We found that in addition to promoters, the newly characterized terminators exhibited large variations in gene expression. These genetic components and integration sites were then used to regulate genes involved in the lycopene biosynthesis pathway, and different levels of lycopene production were achieved. The CRISPR-based gene expression toolkit developed in this study will facilitate the genetic engineering of Y. lipolytica.