Microbiol Resour Announc. 2023 Mar 21:e0126822. doi: 10.1128/mra.01268-22. Online ahead of print.

ABSTRACT

Sinomicrobium sp. strain PAP.21 (EXT111902) was isolated from the coast of Cenderawasih Bay National Park in West Papua, Indonesia. Its genome was assembled into 151 contigs with a total size of 5.439 Mbp, enabling the prediction of its specialized metabolite production capacity.

PMID:36943053 | DOI:10.1128/mra.01268-22

J Bacteriol. 2023 Mar 21:e0047922. doi: 10.1128/jb.00479-22. Online ahead of print.

ABSTRACT

As a master nitrogen regulator in most actinomycetes, GlnR both governs central nitrogen metabolism and regulates many carbon, phosphate, and secondary metabolic pathways. To date, most studies have been focused on the GlnR regulon, while little is known about the transcriptional regulator for glnR itself. It has been observed that glnR transcription can be upregulated in Mycobacterium smegmatis under nitrogen-limited growth conditions; however, the detailed regulatory mechanism is still unclear. Here, we demonstrate that the glnR gene in M. smegmatis is transcriptionally activated by its product GlnR in response to nitrogen limitation. The precise GlnR binding site was successfully characterized in its promoter region using the electrophoretic mobility shift assay and the DNase I footprinting assay. Site mutagenesis and genetic analyses confirmed that the binding site was essential for in vivo self-activation of glnR transcription. Moreover, based on bioinformatic analyses, we discovered that most of the mycobacterial glnR promoter regions (144 out of 147) contain potential GlnR binding sites, and we subsequently proved that the purified M. smegmatis GlnR protein could specifically bind 16 promoter regions that represent 119 mycobacterial species, including Mycobacterium tuberculosis. Together, our findings not only elucidate the transcriptional self-regulation mechanism of glnR transcription in M. smegmatis but also indicate the ubiquity of the mechanism in other mycobacterial species. IMPORTANCE In actinomycetes, the nitrogen metabolism not only is essential for the construction of life macromolecules but also affects the biosynthesis of secondary metabolites and even virulence (e.g., Mycobacterium tuberculosis). The transcriptional regulation of genes involved in nitrogen metabolism has been thoroughly studied and involves the master nitrogen regulator GlnR. However, the transcriptional regulation of glnR itself remains elusive. Here, we demonstrated that GlnR functions as a transcriptional self-activator in response to nitrogen starvation in the fast-growing model Mycobacterium species Mycobacterium smegmatis. We further showed that this self-regulation mechanism could be widespread in other mycobacteria, which might be beneficial for those slow-growing mycobacteria to adapt to the nitrogen-starvation environments such as within human macrophages for M. tuberculosis.

PMID:36943048 | DOI:10.1128/jb.00479-22

mSystems. 2023 Mar 21:e0127022. doi: 10.1128/msystems.01270-22. Online ahead of print.

ABSTRACT

Microbial consortia drive essential processes, ranging from nitrogen fixation in soils to providing metabolic breakdown products to animal hosts. However, it is challenging to translate the composition of microbial consortia into their emergent functional capacities. Community-scale metabolic models hold the potential to simulate the outputs of complex microbial communities in a given environmental context, but there is currently no consensus for what the fitness function of an entire community should look like in the presence of ecological interactions and whether community-wide growth operates close to a maximum. Transitioning from single-taxon genome-scale metabolic models to multitaxon models implies a growth cone without a well-specified growth rate solution for individual taxa. Here, we argue that dynamic approaches naturally overcome these limitations, but they come at the cost of being computationally expensive. Furthermore, we show how two nondynamic, steady-state approaches approximate dynamic trajectories and pick ecologically relevant solutions from the community growth cone with improved computational scalability.

PMID:36943046 | DOI:10.1128/msystems.01270-22

Mol Syst Biol. 2023 Mar 21:e11443. doi: 10.15252/msb.202211443. Online ahead of print.

ABSTRACT

Metabolism is controlled to ensure organismal development and homeostasis. Several mechanisms regulate metabolism, including allosteric control and transcriptional regulation of metabolic enzymes and transporters. So far, metabolism regulation has mostly been described for individual genes and pathways, and the extent of transcriptional regulation of the entire metabolic network remains largely unknown. Here, we find that three-quarters of all metabolic genes are transcriptionally regulated in the nematode Caenorhabditis elegans. We find that many annotated metabolic pathways are coexpressed, and we use gene expression data and the iCEL1314 metabolic network model to define coregulated subpathways in an unbiased manner. Using a large gene expression compendium, we determine the conditions where subpathways exhibit strong coexpression. Finally, we develop "WormClust," a web application that enables a gene-by-gene query of genes to view their association with metabolic (sub)-pathways. Overall, this study sheds light on the ubiquity of transcriptional regulation of metabolism and provides a blueprint for similar studies in other organisms, including humans.

PMID:36942755 | DOI:10.15252/msb.202211443

Org Biomol Chem. 2023 Mar 21. doi: 10.1039/d3ob00060e. Online ahead of print.

ABSTRACT

The plant hormone (S)-abscisic acid (ABA) is a signalling molecule found in all plants that triggers plants' responses to environmental stressors such as heat, drought, and salinity. Metabolism-resistant ABA analogs that confer longer lasting effects require multi-step syntheses and high costs that prevent their application in crop protection. To solve this issue, we have developed a two-step, efficient and scalable synthesis of (+)-tetralone ABA from (S)-ABA methyl ester. A challenging three-carbon insertion and a bicyclic ring formation on (S)-ABA methyl ester was achieved through a highly regioselective Knoevenagel condensation, cyclization, and oxidation in one-pot. Further we have studied the biological activity and metabolism of (+)-tetralone ABA in planta and found the analog is hydroxylated similarly to ABA. The biologically active hydroxylated tetralone ABA has greater persistence than 8'-hydroxy ABA as cyclization to the equivalent of phaseic acid is prevented by the aromatic ring. (+)-tetralone ABA complemented the growth retardation of an Arabidopsis ABA-deficient mutant more effectively than (+)-ABA. Taken together, this new synthesis allows the production of the potent ABA agonist efficiently on an industrial scale.

PMID:36942670 | DOI:10.1039/d3ob00060e

Comput Struct Biotechnol J. 2023 Mar 4;21:1930-1941. doi: 10.1016/j.csbj.2023.02.014. eCollection 2023.

ABSTRACT

Recent progress in our understanding of cancer mostly relies on the systematic profiling of patient samples with high-throughput techniques like transcriptomics. With this approach, one can find gene signatures and networks underlying cancer aggressiveness and therapy resistance. However, omics data alone cannot generate insights into the spatiotemporal aspects of tumor progression. Here, multi-level computational modeling is a promising approach that would benefit from protocols to integrate the data generated by the high-throughput profiling of patient samples. We present a computational workflow to integrate transcriptomics data from tumor patients into hybrid, multi-scale cancer models. In the method, we conduct transcriptomics analysis to select key differentially regulated pathways in therapy responders and non-responders and link them to agent-based model parameters. We then determine global and local sensitivity through systematic model simulations that assess the relevance of parameter variations in triggering therapy resistance. We illustrate the methodology with a de novo generated agent-based model accounting for the interplay between tumor and immune cells in a melanoma micrometastasis. The application of the workflow identifies three distinct scenarios of therapy resistance.

PMID:36942106 | PMC:PMC10024179 | DOI:10.1016/j.csbj.2023.02.014

J Transl Med. 2023 Mar 20;21(1):207. doi: 10.1186/s12967-023-04021-w.

NO ABSTRACT

PMID:36941598 | DOI:10.1186/s12967-023-04021-w

Commun Biol. 2023 Mar 20;6(1):295. doi: 10.1038/s42003-023-04667-8.

ABSTRACT

Prolactin (PRL) is elevated in B-cell-mediated lymphoproliferative diseases and promotes B-cell survival. Whether PRL or PRL receptors drive the evolution of B-cell malignancies is unknown. We measure changes in B cells after knocking down the pro-proliferative, anti-apoptotic long isoform of the PRL receptor (LFPRLR) in vivo in systemic lupus erythematosus (SLE)- and B-cell lymphoma-prone mouse models, and the long plus intermediate isoforms (LF/IFPRLR) in human B-cell malignancies. To knockdown LF/IFPRLRs without suppressing expression of the counteractive short PRLR isoforms (SFPRLRs), we employ splice-modulating DNA oligomers. In SLE-prone mice, LFPRLR knockdown reduces numbers and proliferation of pathogenic B-cell subsets and lowers the risk of B-cell transformation by downregulating expression of activation-induced cytidine deaminase. LFPRLR knockdown in lymphoma-prone mice reduces B-cell numbers and their expression of BCL2 and TCL1. In overt human B-cell malignancies, LF/IFPRLR knockdown reduces B-cell viability and their MYC and BCL2 expression. Unlike normal B cells, human B-cell malignancies secrete autocrine PRL and often express no SFPRLRs. Neutralization of secreted PRL reduces the viability of B-cell malignancies. Knockdown of LF/IFPRLR reduces the growth of human B-cell malignancies in vitro and in vivo. Thus, LF/IFPRLR knockdown is a highly specific approach to block the evolution of B-cell neoplasms.

PMID:36941341 | DOI:10.1038/s42003-023-04667-8

Nat Med. 2023 Mar 20. doi: 10.1038/s41591-023-02248-0. Online ahead of print.

ABSTRACT

Multiomic profiling can reveal population heterogeneity for both health and disease states. Obesity drives a myriad of metabolic perturbations and is a risk factor for multiple chronic diseases. Here we report an atlas of cross-sectional and longitudinal changes in 1,111 blood analytes associated with variation in body mass index (BMI), as well as multiomic associations with host polygenic risk scores and gut microbiome composition, from a cohort of 1,277 individuals enrolled in a wellness program (Arivale). Machine learning model predictions of BMI from blood multiomics captured heterogeneous phenotypic states of host metabolism and gut microbiome composition better than BMI, which was also validated in an external cohort (TwinsUK). Moreover, longitudinal analyses identified variable BMI trajectories for different omics measures in response to a healthy lifestyle intervention; metabolomics-inferred BMI decreased to a greater extent than actual BMI, whereas proteomics-inferred BMI exhibited greater resistance to change. Our analyses further identified blood analyte-analyte associations that were modified by metabolomics-inferred BMI and partially reversed in individuals with metabolic obesity during the intervention. Taken together, our findings provide a blood atlas of the molecular perturbations associated with changes in obesity status, serving as a resource to quantify metabolic health for predictive and preventive medicine.

PMID:36941332 | DOI:10.1038/s41591-023-02248-0

Neuroimage. 2023 Mar 18;271:119987. doi: 10.1016/j.neuroimage.2023.119987. Online ahead of print.

ABSTRACT

Tinnitus is a clinical condition where a sound is perceived without an external sound source. Homeostatic plasticity (HSP), serving to increase neural activity as compensation for the reduced input to the auditory pathway after hearing loss, has been proposed as a mechanism underlying tinnitus. In support, animal models of tinnitus show evidence of increased neural activity after hearing loss, including increased spontaneous and sound-driven firing rate, as well as increased neural noise throughout the auditory processing pathway. Bridging these findings to human tinnitus, however, has proven to be challenging. Here we implement hearing loss-induced HSP in a Wilson-Cowan Cortical Model of the auditory cortex to predict how homeostatic principles operating at the microscale translate to the meso- to macroscale accessible through human neuroimaging. We observed HSP-induced response changes in the model that were previously proposed as neural signatures of tinnitus, but that have also been reported as correlates of hearing loss and hyperacusis. As expected, HSP increased spontaneous and sound-driven responsiveness in hearing-loss affected frequency channels of the model. We furthermore observed evidence of increased neural noise and the appearance of spatiotemporal modulations in neural activity, which we discuss in light of recent human neuroimaging findings. Our computational model makes quantitative predictions that require experimental validation, and may thereby serve as the basis of future human studies of hearing loss, tinnitus, and hyperacusis.

PMID:36940510 | DOI:10.1016/j.neuroimage.2023.119987

Phenomics. 2022 Jun 14;2(5):283-294. doi: 10.1007/s43657-022-00057-y. eCollection 2022 Oct.

ABSTRACT

While disrupted lipid metabolism is a well-established risk factor for hypertension in animal models, the links between various lipidomic signatures and hypertension in human studies remain unclear. We aimed to examine associations between plasma lipidomic profiles and prevalence of hypertension among 2248 community-living Chinese aged 50-70 years. Hypertension was defined according to 2020 International Society of Hypertension global hypertension practice guidelines and 2018 Chinese guidelines. In total, 728 plasma lipidomic species were profiled using high-coverage targeted lipidomics. After multivariate adjustment, including lifestyle, body mass index, blood lipids, and sodium intake, 110 metabolites from nine lipidomic subclasses showed significant associations with hypertension, among which phosphatidylethanolamines (PEs) had the strongest association. Eleven lipidomic signals for hypertension risk were further identified from the nine subclasses, including PE(18:0/18:2) (OR per SD, 1.49; 95% confidence intervals, 1.30-1.69), phosphatidylcholine (PC) (18:0/18:2) (1.27; 1.13-1.43), phosphatidylserine (18:0/18:0) (1.24; 1.09-1.41), lysophosphatidylinositol (18:1) (1.17; 1.06-1.29), triacylglycerol (52:5) (1.38; 1.18-1.61), diacylglycerol (16:0/18:2) (1.42; 1.19-1.69), dihydroceramide (24:0) (1.25; 1.09-1.43), hydroxyl-sphingomyelins (SM[2OH])C34:1 (1.19; 1.07-1.33), lysophosphatidylcholine (20:1) (0.86; 0.78-0.95), SM(OH)C38:1 (0.87; 0.79-0.96), and PC (18:2/20:1) (0.84; 0.75-0.94). Principal component analysis also showed that a factor mainly containing specific PEs was positively associated with hypertension (1.20; 1.09-1.33). Collectively, our study revealed that disturbances in multiple circulating lipidomic subclasses and signatures, especially PEs, were significantly associated with the prevalence of hypertension in middle-aged and elderly Chinese. Future studies are warranted to confirm our findings and determine the mechanisms underlying these associations.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s43657-022-00057-y.

PMID:36939788 | PMC:PMC9590468 | DOI:10.1007/s43657-022-00057-y

Chin Med J (Engl). 2023 Mar 21. doi: 10.1097/CM9.0000000000002201. Online ahead of print.

ABSTRACT

BACKGROUND: Ovarian cancer is one of the most widespread malignant diseases of the female reproductive system worldwide. The plurality of ovarian cancer is diagnosed with metastasis in the abdominal cavity. Epithelial-mesenchymal transition (EMT) exerts a vital role in tumor cell metastasis. However, it remains unclear whether long non-coding RNA (lncRNA) are implicated in EMT and influence ovarian cancer cell invasion and metastasis. This study was designed to investigate the impacts of lncRNA AC005224.4 on ovarian cancer.

METHODS: LncRNA AC005224.4, miR-140-3p, and snail family transcriptional repressor 2 (SNAI2) expression levels in ovarian cancer and normal ovarian tissues were determined using real-time quantitative polymerase chain reaction (qRT-PCR). Cell Counting Kit-8 (CCK-8) and Transwell (migration and invasion) assays were conducted to measure SKOV3 and CAOV-3 cell proliferation and metastasis. E-cadherin, N-cadherin, Snail, and Vimentin contents were detected using Western blot. Nude mouse xenograft assay was utilized to validate AC005224.4 effects in vivo. Dual-luciferase reporter gene assay confirmed the targeted relationship between miR-140-3p and AC005224.4 or SNAI2.

RESULTS: AC005224.4 and SNAI2 upregulation and miR-140-3p downregulation were observed in ovarian cancer tissues and cells. Silencing of AC005224.4 observably moderated SKOV3 and CAOV-3 cell proliferation, migration, invasion, and EMT process in vitro and impaired the tumorigenesis in vivo. miR-140-3p was a target of AC005224.4 and its reduced expression level was mediated by AC005224.4. miR-140-3p mimics decreased the proliferation, migration, and invasion of ovarian cancer cells. SNAI2 was identified as a novel target of miR-140-3p and its expression level was promoted by either AC005224.4 overexpression or miR-140-3p knockdown. Overexpression of SNAI2 also facilitated ovarian cancer cell viability and metastasis.

CONCLUSION: AC005224.4 was confirmed as an oncogene via sponging miR-140-3p and promoted SNAI2 expression, contributing to better understanding of ovarian cancer pathogenesis and shedding light on exploiting the novel lncRNA-directed therapy against ovarian cancer.

PMID:36939239 | DOI:10.1097/CM9.0000000000002201

Am J Physiol Cell Physiol. 2023 Mar 20. doi: 10.1152/ajpcell.00063.2023. Online ahead of print.

ABSTRACT

Ferroportin (Fpn)-expressed at the plasma membrane of macrophages, enterocytes, and hepatocytes-mediates the transfer of cellular iron into the blood plasma. Under the control of the iron-regulatory hormone hepcidin, Fpn serves a critical role in systemic iron homeostasis. Whereas we have previously characterized human Fpn, a great deal of research in iron homeostasis and disorders utilizes mouse models. By way of example, the flatiron mouse, a model of classical ferroportin disease, bears the mutation H32R in Fpn and is characterized by systemic iron deficiency and macrophage iron retention. The flatiron mouse also appears to exhibit a manganese phenotype, raising the possibility that mouse Fpn serves a role in manganese metabolism. At odds with this observation, we have found that human Fpn does not transport manganese, so we considered the possibility that a species difference could explain this discrepancy. We tested the hypothesis that mouse but not human Fpn can transport manganese and performed a comparative analysis of mouse and human Fpn. We examined the functional properties of human Fpn, mouse Fpn, and mutant mouse Fpn by using radiotracer assays in RNA-injected Xenopus oocytes. We found that neither mouse nor human Fpn transports manganese. Mouse and human Fpn share identical properties with respect to substrate profile, calcium dependence, optimal pH, and hepcidin sensitivity. We have also demonstrated that Fpn is not an ATPase pump. Our findings validate the use of mouse models of ferroportin function in iron homeostasis and disease.

PMID:36939203 | DOI:10.1152/ajpcell.00063.2023

Mol Syst Biol. 2023 Mar 20:e11325. doi: 10.15252/msb.202211325. Online ahead of print.

ABSTRACT

The analysis of omic data depends on machine-readable information about protein interactions, modifications, and activities as found in protein interaction networks, databases of post-translational modifications, and curated models of gene and protein function. These resources typically depend heavily on human curation. Natural language processing systems that read the primary literature have the potential to substantially extend knowledge resources while reducing the burden on human curators. However, machine-reading systems are limited by high error rates and commonly generate fragmentary and redundant information. Here, we describe an approach to precisely assemble molecular mechanisms at scale using multiple natural language processing systems and the Integrated Network and Dynamical Reasoning Assembler (INDRA). INDRA identifies full and partial overlaps in information extracted from published papers and pathway databases, uses predictive models to improve the reliability of machine reading, and thereby assembles individual pieces of information into non-redundant and broadly usable mechanistic knowledge. Using INDRA to create high-quality corpora of causal knowledge we show it is possible to extend protein-protein interaction databases and explain co-dependencies in the Cancer Dependency Map.

PMID:36938926 | DOI:10.15252/msb.202211325

Analyst. 2023 Mar 20. doi: 10.1039/d3an00249g. Online ahead of print.

ABSTRACT

Paracetamol (also known as acetaminophen) is an over-the-counter (OTC) drug that is commonly used as an analgesic for mild pain, headache, cold and flu. While in the short term it is a safe and effective medicine, it is sometimes used for attempted suicides particularly in young adults. In such circumstances it is important for rapid diagnosis of overdoses as antidotes can be given to limit liver damage from one of its primary metabolites N-acetyl-p-benzoquinone imine (NAPQI). Unfortunately, the demand for rapid and sensitive analytical techniques to accurately monitor the abuse of OTC drugs has significantly risen. Ideally these techniques would be highly specific, sensitive, reproducible, portable and rapid. In addition, an ideal point of care (PoC) test would enable quantitative detection of drugs and their metabolites present in body fluids. While Raman spectroscopy meets these specifications, there is a need for enhancement of the signal because the Raman effect is weak. In this study, we developed a surface-enhanced Raman scattering (SERS) methodology in conjunction with chemometrics to quantify the amount of paracetamol and its main primary metabolites (viz., paracetamol sulfate, p-acetamidophenyl β-D-glucuronide and NAPQI) in water and artificial urine. The enhancement of the SERS signals was achieved by mixing the drug or xenometabolites with a gold nanoparticle followed by aggregation with 0.045 M NaCl. We found that the SERS data could be collected directly, due to immediate analyte association with the Au surface and colloid aggregation. Accurate and precise measurements were generated, with a limit of detection (LoD) of paracetamol in water and artificial urine at 7.18 × 10-6 M and 2.11 × 10-5 M, respectively, which is well below the limit needed for overdose and indeed normal levels of paracetamol in serum after taking 1 g orally. The predictive values obtained from the analysis of paracetamol in water and artificial urine were also excellent, with the coefficient of determination (Q2) being 0.995 and 0.996, respectively (1 suggests a perfect model). It was noteworthy that when artificial urine was spiked with paracetamol, no aggregating agent was required due to the salt rich medium, which led to spontaneous aggregation. Moreover, for the xenometabolites of paracetamol excellent LoDs were obtained and these ranged from 2.6 × 10-4 M to 5 × 10-5 M with paracetamol sulfate and NAPQI having Q2 values of 0.934 and 0.892 and for p-acetamidophenyl β-D-glucuronide this was slightly lower at 0.6437.

PMID:36938623 | DOI:10.1039/d3an00249g

Heliyon. 2023 Feb 25;9(3):e14007. doi: 10.1016/j.heliyon.2023.e14007. eCollection 2023 Mar.

ABSTRACT

Cellular senescence is a cell fate that prominently impacts physiological and pathophysiological processes. Diverse cellular stresses induce it, and dramatic gene expression changes accompany it. However, determining the interactions comprising the gene regulatory network (GRN) governing senescence remains challenging. Recent advances in signal processing techniques provide opportunities to reconstruct GRNs. Here, we describe a GRN for senescence integrating time-series transcriptome and transcription factor depletion datasets. Specifically, we infer a set of differential equations using the "Sparse Identification of Nonlinear Dynamics" (SINDy) algorithm, discriminate genes with potential hidden regulators, validate the inferred GRN for time-points not included in the training data, and comprehensively benchmark our approach. Our work is a proof of concept for a data-driven GRN reconstruction method, consolidating an iterative, powerful mathematical platform for senescence modeling that can be used to test hypotheses in silico and has the potential for future discoveries of clinical impact.

PMID:36938415 | PMC:PMC10015196 | DOI:10.1016/j.heliyon.2023.e14007

Front Plant Sci. 2023 Mar 2;14:1074935. doi: 10.3389/fpls.2023.1074935. eCollection 2023.

ABSTRACT

Ipomoeapes-caprae (L.) (IPC) is a common species in tropical and subtropical coastal areas and one of the world's most widely distributed plants. It has attracted researchers for its outstanding biological, ecological and medicinal values. It has been reported that the genetic diversity of IPCs located on different continents is very low because of their frequent gene flow. During the long journey of evolution, every aspect of the plant morphologies has evolved to the best adaptivity to the environment, seeking their survival and progeny expansion. However, the fundamental genetic characteristics of IPC and how their seed adapted to the success of population expansion remain unknown. In this study, the fundamental genetic characteristics, including the genome size and the chromosome number of IPC, were investigated. The results showed that IPC's genome size is approximately 0.98-1.08 GB, and the chromosome number is 2n=30, providing the basic information for further genome analysis. In order to decipher the long-distance dispersal secret of this species, the fruit and seed developments, seed morphology, and seed germination were extensively investigated and described. The results showed an exquisite adaptive mechanism of IPC seeds to fulfil the population expansion via ocean currents. The large cavity inside the seeds and the dense tomenta on the surface provide the buoyancy force for the seeds to float on the seawater. The hard seed coats significantly obstructed the water absorption, thus preventing the seed from germination during the dispersal. Meanwhile, the fully developed embryos of IPC also have physiological dormancy. The physical and physiological characteristics of IPC seeds provide insight into the mechanism of their long-distance dispersal across the oceans. Moreover, based on morphological observation and semi-section microscopy, the development pattern of IPC glander trichomes was described, and their physiological functions were also discussed.

PMID:36938054 | PMC:PMC10017971 | DOI:10.3389/fpls.2023.1074935

Front Plant Sci. 2023 Mar 1;14:1123644. doi: 10.3389/fpls.2023.1123644. eCollection 2023.

ABSTRACT

Peanut (Arachis hypogaea L.), one of the leading oilseed crops worldwide, is an important source of vegetable oil, protein, minerals and vitamins. Peanut is widely cultivated in Asia, Africa and America, and China is the largest producer and consumer of peanut. Genetic engineering has shown great potential to alter the DNA makeup of an organism which is largely hindered by the low transformation and screening efficiency including in peanut. DsRed2 is a reporter gene widely utilized in genetic transformation to facilitate the screening of transformants, but never used in peanut genetic transformation. In this study, we have demonstrated the potential of the red fluorescence protein DsRed2 as a visual reporter to improve screening efficiency in peanut. DsRed2 was firstly expressed in protoplasts isolated from peanut cultivar Zhonhua 12 by PEG, and red fluorescence was successfully detected. Then, DsRed2 was expressed in peanut plants Zhonghua 12 driven by 35S promoter via Agrobacterium tumefaciens-mediated transformation. Red fluorescence was visually observed in calli and regenerated shoots, as well as in roots, leaves, flowers, fresh pod shells and mature seeds, suggesting that transgenic screening could be initiated at the early stage of transformation, and continued to the progeny. Upon screening with DsRed2, the positive plant rate was increased from 56.9% to 100%. The transgenic line was then used as the male parent to be crossed with Zhonghua 24, and the hybrid seeds showed red fluorescence as well, indicating that DsRed2 could be applied to hybrid plant identification very efficiently. DsRed2 was also expressed in hairy roots of Huayu 23 via Agrobacterium rhizogenes-mediated transformation, and the transgenic roots were easily selected by red fluorescence. In summary, the DsRed2 is an ideal reporter to achieve maximum screening efficiency and accuracy in peanut genetic transformation.

PMID:36938000 | PMC:PMC10014910 | DOI:10.3389/fpls.2023.1123644

Quant Biol. 2022 Dec;10(4):307-320.

ABSTRACT

BACKGROUND: Pooled CRISPR screen is a promising tool in drug targets or essential genes identification with the utilization of three different systems including CRISPR knockout (CRISPRko), CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa). Aside from continuous improvements in technology, more and more bioinformatics methods have been developed to analyze the data obtained by CRISPR screens which facilitate better understanding of physiological effects.

RESULTS: Here, we provide an overview on the application of CRISPR screens and bioinformatics approaches to analyzing different types of CRISPR screen data. We also discuss mechanisms and underlying challenges for the analysis of dropout screens, sorting-based screens and single-cell screens.

CONCLUSION: Different analysis approaches should be chosen based on the design of screens. This review will help community to better design novel algorithms and provide suggestions for wet-lab researchers to choose from different analysis methods.

PMID:36937794 | PMC:PMC10019185

Front Microbiol. 2023 Mar 2;14:1143770. doi: 10.3389/fmicb.2023.1143770. eCollection 2023.

ABSTRACT

A metabolic model, representing all biochemical reactions in a cell, is a prerequisite for several approaches in systems biology used to explore the metabolic phenotype of an organism. Despite the use of Euglena in diverse industrial applications and as a biological model, there is limited understanding of its metabolic network capacity. The unavailability of the completed genome data and the highly complex evolution of Euglena are significant obstacles to the reconstruction and analysis of its genome-scale metabolic model. In this mini-review, we discuss the current state and challenges of metabolic network reconstruction in Euglena gracilis. We have collated and present the available relevant data for the metabolic network reconstruction of E. gracilis, which could be used to improve the quality of the metabolic model of E. gracilis. Furthermore, we deliver the potential applications of the model in metabolic engineering. Altogether, it is supposed that this mini-review would facilitate the investigation of metabolic networks in Euglena and further lay out a direction for model-assisted metabolic engineering.

PMID:36937274 | PMC:PMC10018167 | DOI:10.3389/fmicb.2023.1143770