Computational prediction of interactions between Paxlovid and prescription drugs.

Pfizer's Paxlovid has recently been approved for the emergency use authorization (EUA) from the US Food and Drug Administration (FDA) for the treatment of mild-to-moderate COVID-19. Drug interactions can be a serious medical problem for COVID-19 patients with underlying medical conditions, such as hypertension and diabetes, who have likely been taking other drugs. Here, we use deep learning to predict potential drug-drug interactions between Paxlovid components (nirmatrelvir and ritonavir) and 2,248 prescription drugs for treating various diseases.

Multiple transcription factors of Arabidopsis thaliana that are activated by LEAFY COTYLEDON 2 regulate triacylglycerol biosynthesis.

Plant triacylglycerols (TAG) are used in food and various industrial feedstocks. LEAFY COTYLEDON 2 (LEC2), a master positive regulator of TAG biosynthesis, regulates a complex network of transcription factors (TFs) during seed development. Aside from WRINKLED1 (WRI1), the TFs regulated by LEC2 related to TAG biosynthesis have not yet been identified. Previously, we identified 25 seed-expressing TFs that were upregulated in Arabidopsis leaves that overexpressed senescence-induced LEC2. In this study, each of the 25 TFs was transiently expressed in the leaves of Nicotiana benthamiana to identify unknown TFs that regulate TAG biosynthesis. The TAG content of the transformed leaves was analyzed using thin layer chromatography and gas chromatography. We observed that five TFs, ARABIDOPSIS RESPONSIVE REGULATOR 21 (ARR21), AINTEGUMENTA-LIKE 6 (AIL6), APETALA2/ETHYLENE RESPONSIVE FACTOR 55 (ERF55), WRKY DNA-BINDING PROTEIN 8 (WRKY8), and ARABIDOPSIS NAC DOMAIN CONTAINING PROTEIN 38 (ANAC038) increased TAG synthesis in the leaves. Among these, the promoters of AIL6, ERF55, WRKY8, and ANAC038 contain RY motifs, which are LEC2-binding sites activated by LEC2. AIL6 overexpression in Arabidopsis increased the total fatty acid (FA) content in seeds and altered the FA composition, with increases in 16:0, 18:1, and 18:2 and decreases in 18:0, 18:3, and 20:1 compared with those in the wild type (WT). AIL6 overexpression activates several FA and TAG biosynthesis genes. Therefore, our study successfully identified several new TFs regulated by LEC2 in TAG biosynthesis and showed that AIL6 increased the TAG content in seeds.

Intrahepatic IgA complex induces polarization of cancer-associated fibroblasts to matrix phenotypes in the tumor microenvironment of HCC.

BACKGROUND AND AIMS: Cancer-associated fibroblasts (CAFs) play key roles in the tumor microenvironment. IgA contributes to inflammation and dismantling antitumor immunity in the human liver. In this study, we aimed to elucidate the effects of the IgA complex on CAFs in Pil Soo Sung the tumor microenvironment of HCC. APPROACH AND RESULTS: CAF dynamics in HCC tumor microenvironment were analyzed through single-cell RNA sequencing of HCC samples. CAFs isolated from 50 HCC samples were treated with mock or serum-derived IgA dimers in vitro. Progression-free survival of patients with advanced HCC treated with atezolizumab and bevacizumab was significantly longer in those with low serum IgA levels ( p <0.05). Single-cell analysis showed that subcluster proportions in the CAF-fibroblast activation protein-α matrix were significantly increased in patients with high serum IgA levels. Flow cytometry revealed a significant increase in the mean fluorescence intensity of fibroblast activation protein in the CD68 + cells from patients with high serum IgA levels ( p <0.001). We confirmed CD71 (IgA receptor) expression in CAFs, and IgA-treated CAFs exhibited higher programmed death-ligand 1 expression levels than those in mock-treated CAFs ( p <0.05). Coculture with CAFs attenuated the cytotoxic function of activated CD8 + T cells. Interestingly, activated CD8 + T cells cocultured with IgA-treated CAFs exhibited increased programmed death-1 expression levels than those cocultured with mock-treated CAFs ( p <0.05). CONCLUSIONS: Intrahepatic IgA induced polarization of HCC-CAFs into more malignant matrix phenotypes and attenuates cytotoxic T-cell function. Our study highlighted their potential roles in tumor progression and immune suppression.

Efficient production of an antitumor precursor actinocin and other medicinal molecules from kynurenine pathway in Escherichia coli.

Kynurenine pathway has a potential to convert L-tryptophan into multiple medicinal molecules. This study aims to explore the biosynthetic potential of kynurenine pathway for the efficient production of actinocin, an antitumor precursor selected as a proof-of-concept target molecule. Kynurenine pathway is first constructed in Escherichia coli by testing various combinations of biosynthetic genes from four different organisms. Metabolic engineering strategies are next performed to improve the production by inhibiting a competing pathway, and enhancing intracellular supply of a cofactor S-adenosyl-L-methionine, and ultimately to produce actinocin from glucose. Metabolome analysis further suggests additional gene overexpression targets, which finally leads to the actinocin titer of 719 mg/L. E. coli strain engineered to produce actinocin is further successfully utilized to produce 350 mg/L of kynurenic acid, a neuroprotectant, and 1401 mg/L of 3-hydroxyanthranilic acid, an antioxidant, also from glucose. These competitive production titers demonstrate the biosynthetic potential of kynurenine pathway as a source of multiple medicinal molecules. The approach undertaken in this study can be useful for the sustainable production of molecules derived from kynurenine pathway, which are otherwise chemically synthesized.

A contribution of metabolic engineering to addressing medical problems: Metabolic flux analysis.

Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA.

Fibrillin2 in chloroplast plastoglobules participates in photoprotection and jasmonate-induced senescence.

Fibrillins (FBNs) are the major structural proteins of plastoglobules (PGs) in chloroplasts. PGs are associated with defense against abiotic and biotic stresses, as well as lipid storage. Although FBN2 is abundant in PGs, its independent function under abiotic stress has not yet been identified. In this study, the targeting of FBN2 to PGs was clearly demonstrated using an FBN2-YFP fusion protein. FBN2 showed higher expression in green photosynthetic tissues and was upregulated at the transcriptional level under high-light stress. The photosynthetic capacity of fbn2 knockout mutants generated using CRISPR/Cas9 technology decreased rapidly compared with that of wild-type (WT) plants under high-light stress. In addition to the photoprotective function of FBN2, fbn2 mutants had lower levels of plastoquinone-9 and plastochromanol-8. The fbn2 mutants were highly sensitive to methyl jasmonate (MeJA) and exhibited root growth inhibition and a pale-green phenotype due to reduced chlorophyll content. Consistently, upon MeJA treatment, the fbn2 mutants showed faster leaf senescence and more rapid chlorophyll degradation with decreased photosynthetic ability compared with the WT plants. The results of this study suggest that FBN2 is involved in protection against high-light stress and acts as an inhibitor of jasmonate-induced senescence in Arabidopsis (Arabidopsis thaliana).

Heterologous overproduction of oviedomycin by refactoring biosynthetic gene cluster and metabolic engineering of host strain Streptomyces coelicolor.

BACKGROUND: Oviedomycin is one among several polyketides known for their potential as anticancer agents. The biosynthetic gene cluster (BGC) for oviedomycin is primarily found in Streptomyces antibioticus. However, because this BGC is usually inactive under normal laboratory conditions, it is necessary to employ systematic metabolic engineering methods, such as heterologous expression, refactoring of BGCs, and optimization of precursor biosynthesis, to allow efficient production of these compounds. RESULTS: Oviedomycin BGC was captured from the genome of Streptomyces antibioticus by a newly constructed plasmid, pCBA, and conjugated into the heterologous strain, S. coelicolor M1152. To increase the production of oviedomycin, clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) system was utilized in an in vitro setting to refactor the native promoters within the ovm BGC. The target promoters of refactoring were selected based on examination of factors such as transcription levels and metabolite profiling. Furthermore, genome-scale metabolic simulation was applied to find overexpression targets that could enhance the biosynthesis of precursors or cofactors related to oviedomycin production. The combined approach led to a significant increase in oviedomycin production, reaching up to 670 mg/L, which is the highest titer reported to date. This demonstrates the potential of the approach undertaken in this study. CONCLUSIONS: The metabolic engineering approach used in this study led to the successful production of a valuable polyketide, oviedomycin, via BGC cloning, promoter refactoring, and gene manipulation of host metabolism aided by genome-scale metabolic simulation. This approach can be also useful for the efficient production of other secondary molecules encoded by 'silent' BGCs.

Recent development of machine learning models for the prediction of drug-drug interactions.

Polypharmacy, the co-administration of multiple drugs, has become an area of concern as the elderly population grows and an unexpected infection, such as COVID-19 pandemic, keeps emerging. However, it is very costly and time-consuming to experimentally examine the pharmacological effects of polypharmacy. To address this challenge, machine learning models that predict drug-drug interactions (DDIs) have actively been developed in recent years. In particular, the growing volume of drug datasets and the advances in machine learning have facilitated the model development. In this regard, this review discusses the DDI-predicting machine learning models that have been developed since 2018. Our discussion focuses on dataset sources used to develop the models, featurization approaches of molecular structures and biological information, and types of DDI prediction outcomes from the models. Finally, we make suggestions for research opportunities in this field.

Systems metabolic engineering of Corynebacterium glutamicum for the efficient production of ß-alanine.

ß-Alanine is an important ß-amino acid with a growing demand in a wide range of applications in chemical and food industries. However, current industrial production of ß-alanine relies on chemical synthesis, which usually involves harmful raw materials and harsh production conditions. Thus, there has been increasing demand for more sustainable, yet efficient production process of ß-alanine. In this study, we constructed Corynebacterium glutamicum strains for the highly efficient production of ß-alanine through systems metabolic engineering. First, aspartate 1-decarboxylases (ADCs) from seven different bacteria were screened, and the Bacillus subtilis ADC showing the most efficient ß-alanine biosynthesis was used to construct a ß-alanine-producing base strain. Next, genome-scale metabolic simulations were conducted to optimize multiple metabolic pathways in the base strain, including phosphotransferase system (PTS)-independent glucose uptake system and the biosynthesis of key precursors, including oxaloacetate and L-aspartate. TCA cycle was further engineered for the streamlined supply of key precursors. Finally, a putative ß-alanine exporter was newly identified, and its overexpression further improved the ß-alanine production. Fed-batch fermentation of the final engineered strain BAL10 (pBA2_tr18) produced 166.6 g/L of ß-alanine with the yield and productivity of 0.28 g/g glucose and 1.74 g/L/h, respectively. To our knowledge, this production performance corresponds to the highest titer, yield and productivity reported to date for the microbial fermentation.

The mysterious role of fibrillin in plastid metabolism: current advances in understanding.

Fibrillins (FBNs) are a family of genes in cyanobacteria, algae, and plants. The proteins they encode possess a lipid-binding motif, exist in various types of plastids, and are associated with lipid bodies called plastoglobules, implicating them in lipid metabolism. FBNs present in the thylakoid and stroma are involved in the storage, transport, and synthesis of lipid molecules for photoprotective functions against high-light stress. In this review, the diversity of subplastid locations in the evolution of FBNs, regulation of FBNs expression by various stresses, and the role of FBNs in plastid lipid metabolism are comprehensively summarized and directions for future research are discussed.

Transcriptional regulation of triacylglycerol accumulation in plants under environmental stress conditions.

Triacylglycerol (TAG), a major energy reserve in lipid form, accumulates mainly in seeds. Although TAG concentrations are usually low in vegetative tissues because of the repression of seed maturation programs, these programs are derepressed upon the exposure of vegetative tissues to environmental stresses. Metabolic reprogramming of TAG accumulation is driven primarily by transcriptional regulation. A substantial proportion of transcription factors regulating seed TAG biosynthesis also participates in stress-induced TAG accumulation in vegetative tissues. TAG accumulation leads to the formation of lipid droplets and plastoglobules, which play important roles in plant tolerance to environmental stresses. Toxic lipid intermediates generated from environmental-stress-induced lipid membrane degradation are captured by TAG-containing lipid droplets and plastoglobules. This review summarizes recent advances in the transcriptional control of metabolic reprogramming underlying stress-induced TAG accumulation, and provides biological insight into the plant adaptive strategy, linking TAG biosynthesis with plant survival.

Systems metabolic engineering of Streptomyces venezuelae for the enhanced production of pikromycin.

Pikromycin is an important precursor of drugs, for example, erythromycin. Hence, systems metabolic engineering for the enhanced pikromycin production can contribute to the development of pikromycin-related drugs. In this study, metabolic genes in Streptomyces venezuelae were systematically engineered for enhanced pikromycin production. For this, a genome-scale metabolic model of S. venezuelae was reconstructed and simulated, which led to the selection of 11 metabolic gene targets. These metabolic genes, including four overexpression targets and seven knockdown targets, were individually engineered first. Next, two overexpression targets and two knockdown targets were selected based on the 11 strains' production performances to engineer two to four of these genes together for the potential synergistic effects on the pikromycin production. As a result, the NM1 strain with AQF52_RS24510 (methenyltetrahydrofolate cyclohydrolase/methylenetetrahydrofolate dehydrogenase) overexpression and AQF52_RS30320 (sulfite reductase) knockdown showed the best production performance among all the 22 strains constructed in this study. Fed-batch fermentation of the NM1 strain produced 295.25 mg/L of pikromycin, by far the best production titer using the native producer S. venezuelae, to the best of our knowledge. The systems metabolic engineering strategy demonstrated herein can also be applied to the overproduction of other secondary metabolites using S. venezuelae.

Chloroplast-localized PITP7 is essential for plant growth and photosynthetic function in Arabidopsis.

Recent studies of chloroplast-localized Sec14-like protein (CPSFL1, also known as phosphatidylinositol transfer protein 7, PITP7) showed that CPSFL1 is necessary for photoautotropic growth and chloroplast vesicle formation in Arabidopsis (Arabidopsis thaliana). Here, we investigated the functional roles of CPSFL1/PITP7 using two A. thaliana mutants carrying a putative null allele (pitp7-1) and a weak allele (pitp7-2), respectively. PITP7 transcripts were undetectable in pitp7-1 and less abundant in pitp7-2 than in the wild-type (WT). The severity of mutant phenotypes, such as plant developmental abnormalities, levels of plastoquinone-9 (PQ-9) and chlorophylls, photosynthetic protein complexes, and photosynthetic performance, were well related to PITP7 transcript levels. The pitp7-1 mutation was seedling lethal and was associated with significantly lower levels of PQ-9 and major photosynthetic proteins. pitp7-2 plants showed greater susceptibility to high-intensity light stress than the WT, attributable to defects in nonphotochemical quenching and photosynthetic electron transport. PITP7 is specifically bound to phosphatidylinositol phosphates (PIPs) in lipid-binding assays in vitro, and the point mutations R82, H125, E162, or K233 reduced the binding affinity of PITP7 to PIPs. Further, constitutive expression of PITP7H125Q or PITP7E162K in pitp7-1 homozygous plants restored autotrophic growth in soil but without fully complementing the mutant phenotypes. Consistent with a previous study, our results demonstrate that PITP7 is essential for plant development, particularly the accumulation of PQ-9 and photosynthetic complexes. We propose a possible role for PITP7 in membrane trafficking of hydrophobic ligands such as PQ-9 and carotenoids through chloroplast vesicle formation or direct binding involving PIPs.

Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers.

High-quality and high-throughput prediction of enzyme commission (EC) numbers is essential for accurate understanding of enzyme functions, which have many implications in pathologies and industrial biotechnology. Several EC number prediction tools are currently available, but their prediction performance needs to be further improved to precisely and efficiently process an ever-increasing volume of protein sequence data. Here, we report DeepEC, a deep learning-based computational framework that predicts EC numbers for protein sequences with high precision and in a high-throughput manner. DeepEC takes a protein sequence as input and predicts EC numbers as output. DeepEC uses 3 convolutional neural networks (CNNs) as a major engine for the prediction of EC numbers, and also implements homology analysis for EC numbers that cannot be classified by the CNNs. Comparative analyses against 5 representative EC number prediction tools show that DeepEC allows the most precise prediction of EC numbers, and is the fastest and the lightest in terms of the disk space required. Furthermore, DeepEC is the most sensitive in detecting the effects of mutated domains/binding site residues of protein sequences. DeepEC can be used as an independent tool, and also as a third-party software component in combination with other computational platforms that examine metabolic reactions.

A safe and sustainable bacterial cellulose nanofiber separator for lithium rechargeable batteries.

Bacterial cellulose nanofiber (BCNF) with high thermal stability produced by an ecofriendly process has emerged as a promising solution to realize safe and sustainable materials in the large-scale battery. However, an understanding of the actual thermal behavior of the BCNF in the full-cell battery has been lacking, and the yield is still limited for commercialization. Here, we report the entire process of BCNF production and battery manufacture. We systematically constructed a strain with the highest yield (31.5%) by increasing metabolic flux and improved safety by introducing a Lewis base to overcome thermochemical degradation in the battery. This report will open ways of exploiting the BCNF as a "single-layer" separator, a good alternative to the existing chemical-derived one, and thus can greatly contribute to solving the environmental and safety issues.

High Light Acclimation Mechanisms Deficient in a PsbS-Knockout Arabidopsis Mutant.

The photosystem II PsbS protein of thylakoid membranes is responsible for regulating the energy-dependent, non-photochemical quenching of excess chlorophyll excited states as a short-term mechanism for protection against high light (HL) stress. However, the role of PsbS protein in long-term HL acclimation processes remains poorly understood. Here we investigate the role of PsbS protein during long-term HL acclimation processes in wild-type (WT) and npq4-1 mutants of Arabidopsis which lack the PsbS protein. During long-term HL illumination, photosystem II photochemical efficiency initially dropped, followed by a recovery of electron transport and photochemical quenching (qL) in WT, but not in npq4-1 mutants. In addition, we observed a reduction in light-harvesting antenna size during HL treatment that ceased after HL treatment in WT, but not in npq4-1 mutants. When plants were adapted to HL, more reactive oxygen species (ROS) were accumulated in npq4-1 mutants compared to WT. Gene expression studies indicated that npq4-1 mutants failed to express genes involved in plastoquinone biosynthesis. These results suggest that the PsbS protein regulates recovery processes such as electron transport and qL during long-term HL acclimation by maintaining plastoquinone biosynthetic gene expression and enhancing ROS homeostasis.

Nuclear import of LIKE HETEROCHROMATIN PROTEIN1 is redundantly mediated by importins α-1, α-2 and α-3.

LIKE HETEROCHROMATIN PROTEIN1 (LHP1) encodes the only plant homologue of the metazoan HETEROCHROMATIN PROTEIN1 (HP1) protein family. The LHP1 protein is necessary for proper epigenetic regulation of a range of developmental processes in plants. LHP1 is a transcriptional repressor of flowering-related genes, such as FLOWERING LOCUS T (FT), FLOWERING LOCUS C (FLC), AGAMOUS (AG) and APETALA 3 (AP3). We found that LHP1 interacts with importin α-1 (IMPα-1), importin α-2 (IMPα-2) and importin α-3 (IMPα-3) both in vitro and in vivo. A genetic approach revealed that triple mutation of impα-1, impα-2 and impα-3 resulted in Arabidopsis plants with a rapid flowering phenotype similar to that of plants with mutations in lhp1 due to the upregulation of FT expression. Nuclear targeting of LHP1 was severely impaired in the impα triple mutant, resulting in the de-repression of LHP1 target genes AG, AP3 and SHATTERPROOF 1 as well as FT. Therefore, the importin proteins IMPα-1, -2 and -3 are necessary for the nuclear import of LHP1.

Predicting biochemical and physiological effects of natural products from molecular structures using machine learning.

Covering: 2016 to 2021Discovery of novel natural products has been greatly facilitated by advances in genome sequencing, genome mining and analytical techniques. As a result, the volume of data for natural products has increased over the years, which started to serve as ingredients for developing machine learning models. In the past few years, a number of machine learning models have been developed to examine various aspects of a molecule by effectively processing its molecular structure. Understanding of the biological effects of natural products can benefit from such machine learning approaches. In this context, this Highlight reviews recent studies on machine learning models developed to infer various biological effects of molecules. A particular attention is paid to molecular featurization, or computational representation of a molecular structure, which is an essential process during the development of a machine learning model. Technical challenges associated with the use of machine learning for natural products are further discussed.

Systems and synthetic biology to elucidate secondary metabolite biosynthetic gene clusters encoded in Streptomyces genomes.

Covering: 2010 to 2020 Over the last few decades, Streptomyces have been extensively investigated for their ability to produce diverse bioactive secondary metabolites. Recent advances in Streptomyces research have been largely supported by improvements in high-throughput technology 'omics'. From genomics, numerous secondary metabolite biosynthetic gene clusters were predicted, increasing their genomic potential for novel bioactive compound discovery. Additional omics, including transcriptomics, translatomics, interactomics, proteomics and metabolomics, have been applied to obtain a system-level understanding spanning entire bioprocesses of Streptomyces, revealing highly interconnected and multi-layered regulatory networks for secondary metabolism. The comprehensive understanding derived from this systematic information accelerates the rational engineering of Streptomyces to enhance secondary metabolite production, integrated with the exploitation of the highly efficient 'Design-Build-Test-Learn' cycle in synthetic biology. In this review, we describe the current status of omics applications in Streptomyces research to better understand the organism and exploit its genetic potential for higher production of valuable secondary metabolites and novel secondary metabolite discovery.

Recent development of antiSMASH and other computational approaches to mine secondary metabolite biosynthetic gene clusters.

Many drugs are derived from small molecules produced by microorganisms and plants, so-called natural products. Natural products have diverse chemical structures, but the biosynthetic pathways producing those compounds are often organized as biosynthetic gene clusters (BGCs) and follow a highly conserved biosynthetic logic. This allows for the identification of core biosynthetic enzymes using genome mining strategies that are based on the sequence similarity of the involved enzymes/genes. However, mining for a variety of BGCs quickly approaches a complexity level where manual analyses are no longer possible and require the use of automated genome mining pipelines, such as the antiSMASH software. In this review, we discuss the principles underlying the predictions of antiSMASH and other tools and provide practical advice for their application. Furthermore, we discuss important caveats such as rule-based BGC detection, sequence and annotation quality and cluster boundary prediction, which all have to be considered while planning for, performing and analyzing the results of genome mining studies.