Serial adaptive laboratory evolution enhances mixed carbon metabolic capacity of Escherichia coli.

Microbes have inherent capacities for utilizing various carbon sources, however they often exhibit sub-par fitness due to low metabolic efficiency. To test whether a bacterial strain can optimally utilize multiple carbon sources, Escherichia coli was serially evolved in L-lactate and glycerol. This yielded two end-point strains that evolved first in L-lactate then in glycerol, and vice versa. The end-point strains displayed a universal growth advantage on single and a mixture of adaptive carbon sources, enabled by a concerted action of carbon source-specialists and generalist mutants. The combination of just four variants of glpK, ppsA, ydcI, and rph-pyrE, accounted for more than 80% of end-point strain fitness. In addition, machine learning analysis revealed a coordinated activity of transcriptional regulators imparting condition-specific regulation of gene expression. The effectiveness of the serial adaptive laboratory evolution (ALE) scheme in bioproduction applications was assessed under single and mixed-carbon culture conditions, in which serial ALE strain exhibited superior productivity of acetoin compared to ancestral strains. Together, systems-level analysis elucidated the molecular basis of serial evolution, which hold potential utility in bioproduction applications.

CRISPRi-Driven Genetic Screening for Designing Novel Microbial Phenotypes.

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 system has enabled rapid advances in genomic engineering and transcriptional regulation. Specifically, CRISPR interference (CRISPRi) system has been used to systematically investigate the gene functions of microbial strains in a high-throughput manner. This method involves growth profiling using cells that have been transformed with the deactivated Cas9 (dCas9) and single-guide RNA (sgRNA) libraries that target individual genes. The fitness scores of each gene are calculated by measuring the abundance of individual sgRNAs during cell growth and represent gene essentiality. In this chapter, a process is described for functional genetic screening using CRISPRi at the whole-genome scale, starting from the synthesis of sgRNA libraries, construction of CRISPRi libraries, and identification of essential genes through growth profiling. The commensal bacterium Bacteroides thetaiotaomicron was used to implement the protocol. This method is expected to be applicable to a broader range of microorganisms to explore the novel phenotypic characteristics of microorganisms.

Reduction-to-synthesis: the dominant approach to genome-scale synthetic biology.

Advances in systems and synthetic biology have propelled the construction of reduced bacterial genomes. Genome reduction was initially focused on exploring properties of minimal genomes, but more recently it has been deployed as an engineering strategy to enhance strain performance. This review provides the latest updates on reduced genomes, focusing on dual-track approaches of top-down reduction and bottom-up synthesis for their construction. Using cases from studies that are based on established industrial workhorse strains, we discuss the construction of a series of synthetic phenotypes that are candidates for biotechnological applications. Finally, we address the possible uses of reduced genomes for biotechnological applications and the needed future research directions that may ultimately lead to the total synthesis of rationally designed genomes.

Automatic Quantitative Assessment for Diagnostic and Therapeutic Response in Rodent Myocardial Infarct Model.

The purpose of this study was to investigate the most appropriate methodological approach for the automatic measurement of rodent myocardial infarct polar map using histogram-based thresholding and unsupervised deep learning (DL)-based segmentation. A rat myocardial infarction model was induced by ligation of the left coronary artery. Positron emission tomography (PET) was performed 60 min after the administration of 18F-fluoro-deoxy-glucose (18F-FDG), and PET was performed after injecting 64Cu-pyruvaldehyde-bis(N4-methylthiosemicarbazone). Single photon emission computed tomography was performed 60 min after injection of 99mTc-hexakis-2-methoxyisobutylisonitrile and 201Tl. Delayed contrast-enhanced magnetic resonance imaging was performed after injecting Gd-DTPA-BMA. Three types of thresholding methods (naive thresholding, Otsu's algorithm, and multi-Gaussian mixture model (MGMM)) were used. DL segmentation methods were based on a convolution neural network and trained with constraints on feature similarity and spatial continuity of the response map extracted from images by the network. The relative infarct sizes measured by histology and estimated R2 for 18F-FDG were 0.8477, 0.7084, 0.8353, and 0.9024 for naïve thresholding, Otsu's algorithm, MGMM, and DL segmentation, respectively. DL-based method improved the accuracy of MI size assessment.

Graph Neural Network Model for Prediction of Non-Small Cell Lung Cancer Lymph Node Metastasis Using Protein-Protein Interaction Network and 18F-FDG PET/CT Radiomics.

The image texture features obtained from 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images of non-small cell lung cancer (NSCLC) have revealed tumor heterogeneity. A combination of genomic data and radiomics may improve the prediction of tumor prognosis. This study aimed to predict NSCLC metastasis using a graph neural network (GNN) obtained by combining a protein-protein interaction (PPI) network based on gene expression data and image texture features. 18F-FDG PET/CT images and RNA sequencing data of 93 patients with NSCLC were acquired from The Cancer Imaging Archive. Image texture features were extracted from 18F-FDG PET/CT images and area under the curve receiver operating characteristic curve (AUC) of each image feature was calculated. Weighted gene co-expression network analysis (WGCNA) was used to construct gene modules, followed by functional enrichment analysis and identification of differentially expressed genes. The PPI of each gene module and genes belonging to metastasis-related processes were converted via a graph attention network. Images and genomic features were concatenated. The GNN model using PPI modules from WGCNA and metastasis-related functions combined with image texture features was evaluated quantitatively. Fifty-five image texture features were extracted from 18F-FDG PET/CT, and radiomic features were selected based on AUC (n = 10). Eighty-six gene modules were clustered by WGCNA. Genes (n = 19) enriched in the metastasis-related pathways were filtered using DEG analysis. The accuracy of the PPI network, derived from WGCNA modules and metastasis-related genes, improved from 0.4795 to 0.5830 (p < 2.75 × 10-12). Integrating PPI of four metastasis-related genes with 18F-FDG PET/CT image features in a GNN model elevated its accuracy over a without image feature model to 0.8545 (95% CI = 0.8401-0.8689, p-value < 0.02). This model demonstrated significant enhancement compared to the model using PPI and 18F-FDG PET/CT derived from WGCNA (p-value < 0.02), underscoring the critical role of metastasis-related genes in prediction model. The enhanced predictive capability of the lymph node metastasis prediction GNN model for NSCLC, achieved through the integration of comprehensive image features with genomic data, demonstrates promise for clinical implementation.

AXL/WRNIP1 Mediates Replication Stress Response and Promotes Therapy Resistance and Metachronous Metastasis in HER2+ Breast Cancer.

Therapy resistance and metastatic progression are primary causes of cancer-related mortality. Disseminated tumor cells possess adaptive traits that enable them to reprogram their metabolism, maintain stemness, and resist cell death, facilitating their persistence to drive recurrence. The survival of disseminated tumor cells also depends on their ability to modulate replication stress in response to therapy while colonizing inhospitable microenvironments. In this study, we discovered that the nuclear translocation of AXL, a TAM receptor tyrosine kinase, and its interaction with WRNIP1, a DNA replication stress response factor, promotes the survival of HER2+ breast cancer cells that are resistant to HER2-targeted therapy and metastasize to the brain. In preclinical models, knocking down or pharmacologically inhibiting AXL or WRNIP1 attenuated protection of stalled replication forks. Furthermore, deficiency or inhibition of AXL and WRNIP1 also prolonged metastatic latency and delayed relapse. Together, these findings suggest that targeting the replication stress response, which is a shared adaptive mechanism in therapy-resistant and metastasis-initiating cells, could reduce metachronous metastasis and enhance the response to standard-of-care therapies. SIGNIFICANCE: Nuclear AXL and WRNIP1 interact and mediate replication stress response, promote therapy resistance, and support metastatic progression, indicating that targeting the AXL/WRNIP1 axis is a potentially viable therapeutic strategy for breast cancer.

Defect-Engineered Semiconducting van der Waals Thin Film at Metal-Semiconductor Interface of Field-Effect Transistors.

The significance of metal-semiconductor interfaces and their impact on electronic device performance have gained increasing attention, with a particular focus on investigating the contact metal. However, another avenue of exploration involves substituting the contact metal at the metal-semiconductor interface of field-effect transistors with semiconducting layers to introduce additional functionalities to the devices. Here, a scalable approach for fabricating metal-oxide-semiconductor (channel)-semiconductor (interfacial layer) field-effect transistors is proposed by utilizing solution-processed semiconductors, specifically semiconducting single-walled carbon nanotubes and molybdenum disulfide, as the channel and interfacial semiconducting layers, respectively. The work function of the interfacial MoS2 is modulated by controlling the sulfur vacancy concentration through chemical treatment, which results in distinctive energy band alignments within a single device configuration. The resulting band alignments lead to multiple functionalities, including multivalued transistor characteristics and multibit nonvolatile memory (NVM) behavior. Moreover, leveraging the stable NVM properties, we demonstrate artificial synaptic devices with 88.9% accuracy of MNIST image recognition.

Systems and synthetic biology-driven engineering of live bacterial therapeutics.

The past decade has seen growing interest in bacterial engineering for therapeutically relevant applications. While early efforts focused on repurposing genetically tractable model strains, such as Escherichia coli, engineering gut commensals is gaining traction owing to their innate capacity to survive and stably propagate in the intestine for an extended duration. Although limited genetic tractability has been a major roadblock, recent advances in systems and synthetic biology have unlocked our ability to effectively harness native gut commensals for therapeutic and diagnostic purposes, ranging from the rational design of synthetic microbial consortia to the construction of synthetic cells that execute "sense-and-respond" logic operations that allow real-time detection and therapeutic payload delivery in response to specific signals in the intestine. In this review, we outline the current progress and latest updates on microbial therapeutics, with particular emphasis on gut commensal engineering driven by synthetic biology and systems understanding of their molecular phenotypes. Finally, the challenges and prospects of engineering gut commensals for therapeutic applications are discussed.

Deep Neural Network-Based Automatic Dicentric Chromosome Detection Using a Model Pretrained on Common Objects.

Dicentric chromosome assay (DCA) is one of the cytogenetic dosimetry methods where the absorbed dose is estimated by counting the number of dicentric chromosomes, which is a major radiation-induced change in DNA. However, DCA is a time-consuming task and requires technical expertise. In this study, a neural network was applied for automating the DCA. We used YOLOv5, a one-stage detection algorithm, to mitigate these limitations by automating the estimation of the number of dicentric chromosomes in chromosome metaphase images. YOLOv5 was pretrained on common object datasets. For training, 887 augmented chromosome images were used. We evaluated the model using validation and test datasets with 380 and 300 images, respectively. With pretrained parameters, the trained model detected chromosomes in the images with a maximum F1 score of 0.94 and a mean average precision (mAP) of 0.961. Conversely, when the model was randomly initialized, the training performance decreased, with a maximum F1 score and mAP of 0.82 and 0.873%, respectively. These results confirm that the model could effectively detect dicentric chromosomes in an image. Consequently, automatic DCA is expected to be conducted based on deep learning for object detection, requiring a relatively small amount of chromosome data for training using the pretrained network.

Deep Learning-Based Delayed PET Image Synthesis from Corresponding Early Scanned PET for Dosimetry Uptake Estimation.

The acquisition of in vivo radiopharmaceutical distribution through imaging is time-consuming due to dosimetry, which requires the subject to be scanned at several time points post-injection. This study aimed to generate delayed positron emission tomography images from early images using a deep-learning-based image generation model to mitigate the time cost and inconvenience. Eighteen healthy participants were recruited and injected with [18F]Fluorodeoxyglucose. A paired image-to-image translation model, based on a generative adversarial network (GAN), was used as the generation model. The standardized uptake value (SUV) mean of the generated image of each organ was compared with that of the ground-truth. The least square GAN and perceptual loss combinations displayed the best performance. As the uptake time of the early image became closer to that of the ground-truth image, the translation performance improved. The SUV mean values of the nominated organs were estimated reasonably accurately for the muscle, heart, liver, and spleen. The results demonstrate that the image-to-image translation deep learning model is applicable for the generation of a functional image from another functional image acquired from normal subjects, including predictions of organ-wise activity for specific normal organs.

A motif in the 5'untranslated region of messenger RNAs regulates protein synthesis in a S6 kinase-dependent manner.

The 5' untranslated regions (UTRs) in messenger RNAs (mRNAs) play an important role in the regulation of protein synthesis. We had previously identified a group of mRNAs that includes human semaphorin 7A (SEMA7A) whose translation is upregulated by the Erk/p90S6K pathway in human eosinophils, with a potential negative impact in asthma and airway inflammation. In the current study, we aimed to find a common 5'UTR regulatory cis-element, and determine its impact on protein synthesis. We identified a common and conserved 5'UTR motif GGCTG-[(C/G)T(C/G)]n-GCC that was present in this group of mRNAs. Mutations of the first two GG bases in this motif in SEMA7A 5'UTR led to a complete loss of S6K activity dependence for maximal translation. In conclusion, the newly identified 5'UTR motif present in SEMA7A has a critical role in regulating S6K-dependent protein synthesis.

Limiting mitochondrial plasticity by targeting DRP1 induces metabolic reprogramming and reduces breast cancer brain metastases.

Disseminated tumor cells with metabolic flexibility to utilize available nutrients in distal organs persist, but the precise mechanisms that facilitate metabolic adaptations remain unclear. Here we show fragmented mitochondrial puncta in latent brain metastatic (Lat) cells enable fatty acid oxidation (FAO) to sustain cellular bioenergetics and maintain redox homeostasis. Depleting the enriched dynamin-related protein 1 (DRP1) and limiting mitochondrial plasticity in Lat cells results in increased lipid droplet accumulation, impaired FAO and attenuated metastasis. Likewise, pharmacological inhibition of DRP1 using a small-molecule brain-permeable inhibitor attenuated metastatic burden in preclinical models. In agreement with these findings, increased phospho-DRP1 expression was observed in metachronous brain metastasis compared with patient-matched primary tumors. Overall, our findings reveal the pivotal role of mitochondrial plasticity in supporting the survival of Lat cells and highlight the therapeutic potential of targeting cellular plasticity programs in combination with tumor-specific alterations to prevent metastatic recurrences.

Minireview: Engineering evolution to reconfigure phenotypic traits in microbes for biotechnological applications.

Adaptive laboratory evolution (ALE) has long been used as the tool of choice for microbial engineering applications, ranging from the production of commodity chemicals to the innovation of complex phenotypes. With the advent of systems and synthetic biology, the ALE experimental design has become increasingly sophisticated. For instance, implementation of in silico metabolic model reconstruction and advanced synthetic biology tools have facilitated the effective coupling of desired traits to adaptive phenotypes. Furthermore, various multi-omic tools now enable in-depth analysis of cellular states, providing a comprehensive understanding of the biology of even the most genomically perturbed systems. Emerging machine learning approaches would assist in streamlining the interpretation of massive and multiplexed datasets and promoting our understanding of complexity in biology. This review covers some of the representative case studies among the 700 independent ALE studies reported to date, outlining key ideas, principles, and important mechanisms underlying ALE designs in bioproduction and synthetic cell engineering, with evidence from literatures to aid comprehension.

Cell Competition Shapes Metastatic Latency and Relapse.

Cell competition, a fitness-sensing process, is essential for tissue homeostasis. Using cancer metastatic latency models, we show that cell competition results in the displacement of latent metastatic (Lat-M) cells from the primary tumor. Lat-M cells resist anoikis and survive as residual metastatic disease. A memodeled extracellular matrix facilitates Lat-M cell displacement and survival in circulation. Disrupting cell competition dynamics by depleting secreted protein and rich in cysteine (SPARC) reduced displacement from orthotopic tumors and attenuated metastases. In contrast, depletion of SPARC after extravasation in lung-resident Lat-M cells increased metastatic outgrowth. Furthermore, multiregional transcriptomic analyses of matched primary tumors and metachronous metastases from patients with kidney cancer identified tumor subclones with Lat-M traits. Kidney cancer enriched for these Lat-M traits had a rapid onset of metachronous metastases and significantly reduced disease-free survival. Thus, an unexpected consequence of cell competition is the displacement of cells with Lat-M potential, thereby shaping metastatic latency and relapse. SIGNIFICANCE: We demonstrate that cell competition within the primary tumor results in the displacement of Lat-M cells. We further show the impact of altering cell competition dynamics on metastatic incidence that may guide strategies to limit metastatic recurrences. This article is highlighted in the In This Issue feature, p. 1.

Translation suppression underlies the restrained COVID-19 mRNA vaccine response in the high-risk immunocompromised group.

Background: Immunocompromised (IC) patients show diminished immune response to COVID-19 mRNA vaccines (Co-mV). To date, there is no 'empirical' evidence to link the perturbation of translation, a rate-limiting step for mRNA vaccine efficiency (VE), to the dampened response of Co-mV. Materials and methods: Impact of immunosuppressants (ISs), tacrolimus (T), mycophenolate (M), rapamycin/sirolimus (S), and their combinations on Pfizer Co-mV translation were determined by the Spike (Sp) protein expression following Co-mV transfection in HEK293 cells. In vivo impact of ISs on SARS-CoV-2 spike specific antigen (SpAg) and associated antibody levels (IgGSp) in serum were assessed in Balb/c mice after two doses (2D) of the Pfizer vaccine. Spike Ag and IgGSp levels were assessed in 259 IC patients and 50 healthy controls (HC) who received 2D of Pfizer or Moderna Co-mV as well as in 67 immunosuppressed solid organ transplant (SOT) patients and 843 non-transplanted (NT) subjects following three doses (3D) of Co-mV. Higher Co-mV concentrations and transient drug holidays were evaluated. Results: We observed significantly lower IgGSP response in IC patients (p<0.0001) compared to their matched controls in 2D and 3D Co-mV groups. IC patients on M or S showed a profound dampening of IgGSP response relative to those that were not on these drugs. M and S, when used individually or in combination, significantly attenuated the Co-mV-induced Sp expression, whereas T did not exert significant influence. Sirolimus combo pretreatment in vivo significantly attenuated the Co-mV induced IgMSp and IgGSp production, which correlated with a decreasing trend in the early levels (after day 1) of Co-mV induced Sp immunogen levels. Neither higher Co-mV concentrations (6µg) nor withholding S for 1-day could overcome the inhibition of Sp protein levels. Interestingly, 3-days S holiday or using T alone rescued Sp levels in vitro. Conclusions: This is the first study to demonstrate that ISs, sirolimus and mycophenolate inhibited Co-mV-induced Sp protein synthesis via translation repression. Selective use of tacrolimus or drug holiday of sirolimus can be a potential means to rescue translation-dependent Sp protein production. These findings lay a strong foundation for guiding future studies aimed at improving Co-mV responses in high-risk IC patients.

Functional Characterization of lncRNA152 as an Angiogenesis-Inhibiting Tumor Suppressor in Triple-Negative Breast Cancers.

Long noncoding RNAs have been implicated in many of the hallmarks of cancer. Herein, we found that the expression of lncRNA152 (lnc152; a.k.a. DRAIC), which we annotated previously, is highly upregulated in luminal breast cancer (LBC) and downregulated in triple-negative breast cancer (TNBC). Knockdown of lnc152 promotes cell migration and invasion in LBC cell lines. In contrast, ectopic expression of lnc152 inhibits growth, migration, invasion, and angiogenesis in TNBC cell lines. In mice, lnc152 inhibited the growth of TNBC cell xenografts, as well as metastasis of TNBC cells in an intracardiac injection model. Transcriptome analysis of the xenografts indicated that lnc152 downregulates genes controlling angiogenesis. Using pull down assays followed by LC/MS-MS, we identified RBM47, a known tumor suppressor in breast cancer, as a lnc152-interacting protein. The effects of lnc152 in TNBC cells are mediated, in part, by regulating the expression of RBM47. Collectively, our results demonstrate that lnc152 is an angiogenesis-inhibiting tumor suppressor that attenuates the aggressive cancer-related phenotypes found in TNBC. IMPLICATIONS: This study identifies lncRNA152 as an angiogenesis-inhibiting tumor suppressor that attenuates the aggressive cancer-related phenotypes found in TNBC by upregulating the expression of the tumor suppressor RBM47. As such, lncRNA152 may serve as a biomarker to track aggressiveness of breast cancer, as well as therapeutic target for treating TNBC.

Optimized protocol for stable isotope tracing and steady-state metabolomics in mouse HER2+ breast cancer brain metastasis.

Analyzing the metabolic dependencies of tumor cells is vital for cancer diagnosis and treatment. Here, we describe a protocol for 13C-stable glucose and glutamine isotope tracing in mice HER2+ breast cancer brain metastatic lesions. We describe how to inject cancer cells intracardially to generate brain metastatic lesions in mice. We then detail how to perform 13C-stable isotope infusion in mice with established brain metastasis. Finally, we outline steps for sample collection, processing for metabolite extraction, and analyzing mass spectrometry data. For complete details on the use and execution of this protocol, please refer to Parida et al. (2022).

Synthetic 3'-UTR valves for optimal metabolic flux control in Escherichia coli.

As the design of genetic circuitry for synthetic biology becomes more sophisticated, diverse regulatory bioparts are required. Despite their importance, well-characterized 3'-untranslated region (3'-UTR) bioparts are limited. Thus, transcript 3'-ends require further investigation to understand the underlying regulatory role and applications of the 3'-UTR. Here, we revisited the use of Term-Seq in the Escherichia coli strain K-12 MG1655 to enhance our understanding of 3'-UTR regulatory functions and to provide a diverse collection of tunable 3'-UTR bioparts with a wide termination strength range. Comprehensive analysis of 1,629 transcript 3'-end positions revealed multiple 3'-termini classes generated through transcription termination and RNA processing. The examination of individual Rho-independent terminators revealed a reduction in downstream gene expression over a wide range, which led to the design of novel synthetic metabolic valves that control metabolic fluxes in branched pathways. These synthetic metabolic valves determine the optimal balance of heterologous pathways for maximum target biochemical productivity. The regulatory strategy using 3'-UTR bioparts is advantageous over promoter- or 5'-UTR-based transcriptional control as it modulates gene expression at transcription levels without trans-acting element requirements (e.g. transcription factors). Our results provide a foundational platform for 3'-UTR engineering in synthetic biology applications.

Metabolic diversity within breast cancer brain-tropic cells determines metastatic fitness.

HER2+ breast cancer patients are presented with either synchronous (S-BM), latent (Lat), or metachronous (M-BM) brain metastases. However, the basis for disparate metastatic fitness among disseminated tumor cells of similar oncotype within a distal organ remains unknown. Here, employing brain metastatic models, we show that metabolic diversity and plasticity within brain-tropic cells determine metastatic fitness. Lactate secreted by aggressive metastatic cells or lactate supplementation to mice bearing Lat cells limits innate immunosurveillance and triggers overt metastasis. Attenuating lactate metabolism in S-BM impedes metastasis, while M-BM adapt and survive as residual disease. In contrast to S-BM, Lat and M-BM survive in equilibrium with innate immunosurveillance, oxidize glutamine, and maintain cellular redox homeostasis through the anionic amino acid transporter xCT. Moreover, xCT expression is significantly higher in matched M-BM brain metastatic samples compared to primary tumors from HER2+ breast cancer patients. Inhibiting xCT function attenuates residual disease and recurrence in these preclinical models.

Adaptive laboratory evolution of Escherichia coli W enhances gamma-aminobutyric acid production using glycerol as the carbon source.

The microbial conversion of glycerol into value-added commodity products has emerged as an attractive means to meet the demands of biosustainability. However, glycerol is a non-preferential carbon source for productive fermentation because of its low energy density. We employed evolutionary and metabolic engineering in tandem to construct an Escherichia coli strain with improved GABA production using glycerol as the feedstock carbon. Adaptive evolution of E. coli W under glycerol-limited conditions for 1300 generations harnessed an adapted strain with a metabolic system optimized for glycerol utilization. Mutation profiling, enzyme kinetic assays, and transcriptome analysis of the adapted strain allowed us to decipher the basis of glycerol adaptation at the molecular level. Importantly, increased substrate influx mediated by the mutant glpK and modulation of intracellular cAMP levels were the key drivers of improved fitness in the glycerol-limited condition. Leveraging the enhanced capability of glycerol utilization in the strain, we constructed a GABA-producing E. coli W-derivative with superior GABA production compared to the wild-type. Furthermore, rationally designed inactivation of the non-essential metabolic genes, including ackA, mgsA, and gabT, in the glycerol-adapted strain improved the final GABA titer and specific productivity by 3.9- and 4.3-fold, respectively, compared with the wild-type.