CRACD loss induces neuroendocrine cell plasticity of lung adenocarcinoma.

Tumor cell plasticity contributes to intratumoral heterogeneity and therapy resistance. Through cell plasticity, some lung adenocarcinoma (LUAD) cells transform into neuroendocrine (NE) tumor cells. However, the mechanisms of NE cell plasticity remain unclear. CRACD (capping protein inhibiting regulator of actin dynamics), a capping protein inhibitor, is frequently inactivated in cancers. CRACD knockout (KO) is sufficient to de-repress NE-related gene expression in the pulmonary epithelium and LUAD cells. In LUAD mouse models, Cracd KO increases intratumoral heterogeneity with NE gene expression. Single-cell transcriptomic analysis showed that Cracd KO-induced NE cell plasticity is associated with cell de-differentiation and stemness-related pathway activation. The single-cell transcriptomic analysis of LUAD patient tumors recapitulates that the distinct LUAD NE cell cluster expressing NE genes is co-enriched with impaired actin remodeling. This study reveals the crucial role of CRACD in restricting NE cell plasticity that induces cell de-differentiation of LUAD.

Single-cell transcriptome analysis reveals subtype-specific clonal evolution and microenvironmental changes in liver metastasis of pancreatic adenocarcinoma and their clinical implications.

BACKGROUND: Intratumoral heterogeneity (ITH) and tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) play important roles in tumor evolution and patient outcomes. However, the precise characterization of diverse cell populations and their crosstalk associated with PDAC progression and metastasis is still challenging. METHODS: We performed single-cell RNA sequencing (scRNA-seq) of treatment-naïve primary PDAC samples with and without paired liver metastasis samples to understand the interplay between ITH and TME in the PDAC evolution and its clinical associations. RESULTS: scRNA-seq analysis revealed that even a small proportion (22%) of basal-like malignant ductal cells could lead to poor chemotherapy response and patient survival and that epithelial-mesenchymal transition programs were largely subtype-specific. The clonal homogeneity significantly increased with more prevalent and pronounced copy number gains of oncogenes, such as KRAS and ETV1, and losses of tumor suppressor genes, such as SMAD2 and MAP2K4, along PDAC progression and metastasis. Moreover, diverse immune cell populations, including naïve SELLhi regulatory T cells (Tregs) and activated TIGIThi Tregs, contributed to shaping immunosuppressive TMEs of PDAC through cellular interactions with malignant ductal cells in PDAC evolution. Importantly, the proportion of basal-like ductal cells negatively correlated with that of immunoreactive cell populations, such as cytotoxic T cells, but positively correlated with that of immunosuppressive cell populations, such as Tregs. CONCLUSION: We uncover that the proportion of basal-like subtype is a key determinant for chemotherapy response and patient outcome, and that PDAC clonally evolves with subtype-specific dosage changes of cancer-associated genes by forming immunosuppressive microenvironments in its progression and metastasis.

Establishment of a patient-specific avatar organoid model derived from endoscopic ultrasonography-guided fine needle biopsy for timely clinical application in pancreatic ductal adenocarcinoma.

BACKGROUND AND AIMS: Pancreatic ductal adenocarcinoma (PDAC) has the worst survival rate among tumors. At the time of diagnosis, more than 80% of PDACs are considered to be surgically unresectable, and there is an unmet need for treatment options in these inoperable PDACs. This study aimed to establish a patient-derived organoid (PDO) platform from endoscopic ultrasound-guided fine needle biopsy (EUS-FNB) collected at diagnosis and to determine its clinical applicability for the timely treatment of unresectable PDAC. METHODS: Patients with suspected PDAC were prospectively enrolled at the Samsung Medical Center from 2015 to 2019. PDAC tissues were acquired by means of EUS-FNB to establish PDAC PDOs, which were comprehensively analyzed for histology, genomic sequencing, and high-throughput screening (HTS) drug sensitivity test. RESULTS: PDAC PDOs were established with a success rate of 83.2% (94/113). It took approximately 3 weeks from acquiring minimal EUS-FNB specimens to generating sufficient PDAC PDOs for the simultaneous HTS drug sensitivity test and genomic sequencing. The high concordance between PDAC tissues and matched PDOs was confirmed, and whole-exome sequencing revealed the increased detection of genetic alterations in PDOs compared with EUS-FNB tissues. The HTS drug sensitivity test showed clinical correlation between the ex vivo PDO response and the actual chemotherapeutic response of the study patients in the real world (13 out of 15 cases). In addition, whole-transcriptome sequencing identified candidate genes associated with nab-paclitaxel resistance, such as ITGB7, ANPEP, and ST3GAL1. CONCLUSIONS: This PDAC PDO platform allows several therapeutic drugs to be tested within a short time window and opens the possibility for timely personalized medicine as a "patient avatar model" in clinical practice.

Best method to determine DNA G-quadruplex folding: The 1H-13C HSQC NMR experiment.

NMR spectroscopy is the major method for G-quadruplex structure determination under physiologically relevant solution conditions. Unlike duplex B-DNA, in which all nucleotides adopt an anti glycosidic conformation, the core tetrad-guanines in a G-quadruplex can adopt anti or syn glycosidic conformation depending on the folding structure. An experimental method that can clearly and unambiguously determine syn and anti tetrad-Gs in a G-quadruplex is highly desirable and necessary. In the present study, we exploit the advantages of the 1H-13C HSQC experiment to determine tetrad-G's glycosidic conformation and thus folding topology of G-quadruplexes. We use several examples to demonstrate the clear and straightforward determination of the guanine glycosidic conformations and G-quadruplex folding structures. Moreover, 1H-13C HSQC data can readily identify adenine H2 resonances as well as determine unusual syn conformation in loop and flanking sequences, a challenging task by standard 2D NOESY.

Single-cell RNA Sequencing Reveals Novel Cellular Factors for Response to Immunosuppressive Therapy in Aplastic Anemia.

Aplastic anemia (AA) is a lethal hematological disorder; however, its pathogenesis is not fully understood. Although immunosuppressive therapy (IST) is a major treatment option for AA, one-third of patients do not respond to IST and its resistance mechanism remains elusive. To understand AA pathogenesis and IST resistance, we performed single-cell RNA sequencing (scRNA-seq) of bone marrow (BM) from healthy controls and patients with AA at diagnosis. We found that CD34+ early-stage erythroid precursor cells and PROM1+ hematopoietic stem cells were significantly depleted in AA, which suggests that the depletion of CD34+ early-stage erythroid precursor cells and PROM1+ hematopoietic stem cells might be one of the major mechanisms for AA pathogenesis related with BM-cell hypoplasia. More importantly, we observed the significant enrichment of CD8+ T cells and T cell-activating intercellular interactions in IST responders, indicating the association between the expansion and activation of T cells and the positive response of IST in AA. Taken together, our findings represent a valuable resource offering novel insights into the cellular heterogeneity in the BM of AA and reveal potential biomarkers for IST, building the foundation for future precision therapies in AA.

RAMP: response-aware multi-task learning with contrastive regularization for cancer drug response prediction.

The accurate prediction of cancer drug sensitivity according to the multiomics profiles of individual patients is crucial for precision cancer medicine. However, the development of prediction models has been challenged by the complex crosstalk of input features and the resistance-dominant drug response information contained in public databases. In this study, we propose a novel multidrug response prediction framework, response-aware multitask prediction (RAMP), via a Bayesian neural network and restrict it by soft-supervised contrastive regularization. To utilize network embedding vectors as representation learning features for heterogeneous networks, we harness response-aware negative sampling, which applies cell line-drug response information to the training of network embeddings. RAMP overcomes the prediction accuracy limitation induced by the imbalance of trained response data based on the comprehensive selection and utilization of drug response features. When trained on the Genomics of Drug Sensitivity in Cancer dataset, RAMP achieved an area under the receiver operating characteristic curve > 89%, an area under the precision-recall curve > 59% and an $\textrm{F}_1$ score > 52% and outperformed previously developed methods on both balanced and imbalanced datasets. Furthermore, RAMP predicted many missing drug responses that were not included in the public databases. Our results showed that RAMP will be suitable for the high-throughput prediction of cancer drug sensitivity and will be useful for guiding cancer drug selection processes. The Python implementation for RAMP is available at https://github.com/hvcl/RAMP.

Spectral noise and data reduction using a long short-term memory network for nonlinear ultrasonic modulation-based fatigue crack detection.

This paper presents a spectral noise and data reduction technique based on long short-term memory (LSTM) network for nonlinear ultrasonic modulation-based fatigue crack detection. The amplitudes of the nonlinear modulation components created by a micro fatigue crack are often very small and masked by noise. In addition, the collection of large amounts of data is often undesirable owing to the limited power, data storage, and data transmission bandwidth of monitoring systems. To tackle the issues, an LSTM network was applied to ultrasonic signals to reduce the noise level and the amount of data. The proposed technique offers the following benefits: (1) spectral noise reduction using the LSTM network for ultrasonic signals and (2) data reduction without compromising the spectral density amplitude of the existing nonlinear modulation components. Finally, the performance evaluation was conducted using the data obtained from complex geometry and real structure under external noises, indicating that the proposed method can be applied to various structures.

Cellular plasticity and immune microenvironment of malignant pleural effusion are associated with EGFR-TKI resistance in non-small-cell lung carcinoma.

Malignant pleural effusion (MPE) is a complication of lung cancer that can be used as an alternative method for tissue sampling because it is generally simple and minimally invasive. Our study evaluated the diagnostic potential of non-small-cell lung carcinoma (NSCLC)-associated MPE in terms of understanding tumor heterogeneity and identifying response factors for EGFR tyrosine kinase inhibitor (TKI) therapy. We performed a single-cell RNA sequencing analysis of 31,743 cells isolated from the MPEs of 9 patients with NSCLC (5 resistant and 4 sensitive to EGFR TKI) with EGFR mutations. Interestingly, lung epithelial precursor-like cells with upregulated GNB2L1 and CAV1 expression were enriched in the EGFR TKI-resistant group. Moreover, GZMK upregulated transitional effector T cells, and plasmacytoid dendritic cells were significantly enriched in the EGFR TKI-resistant patients. Our results suggest that cellular plasticity and immunosuppressive microenvironment in MPEs are potentially associated with the TKI response of patients with EGFR-mutated NSCLC.

Drug-likeness scoring based on unsupervised learning.

Drug-likeness prediction is important for the virtual screening of drug candidates. It is challenging because the drug-likeness is presumably associated with the whole set of necessary properties to pass through clinical trials, and thus no definite data for regression is available. Recently, binary classification models based on graph neural networks have been proposed but with strong dependency of their performances on the choice of the negative set for training. Here we propose a novel unsupervised learning model that requires only known drugs for training. We adopted a language model based on a recurrent neural network for unsupervised learning. It showed relatively consistent performance across different datasets, unlike such classification models. In addition, the unsupervised learning model provides drug-likeness scores that well separate distributions with increasing mean values in the order of datasets composed of molecules at a later step in a drug development process, whereas the classification model predicted a polarized distribution with two extreme values for all datasets presumably due to the overconfident prediction for unseen data. Thus, this new concept offers a pragmatic tool for drug-likeness scoring and further can be applied to other biochemical applications.

Microbiome of Saliva and Plaque in Children According to Age and Dental Caries Experience.

Dental caries are one of the chronic diseases caused by organic acids made from oral microbes. However, there was a lack of knowledge about the oral microbiome of Korean children. The aim of this study was to analyze the metagenome data of the oral microbiome obtained from Korean children and to discover bacteria highly related to dental caries with machine learning models. Saliva and plaque samples from 120 Korean children aged below 12 years were collected. Bacterial composition was identified using Illumina HiSeq sequencing based on the V3-V4 hypervariable region of the 16S rRNA gene. Ten major genera accounted for approximately 70% of the samples on average, including Streptococcus, Neisseria, Corynebacterium, and Fusobacterium. Differential abundant analyses revealed that Scardovia wiggsiae and Leptotrichia wadei were enriched in the caries samples, while Neisseria oralis was abundant in the non-caries samples of children aged below 6 years. The caries and non-caries samples of children aged 6-12 years were enriched in Streptococcus mutans and Corynebacterium durum, respectively. The machine learning models based on these differentially enriched taxa showed accuracies of up to 83%. These results confirmed significant alterations in the oral microbiome according to dental caries and age, and these differences can be used as diagnostic biomarkers.

Single-cell RNA sequencing reveals distinct cellular factors for response to immunotherapy targeting CD73 and PD-1 in colorectal cancer.

BACKGROUND: Although cancer immunotherapy is one of the most effective advanced-stage cancer therapies, no clinically approved cancer immunotherapies currently exist for colorectal cancer (CRC). Recently, programmed cell death protein 1 (PD-1) blockade has exhibited clinical benefits according to ongoing clinical trials. However, ongoing clinical trials for cancer immunotherapies are focused on PD-1 signaling inhibitors such as pembrolizumab, nivolumab, and atezolizumab. In this study, we focused on revealing the distinct response mechanism for the potent CD73 ectoenzyme selective inhibitor AB680 as a promising drug candidate that functions by blocking tumorigenic ATP/adenosine signaling in comparison to current therapeutics that block PD-1 to assess the value of this drug as a novel immunotherapy for CRC. METHODS: To understand the distinct mechanism of AB680 in comparison to that of a neutralizing antibody against murine PD-1 used as a PD-1 blocker, we performed single-cell RNA sequencing of CD45+ tumor-infiltrating lymphocytes from untreated controls (n=3) and from AB680-treated (n=3) and PD-1-blockade-treated murine CRC in vivo models. We also used flow cytometry, Azoxymethane (AOM)/Dextran Sulfate Sodium (DSS) models, and in vitro functional assays to validate our new findings. RESULTS: We initially observed that the expressions of Nt5e (a gene for CD73) and Entpd1 (a gene for CD39) affect T cell receptor (TCR) diversity and transcriptional profiles of T cells, thus suggesting their critical roles in T cell exhaustion within tumor. Importantly, PD-1 blockade significantly increased the TCR diversity of Entpd1-negative T cells and Pdcd1-positive T cells. Additionally, we determined that AB680 improved the anticancer functions of immunosuppressed cells such as Treg and exhausted T cells, while the PD-1 blocker quantitatively reduced Malat1high Treg and M2 macrophages. We also verified that PD-1 blockade induced Treg depletion in AOM/DSS CRC in vivo models, and we confirmed that AB680 treatment caused increased activation of CD8+ T cells using an in vitro T cell assay. CONCLUSIONS: The intratumoral immunomodulation of CD73 inhibition is distinct from PD-1 inhibition and exhibits potential as a novel anticancer immunotherapy for CRC, possibly through a synergistic effect when combined with PD-1 blocker treatments. This study may contribute to the ongoing development of anticancer immunotherapies targeting refractory CRC.

Loss of BubR1 acetylation provokes replication stress and leads to complex chromosomal rearrangements.

Accurate chromosomal segregation during mitosis is regulated by the spindle assembly checkpoint (SAC). SAC failure results in aneuploidy, a hallmark of cancer. However, many studies have suggested that aneuploidy alone is not oncogenic. We have reported that BubR1 acetylation deficiency in mice (K243R/+) caused spontaneous tumorigenesis via weakened SAC signaling and unstable chromosome-spindle attachment, resulting in massive chromosomal mis-segregation. In addition to aneuploidy, cells derived from K243R/+ mice exhibited moderate genetic instability and chromosomal translocation. Here, we investigated how the loss of BubR1 acetylation led to genetic instability and chromosomal rearrangement. To rescue all chromosomal abnormalities generated by the loss of BubR1 acetylation during development, K243R/+ mice were crossed with p53-deficient mice. Genome-wide sequencing and spectral karyotyping of tumors derived from these double-mutant mice revealed that BubR1 acetylation deficiency was associated with complex chromosomal rearrangements, including Robertsonian-like whole-arm translocations. By analyzing the telomeres and centromeres in metaphase chromosome spreads, we found that BubR1 acetylation deficiency increased the collapse of stalled replication forks, commonly referred to as replication stress, and led to DNA damage and chromosomal rearrangements. BubR1 mutations that are critical in interacting with PCAF acetyltransferase and acetylating K250, L249F and A251P, were found from human cancers. Furthermore, a subset of human cancer cells exhibiting whole-arm translocation also displayed defects in BubR1 acetylation, supporting that defects in BubR1 acetylation in mitosis contributes to tumorigenesis. Collectively, loss of BubR1 acetylation provokes replication stress, particularly at the telomeres, leading to genetic instability and chromosomal rearrangement.

Treatment strategy for papillary renal cell carcinoma type 2: a case series of seven patients treated based on next generation sequencing data.

BACKGROUND: Papillary renal cell carcinoma type 2 (PRCC2) is refractory to systemic treatment and has a dismal prognosis. Previous studies showed that genetic alterations in PRCC2 were heterogeneous regardless of germline or somatic mutations. In this study, we aimed to perform precision treatment of PRCC2 based on genetic information. METHODS: We performed exome and genome sequencing of tumor tissues and matched normal samples. Based on sequencing data, we treated patients with metastatic PRCC2 using precision oncology. RESULTS: Four patients underwent curative surgery of PRCC2 and three patients had metastatic PRCC2. All PRCC2 heterogeneously harbored own driver mutations. Two out of the three patients with metastatic disease had fumarate hydratase (FH) germline mutations. One patient with a germline FH mutation was diagnosed with hereditary leiomyomatosis RCC. He was treated with bevacizumab and erlotinib combination and showed a durable response. The other metastatic PRCC2 patient harboring a germline FH mutation had an additional somatic FH mutation and was durably controlled with pazopanib. Other metastatic PRCC2 patient with somatic PBRM1 and SETD2 mutations had over 5 years of overall survival with axitinib treatment. CONCLUSIONS: We performed precision systemic treatment based on genetic information. Genome sequencing could help identify candidates for targeted therapy in PRCC2, a genetically heterogeneous disease.

Prediction of Chronic Periodontitis Severity Using Machine Learning Models Based On Salivary Bacterial Copy Number.

Periodontitis is a widespread chronic inflammatory disease caused by interactions between periodontal bacteria and homeostasis in the host. We aimed to investigate the performance and reliability of machine learning models in predicting the severity of chronic periodontitis. Mouthwash samples from 692 subjects (144 healthy controls and 548 generalized chronic periodontitis patients) were collected, the genomic DNA was isolated, and the copy numbers of nine pathogens were measured using multiplex qPCR. The nine pathogens are as follows: Porphyromonas gingivalis (Pg), Tannerella forsythia (Tf), Treponema denticola (Td), Prevotella intermedia (Pi), Fusobacterium nucleatum (Fn), Campylobacter rectus (Cr), Aggregatibacter actinomycetemcomitans (Aa), Peptostreptococcus anaerobius (Pa), and Eikenella corrodens (Ec). By adding the species one by one in order of high accuracy to find the optimal combination of input features, we developed an algorithm that predicts the severity of periodontitis using four machine learning techniques. The accuracy was the highest when the models classified "healthy" and "moderate or severe" periodontitis (H vs. M-S, average accuracy of four models: 0.93, AUC = 0.96, sensitivity of 0.96, specificity of 0.81, and diagnostic odds ratio = 112.75). One or two red complex pathogens were used in three models to distinguish slight chronic periodontitis patients from healthy controls (average accuracy of 0.78, AUC = 0.82, sensitivity of 0.71, and specificity of 0.84, diagnostic odds ratio = 12.85). Although the overall accuracy was slightly reduced, the models showed reliability in predicting the severity of chronic periodontitis from 45 newly obtained samples. Our results suggest that a well-designed combination of salivary bacteria can be used as a biomarker for classifying between a periodontally healthy group and a chronic periodontitis group.

Korean Genome Project: 1094 Korean personal genomes with clinical information.

We present the initial phase of the Korean Genome Project (Korea1K), including 1094 whole genomes (sequenced at an average depth of 31×), along with data of 79 quantitative clinical traits. We identified 39 million single-nucleotide variants and indels of which half were singleton or doubleton and detected Korean-specific patterns based on several types of genomic variations. A genome-wide association study illustrated the power of whole-genome sequences for analyzing clinical traits, identifying nine more significant candidate alleles than previously reported from the same linkage disequilibrium blocks. Also, Korea1K, as a reference, showed better imputation accuracy for Koreans than the 1KGP panel. As proof of utility, germline variants in cancer samples could be filtered out more effectively when the Korea1K variome was used as a panel of normals compared to non-Korean variome sets. Overall, this study shows that Korea1K can be a useful genotypic and phenotypic resource for clinical and ethnogenetic studies.

Flightless-1 inhibits ER stress-induced apoptosis in colorectal cancer cells by regulating Ca2+ homeostasis.

The endoplasmic reticulum (ER) stress response is an adaptive mechanism that is activated upon disruption of ER homeostasis and protects the cells against certain harmful environmental stimuli. However, critical and prolonged cell stress triggers cell death. In this study, we demonstrate that Flightless-1 (FliI) regulates ER stress-induced apoptosis in colon cancer cells by modulating Ca2+ homeostasis. FliI was highly expressed in both colon cell lines and colorectal cancer mouse models. In a mouse xenograft model using CT26 mouse colorectal cancer cells, tumor formation was slowed due to elevated levels of apoptosis in FliI-knockdown (FliI-KD) cells. FliI-KD cells treated with ER stress inducers, thapsigargin (TG), and tunicamycin exhibited activation of the unfolded protein response (UPR) and induction of UPR-related gene expression, which eventually triggered apoptosis. FliI-KD increased the intracellular Ca2+ concentration, and this upregulation was caused by accelerated ER-to-cytosolic efflux of Ca2+. The increase in intracellular Ca2+ concentration was significantly blocked by dantrolene and tetracaine, inhibitors of ryanodine receptors (RyRs). Dantrolene inhibited TG-induced ER stress and decreased the rate of apoptosis in FliI-KD CT26 cells. Finally, we found that knockdown of FliI decreased the levels of sorcin and ER Ca2+ and that TG-induced ER stress was recovered by overexpression of sorcin in FliI-KD cells. Taken together, these results suggest that FliI regulates sorcin expression, which modulates Ca2+ homeostasis in the ER through RyRs. Our findings reveal a novel mechanism by which FliI influences Ca2+ homeostasis and cell survival during ER stress.

Androgen-induced expression of DRP1 regulates mitochondrial metabolic reprogramming in prostate cancer.

Androgen receptor (AR) signaling plays a central role in metabolic reprogramming for prostate cancer (PCa) growth and progression. Mitochondria are metabolic powerhouses of the cell and support several hallmarks of cancer. However, the molecular links between AR signaling and the mitochondria that support the metabolic demands of PCa cells are poorly understood. Here, we demonstrate increased levels of dynamin-related protein 1 (DRP1), a mitochondrial fission mediator, in androgen-sensitive and castration-resistant AR-driven PCa. AR signaling upregulates DRP1 to form the VDAC-MPC2 complex, increases pyruvate transport into mitochondria, and supports mitochondrial metabolism, including oxidative phosphorylation and lipogenesis. DRP1 inhibition activates the cellular metabolic stress response, which involves AMPK phosphorylation, induction of autophagy, and the ER unfolded protein response, and attenuates androgen-induced proliferation. Additionally, DRP1 expression facilitates PCa cell survival under diverse metabolic stress conditions, including hypoxia and oxidative stress. Moreover, we found that increased DRP1 expression was indicative of poor prognosis in patients with castration-resistant PCa. Collectively, our findings link androgen signaling-mediated mitochondrial dynamics to metabolic reprogramming; moreover, they have important implications for understanding PCa progression.

Raptor genomes reveal evolutionary signatures of predatory and nocturnal lifestyles.

BACKGROUND: Birds of prey (raptors) are dominant apex predators in terrestrial communities, with hawks (Accipitriformes) and falcons (Falconiformes) hunting by day and owls (Strigiformes) hunting by night. RESULTS: Here, we report new genomes and transcriptomes for 20 species of birds, including 16 species of birds of prey, and high-quality reference genomes for the Eurasian eagle-owl (Bubo bubo), oriental scops owl (Otus sunia), eastern buzzard (Buteo japonicus), and common kestrel (Falco tinnunculus). Our extensive genomic analysis and comparisons with non-raptor genomes identify common molecular signatures that underpin anatomical structure and sensory, muscle, circulatory, and respiratory systems related to a predatory lifestyle. Compared with diurnal birds, owls exhibit striking adaptations to the nocturnal environment, including functional trade-offs in the sensory systems, such as loss of color vision genes and selection for enhancement of nocturnal vision and other sensory systems that are convergent with other nocturnal avian orders. Additionally, we find that a suite of genes associated with vision and circadian rhythm are differentially expressed in blood tissue between nocturnal and diurnal raptors, possibly indicating adaptive expression change during the transition to nocturnality. CONCLUSIONS: Overall, raptor genomes show genomic signatures associated with the origin and maintenance of several specialized physiological and morphological features essential to be apex predators.

Potential involvement of Drosophila flightless-1 in carbohydrate metabolism.

A previous study of ours indicated that Drosophila flightless-1 controls lipid metabolism, and that there is an accumulation of triglycerides in flightless-1 (fliI)-mutant flies, where this mutation triggers metabolic stress and an obesity phenotype. Here, with the aim of characterizing the function of FliI in metabolism, we analyzed the levels of gene expression and metabolites in fliI-mutant flies. The levels of enzymes related to glycolysis, lipogenesis, and the pentose phosphate pathway increased in fliI mutants; this result is consistent with the levels of metabolites corresponding to a metabolic pathway. Moreover, high-throughput RNA sequencing revealed that Drosophila FliI regulates the expression of genes related to biological processes such as chromosome organization, carbohydrate metabolism, and immune reactions. These results showed that Drosophila FliI regulates the expression of metabolic genes, and that dysregulation of the transcription controlled by FliI gives rise to metabolic stress and problems in the development and physiology of Drosophila. [BMB Reports 2018; 51(9): 462-467].