Lysine-Rich Polypeptide Modulates Forkhead Box O3 and Phosphoinositide 3-Kinase-Protein Kinase B Pathway To Induce Apoptosis in Breast Cancer.

The PI3K/AKT/FOXO3 pathway is one of the most frequently involved signaling pathways in cancer, including breast cancer. Therefore, we synthesized a novel lysine-rich polypeptide (Lys-PP) using de novo assembly method and evaluated its anticancer effect. We characterized the structural and physicochemical properties of Lys-PP using various techniques. Later, we used integrated approaches such as in silico, in vitro, and in vivo analysis to confirm the anticancer and therapeutic effect of Lys-PP. First, RNA sequencing suggests Lys-PP disrupted the central carbon metabolic pathway through the modulation of prolactin signaling. Additionally, docking analysis also confirmed the significant association of PI3K/AKT and FOXO3 pathway to induce an apoptotic effect on cancer. Second, Lys-PP exhibited a significant cytotoxicity effect against MDA-MB-231 but no cytotoxic effects on RAW 264.7 and HEK-293, respectively. The cytotoxic effect of Lys-PP-induced apoptosis by an increase in FOXO3a protein expression and a decrease in PI3K/AKT pathway was confirmed by quantitative real-time polymerase chain reaction, immunoblotting, and fluorescent microscopy. Later, immunohistochemistry and hematoxylin and eosin staining on MDA-MD-231 showed increased FOXO3a expression and cell death in the xenograft mice model. Further, liver function, metabolic health, or lipid profile upon Lys-PP showed the absence of significant modulation in the biomarkers except for kidney-related biomarkers. Overall, our comprehensive study provides the first evidence of Lys-PP antibreast cancer action, which could serve as a potential treatment in an alternative or complementary medicine practice.

Microbiota Association and Profiling of Gingival Sulci and Root Canals of Teeth with Primary or Secondary/Persistent Endodontic Infections.

INTRODUCTION: Microbiota associated with primary endodontic infection (PEI) and secondary/persistent endodontic infection (SPEI) must be characterized to elucidate pathogenesis in apical periodontitis and bacterial biomarkers identified for diagnostic and therapeutic applications. METHODS: This study analyzed the microbial community profiles of root canals and gingival sulci (sulcus-E) for teeth with PEI (n = 10) or SPEI (n = 10), using the Illumina MiSeq platform. Bacterial samples from gingival sulci (sulcus-C) of healthy contralateral teeth served as controls. RESULTS: There were 15 phyla, 177 genera, and 340 species identified. The number and diversity of bacteria in root canals did not differ significantly between PEI and SPEI. Proteobacteria, Firmicutes, Fusobacteria, Bacteroidetes, and Actinobacteria were the dominant phyla in both groups. At the genus level, Lancefieldella, Bifidobacterium, Stomatobaculum, and Schaalia were enriched in root canals with SPEI. Of significance, Lancefieldella was observed in both root canals and sulcus-E of teeth with SPEI. At the species level, Neisseria macacae, Streptococcus gordonii, Bifidobacterium dentium, Stomatobaculum longum, and Schaalia odontolytica were increased significantly in root canals with SPEI compared to PEI. Oribacterium species, Streptococcus salivarius, Lancefieldella parvula, Prevotella denticola, and Oribacterium asaccharolyticum were more abundant in sulcus-E of teeth with SPEI compared to PEI. CONCLUSIONS: There were distinctive and differing predominant bacterial species associated with the root canals and gingival sulci between teeth with PEI and SPEI. Specific bacteria identified in sulcus-E and root canals of teeth with SPEI could serve as noninvasive diagnostic biomarkers for detecting SPEI.

Kernel-Based Independence Tests for Causal Structure Learning on Functional Data.

Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics and engineering. Such measurements can be viewed as realisations of an underlying smooth process sampled over the continuum. However, traditional methods for independence testing and causal learning are not directly applicable to such data, as they do not take into account the dependence along the functional dimension. By using specifically designed kernels, we introduce statistical tests for bivariate, joint, and conditional independence for functional variables. Our method not only extends the applicability to functional data of the Hilbert-Schmidt independence criterion (hsic) and its d-variate version (d-hsic), but also allows us to introduce a test for conditional independence by defining a novel statistic for the conditional permutation test (cpt) based on the Hilbert-Schmidt conditional independence criterion (hscic), with optimised regularisation strength estimated through an evaluation rejection rate. Our empirical results of the size and power of these tests on synthetic functional data show good performance, and we then exemplify their application to several constraint- and regression-based causal structure learning problems, including both synthetic examples and real socioeconomic data.

Anti-Atopic Dermatitis Effect of TPS240, a Novel Therapeutic Peptide, via Suppression of NF-κB and STAT3 Activation.

Atopic dermatitis (AD) is a relapsing skin disease with persistent inflammation as a causal factor for symptoms and disease progression. Current therapies provide only temporary relief and require long-term usage accompanied by side effects due to persistent relapses. A short peptide, TPS240, has been tested for its potential to subside AD. In this study, we confirmed the anti-atopic effect of TPS240 in vivo and in vitro using a DNCB-induced AD mouse model and TNF-α/IFN-γ-stimulated HaCaT cells. In the AD mouse model, topical treatment with TPS240 diminished AD-like skin lesions and symptoms such as epidermal thickening and mast cell infiltration induced by DNCB, similar to the existing treatment, dexamethasone (Dex). Furthermore, skin atrophy, weight loss, and abnormal organ weight changes observed in the Dex-treated group were not detected in the TPS240-treated group. In TNF-α/IFN-γ-stimulated HaCaT cells, TPS240 reduced the expression of the inflammatory chemokines CCL17 and CCL22 and the pruritic cytokines TSLP and IL-31 by inhibiting NF-κB and STAT3 activation. These results suggest that TPS240 has an anti-atopic effect through immunomodulation of AD-specific cytokines and chemokines and can be used as a candidate drug for the prevention and treatment of AD that can solve the safety problems of existing treatments.

Mitochondrial glutamate transporter SLC25A22 uni-directionally export glutamate for metabolic rewiring in radioresistant glioblastoma.

Glioblastoma Multiforme (GBM) is a malignant primary brain tumor. Radiotherapy, one of the standard treatments for GBM patients, could induce GBM radioresistance via rewiring cellular metabolism. However, the precise mechanism attributing to GBM radioresistance or targeting strategies to overcome GBM radioresistance are lacking. Here, we demonstrate that SLC25A22, a mitochondrial bi-directional glutamate transporter, is upregulated and showed uni-directionality from mitochondria to cytosol in radioresistant GBM cells, resulting in accumulating cytosolic glutamate. However, mitochondrial glutaminolysis-mediated TCA cycle metabolites and OCR are maintained constantly. The accumulated cytosolic glutamate enhances the glutathione (GSH) production and proline synthesis in radioresistant GBM cells. Increased GSH protects cells against ionizing radiation (IR)-induced reactive oxygen species (ROS) whereas increased proline, a rate-limiting substrate for collagen biosynthesis, induces extracellular matrix (ECM) remodeling, leading to GBM invasive phenotypes. Finally, we discover that genetic inhibition of SLC25A22 using miR-184 mimic decreases GBM radioresistance and aggressiveness both in vitro and in vivo. Collectively, our study suggests that SLC25A22 upregulation confers GBM radioresistance by rewiring glutamate metabolism, and SLC25A22 could be a significant therapeutic target to overcome GBM radioresistance.

Comparison of Muscle Activity and Muscle Thickness According to Knee Flexion Angle during Supine Bridge Exercises using the Abdominal Drawing-in Maneuver on an Unstable Surface.

This study evaluated changes in deep trunk muscle thickness and lower extremity muscle activities during bridge exercises with the abdominal drawing-in maneuver. Bridge exercises were conducted on an unstable surface at different knee flexion angles (60º, 90º and 120º), with the aim of identifying more effective angles for bridge exercises. This study included 21 healthy adults, aged 20-27 years. Biceps femoris (BF), rectus abdominis, and rectus femoris activity was measured using surface electromyography. The thicknesses of the transverse abdominis (TrA), external oblique (EO) and internal oblique (IO) muscles were measured. BF (p = 0.000, partial η2 = 0.670) activity increased considerably as the knee flexion angle decreased. TrA (p = 0.000, partial η2 = 0.883) and IO (p = 0.000, partial η2 = 0.892) thickness significantly increased, while EO (p = 0.000, partial η2 = 0.893) thickness decreased as the knee flexion angle decreased. When performing bridge exercises using the abdominal drawing-in maneuver on an unstable surface, the knee flexion angles should be at 120º and 60º to increase trunk stability and lower extremity muscle activity, respectively.

Parnassin, a Novel Therapeutic Peptide, Alleviates Skin Lesions in a DNCB-Induced Atopic Dermatitis Mouse Model.

Atopic dermatitis (AD) is a chronic inflammatory skin disease which requires continuous treatment due to its relapsing nature. The current treatment includes steroids and nonsteroidal agents targeting inflammation but long-term administration causes various side effects such as skin atrophy, hirsutism, hypertension and diarrhea. Thus, there is an unmet need for safer and effective therapeutic agents in the treatment of AD. Peptides are small biomolecule drugs which are highly potent and remarkably have less side effects. Parnassin is a tetrapeptide with predicted anti-microbial activity curated from Parnassius bremeri transcriptome data. In this study, we confirmed the effect of parnassin on AD using a DNCB-induced AD mouse model and TNF-α/IFN-γ-stimulated HaCaT cells. In the AD mouse model, topical administration of parnassin improved skin lesions and symptoms in AD mice, such as epidermal thickening and mast cell infiltration, similar to the existing treatment, dexamethasone, and did not affect body weight, or the size and weight of spleen. In TNF-α/IFN-γ-stimulated HaCaT cells, parnassin inhibited the expression of Th2-type chemokine CCL17 and CCL22 genes by suppressing JAK2 and p38 MAPK signaling kinases and their downstream transcription factor STAT1. Parnassin also significantly reduced the gene expression of TSLP and IL-31, which are pruritus-inducing cytokines. These findings suggested that parnassin alleviates AD-like lesions via its immunomodulatory effects and can be used as a candidate drug for the prevention and treatment of AD because it is safer than existing treatments.

Light and scanning electron microscopy of aecia and aeciospores of Cronartium ribicola on Pinus koraiensis branch tissues.

Morphological characteristics of aecia and aeciospores of Cronartium ribicola on Pinus koraiensis branch tissues were investigated using light and field emission scanning electron microscopy (FESEM). Mature P. koraiensis trees in Jeongseon, Korea, showed yellowish aecia on stems and branches. Aecia and surrounding tissues were excised from the lesions and vapor-fixed for FESEM imaging, which revealed morphology including intact blister-shaped, flattened, and burst forms. Light microscopy revealed yellowish aeciospores having surface projections. Aeciospores were mostly ovoid and measured approximately 20 µm long. The FESEM showed irregularly shaped cracks on the aecia that had erupted through the bark of P. koraiensis. Some aeciospores had germinated, producing two germ tubes from a spore in a burst aecium. Aeciospores had both smooth and verrucose regions on the surface, and some had concave or convex regions. Aeciospore layers and underlying fungal matrices including aecial columns were obvious in the cross-sections of aecia. Approximately 1 µm-high wart-like surface projections could be resolved and comprised less than 10 angular platelets stacked in vertical rows. Remains of the primary spore wall were present between surface projections. These results provide insights into the morphology of the heteroecious rust fungus with the help of vapor fixation and high-resolution surface imaging.

Classification of benign and malignant subtypes of breast cancer histopathology imaging using hybrid CNN-LSTM based transfer learning.

BACKGROUND: Grading of cancer histopathology slides requires more pathologists and expert clinicians as well as it is time consuming to look manually into whole-slide images. Hence, an automated classification of histopathological breast cancer sub-type is useful for clinical diagnosis and therapeutic responses. Recent deep learning methods for medical image analysis suggest the utility of automated radiologic imaging classification for relating disease characteristics or diagnosis and patient stratification. METHODS: To develop a hybrid model using the convolutional neural network (CNN) and the long short-term memory recurrent neural network (LSTM RNN) to classify four benign and four malignant breast cancer subtypes. The proposed CNN-LSTM leveraging on ImageNet uses a transfer learning approach in classifying and predicting four subtypes of each. The proposed model was evaluated on the BreakHis dataset comprises 2480 benign and 5429 malignant cancer images acquired at magnifications of 40×, 100×, 200× and 400×. RESULTS: The proposed hybrid CNN-LSTM model was compared with the existing CNN models used for breast histopathological image classification such as VGG-16, ResNet50, and Inception models. All the models were built using three different optimizers such as adaptive moment estimator (Adam), root mean square propagation (RMSProp), and stochastic gradient descent (SGD) optimizers by varying numbers of epochs. From the results, we noticed that the Adam optimizer was the best optimizer with maximum accuracy and minimum model loss for both the training and validation sets. The proposed hybrid CNN-LSTM model showed the highest overall accuracy of 99% for binary classification of benign and malignant cancer, and, whereas, 92.5% for multi-class classifier of benign and malignant cancer subtypes, respectively. CONCLUSION: To conclude, the proposed transfer learning approach outperformed the state-of-the-art machine and deep learning models in classifying benign and malignant cancer subtypes. The proposed method is feasible in classification of other cancers as well as diseases.

Transition in vaginal Lactobacillus species during pregnancy and prediction of preterm birth in Korean women.

The predominance of vaginal Lactobacillus species, specifically L. crispatus, is important for pregnancy maintenance, but varies by race. The composition of the vaginal microbiome can affect susceptibility to adverse pregnancy outcomes. We performed 16S rRNA gene amplicon sequencing on vaginal swabs taken from Korean pregnant women. Here, we report the transition of Lactobacillus spp. in samples of full-term birth (FTB) collected longitudinally in the second and third trimesters of pregnancy in a cohort study (n = 23) and their association with Lactobacillus abundance and preterm birth (PTB) in a case-control study (n = 200). Lactobacillus species, which was dominant in FTB samples including those that received interventions in the second trimester, did not change until 37 weeks of gestation. However, L. crispatus was replaced by other Lactobacillus species after 37 weeks. The PTB risk showed a closer association with the Lactobacillus abundance than with community state type determined by Lactobacillus species. PTB was associated with less than 90% of Lactobacillus abundance and an increase in Ureplasma parvum in the second trimester. Thus, the vaginal microbiome may change in preparation for childbirth in response to multiple intrinsic factors after 37 weeks of gestation. Monitoring the Lactobacillus abundance may help improve the reliability of microbial PTB biomarkers.

Developmental Transcriptome Analysis of Red-Spotted Apollo Butterfly, Parnassius bremeri.

Parnassius bremeri (P. bremeri), a member of the genus Snow Apollo in the swallowtail family (Papilionidae), is a high alpine butterfly that lives in Russia, Korea, and China. It is an endangered wildlife (Class I) in South Korea and is a globally endangered species. The lack of transcriptomic and genomic resources of P. bremeri significantly hinders the study of its population genetics and conservation. The detailed information of the developmental stage-specific gene expression patterns of P. bremeri is of great demand for its conservation. However, the molecular mechanism underlying the metamorphic development of P. bremeri is still unknown. In the present study, the differentially expressed genes (DEGs) across the metamorphic developmental stages were compared using high-throughput transcriptome sequencing. We identified a total of 72,161 DEGs from eight comparisons. GO enrichment analysis showed that a range of DEGs were responsible for cuticle development and the melanin biosynthetic pathway during larval development. Pathway analysis suggested that the signaling pathways, such as the Wnt signaling pathway, hedgehog signaling pathway and Notch signaling pathway, are regulated during the developmental stages of P. bremeri. Furthermore, sensory receptors were also activated, especially during the larval to adult transition stage. Collectively, the results of this study provide a preliminary foundation and understanding of the molecular mechanism in their transcriptomes for further research on the metamorphic development of P. bremeri.

Urinary microbiome profile in men with genitourinary malignancies.

PURPOSE: Recent advances in molecular biology technology have allowed identification of microbial communities in the urinary tract, and urinary microbiome is associated with various urological diseases. In this study, we aimed to characterize the urinary microbiome of genitourinary malignancies. MATERIALS AND METHODS: Metagenomic analysis of urinary DNA was performed in 85 patients including 30 with bladder cancer (BC), 27 with prostate cancer (PC), 12 with renal cancer (RC), and 16 with non-cancer (NC). 16S rRNA gene sequencing was conducted after amplification of the V3-V4 region. RESULTS: PC and RC had significantly lower Shannon index than BC, and beta diversity showed significantly different microbiome composition between four groups. We identified six genera of Cutibacterium, Peptoniphilus, Sphingomonas, Staphylococcus, Micrococcus, and Moraxella, which showed significantly different abundance between the four groups. When each of the malignancies were compared to NC at the species level, Micrococcus sp. was significantly increased in BC. We also identified 12 and five species with increased populations in PC and RC, respectively. Of these, Cutibacterium acnes, Cutibacterium granulosum, Peptoniphilus lacydonensis, and Tessaracoccus were significantly increased in both PC and RC. CONCLUSIONS: Urinary microbiome composition was different depending on the type of genitourinary malignancies, and we identified bacteria that are significantly associated with each type of malignancy. Specifically, several bacterial species were associated both PC and RC, suggesting that PC and RC share a similar pathogenesis-related urinary microbiome.

Identification of Novel Ribonucleotide Reductase Inhibitors for Therapeutic Application in Bile Tract Cancer: An Advanced Pharmacoinformatics Study.

Biliary tract cancer (BTC) is constituted by a heterogeneous group of malignant tumors that may develop in the biliary tract, and it is the second most common liver cancer. Human ribonucleotide reductase M1 (hRRM1) has already been proven to be a potential BTC target. In the current study, a de novo design approach was used to generate novel and effective chemical therapeutics for BTC. A set of comprehensive pharmacoinformatics approaches was implemented and, finally, seventeen potential molecules were found to be effective for the modulation of hRRM1 activity. Molecular docking, negative image-based ShaEP scoring, absolute binding free energy, in silico pharmacokinetics, and toxicity assessments corroborated the potentiality of the selected molecules. Almost all molecules showed higher affinity in comparison to gemcitabine and naphthyl salicylic acyl hydrazone (NSAH). On binding interaction analysis, a number of critical amino acids was found to hold the molecules at the active site cavity. The molecular dynamics (MD) simulation study also indicated the stability between protein and ligands. High negative MM-GBSA (molecular mechanics generalized Born and surface area) binding free energy indicated the potentiality of the molecules. Therefore, the proposed molecules might have the potential to be effective therapeutics for the management of BTC.

A Study on the Prediction of Cancer Using Whole-Genome Data and Deep Learning.

The number of patients diagnosed with cancer continues to increasingly rise, and has nearly doubled in 20 years. Therefore, predicting cancer occurrence has a significant impact on reducing medical costs, and preventing cancer early can increase survival rates. In the data preprocessing step, since individual genome data are used as input data, they are classified as individual genome data. Subsequently, data embedding is performed in character units, so that it can be used in deep learning. In the deep learning network schema, using preprocessed data, a character-based deep learning network learns the correlation between individual feature data and predicts cancer occurrence. To evaluate the objective reliability of the method proposed in this study, various networks published in other studies were compared and evaluated using the TCGA dataset. As a result of comparing various networks published in other studies using the same data, excellent results were obtained in terms of accuracy, sensitivity, and specificity. Thus, the superiority of the effectiveness of deep learning networks in predicting cancer occurrence using individual whole-genome data was demonstrated. From the results of the confusion matrix, the validity of the model for predicting the cancer using an individual's whole-genome data and the deep learning proposed in this study was proven. In addition, the AUC, which is the area under the ROC curve, which judges the efficiency of diagnosis as a performance evaluation index of the model, was found to be 90% or more, good classification results were derived. The objectives of this study were to use individual genome data for 12 cancers as input data to analyze the whole genome pattern, and to not separately use reference genome sequence data of normal individuals. In addition, several mutation types, including SNV, DEL, and INS, were applied.

Appressed, directed and silicified trichomes of the kudzu climbing vine.

Kudzu (Pueraria montana var. lobata) is becoming one of the most prevalent climbing vines in urban forests. Here, surface characteristics of kudzu leaves and stems are investigated using field-emission scanning electron microscopy and X-ray microanalysis. The leaf and stem surfaces were characterized by different types of trichomes. No adhesive pads, modified hooks or tendrils were found on kudzu vines. Trichomes on the leaves and stems of kudzu could be categorized into (i) complex bulbous glandular trichomes and (ii) needle-shaped nonglandular trichomes (NSNGTs). Two morphotypes of the nonglandular trichomes were present on the stems: (i) long hairy nonglandular trichomes and (ii) short NSNGTs. The leaf trichomes were appressed and oriented in an uphill direction, whereas the stem trichomes were appressed and oriented in a downhill direction. This opposite trichome orientation appears to facilitate climbing and prohibits slipping of kudzu vines through differential friction between the plants and the kudzu vines. X-ray microanalysis revealed a distinct accumulation of silicon in the leaf and stem trichomes. These results suggest that appressed, directed and silicified trichomes may contribute to the twining behavior of kudzu vines.

Identification of Potential Cytochrome P450 3A5 Inhibitors: An Extensive Virtual Screening through Molecular Docking, Negative Image-Based Screening, Machine Learning and Molecular Dynamics Simulation Studies.

Cytochrome P450 3A5 (CYP3A5) is one of the crucial CYP family members and has already proven to be an important drug target for cardiovascular diseases. In the current study, the PubChem database was screened through molecular docking and high-affinity molecules were adopted for further assessment. A negative image-based (NIB) model was used for a similarity search by considering the complementary shape and electrostatics of the target and small molecules. Further, the molecules were segregated into active and inactive groups through six machine learning (ML) matrices. The active molecules found in each ML model were used for in silico pharmacokinetics and toxicity assessments. A total of five molecules followed the acceptable pharmacokinetics and toxicity profiles. Several potential binding interactions between the proposed molecules and CYP3A5 were observed. The dynamic behavior of the selected molecules in the CYP3A5 was explored through a molecular dynamics (MD) simulation study. Several parameters obtained from the MD simulation trajectory explained the stability of the protein-ligand complexes in dynamic states. The high binding affinity of each molecule was revealed by the binding free energy calculation through the MM-GBSA methods. Therefore, it can be concluded that the proposed molecules might be potential CYP3A5 molecules for therapeutic application in cardiovascular diseases subjected to in vitro/in vivo validations.

A non-parametric Hawkes model of the spread of Ebola in west Africa.

Recently developed methods for the non-parametric estimation of Hawkes point process models facilitate their application for describing and forecasting the spread of epidemic diseases. We use data from the 2014 Ebola outbreak in West Africa to evaluate how well a simple Hawkes point process model can forecast the spread of Ebola virus in Guinea, Sierra Leone, and Liberia. For comparison, SEIR models that fit previously to the same data are evaluated using identical metrics. To test the predictive power of each of the models, we simulate the ability to make near real-time predictions during an actual outbreak by using the first 75% of the data for estimation and the subsequent 25% of the data for evaluation. Forecasts generated from Hawkes models more accurately describe the spread of Ebola in each of the three countries investigated and result in a 38% reduction in RMSE for weekly case estimation across all countries when compared to SEIR models (total RMSE of 59.8 cases/week using SEIR compared to 37.1 for Hawkes). We demonstrate that the improved fit from Hawkes modeling cannot be attributed to overfitting and evaluate the advantages and disadvantages of Hawkes models in general for forecasting the spread of epidemic diseases.

X-ray computed tomography, electron microscopy, and energy-dispersive X-ray spectroscopy of severed Zelkova serrata roots (Japanese elm tree).

X-ray computed tomography (XCT), field emission scanning electron microscopy (FESEM), and energy-dispersive X-ray spectroscopy (EDS) were evaluated for imaging and element identification of woody plant roots. Lateral roots of Japanese zelkova (Zelkova serrata) were severed in spring and maintained in soil for six months. The lateral roots were observed using XCT without maceration and sectioning. The general wood characteristics were discernible to reveal the bark and xylem structures in contrast-inverted tomograms. Virtual sections showed a newly formed ring of woundwood encircling the severed lateral roots. FESEM exhibited secondary xylem structures in which tyloses, fungal hyphae, and aggregates were present. While silicon was dispersed in and around the fungal hyphae, calcium was localized as distinct aggregates using EDS. These results suggest that the combined use of XCT, FESEM, and EDS has merit into the morphological assessment of tree health care, providing virtual sections, high-resolution images, and element composition from an entire woodblock.

RNA-Seq-based transcriptome analysis of methicillin-resistant Staphylococcus aureus growth inhibition by propionate.

Staphylococcus aureus is a pathogen that causes a variety of infectious diseases such as pneumonia, endocarditis, and septic shock. Methicillin-resistant S. aureus (MRSA) evades virtually all available treatments, creating the need for an alternative control strategy. Although we previously demonstrated the inhibitory effect of sodium propionate (NaP) on MRSA, the regulatory mechanism of this effect remains unclear. In this study, we investigated the regulatory mechanism responsible for the inhibitory effect of NaP on MRSA using RNA-Seq analysis. Total RNAs were isolated from non-treated and 50 mM NaP-treated S. aureus USA300 for 3 h and transcriptional profiling was conducted by RNA-Seq analysis. A total of 171 differentially expressed genes (DEGs) with log2 fold change ≥2 and p < 0.05 was identified in the NaP treatment group compared with the control group. Among the 171 genes, 131 were up-regulated and 40 were down-regulated. Upon gene ontology (GO) annotation analysis, total 26 specific GO terms in "Biological process," "Molecular function," and "Cellular component" were identified in MRSA treated with NaP for 3 h. "Purine metabolism"; "riboflavin metabolism"; and "glycine, serine, and threonine metabolism" were identified as major altered metabolic pathways among the eight significantly enriched KEGG pathways in MRSA treated with NaP. Furthermore, the MRSA strains deficient in purF, ilvA, ribE, or ribA, which were the up-regulated DEGs in the metabolic pathways, were more susceptible to NaP than wild-type MRSA. Collectively, these results demonstrate that NaP attenuates MRSA growth by altering its metabolic pathways, suggesting that NaP can be used as a potential bacteriostatic agent for prevention of MRSA infection.

Nonparametric estimation of recursive point processes with application to mumps in Pennsylvania.

The self-exciting Hawkes point process model (Hawkes, 1971) has been used to describe and forecast communicable diseases. A variant of the Hawkes model, called the recursive model, was proposed by Schoenberg et al. (2019) and has been shown to fit well to various epidemic disease datasets. Unlike the Hawkes model, the recursive model allows the productivity to vary as the overall rate of incidence of the disease varies. Here, we extend the data-driven nonparametric expectation-maximization method of Marsan and Lengliné (2008) in order to fit the recursive model without assuming a particular functional form for the productivity. The nonparametric recursive model is trained to fit to weekly reported cases of mumps in Pennsylvania during the January 1970-September 1990 time frame and then assessed using one week forecasts for the October 1990-December 2001 time period. Both its training and predictive ability are evaluated compared to that of other candidate models, such as Hawkes and SVEILR (susceptible, vaccinated, exposed, infected, lightly infected, recovered) compartmental models.