Genome-wide search algorithms for identifying dynamic gene co-expression via Bayesian variable selection.

A wealth of gene expression data generated by high-throughput techniques provides exciting opportunities for studying gene-gene interactions systematically. Gene-gene interactions in a biological system are tightly regulated and are often highly dynamic. The interactions can change flexibly under various internal cellular signals or external stimuli. Previous studies have developed statistical methods to examine these dynamic changes in gene-gene interactions. However, due to the massive number of possible gene combinations that need to be considered in a typical genomic dataset, intensive computation is a common challenge for exploring gene-gene interactions. On the other hand, oftentimes only a small proportion of gene combinations exhibit dynamic co-expression changes. To solve this problem, we propose Bayesian variable selection approaches based on spike-and-slab priors. The proposed algorithms reduce the computational intensity by focusing on identifying subsets of promising gene combinations in the search space. We also adopt a Bayesian multiple hypothesis testing procedure to identify strong dynamic gene co-expression changes. Simulation studies are performed to compare the proposed approaches with existing exhaustive search heuristics. We demonstrate the implementation of our proposed approach to study the association between gene co-expression patterns and overall survival using the RNA-sequencing dataset from The Cancer Genome Atlas breast cancer BRCA-US project.

Long-term patient-reported donor-site morbidity after free peroneal fasciocutaneous flap in head and neck reconstruction.

OBJECTIVE: Fasciocutaneous free flap based on the peroneal artery (boneless version) is an option in our practice for head and neck reconstruction. However, the associated donor-site morbidity has rarely been discussed. Thus, this study investigated the long-term patient-reported donor-site morbidity associated with peroneal flaps. METHODS: In this single-center, retrospective, observational study, 39 patients who underwent a free peroneal flap were enrolled. We evaluated donor-site morbidity with a modified questionnaire from Enneking et al. and Bodde et al. RESULTS: Patient-reported daily life limitation was relatively low (5/39; 12.9%). Donor-site morbidities, namely pain (4/39; 10.3%), sensory disturbance (9/39; 23.1%), and walking limitation (9/39; 23.1%) were reported; most were rated minimal in severity. Among patients with walking limitation, muscle weakness (3/39; 7.7%), ankle instability (6/39; 15.4%), and gait alternation (6/39; 15.4%) were reported. Six patients developed claw toe. CONCLUSION: Balancing successful reconstruction and donor-site morbidity is challenging. This long-term patient-reported survey revealed that harvesting peroneal flaps resulted in minimal or minor donor-site morbidity with no obvious impacts on the patients' daily quality of life. Although free radial forearm flaps and anterolateral thigh flaps are standard, free peroneal flaps have been proven reliable, with acceptable donor-site morbidity.

Flexible copula model for integrating correlated multi-omics data from single-cell experiments.

With recent advances in technologies to profile multi-omics data at the single-cell level, integrative multi-omics data analysis has been increasingly popular. It is increasingly common that information such as methylation changes, chromatin accessibility, and gene expression are jointly collected in a single-cell experiment. In biomedical studies, it is often of interest to study the associations between various data types and to examine how these associations might change according to other factors such as cell types and gene regulatory components. However, since each data type usually has a distinct marginal distribution, joint analysis of these changes of associations using multi-omics data is statistically challenging. In this paper, we propose a flexible copula-based framework to model covariate-dependent correlation structures independent of their marginals. In addition, the proposed approach could jointly combine a wide variety of univariate marginal distributions, either discrete or continuous, including the class of zero-inflated distributions. The performance of the proposed framework is demonstrated through a series of simulation studies. Finally, it is applied to a set of experimental data to investigate the dynamic relationship between single-cell RNA sequencing, chromatin accessibility, and DNA methylation at different germ layers during mouse gastrulation.

Modeling dynamic correlation in zero-inflated bivariate count data with applications to single-cell RNA sequencing data.

Interactions between biological molecules in a cell are tightly coordinated and often highly dynamic. As a result of these varying signaling activities, changes in gene coexpression patterns could often be observed. The advancements in next-generation sequencing technologies bring new statistical challenges for studying these dynamic changes of gene coexpression. In recent years, methods have been developed to examine genomic information from individual cells. Single-cell RNA sequencing (scRNA-seq) data are count-based, and often exhibit characteristics such as overdispersion and zero inflation. To explore the dynamic dependence structure in scRNA-seq data and other zero-inflated count data, new approaches are needed. In this paper, we consider overdispersion and zero inflation in count outcomes and propose a ZEro-inflated negative binomial dynamic COrrelation model (ZENCO). The observed count data are modeled as a mixture of two components: success amplifications and dropout events in ZENCO. A latent variable is incorporated into ZENCO to model the covariate-dependent correlation structure. We conduct simulation studies to evaluate the performance of our proposed method and to compare it with existing approaches. We also illustrate the implementation of our proposed approach using scRNA-seq data from a study of minimal residual disease in melanoma.

Toward uncharted territory of cellular heterogeneity: advances and applications of single-cell RNA-seq.

Among single-cell analysis technologies, single-cell RNA-seq (scRNA-seq) has been one of the front runners in technical inventions. Since its induction, scRNA-seq has been well received and undergone many fast-paced technical improvements in cDNA synthesis and amplification, processing and alignment of next generation sequencing reads, differentially expressed gene calling, cell clustering, subpopulation identification, and developmental trajectory prediction. scRNA-seq has been exponentially applied to study global transcriptional profiles in all cell types in humans and animal models, healthy or with diseases, including cancer. Accumulative novel subtypes and rare subpopulations have been discovered as potential underlying mechanisms of stochasticity, differentiation, proliferation, tumorigenesis, and aging. scRNA-seq has gradually revealed the uncharted territory of cellular heterogeneity in transcriptomes and developed novel therapeutic approaches for biomedical applications. This review of the advancement of scRNA-seq methods provides an exploratory guide of the quickly evolving technical landscape and insights of focused features and strengths in each prominent area of progress.

Haematogenous Klebsiella pneumoniae osteomyelitis.

OBJECTIVES: Klebsiella pneumoniae infection has been associated with alcoholic and diabetic patient populations, especially in Asian populations. K. pneumonia wound infection is common, but K. pneumonia without wound osteomyelitis (OM) is relatively rare. However, the pathogenesis of haematogenous K. pneumonia without open wound OM still unclear until now. In our research, we are trying to collect patients with haematogenous K. pneumonia osteomyelitis (K.p OM) at our hospital and to evaluate their contributing factors. METHODS: We compiled a retrospective database of haematogenous K. pneumonia osteomyelitis (K.p OM) from 1990 to 2019 at our hospital. Patients' bone cultures without K. pneumonia infection were excluded. Sixteen patients with haematogenous K.p OM were recruited. Patients' basic information, comorbidities, wound history, the biochemical examination of the blood, bacterial blood, bone, urine, and liver abscess cultures, the location of OM, corresponding treatments, and post operation K.p wound infection history were reviewed retrospectively. The collected data were analyzed using SPSS software. RESULTS: Unwounded haematogenous K.p OM had a statistically significant and positive correlation with liver insufficiency (P = .037; OR = 2.200), advanced age (≥ 65 years) (P = .037; OR = 2.200) and male gender (P = .03; OR = 1.833). DM, hypertension, steroid usage, GI or GU tract K.p infection, post operation K.p wound infection, hypoalbuminemia, and the location of K.p OM had no significant relationship to outcomes. CONCLUSION: Male patients of advanced age (> 65 years) and patients with liver insufficiency, including liver cirrhosis and hepatitis, have a strong correlation with unwounded haematogenous K.p OM.

Coexposure to Inhaled Aldehydes or Carbon Dioxide Enhances the Carcinogenic Properties of the Tobacco-Specific Nitrosamine 4-Methylnitrosamino-1-(3-pyridyl)-1-butanone in the A/J Mouse Lung.

Tobacco smoke is a complex mixture of chemicals, many of which are toxic and carcinogenic. Hazard assessments of tobacco smoke exposure have predominantly focused on either single chemical exposures or the more complex mixtures of tobacco smoke or its fractions. There are fewer studies exploring interactions between specific tobacco smoke chemicals. Aldehydes such as formaldehyde and acetaldehyde were hypothesized to enhance the carcinogenic properties of the human carcinogen, 4-methylnitrosamino-1-(3-pyridyl)-1-butanone (NNK) through a variety of mechanisms. This hypothesis was tested in the established NNK-induced A/J mouse lung tumor model. A/J mice were exposed to NNK (intraperitoneal injection, 0, 2.5, or 7.5 µmol in saline) in the presence or absence of acetaldehyde (0 or 360 ppmv) or formaldehyde (0 or 17 ppmv) for 3 h in a nose-only inhalation chamber, and lung tumors were counted 16 weeks later. Neither aldehyde by itself induced lung tumors. However, mice receiving both NNK and acetaldehyde or formaldehyde had more adenomas with dysplasia or progression than those receiving only NNK, suggesting that aldehydes may increase the severity of NNK-induced lung adenomas. The aldehyde coexposure did not affect the levels of NNK-derived DNA adduct levels. Similar studies tested the ability of a 3 h nose-only carbon dioxide (0, 5, 10, or 15%) coexposure to influence lung adenoma formation by NNK. While carbon dioxide alone was not carcinogenic, it significantly increased the number of NNK-derived lung adenomas without affecting NNK-derived DNA damage. These studies indicate that the chemicals in tobacco smoke work together to form a potent lung carcinogenic mixture.

Bisphosphonate-Related Osteonecrosis of the Jaw.

OBJECTIVES: Antiresorptive agents for bone pain were widely used to treat patients with advanced osteoporosis, multiple myeloma, and bone metastatic cancer. In recent years, however, bisphosphonate-related osteonecrosis of the jaw (BRONJ) has been a rare but major complication of this therapy. Most patients with BRONJ undergo dental procedures during treatment with antiresorptive agents. However, BRONJ may also occur spontaneously. This study reports 13 BRONJ patient cases at Kaohsiung Veterans General Hospital, Taiwan, and their related treatments. We also compare patients with cancer with patients with osteoporosis in treatment outcomes. METHODS: Thirteen symptomatic patients with BRONJ were reviewed between 1985 and 2018 at Kaohsiung Veterans General Hospital. We included patients at advanced stage who were hospitalized for infection control of osteonecrosis of the jaw and excluded asymptomatic patients at stage 0 and stage 1. Four multiple myeloma, 3 patients with bone metastatic breast cancer and 6 patients with advanced osteoporosis (average ages, 63.57 ± 14.54 years in cancer patients and 79.5 ± 9.31 years in osteoporosis patients; average drug durations, 25.86 ± 27.23 months in cancer patients and 58.33 ± 23.87 months in osteoporosis patients; average follow-up times, 22.71 ± 14.46 months in cancer patients and 28.08 ± 36.35 months in osteoporosis patients) were included. RESULTS: Seven patients were defined as having stage 3 (53.8%) and 6 as having stage 2 (46.2%) medication-related osteonecrosis of the jaw, according to the American Association of Oral and Maxillofacial Surgeons classification. The complete response rate with totally healed mucosa was 61.5%. Four cancer patients received free fibular flap (FFF) reconstruction with a high complete response rate (100%). All of them had a relatively better performance status, and the average age was also younger than osteoporosis patients. CONCLUSION: Free fibular flap with a high complete response rate may improve pain relief and infection control for patients with BRONJ. Younger age is seemed to be a great indicator for FFF, but poor self-care ability (Eastern Cooperative Oncology Group status >3) is not suitable for these surgical treatments.

Flexible bivariate correlated count data regression.

Multivariate count data are common in many disciplines. The variables in such data often exhibit complex positive or negative dependency structures. We propose three Bayesian approaches to modeling bivariate count data by simultaneously considering covariate-dependent means and correlation. A direct approach utilizes a bivariate negative binomial probability mass function developed in Famoye (2010, Journal of Applied Statistics). The second approach fits bivariate count data indirectly using a bivariate Poisson-gamma mixture model. The third approach is a bivariate Gaussian copula model. Based on the results from simulation analyses, the indirect and copula approaches perform better overall than the direct approach in terms of model fitting and identifying covariate-dependent association. The proposed approaches are applied to two RNA-sequencing data sets for studying breast cancer and melanoma (BRCA-US and SKCM-US), respectively, obtained through the International Cancer Genome Consortium.

The Arabidopsis transcription factor AINTEGUMENTA orchestrates patterning genes and auxin signaling in the establishment of floral growth and form.

Understanding how flowers form is an important problem in plant biology, as human food supply depends on flower and seed production. Flower development also provides an excellent model for understanding how cell division, expansion and differentiation are coordinated during organogenesis. In the model plant Arabidopsis thaliana, floral organogenesis requires AINTEGUMENTA (ANT) and AINTEGUMENTA-LIKE 6 (AIL6)/PLETHORA 3 (PLT3), two members of the Arabidopsis AINTEGUMENTA-LIKE/PLETHORA (AIL/PLT) transcription factor family. Together, ANT and AIL6/PLT3 regulate aspects of floral organogenesis, including floral organ initiation, growth, identity specification and patterning. Previously, we used RNA-Seq to identify thousands of genes with disrupted expression in ant ail6 mutant flowers, indicating that ANT and AIL6/PLT3 influence a vast transcriptional network. The immediate downstream targets of ANT and AIL6/PLT3 in flowers are unknown, however. To identify direct targets of ANT regulation, we performed an RNA-Seq time-course experiment in which we induced ANT activity in transgenic plants bearing an ANT-glucocorticoid receptor fusion construct. In addition, we performed a ChIP-Seq experiment that identified ANT binding sites in developing flowers. These experiments identified 200 potential ANT target genes based on their proximity to ANT binding sites and differential expression in response to ANT. These 200 candidate target genes were involved in functions such as polarity specification, floral organ development, meristem development and auxin signaling. In addition, we identified several genes associated with lateral organ growth that may mediate the role of ANT in organ size control. These results reveal new features of the ANT transcriptional network by linking ANT to previously unknown regulatory targets.

Using sufficient direction factor model to analyze latent activities associated with breast cancer survival.

High-dimensional gene expression data often exhibit intricate correlation patterns as the result of coordinated genetic regulation. In practice, however, it is difficult to directly measure these coordinated underlying activities. Analysis of breast cancer survival data with gene expressions motivates us to use a two-stage latent factor approach to estimate these unobserved coordinated biological processes. Compared to existing approaches, our proposed procedure has several unique characteristics. In the first stage, an important distinction is that our procedure incorporates prior biological knowledge about gene-pathway membership into the analysis and explicitly model the effects of genetic pathways on the latent factors. Second, to characterize the molecular heterogeneity of breast cancer, our approach provides estimates specific to each cancer subtype. Finally, our proposed framework incorporates sparsity condition due to the fact that genetic networks are often sparse. In the second stage, we investigate the relationship between latent factor activity levels and survival time with censoring using a general dimension reduction model in the survival analysis context. Combining the factor model and sufficient direction model provides an efficient way of analyzing high-dimensional data and reveals some interesting relations in the breast cancer gene expression data.

Clinical Importance and Risk Factors for Postoperative Late-Onset Pneumonia After Major Oral Cancer Surgery With Microvascular Reconstruction.

BACKGROUND: Pulmonary complications are common among patients who have undergone major oral cancer surgery with microvascular reconstruction. Current literatures focused on early-onset pneumonia in the postoperative acute stage. In contrast, we are aiming to identify the clinical importance and the risk factors associated with late-onset pneumonia in oral cancer patients after acute stage. METHODS: In total, 195 patients were included from May 2014 to December 2016 and followed up for up to 1 year after surgery. Their medical histories were reviewed to identify the risk factors of late-onset pneumonia and outcome. Primary outcome was late-onset pneumonia. Other outcome measures included early-onset pneumonia, tumor recurrence, and death within 1 year after surgery. RESULTS: Patients with late-onset pneumonia have demonstrated a significantly higher rate of tumor recurrence (P < 0.001) and death within 1 year (P < 0.001). Independent risk factors of late-onset pneumonia identified were age (P = 0.031), previous radiotherapy (P = 0.017), postoperative radiotherapy (P = 0.002), flap size (P = 0.001), flap type other than osteocutaneous fibula flap (P = 0.009), and tumor recurrence (P < 0.001). CONCLUSIONS: Late-onset pneumonia can act as a warning sign for oral cancer patients who have received microsurgical reconstruction, for its high correlation with tumor recurrence and mortality rate.

Individual Differences in the Response of Human ß-Lymphoblastoid Cells to the Cytotoxic, Mutagenic, and DNA-Damaging Effects of a DNA Methylating Agent, N-Methylnitrosourethane.

Metabolic activation of many carcinogens leads to formation of reactive intermediates that form DNA adducts. These adducts are cytotoxic when they interfere with cell division. They can also cause mutations by miscoding during DNA replication. Therefore, an individual's risk of developing cancer will depend on the balance between these processes as well as their ability to repair the DNA damage. Our hypothesis is that variations of genes participating in DNA damage repair and response pathways play significant roles in an individual's risk of developing tobacco-related cancers. To test this hypothesis, 61 human B-lymphocyte cell lines from the International HapMap project were phenotyped for their sensitivity to the cytotoxic and genotoxic properties of a model methylating agent, N-nitroso-N-methylurethane (NMUr). Cell viability was measured using a luciferase-based assay. Repair of the mutagenic and toxic DNA adduct, O6-methylguanine (O6-mG), was monitored by LC-MS/MS analysis. Genotoxic potential of NMUr was assessed employing a flow-cytometry based in vitro mutagenesis assay in the phosphatidylinositol-glycan biosynthesis class-A (PIG-A) gene. A wide distribution of responses to NMUr was observed with no correlation to gender or ethnicity. While the rate of O6-mG repair partially influenced the toxicity of NMUr, it did not appear to be the major factor affecting individual susceptibility to the mutagenic effects of NMUr. Genome-wide analysis identified several novel single nucleotide polymorphisms to be explored in future functional validation studies for a number of the toxicological end points.

Meta-analytic framework for modeling genetic coexpression dynamics.

Methods for exploring genetic interactions have been developed in an attempt to move beyond single gene analyses. Because biological molecules frequently participate in different processes under various cellular conditions, investigating the changes in gene coexpression patterns under various biological conditions could reveal important regulatory mechanisms. One of the methods for capturing gene coexpression dynamics, named liquid association (LA), quantifies the relationship where the coexpression between two genes is modulated by a third "coordinator" gene. This LA measure offers a natural framework for studying gene coexpression changes and has been applied increasingly to study regulatory networks among genes. With a wealth of publicly available gene expression data, there is a need to develop a meta-analytic framework for LA analysis. In this paper, we incorporated mixed effects when modeling correlation to account for between-studies heterogeneity. For statistical inference about LA, we developed a Markov chain Monte Carlo (MCMC) estimation procedure through a Bayesian hierarchical framework. We evaluated the proposed methods in a set of simulations and illustrated their use in two collections of experimental data sets. The first data set combined 10 pancreatic ductal adenocarcinoma gene expression studies to determine the role of possible coordinator gene USP9X in the Hippo pathway. The second experimental data set consisted of 907 gene expression microarray Escherichia coli experiments from multiple studies publicly available through the Many Microbe Microarray Database website (http://m3d.bu.edu/) and examined genes that coexpress with serA in the presence of coordinator gene Lrp.

Whole-Blood Transcriptional Signatures Composed of Erythropoietic and NRF2-Regulated Genes Differ Between Cerebral Malaria and Severe Malarial Anemia.

Background: Among the severe malaria syndromes, severe malarial anemia (SMA) is the most common, whereas cerebral malaria (CM) is the most lethal. However, the mechanisms that lead to CM and SMA are unclear. Methods: We compared transcriptomic profiles of whole blood obtained from Ugandan children with acute CM (n = 17) or SMA (n = 17) and community children without Plasmodium falciparum infection (n = 12) and determined the relationships among gene expression, hematological indices, and relevant plasma biomarkers. Results: Both CM and SMA demonstrated predominantly upregulated enrichment of dendritic cell activation, inflammatory/Toll-like receptor/chemokines, and monocyte modules, but downregulated enrichment of lymphocyte modules. Nuclear factor, erythroid 2 like 2 (Nrf2)-regulated genes were overexpressed in children with SMA relative to CM, with the highest expression in children with both SMA and sickle cell disease (HbSS), corresponding with elevated plasma heme oxygenase-1 in this group. Erythroid and reticulocyte-specific signatures were markedly decreased in CM relative to SMA despite higher hemoglobin levels and appropriate increases in erythropoietin. Viral sensing/interferon-regulatory factor 2 module expression and plasma interferon-inducible protein-10/CXCL10 negatively correlated with reticulocyte-specific signatures. Conclusions: Compared with SMA, CM is associated with downregulation of Nrf2-related and erythropoiesis signatures by whole-blood transcriptomics. Future studies are needed to confirm these findings and assess pathways that may be amenable to interventions to ameliorate CM and SMA.

A Bayesian hierarchical model for demand curve analysis.

Drug self-administration experiments are a frequently used approach to assess the abuse liability and reinforcing property of a compound. It has been used to assess the abuse liabilities of various substances such as psychomotor stimulants and hallucinogens, food, nicotine, and alcohol. The demand curve generated from a self-administration study describes how demand of a drug or non-drug reinforcer varies as a function of price. With the approval of the 2009 Family Smoking Prevention and Tobacco Control Act, demand curve analysis provides crucial evidence to inform the US Food and Drug Administration's policy on tobacco regulation because it produces several important quantitative measurements to assess the reinforcing strength of nicotine. The conventional approach popularly used to analyze the demand curve data is individual-specific non-linear least square regression. The non-linear least square approach sets out to minimize the residual sum of squares for each subject in the dataset; however, this one-subject-at-a-time approach does not allow for the estimation of between- and within-subject variability in a unified model framework. In this paper, we review the existing approaches to analyze the demand curve data, non-linear least square regression, and the mixed effects regression and propose a new Bayesian hierarchical model. We conduct simulation analyses to compare the performance of these three approaches and illustrate the proposed approaches in a case study of nicotine self-administration in rats. We present simulation results and discuss the benefits of using the proposed approaches.

Daily and Nondaily Smoking Varies by Acculturation among English-Speaking, US Latino Men and Women.

Background: Higher smoking prevalence and quantity (cigarettes per day) has been linked to acculturation in the United States among Latinas, but not Latino men. Our study examines variation between a different and increasingly important target behavior, smoking level (nondaily vs daily) and acculturation by sex. Methods: An online English-language survey was administered to 786 Latino smokers during July through August 2012. The Brief Acculturation Rating Scale for Mexican Americans-II (ARSMA-II) and other acculturation markers were used. Multinomial logistic regression models were implemented to assess the association between smoking levels (nondaily, light daily, and moderate/heavy daily) with acculturation markers. Results: Greater ARMSA-II scores (relative risk ratio, RRR=.81, 95% CI: .72-.91) and being born inside the United States (RRR=.42, 95% CI: .24-.74) were associated with lower relative risk of nondaily smoking. Greater Latino orientation (RRR=1.29, 95% CI: 1.11-1.48) and preference for Spanish language (RRR=1.06, 95% CI: 1.02-1.10) and media (RRR=1.12, 95% CI: 1.05-1.20) were associated with higher relative risk of nondaily smoking. The relationship between acculturation and smoking level did not differ by sex. Conclusion: This study found that among both male and female, English-speaking Latino smokers, nondaily smoking was associated with lower acculturation, while daily smoking was linked with higher acculturation.

A Bayesian hierarchical model for demand curve analysis.

Drug self-administration experiments are a frequently used approach to assessing the abuse liability and reinforcing property of a compound. It has been used to assess the abuse liabilities of various substances such as psychomotor stimulants and hallucinogens, food, nicotine, and alcohol. The demand curve generated from a self-administration study describes how demand of a drug or non-drug reinforcer varies as a function of price. With the approval of the 2009 Family Smoking Prevention and Tobacco Control Act, demand curve analysis provides crucial evidence to inform the US Food and Drug Administration's policy on tobacco regulation, because it produces several important quantitative measurements to assess the reinforcing strength of nicotine. The conventional approach popularly used to analyze the demand curve data is individual-specific non-linear least square regression. The non-linear least square approach sets out to minimize the residual sum of squares for each subject in the dataset; however, this one-subject-at-a-time approach does not allow for the estimation of between- and within-subject variability in a unified model framework. In this paper, we review the existing approaches to analyze the demand curve data, non-linear least square regression, and the mixed effects regression and propose a new Bayesian hierarchical model. We conduct simulation analyses to compare the performance of these three approaches and illustrate the proposed approaches in a case study of nicotine self-administration in rats. We present simulation results and discuss the benefits of using the proposed approaches.

Evaluation of the prebiotic effects of citrus pectin hydrolysate.

Citrus pectin enzyme hydrolysate (PEH) of different hydrolysis time intervals (6 hours, PEH-6; 12 hours, PEH-12; 24 hours, PEH-24; or 48 hours, PEH-48) or concentrations (1%, 2%, and 4%) was tested for its growth stimulation effect on two probiotics, Bifidobacterium bifidum and Lactobacillus acidophilus. Higher monosaccharide concentrations and smaller molecular weights of PEHs were obtained by prolonging the hydrolysis time. In addition, higher PEH concentrations resulted in significantly higher (p < 0.05) probiotic populations, pH reduction, and increase in total titratable acidity than the glucose-free MRS negative control. Furthermore, significantly higher populations in the low pH environment and longer survival time in nonfat milk (p < 0.05) were observed when the two probiotics were incubated in media supplemented with 2% PEH-24, than in glucose and the negative control. In comparison with other prebiotics, addition of 2% PEH-24 resulted in a more significant increase in the probiotic population (p < 0.05) than in the commercial prebiotics. This study demonstrated that PEH derived from citrus pectin could be an effective prebiotic to enhance the growth, fermentation, acid tolerance, and survival in nonfat milk for the tested probiotics.

Acquired Tamoxifen Resistance in MCF-7 Breast Cancer Cells Requires Hyperactivation of eIF4F-Mediated Translation.

While selective estrogen receptor modulators, such as tamoxifen, have contributed to increased survival in patients with hormone receptor-positive breast cancer, the development of resistance to these therapies has led to the need to investigate other targetable pathways involved in oncogenic signaling. Approval of the mTOR inhibitor everolimus in the therapy of secondary endocrine resistance demonstrates the validity of this approach. Importantly, mTOR activation regulates eukaryotic messenger RNA translation. Eukaryotic translation initiation factor 4E (eIF4E), a component of the cap-dependent translation complex eIF4F, confers resistance to drug-induced apoptosis when overexpressed in multiple cell types. The eIF4F complex is downstream of multiple oncogenic pathways, including mTOR, making it an appealing drug target. Here, we show that the eIF4F translation pathway was hyperactive in tamoxifen-resistant (TamR) MCF-7L breast cancer cells. While overexpression of eIF4E was not sufficient to confer resistance to tamoxifen in MCF-7L cells, its function was necessary to maintain resistance in TamR cells. Targeting the eIF4E subunit of the eIF4F complex through its degradation using an antisense oligonucleotide (ASO) or via sequestration using a mutant 4E-BP1 inhibited the proliferation and colony formation of TamR cells and partially restored sensitivity to tamoxifen. Further, the use of these agents also resulted in cell cycle arrest and induction of apoptosis in TamR cells. Finally, the use of a pharmacologic agent which inhibited the eIF4E-eIF4G interaction also decreased the proliferation and anchorage-dependent colony formation in TamR cells. These results highlight the eIF4F complex as a promising target for patients with acquired resistance to tamoxifen and, potentially, other endocrine therapies.