Dual Targeted Therapy with p53 siRNA and Epigallocatechingallate in a Triple Negative Breast Cancer Cell Model

Triple-negative breast cancer (TNBC) is a highly aggressive phenotype that is resistant to standard therapy. Thus, the development of alternative therapeutic strategies for TNBC is essential. The purpose of our in vitro study was to evaluate the impact of p53 gene silencing in conjunction with the administration of a natural compound, epigallocatechingallate (EGCG). RT²Profiler PCR Array technology was used to evaluate the impact of dual treatment on the main genes involved in apoptosis in the Hs578T cell culture model of TNBC. Gene expression analysis revealed 28 genes were significantly altered (16 upregulated and 12 downregulated) in response to combined p53 siRNA and EGCG treatment. Further analysis revealed that p53 siRNA and EGCG dual therapy leads to the activation of pro-apoptotic genes and the inhibition of pro-survival genes, autophagy, and cell network formation. These results indicate that this dual therapy targets both the apoptotic and angiogenic pathways, which may improve treatment effectiveness for tumors resistant to conventional treatment.     

Dynamic changes in the structure of microbial communities in Baltic Sea coastal seawater microcosms modified by crude oil,shale oil or diesel fuel

The coastal waters of the Baltic Sea are constantly threatened by oil spills, due to the extensive transportation of oil products across the sea. To characterise the hydrocarbon-degrading bacterial community of this marine area, microcosm experiments on diesel fuel, crude oil and shale oil were performed. Analysis of these microcosms, using alkane monooxygenase (alkB) and 16S rRNA marker genes in PCR-DGGE experiments, demonstrated that substrate type and concentration strongly influence species composition and the occurrence of alkB genes in respective oil degrading bacterial communities. Gammaproteobacteria (particularly the genus Pseudomonas) and Alphaproteobacteria were dominant in all microcosms treated with oils. All alkB genes carried by bacterial isolates (40 strains), and 8 of the 11 major DGGE bands from the microcosms, had more than 95% sequence identity with the alkB genes of Pseudomonas fluorescens. However, the closest relatives of the majority of sequences (54 sequences from 79) of the alkB gene library from initially collected seawater DNA were Actinobacteria. alkB gene expression, induced by hexadecane, was recorded in isolated bacterial strains. Thus, complementary culture dependent and independent methods provided a more accurate picture about the complex seawater microbial communities of the Baltic Sea.     

Lessons learned through listening to biology students during a transition to online learning in the wake of the COVID‐19 pandemic

During the Spring Semester of 2020, an outbreak of a novel coronavirus (SARS‐CoV‐2) and the illnesses it caused (COVID‐19) led to widespread cancelling of on‐campus instruction at colleges and universities in the United States and other countries around the world. Response to the pandemic in university settings included a rapid and unexpected shift to online learning for faculty and students. The transition to teaching and learning online posed many challenges, and the experiences of students during this crisis may inform future planning for distance learning experiences during the ongoing pandemic and beyond. Herein, we discuss the experiences of first‐ and second‐year university students enrolled in a biology seminar course as their classes migrated to online environments. Drawing on reported student experiences and prior research and resources, we discuss the ways we will adjust our own teaching for future iterations of the course while offering recommendations for instructors tasked with teaching in online environments.     

The completely sequenced plasmid pEST4011 contains a novel IncP1 backbone and a catabolic transposon harboring tfd genes for 2,4-dichlorophenoxyacetic acid degradation

The herbicide 2,4-dichlorophenoxyacetic acid (2,4-D)-degrading bacterium Achromobacter xylosoxidans subsp. denitrificans strain EST4002 contains plasmid pEST4011. This plasmid ensures its host a stable 2,4-D(+) phenotype. We determined the complete 76,958-bp nucleotide sequence of pEST4011. This plasmid is a deletion and duplication derivative of pD2M4, the 95-kb highly unstable laboratory ancestor of pEST4011, and was self-generated during different laboratory manipulations performed to increase the stability of the 2,4-D(+) phenotype of the original strain, strain D2M4(pD2M4). The 47,935-bp catabolic region of pEST4011 forms a transposon-like structure with identical copies of the hybrid insertion element IS1071::IS1471 at the two ends. The catabolic regions of pEST4011 and pJP4, the best-studied 2,4-D-degradative plasmid, both contain homologous, tfd-like genes for complete 2,4-D degradation, but they have little sequence similarity other than that. The backbone genes of pEST4011 are most similar to the corresponding genes of broad-host-range self-transmissible IncP1 plasmids. The backbones of the other three IncP1 catabolic plasmids that have been sequenced (the 2,4-D-degradative plasmid pJP4, the haloacetate-catabolic plasmid pUO1, and the atrazine-catabolic plasmid pADP-1) are nearly identical to the backbone of R751, the archetype plasmid of the IncP1 beta subgroup. We show that despite the overall similarity in plasmid organization, the pEST4011 backbone is sufficiently different (51 to 86% amino acid sequence identity between individual backbone genes) from the backbones of members of the three IncP1 subgroups (the alpha, beta, and gamma subgroups) that it belongs to a new IncP1subgroup, the delta subgroup. This conclusion was also supported by a phylogenetic analysis of the trfA2, korA, and traG gene products of different IncP1 plasmids.     

Antibody response against saliva antigens of Anopheles gambiae and Aedes aegypti in travellers in tropical Africa

Exposure to vectors of infectious diseases has been associated with antibody responses against salivary antigens of arthropods among people living in endemic areas. This immune response has been proposed as a surrogate marker of exposure to vectors appropriate for evaluating the protective efficacy of antivectorial devices. The existence and potential use of such antibody responses in travellers transiently exposed to Plasmodium or arbovirus vectors in tropical areas has never been investigated. The IgM and IgG antibody responses of 88 French soldiers against the saliva of Anopheles gambiae and Aedes aegypti were evaluated before and after a 5-month journey in tropical Africa. Antibody responses against Anopheles and Aedes saliva increased significantly in 41% and 15% of the individuals, respectively, and appeared to be specific to the mosquito genus. A proteomic and immunoproteomic analysis of anopheles and Aedes saliva allowed for the identification of some antigens that were recognized by most of the exposed individuals. These results suggest that antibody responses to the saliva of mosquitoes could be considered as specific surrogate markers of exposure of travellers to mosquito vectors that transmit arthropod borne infections.     

A longitudinal study to assess the persistence of vancomycin-resistant Enterococcus faecium (VREF) on an intensive broiler farm in the United Kingdom

Seven years after the ban of avoparcin, VREF could still be isolated within sectors of the UK broiler industry. The aim of this study was to assess whether there is a carryover of VREF between consecutive flocks of birds, to conduct a preliminary investigation of possible routes of entry of VREF into broiler houses and to follow the dynamics of VREF shed by growing birds. A series of nine visits were made to two of six houses on a conventional broiler farm. A total of 343 vanA VREF were recovered from environmental (95/843) and faecal (248/416) samples. Significant differences were observed in the carryover of VREF between pre- and postcohort postcleaning and disinfection visits (RR 0.57, P=0.006). Ninety-nine percent of the VREF isolates were resistant to more than five antimicrobials, with 42 isolates (n=49) positive for erm(B) and 32 (n=40) for vat(E). Pulsed field gel electrophoresis (PFGE) typing identified 50 PFGE types within 15 different PFGE clusters of 90% similarity, demonstrating a high level of genetic diversity within VREF populations from epidemiologically related broiler flocks and broiler houses. Further characterization of Tn1546 from different clones showed a low diversity of Tn-types, suggesting horizontal transfer of resistance determinants between different genetic clones. Thus, this study does not only show the persistence of VREF but also of multi-drug resistant lineages of VREF.     

Patterns of Metabolite Changes Identified from Large-Scale Gene Perturbations in Arabidopsis Using a Genome-Scale Metabolic Network

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes.Rational and quantitative assessment of metabolic changes in response to genetic modification (GM) is an open question and in need of innovative solutions. Nontargeted metabolite profiling can detect thousands of compounds, but it is not easy to understand the significance of the changed metabolites in the biochemical and biological context of the organism. To better assess the changes in metabolites from nontargeted metabolomics studies, it is important to examine the changed metabolites in the context of the genome-scale metabolic network of the organism.Metabolomics is a technique that aims to quantify all the metabolites in a biological system (Nikolau and Wurtele, 2007; Nicholson and Lindon, 2008; Roessner and Bowne, 2009). It has been used widely in studies ranging from disease diagnosis (Holmes et al., 2008; DeBerardinis and Thompson, 2012) and drug discovery (Cascante et al., 2002; Kell, 2006) to metabolic reconstruction (Feist et al., 2009; Kim et al., 2012) and metabolic engineering (Keasling, 2010; Lee et al., 2011). Metabolomic studies have demonstrated the possibility of identifying gene functions from changes in the relative concentrations of metabolites (metabotypes or metabolic signatures; Ebbels et al., 2004) in various species including yeast (Saccharomyces cerevisiae; Raamsdonk et al., 2001; Allen et al., 2003), Arabidopsis (Arabidopsis thaliana; Brotman et al., 2011), tomato (Solanum lycopersicum; Schauer et al., 2006), and maize (Zea mays; Riedelsheimer et al., 2012). Metabolomics has also been used to better understand how plants interact with their environments (Field and Lake, 2011), including their responses to biotic and abiotic stresses (Dixon et al., 2006; Arbona et al., 2013), and to predict important agronomic traits (Riedelsheimer et al., 2012). Metabolite profiling has been performed on many plant species, including angiosperms such as Arabidopsis, poplar (Populus trichocarpa), and Catharanthus roseus (Sumner et al., 2003; Rischer et al., 2006), basal land plants such as Selaginella moellendorffii and Physcomitrella patens (Erxleben et al., 2012; Yobi et al., 2012), and Chlamydomonas reinhardtii (Fernie et al., 2012; Davis et al., 2013). With the availability of whole genome sequences of various species, metabolomics has the potential to become a useful tool for elucidating the functions of genes using large-scale systematic analyses (Fiehn et al., 2000; Saito and Matsuda, 2010; Hur et al., 2013).Although metabolomics data have the potential for identifying the roles of genes that are associated with metabolic phenotypes, the biochemical mechanisms that link functions of genes with metabolic phenotypes are still poorly characterized. For example, we do not yet know the principles behind how perturbing the expression of a single gene changes the metabolic system as a whole. Large-scale metabolomics data have provided useful resources for linking phenotypes to genotypes (Fiehn et al., 2000; Roessner et al., 2001; Tikunov et al., 2005; Schauer et al., 2006; Lu et al., 2011; Fukushima et al., 2014). For example, Lu et al. (2011) compared morphological and metabolic phenotypes from more than 5,000 Arabidopsis chloroplast mutants using gas chromatography (GC)- and liquid chromatography (LC)-mass spectrometry (MS). Fukushima et al. (2014) generated metabolite profiles from various characterized and uncharacterized mutant plants and clustered the mutants with similar metabolic phenotypes by conducting multidimensional scaling with quantified metabolic phenotypes. Nonetheless, representation and analysis of such a large amount of data remains a challenge for scientific discovery (Lu et al., 2011). In addition, these studies do not examine the topological and functional characteristics of metabolic changes in the context of a genome-scale metabolic network. To understand the relationship between genotype and metabolic phenotype, we need to investigate the metabolic changes caused by perturbing the expression of a gene in a genome-scale metabolic network perspective, because metabolic pathways are not independent biochemical factories but are components of a complex network (Berg et al., 2002; Merico et al., 2009).Much progress has been made in the last 2 decades to represent metabolism at a genome scale (Terzer et al., 2009). The advances in genome sequencing and emerging fields such as biocuration and bioinformatics enabled the representation of genome-scale metabolic network reconstructions for model organisms (Bassel et al., 2012). Genome-scale metabolic models have been built and applied broadly from microbes to plants. The first step toward modeling a genome-scale metabolism in a plant species started with developing a genome-scale metabolic pathway database for Arabidopsis (AraCyc; Mueller et al., 2003) from reference pathway databases (Kanehisa and Goto, 2000; Karp et al., 2002; Zhang et al., 2010). Genome-scale metabolic pathway databases have been built for several plant species (Mueller et al., 2005; Zhang et al., 2005, 2010; Urbanczyk-Wochniak and Sumner, 2007; May et al., 2009; Dharmawardhana et al., 2013; Monaco et al., 2013, 2014; Van Moerkercke et al., 2013; Chae et al., 2014; Jung et al., 2014). Efforts have been made to develop predictive genome-scale metabolic models using enzyme kinetics and stoichiometric flux-balance approaches (Sweetlove et al., 2008). de Oliveira Dal’Molin et al. (2010) developed a genome-scale metabolic model for Arabidopsis and successfully validated the model by predicting the classical photorespiratory cycle as well as known key differences between redox metabolism in photosynthetic and nonphotosynthetic plant cells. Other genome-scale models have been developed for Arabidopsis (Poolman et al., 2009; Radrich et al., 2010; Mintz-Oron et al., 2012), C. reinhardtii (Chang et al., 2011; Dal’Molin et al., 2011), maize (Dal’Molin et al., 2010; Saha et al., 2011), sorghum (Sorghum bicolor; Dal’Molin et al., 2010), and sugarcane (Saccharum officinarum; Dal’Molin et al., 2010). These predictive models have the potential to be applied broadly in fields such as metabolic engineering, drug target discovery, identification of gene function, study of evolutionary processes, risk assessment of genetically modified crops, and interpretations of mutant phenotypes (Feist and Palsson, 2008; Ricroch et al., 2011).Here, we interrogate the metabotypes caused by 136 single gene perturbations of Arabidopsis by analyzing the relative concentration changes of 1,348 chemically identified metabolites using a reconstructed genome-scale metabolic network. We examine the characteristics of the changed metabolites (the metabolites whose relative concentrations were significantly different in mutants relative to the wild type) in the metabolic network to uncover biological and topological consequences of the perturbed genes.     

Effect of older age on treatment decisions and outcomes among patients with traumatic spinal cord injury

Background:

Older people are at increased risk of traumatic spinal cord injury from falls. We evaluated the impact of older age (≥ 70 yr) on treatment decisions and outcomes.

Methods:

We identified patients with traumatic spinal cord injury for whom consent and detailed data were available from among patients recruited (2004–2013) at any of the 31 acute care and rehabilitation hospitals participating in the Rick Hansen Spinal Cord Injury Registry. Patients were assessed by age group (< 70 v. ≥ 70 yr). The primary outcome was the rate of acute surgical treatment. We used bivariate and multivariate regression models to assess patient and injury-related factors associated with receiving surgical treatment and with the timing of surgery after arrival to a participating centre.

Results:

Of the 1440 patients included in our study cohort, 167 (11.6%) were 70 years or older at the time of injury. Older patients were more likely than younger patients to be injured by falling (83.1% v. 37.4%; p < 0.001), to have a cervical injury (78.0% v. 61.6%; p = 0.001), to have less severe injuries on admission (American Spinal Injury Association Impairment Scale grade C or D: 70.5% v. 46.9%; p < 0.001), to have a longer stay in an acute care hospital (median 35 v. 28 d; p < 0.005) and to have a higher in-hospital mortality (4.2% v. 0.6%; p < 0.001). Multivariate analysis did not show that age of 70 years or more at injury was associated with a decreased likelihood of surgical treatment (adjusted odds ratio [OR] 0.48, 95% confidence interval [CI] 0.22–1.07). An unplanned sensitivity analysis with different age thresholds showed that a threshold of 65 years was associated with a decreased chance of surgical treatment (OR 0.39, 95% CI 0.19–0.80). Older patients who underwent surgical treatment had a significantly longer wait time from admission to surgery than younger patients (37 v. 19 h; p < 0.001).

Interpretation:

We found chronological age to be a factor influencing treatment decisions but not at the 70-year age threshold that we had hypothesized. Older patients waited longer for surgery and had a substantially higher in-hospital mortality despite having less severe injuries than younger patients. Further research into the link between treatment delays and outcomes among older patients could inform surgical guideline development.Globally there has been an epidemiologic shift in the age of patients who sustain a traumatic spinal cord injury.^1–3 Although most people who have traumatic spinal cord injuries are 16–30 years old, there has been a progressive increase in the number who are over 70. The average age at injury has increased from 29 to 40 years.⁴ By 2032, patients over 70 are predicted to account for most patients with new traumatic spinal cord injuries.⁵ This change is attributed in part to aging baby boomers. It is unknown whether the management and outcomes of these older patients differ compared with younger patients.Older patients typically have more comorbid conditions, including cardiovascular disease, respiratory disorders, cerebrovascular disease and dementia, which are thought to increase their risk of perioperative adverse events.⁶ The use of anticoagulants for cardiac and cerebrovascular indications can delay timely surgical interventions. Older patients are also at increased risk of postoperative and medication-related adverse events, such as delirium.⁷ As a direct consequence of this perceived risk of perioperative adverse events and ambiguity about the optimal treatment for spinal cord injury in older patients, surgeons may deliberate for some time before making a clear therapeutic decision, they may choose nonoperative treatment,⁸ or they may delay the surgical treatment in an effort to optimize the patient’s condition medically.Given the increasing incidence of traumatic spinal cord injury in older adults, and the potential for differences in treatment among older and younger patients, we evaluated the impact of age on treatment decisions and outcomes among patients with traumatic spinal cord injury. We hypothesized that surgical management would differ at an age threshold of 70 years.