A secure semi-field system for the study of Aedes aegypti

Background

New contained semi-field cages are being developed and used to test novel vector control strategies of dengue and malaria vectors. We herein describe a new Quarantine Insectary Level-2 (QIC-2) laboratory and field cages (James Cook University Mosquito Research Facility Semi-Field System; MRF SFS) that are being used to measure the impact of the endosymbiont Wolbachia pipientis on populations of Aedes aegypti in Cairns Australia.

Methodology/Principal Findings

The MRF consists of a single QIC-2 laboratory/insectary that connects through a central corridor to two identical QIC-2 semi-field cages. The semi-field cages are constructed of two layers of 0.25 mm stainless steel wire mesh to prevent escape of mosquitoes and ingress of other insects. The cages are covered by an aluminum security mesh to prevent penetration of the cages by branches and other missiles in the advent of a tropical cyclone. Parts of the cage are protected from UV light and rainfall by 90% shade cloth and a vinyl cover. A wooden structure simulating the understory of a Queenslander-style house is also situated at one end of each cage. The remainder of the internal aspect of the cage is covered with mulch and potted plants to emulate a typical yard. An air conditioning system comprised of two external ACs that feed cooled, moistened air into the cage units. The air is released from the central ceiling beam from a long cloth tube that disperses the airflow and also prevents mosquitoes from escaping the cage via the AC system. Sensors located inside and outside the cage monitor ambient temperature and relative humidity, with AC controlled to match ambient conditions. Data loggers set in the cages and outside found a <2°C temperature difference. Additional security features include air curtains over exit doors, sticky traps to monitor for escaping mosquitoes between layers of the mesh, a lockable vestibule leading from the connecting corridor to the cage and from inside to outside of the insectary, and screened (0.25 mm mesh) drains within the insectary and the cage. A set of standard operating procedures (SOP) has been developed to ensure that security is maintained and for enhanced surveillance for escaping mosquitoes on the JCU campus where the MRF is located. A cohort of male and female Aedes aegypti mosquitoes were released in the cage and sampled every 3–4 days to determine daily survival within the cage; log linear regression from BG-sentinel trapping collections produced an estimated daily survival of 0.93 and 0.78 for females and males, respectively.

Conclusions/Significance

The MRF SFS allows us to test novel control strategies within a secure, contained environment. The air-conditioning system maintains conditions within the MRF cages comparable to outside ambient conditions. This cage provides a realistic transitional platform between the laboratory and the field in which to test novel control measures on quarantine level insects.     

Identification of DNA repair gene Ercc1 as a novel target in melanoma

Increased expression of DNA repair genes contributes to the extreme resistance shown by melanoma to conventional DNA-damaging chemotherapeutics. One such chemotherapeutic effective against a range of other cancers, but not melanoma, is cisplatin. The DNA repair protein, ERCC1, is needed to remove cisplatin-induced DNA damage. We have shown that ERCC1 is essential for melanoma growth and resistance to cisplatin in a mouse xenograft model. Untreated xenografts of our transformed Ercc1-proficient melanocyte cell line grew very rapidly as malignant melanoma. Cisplatin treatment caused initial shrinkage of xenografts, but cisplatin-resistant regrowth soon followed. Cells reisolated into culture had twofold elevated levels of ERCC1 compared to both input cells and cells reisolated from untreated xenografts. An isogenic Ercc1-deficient derivative grew equally well in vitro as the Ercc1-proficient melanocyte cell line. However, in xenografts, the Ercc1-deficient melanomas were much slower to establish and were completely cured by just two cisplatin treatments.     

Signaling-mediated cross-talk modulates swarming and biofilm formation in a coral pathogen Serratia marcescens

Interactions within microbial communities associated with marine holobionts contribute importantly to the health of these symbiotic organisms formed by invertebrates, dinoflagellates and bacteria. However, mechanisms that control invertebrate-associated microbiota are not yet fully understood. Hydrophobic compounds that were isolated from surfaces of asymptomatic corals inhibited biofilm formation by the white pox pathogen Serratia marcescens PDL100, indicating that signals capable of affecting the associated microbiota are produced in situ. However, neither the origin nor structures of these signals are currently known. A functional survey of bacteria recovered from coral mucus and from cultures of the dinoflagellate Symbiodinium spp. revealed that they could alter swarming and biofilm formation in S. marcescens. As swarming and biofilm formation are inversely regulated, the ability of some native α-proteobacteria to affect both behaviors suggests that the α-proteobacterial signal(s) target a global regulatory switch controlling the behaviors in the pathogen. Isolates of Marinobacter sp. inhibited both biofilm formation and swarming in S. marcescens PDL100, without affecting growth of the coral pathogen, indicative of the production of multiple inhibitors, likely targeting lower level regulatory genes or functions. A multi-species cocktail containing these strains inhibited progression of a disease caused by S. marcescens in a model polyp Aiptasia pallida. An α-proteobacterial isolate 44B9 had a similar effect. Even though ∼4% of native holobiont-associated bacteria produced compounds capable of triggering responses in well-characterized N-acyl homoserine lactone (AHL) biosensors, there was no strong correlation between the production of AHL-like signals and disruption of biofilms or swarming in S. marcescens.     

Development of Temporal Temperature Gradient Electrophoresis for Characterising Methanogen Diversity

Temporal temperature gradient electrophoretic (TTGE) analysis of 16S rDNA sequences was optimized to monitor the methanogen population present in water and sediments of a small eutrophic lake, Priest Pot, in the English Lake district. The production of nonrepresentative TTGE profiles due to the generation of polymerase chain reaction (PCR) artifacts initially proved problematical. The use of a proofreading polymerase in the PCR was found to be essential and fully optimized protocols were established and tested to ensure confidence that the TTGE profiles truly reflected sequence diversity. TTGE analysis revealed the methanogen population to be less diverse in water than in sediment. The most genetic diversity was observed in TTGE profiles of sediment DNA isolated in winter and the least was in sediment DNA isolated in summer. DNA sequencing analysis of bands recovered from TTGE gels revealed the presence of two methanogen communities. One clustered with Methanosaeta species and the other with the Methanomicrobiales. Many sequences showed low DNA sequence similarity to known methanogens, suggesting that Priest Pot harbors previously undescribed methanogen species.     

Comparative analysis of the CD19+ and CD138+ cell antibody repertoires in the cerebrospinal fluid of patients with multiple sclerosis

Increased amounts of intrathecally synthesized IgG and oligoclonal bands have long been recognized as a hallmark of multiple sclerosis (MS). B cells and plasma cells are components of the inflammatory infiltrates in both active and chronic MS lesions, and increased numbers of these cells are present in MS cerebrospinal fluid (CSF). Single-cell RT-PCR was used to analyze both the CD19+ B cell and CD138+ plasma cell populations in CSF of two patients with clinically definite MS and of one MS patient whose CSF was obtained after a clinically isolated syndrome, but before the second episode. Sequence analysis of amplified IgG V region sequences identified the rearranged germline segments, extent of somatic mutation, and clonal relationships within and between the two cell populations in the three MS patients. Expanded B cell and plasma cell clones were detected in each MS CSF and in all three patients the CD138+ IgG repertoire was more restricted. However, little if any significant sequence overlap was observed between the CD19+ and CD138+ repertoires of each donor. Detection of plasma cell clones by single-cell PCR will facilitate the in vitro production of recombinant Abs useful in identifying disease-relevant Ags.     

PPIDomainMiner: Inferring domain-domain interactions from multiple sources of protein-protein interactions

Many biological processes are mediated by protein-protein interactions (PPIs). Because protein domains are the building blocks of proteins, PPIs likely rely on domain-domain interactions (DDIs). Several attempts exist to infer DDIs from PPI networks but the produced datasets are heterogeneous and sometimes not accessible, while the PPI interactome data keeps growing.We describe a new computational approach called “PPIDM” (Protein-Protein Interactions Domain Miner) for inferring DDIs using multiple sources of PPIs. The approach is an extension of our previously described “CODAC” (Computational Discovery of Direct Associations using Common neighbors) method for inferring new edges in a tripartite graph. The PPIDM method has been applied to seven widely used PPI resources, using as “Gold-Standard” a set of DDIs extracted from 3D structural databases. Overall, PPIDM has produced a dataset of 84,552 non-redundant DDIs. Statistical significance (p-value) is calculated for each source of PPI and used to classify the PPIDM DDIs in Gold (9,175 DDIs), Silver (24,934 DDIs) and Bronze (50,443 DDIs) categories. Dataset comparison reveals that PPIDM has inferred from the 2017 releases of PPI sources about 46% of the DDIs present in the 2020 release of the 3did database, not counting the DDIs present in the Gold-Standard. The PPIDM dataset contains 10,229 DDIs that are consistent with more than 13,300 PPIs extracted from the IMEx database, and nearly 23,300 DDIs (27.5%) that are consistent with more than 214,000 human PPIs extracted from the STRING database. Examples of newly inferred DDIs covering more than 10 PPIs in the IMEx database are provided.Further exploitation of the PPIDM DDI reservoir includes the inventory of possible partners of a protein of interest and characterization of protein interactions at the domain level in combination with other methods. The result is publicly available at http://ppidm.loria.fr/.     

S-glutathionylation activates STIM1 and alters mitochondrial homeostasis

Oxidant stress influences many cellular processes, including cell growth, differentiation, and cell death. A well-recognized link between these processes and oxidant stress is via alterations in Ca²⁺ signaling. However, precisely how oxidants influence Ca²⁺ signaling remains unclear. Oxidant stress led to a phenotypic shift in Ca²⁺ mobilization from an oscillatory to a sustained elevated pattern via calcium release–activated calcium (CRAC)–mediated capacitive Ca²⁺ entry, and stromal interaction molecule 1 (STIM1)– and Orai1-deficient cells are resistant to oxidant stress. Functionally, oxidant-induced Ca²⁺ entry alters mitochondrial Ca²⁺ handling and bioenergetics and triggers cell death. STIM1 is S-glutathionylated at cysteine 56 in response to oxidant stress and evokes constitutive Ca²⁺ entry independent of intracellular Ca²⁺ stores. These experiments reveal that cysteine 56 is a sensor for oxidant-dependent activation of STIM1 and demonstrate a molecular link between oxidant stress and Ca²⁺ signaling via the CRAC channel.     

Machine Learning for Detecting Gene-Gene Interactions

Complex interactions among genes and environmental factors are known to play a role in common human disease aetiology. There is a growing body of evidence to suggest that complex interactions are 'the norm' and, rather than amounting to a small perturbation to classical Mendelian genetics, interactions may be the predominant effect. Traditional statistical methods are not well suited for detecting such interactions, especially when the data are high dimensional (many attributes or independent variables) or when interactions occur between more than two polymorphisms. In this review, we discuss machine-learning models and algorithms for identifying and characterising susceptibility genes in common, complex, multifactorial human diseases. We focus on the following machine-learning methods that have been used to detect gene-gene interactions: neural networks, cellular automata, random forests, and multifactor dimensionality reduction. We conclude with some ideas about how these methods and others can be integrated into a comprehensive and flexible framework for data mining and knowledge discovery in human genetics.