Correlated mutations via regularized multinomial regression

Background In addition to sequence conservation, protein multiple sequence alignments contain evolutionary signal in the form of correlated variation among amino acid positions. This signal indicates positions in the sequence that influence each other, and can be applied for the prediction of intra- or intermolecular contacts. Although various approaches exist for the detection of such correlated mutations, in general these methods utilize only pairwise correlations. Hence, they tend to conflate direct and indirect dependencies. Results We propose RMRCM, a method for Regularized Multinomial Regression in order to obtain Correlated Mutations from protein multiple sequence alignments. Importantly, our method is not restricted to pairwise (column-column) comparisons only, but takes into account the network nature of relationships between protein residues in order to predict residue-residue contacts. The use of regularization ensures that the number of predicted links between columns in the multiple sequence alignment remains limited, preventing overprediction. Using simulated datasets we analyzed the performance of our approach in predicting residue-residue contacts, and studied how it is influenced by various types of noise. For various biological datasets, validation with protein structure data indicates a good performance of the proposed algorithm for the prediction of residue-residue contacts, in comparison to previous results. RMRCM can also be applied to predict interactions (in addition to only predicting interaction sites or contact sites), as demonstrated by predicting PDZ-peptide interactions. Conclusions A novel method is presented, which uses regularized multinomial regression in order to obtain correlated mutations from protein multiple sequence alignments

The Predictability of Phytophagous Insect Communities: Host Specialists as Habitat Specialists

The difficulties specialized phytophagous insects face in finding habitats with an appropriate host should constrain their dispersal. Within the concept of metacommunities, this leads to the prediction that host-plant specialists should sort into local assemblages according to the local environmental conditions, i.e. habitat conditions, whereas assemblages of host-plant generalists should depend also on regional processes. Our study aimed at ranking the importance of local environmental factors and species composition of the vegetation for predicting the species composition of phytophagous moth assemblages with either a narrow or a broad host range. Our database consists of 351,506 specimens representing 820 species of nocturnal Macrolepidoptera sampled between 1980 and 2006 using light traps in 96 strict forest reserves in southern Germany. Species were grouped as specialists or generalists according to the food plants of the larvae; specialists use host plants belonging to one genus. We used predictive canonical correspondence and co-correspondence analyses to rank the importance of local environmental factors, the species composition of the vegetation and the role of host plants for predicting the species composition of host-plant specialists and generalists. The cross-validatory fit for predicting the species composition of phytophagous moths was higher for host-plant specialists than for host-plant generalists using environmental factors as well as the composition of the vegetation. As expected for host-plant specialists, the species composition of the vegetation was a better predictor of the composition of these assemblages than the environmental variables. But surprisingly, this difference for specialized insects was not due to the occurrence of their host plants. Overall, our study supports the idea that owing to evolutionary constraints in finding a host, host-plant specialists and host-plant generalists follow two different models of metacommunities: the species-sorting and the mass-effect model

Benthic invertebrate and microbial biodiversity in sub-tropical urban rivers:Correlations with environmental variables and emerging chemicals

Urban rivers often function as sinks for various contaminants potentially placing the benthic communities at risk of exposure. We performed a comprehensive biological survey of the benthic macroinvertebrate and bacterial community compositions in six rivers from the suburb to the central urban area of Guangzhou city (South China), and evaluated their correlations with emerging organic contaminants, heavy metals and nutrients. Overall, the benthic macroinvertebrate community shifted from molluscs to oligochaete from the suburban to the central urban rivers that receive treated and untreated sewage. An exception was the site in the Sha River where chironomids were most abundant. The differences in macroinvertebrate community assemblages were significantly associated with chromium, total phosphorus, galaxolide, triclosan and sand content in the sediment. There was no significant difference in benthic macroinvertebrate composition between the dry and wet season. As assessed by double constrained ordination, sexual reproduction was the only trait of benthic macroinvertebrates that showed a significant correlation with pollution variables, as it was significantly positively correlated with chromium and total phosphorus. This suggests that r-strategist occurs in polluted sampling sites. The benthic bacterial community composition showed a significant difference between seasons and among the Liuxi River, Zhujiang River and central urban rivers. The differences in community composition of the benthic bacteria were significantly correlated with galaxolide, total phosphorus, lead and triclosan. These results suggest that input of treated and untreated sewage significantly altered the benthic macroinvertebrate and bacterial community compositions in urban rivers.</p

Field Evaluation of Traditionally Used Plant-Based Insect Repellents and Fumigants Against the Malaria Vector Anopheles darlingi in Riberalta, Bolivian Amazon

Inexpensive insect repellents may be needed to supplement the use of impregnated bed-nets in the Amazon region, where the primary malaria vector, Anopheles darlingi (Root), is exophilic and feeds in the early evening. Three plants that are traditionally used to repel mosquitoes in Riberalta, Bolivian Amazon, were identified by focus group, and then they were tested against An. darlingi as well as Mansonia indubitans (Dyar & Shannon)/Mansonia titillans (Walker). Cymbopogon citratus (Staph), Guatemalan lemongrass, essential oil at 25% was used as a skin repellent, and it provided 74% protection for 2.5 h against predominantly An. darlingi and 95% protection for 2.5 h against Mansonia spp. Attalea princeps (name not verified) husks, burned on charcoal in the traditional way provided 35 and 51% protection against An. darlingi and Mansonia spp., respectively. Kerosene lamps, often used to light rural homes, were used as a heat source to volatilize 100% Mentha arvensis (Malinv ex. Bailey) essential oil, and they reduced biting by 41% inside traditional homes against Mansonia spp., although they were ineffective outdoors against An. darlingi. All three plant-based repellents provided significant protection compared with controls. Plant-based repellents, although less effective than synthetic alternatives, were shown by focus groups to be more culturally acceptable in this setting, in particular para-menthane-3, 8, idol derived from lemon eucalyptus, Corymbia citriodora (Hook). Plant-based repellents have the potential to be produced locally and therefore sold more cheaply than synthetic commercial repellents. Importantly, their low cost may encourage user compliance among indigenous and marginalized populations

Impact of Gut Bacteria on the Infection and Transmission of Pathogenic Arboviruses by Biting Midges and Mosquitoes

Tripartite interactions among insect vectors, midgut bacteria, and viruses may determine the ability of insects to transmit pathogenic arboviruses. Here, we investigated the impact of gut bacteria on the susceptibility of Culicoides nubeculosus and Culicoides sonorensis biting midges for Schmallenberg virus, and of Aedes aegypti mosquitoes for Zika and chikungunya viruses. Gut bacteria were manipulated by treating the adult insects with antibiotics. The gut bacterial communities were investigated using Illumina MiSeq sequencing of 16S rRNA, and susceptibility to arbovirus infection was tested by feeding insects with an infectious blood meal. Antibiotic treatment led to changes in gut bacteria for all insects. Interestingly, the gut bacterial composition of untreated Ae. aegypti and C. nubeculosus showed Asaia as the dominant genus, which was drastically reduced after antibiotic treatment. Furthermore, antibiotic treatment resulted in relatively more Delftia bacteria in both biting midge species, but not in mosquitoes. Antibiotic treatment and subsequent changes in gut bacterial communities were associated with a significant, 1.8-fold increased infection rate of C. nubeculosus with Schmallenberg virus, but not for C. sonorensis. We did not find any changes in infection rates for Ae. aegypti mosquitoes with Zika or chikungunya virus. We conclude that resident gut bacteria may dampen arbovirus transmission in biting midges, but not so in mosquitoes. Use of antimicrobial compounds at livestock farms might therefore have an unexpected contradictory effect on the health of animals, by increasing the transmission of viral pathogens by biting midges.</p

QTL linkage analysis of connected populations using ancestral marker and pedigree information

The common assumption in quantitative trait locus (QTL) linkage mapping studies that parents of multiple connected populations are unrelated is unrealistic for many plant breeding programs. We remove this assumption and propose a Bayesian approach that clusters the alleles of the parents of the current mapping populations from locus-specific identity by descent (IBD) matrices that capture ancestral marker and pedigree information. Moreover, we demonstrate how the parental IBD data can be incorporated into a QTL linkage analysis framework by using two approaches: a Threshold IBD model (TIBD) and a Latent Ancestral Allele Model (LAAM). The TIBD and LAAM models are empirically tested via numerical simulation based on the structure of a commercial maize breeding program. The simulations included a pilot dataset with closely linked QTL on a single linkage group and 100 replicated datasets with five linkage groups harboring four unlinked QTL. The simulation results show that including parental IBD data (similarly for TIBD and LAAM) significantly improves the power and particularly accuracy of QTL mapping, e.g., position, effect size and individuals’ genotype probability without significantly increasing computational demand

Bayesian Markov Random Field Analysis for Protein Function Prediction Based on Network Data

Inference of protein functions is one of the most important aims of modern biology. To fully exploit the large volumes of genomic data typically produced in modern-day genomic experiments, automated computational methods for protein function prediction are urgently needed. Established methods use sequence or structure similarity to infer functions but those types of data do not suffice to determine the biological context in which proteins act. Current high-throughput biological experiments produce large amounts of data on the interactions between proteins. Such data can be used to infer interaction networks and to predict the biological process that the protein is involved in. Here, we develop a probabilistic approach for protein function prediction using network data, such as protein-protein interaction measurements. We take a Bayesian approach to an existing Markov Random Field method by performing simultaneous estimation of the model parameters and prediction of protein functions. We use an adaptive Markov Chain Monte Carlo algorithm that leads to more accurate parameter estimates and consequently to improved prediction performance compared to the standard Markov Random Fields method. We tested our method using a high quality S.cereviciae validation network with 1622 proteins against 90 Gene Ontology terms of different levels of abstraction. Compared to three other protein function prediction methods, our approach shows very good prediction performance. Our method can be directly applied to protein-protein interaction or coexpression networks, but also can be extended to use multiple data sources. We apply our method to physical protein interaction data from S. cerevisiae and provide novel predictions, using 340 Gene Ontology terms, for 1170 unannotated proteins and we evaluate the predictions using the available literature