Pulmonary administered palmitic-acid modified exendin-4 peptide prolongs hypoglycemia in type 2 diabetic db/db mice

Hypoglycemia caused by palmitic-acid modified exendin-4 (Pal-Ex4) administered via the pulmonary route was evaluated and compared with that caused by native Ex4. Pal-Ex4 and Ex4 in solution (each 50 μl) were administered using a microsprayer directly into the trachea of type 2 diabetic db/db mice at 75 or 150 nmol/kg. The lung depositions of Cy5.5-labeled Ex4 or Pal-Ex4 were monitored using an infrared imaging system after administration. The hypoglycemia caused by Pal-Ex4 was found to be 3.4 and 2.3 times greater than that caused by native Ex4 at 75 and 150 nmol/kg, respectively. Furthermore, time to blood glucose level (BGL) rebound to >150 mg/dl for Pal-Ex4 was 3.5 times greater than that of Ex4 (18.1 h vs. 5.2 h at 150 nmol/kg). In particular, the time taken for Pal-Ex4 to reach a BGL nadir was significantly greater than that of Ex4 (~8 h versus 4 h). Furthermore, lung deposition images clearly showed that Pal-Ex4 was slowly absorbed from lungs and barely distributed into kidneys until 8 h post-administration. It is likely that the prolonged hypoglycemia exhibited by Pal-Ex4 was due to; (i) delayed absorption in the lungs and (ii) albumin-binding in the circulation. The study demonstrates that palmitic acid-modified exendin-4 should be viewed as a long-acting inhalation candidate for the treatment of type 2 diabetes.     

Comparative Genome-Scale Reconstruction of Gapless Metabolic Networks for Present and Ancestral Species

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/.     

Moose (Alces alces) reacts to high summer temperatures by utilizing thermal shelters in boreal forests – an analysis based on airborne laser scanning of the canopy structure at moose locations

The adaptation of different species to warming temperatures has been increasingly studied. Moose (Alces alces) is the largest of the ungulate species occupying the northern latitudes across the globe, and in Finland it is the most important game species. It is very well adapted to severe cold temperatures, but has a relatively low tolerance to warm temperatures. Previous studies have documented changes in habitat use by moose due to high temperatures. In many of these studies, the used areas have been classified according to how much thermal cover they were assumed to offer based on satellite/aerial imagery data. Here, we identified the vegetation structure in the areas used by moose under different thermal conditions. For this purpose, we used airborne laser scanning (ALS) data extracted from the locations of GPS‐collared moose. This provided us with detailed information about the relationships between moose and the structure of forests it uses in different thermal conditions and we were therefore able to determine and differentiate between the canopy structures at locations occupied by moose during different thermal conditions. We also discovered a threshold beyond which moose behaviour began to change significantly: as day temperatures began to reach 20 °C and higher, the search for areas with higher and denser canopies during daytime became evident. The difference was clear when compared to habitat use at lower temperatures, and was so strong that it provides supporting evidence to previous studies, suggesting that moose are able to modify their behaviour to cope with high temperatures, but also that the species is likely to be affected by warming climate.     

Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions

Specialised metabolites from microbial sources are well-known for their wide range of biomedical applications, particularly as antibiotics. When mining paired genomic and metabolomic data sets for novel specialised metabolites, establishing links between Biosynthetic Gene Clusters (BGCs) and metabolites represents a promising way of finding such novel chemistry. However, due to the lack of detailed biosynthetic knowledge for the majority of predicted BGCs, and the large number of possible combinations, this is not a simple task. This problem is becoming ever more pressing with the increased availability of paired omics data sets. Current tools are not effective at identifying valid links automatically, and manual verification is a considerable bottleneck in natural product research. We demonstrate that using multiple link-scoring functions together makes it easier to prioritise true links relative to others. Based on standardising a commonly used score, we introduce a new, more effective score, and introduce a novel score using an Input-Output Kernel Regression approach. Finally, we present NPLinker, a software framework to link genomic and metabolomic data. Results are verified using publicly available data sets that include validated links.     

Relative contributions of local and regional factors to species richness and total density of butterflies and moths in semi-natural grasslands

Metapopulation theory predicts that species richness and total population density of habitat specialists increase with increasing area and regional connectivity of the habitat. To test these predictions, we examined the relative contributions of habitat patch area, connectivity of the regional habitat network and local habitat quality to species richness and total density of butterflies and day-active moths inhabiting semi-natural grasslands. We studied butterflies and moths in 48 replicate landscapes situated in southwest Finland, including a focal patch and the surrounding network of other semi-natural grasslands within a radius of 1.5 km from the focal patch. By applying the method of hierarchical partitioning, which can distinguish between independent and joint contributions of individual explanatory variables, we observed that variables of the local habitat quality (e.g. mean vegetation height and nectar plant abundance) generally showed the highest independent effect on species richness and total density of butterflies and moths. Habitat area did not show a significant independent contribution to species richness and total density of butterflies and moths. The effect of habitat connectivity was observed only for total density of the declining butterflies and moths. These observations indicate that the local habitat quality is of foremost importance in explaining variation in species richness and total density of butterflies and moths. In addition, declining butterflies and moths have larger populations in well-connected networks of semi-natural grasslands. Our results suggest that, while it is crucial to maintain high-quality habitats by management, with limited resources it would be appropriate to concentrate grassland management and restoration to areas with well-connected grassland networks in which the declining species currently have their strongest populations. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways

A wide variety of 1) parametric regression models and 2) co-expression networks have been developed for finding gene-by-gene interactions underlying complex traits from expression data. While both methodological schemes have their own well-known benefits, little is known about their synergistic potential. Our study introduces their methodological fusion that cross-exploits the strengths of individual approaches via a built-in information-sharing mechanism. This fusion is theoretically based on certain trait-conditioned dependency patterns between two genes depending on their role in the underlying parametric model. Resulting trait-specific co-expression network estimation method 1) serves to enhance the interpretation of biological networks in a parametric sense, and 2) exploits the underlying parametric model itself in the estimation process. To also account for the substantial amount of intrinsic noise and collinearities, often entailed by expression data, a tailored co-expression measure is introduced along with this framework to alleviate related computational problems. A remarkable advance over the reference methods in simulated scenarios substantiate the method’s high-efficiency. As proof-of-concept, this synergistic approach is successfully applied in survival analysis, with acute myeloid leukemia data, further highlighting the framework’s versatility and broad practical relevance.     

A low‐cost,computer‐controlled robotic flower system for behavioral experiments

Human observations during behavioral studies are expensive, time‐consuming, and error prone. For this reason, automatization of experiments is highly desirable, as it reduces the risk of human errors and workload. The robotic system we developed is simple and cheap to build and handles feeding and data collection automatically. The system was built using mostly off‐the‐shelf components and has a novel feeding mechanism that uses servos to perform refill operations. We used the robotic system in two separate behavioral studies with bumblebees (Bombus terrestris): The system was used both for training of the bees and for the experimental data collection. The robotic system was reliable, with no flight in our studies failing due to a technical malfunction. The data recorded were easy to apply for further analysis. The software and the hardware design are open source. The development of cheap open‐source prototyping platforms during the recent years has opened up many possibilities in designing of experiments. Automatization not only reduces workload, but also potentially allows experimental designs never done before, such as dynamic experiments, where the system responds to, for example, learning of the animal. We present a complete system with hardware and software, and it can be used as such in various experiments requiring feeders and collection of visitation data. Use of the system is not limited to any particular experimental setup or even species.     

Cognitive disturbances in the cuprizone model of multiple sclerosis

Cognitive problems frequently accompany neurological manifestations of multiple sclerosis (MS). However, during screening of preclinical candidates, assessments of behaviour in mouse models of MS typically focus on locomotor activity. In the present study, we analysed cognitive behaviour of 9 to 10-week-old female C57Bl/6J mice orally administered with the toxin cuprizone that induces demyelination, a characteristic feature of MS. Animals received 400 mg/kg cuprizone daily for 2 or 4 weeks, and their performance was compared with that of vehicle-treated mice. Cuprizone-treated animals showed multiple deficits in short touchscreen-based operant tasks: they responded more slowly to visual stimuli, rewards and made more errors in a simple rule-learning task. In contextual/cued fear conditioning experiments, cuprizone-treated mice showed significantly lower levels of contextual freezing than vehicle-treated mice. Diffusion tensor imaging showed treatment-dependent changes in fractional anisotropy as well as in axial and mean diffusivities in different white matter areas. Lower values of fractional anisotropy and axial diffusivity in cuprizone-treated mice indicated developing demyelination and/or axonal damage. Several diffusion tensor imaging measurements correlated with learning parameters. Our results show that translational touchscreen operant tests and fear conditioning paradigms can reliably detect cognitive consequences of cuprizone treatment. The suggested experimental approach enables screening novel MS drug candidates in longitudinal experiments for their ability to improve pathological changes in brain structure and reverse cognitive deficits.