Incorporating Field Studies into Species Distribution and Climate Change Modelling: A Case Study of the Koomal Trichosurus vulpecula hypoleucus (Phalangeridae)

Species distribution models (SDMs) are an effective way of predicting the potential distribution of species and their response to environmental change. Most SDMs apply presence data to a relatively generic set of predictive variables such as climate. However, this weakens the modelling process by overlooking the responses to more cryptic predictive variables. In this paper we demonstrate a means by which data gathered from an intensive animal trapping study can be used to enhance SDMs by combining field data with bioclimatic modelling techniques to determine the future potential distribution for the koomal (Trichosurus vulpecula hypoleucus). The koomal is a geographically isolated subspecies of the common brushtail possum, endemic to south-western Australia. Since European settlement this taxon has undergone a significant reduction in distribution due to its vulnerability to habitat fragmentation, introduced predators and tree/shrub dieback caused by a virulent group of plant pathogens of the genus Phytophthora. An intensive field study found: 1) the home range for the koomal rarely exceeded 1 km in in length at its widest point; 2) areas heavily infested with dieback were not occupied; 3) gap crossing between patches (>400 m) was common behaviour; 4) koomal presence was linked to the extent of suitable vegetation; and 5) where the needs of koomal were met, populations in fragments were demographically similar to those found in contiguous landscapes. We used this information to resolve a more accurate SDM for the koomal than that created from bioclimatic data alone. Specifically, we refined spatial coverages of remnant vegetation and dieback, to develop a set of variables that we combined with selected bioclimatic variables to construct models. We conclude that the utility value of an SDM can be enhanced and given greater resolution by identifying variables that reflect observed, species-specific responses to landscape parameters and incorporating these responses into the model.     

Protein Paucimannosylation Is an Enriched N‐Glycosylation Signature of Human Cancers

While aberrant protein glycosylation is a recognized characteristic of human cancers, advances in glycoanalytics continue to discover new associations between glycoproteins and tumorigenesis. This glycomics‐centric study investigates a possible link between protein paucimannosylation, an under‐studied class of human N‐glycosylation [Man_1‐3GlcNAc₂Fuc_0‐1], and cancer. The paucimannosidic glycans (PMGs) of 34 cancer cell lines and 133 tissue samples spanning 11 cancer types and matching non‐cancerous specimens are profiled from 467 published and unpublished PGC‐LC‐MS/MS N‐glycome datasets collected over a decade. PMGs, particularly Man_2‐3GlcNAc₂Fuc₁, are prominent features of 29 cancer cell lines, but the PMG level varies dramatically across and within the cancer types (1.0–50.2%). Analyses of paired (tumor/non‐tumor) and stage‐stratified tissues demonstrate that PMGs are significantly enriched in tumor tissues from several cancer types including liver cancer (p = 0.0033) and colorectal cancer (p = 0.0017) and is elevated as a result of prostate cancer and chronic lymphocytic leukaemia progression (p < 0.05). Surface expression of paucimannosidic epitopes is demonstrated on human glioblastoma cells using immunofluorescence while biosynthetic involvement of N‐acetyl‐β‐hexosaminidase is indicated by quantitative proteomics. This intriguing association between protein paucimannosylation and human cancers warrants further exploration to detail the biosynthesis, cellular location(s), protein carriers, and functions of paucimannosylation in tumorigenesis and metastasis.     

Genetically and Phenotypically Distinct Pseudomonas aeruginosa Cystic Fibrosis Isolates Share a Core Proteomic Signature

The opportunistic pathogen Pseudomonas aeruginosa is among the main colonizers of the lungs of cystic fibrosis (CF) patients. We have isolated and sequenced several P. aeruginosa isolates from the sputum of CF patients and compared them with each other and with the model strain PAO1. Phenotypic analysis of CF isolates showed significant variability in colonization and virulence-related traits suggesting different strategies for adaptation to the CF lung. Genomic analysis indicated these strains shared a large set of core genes with the standard laboratory strain PAO1, and identified the genetic basis for some of the observed phenotypic differences. Proteomics revealed that in a conventional laboratory medium PAO1 expressed 827 proteins that were absent in the CF isolates while the CF isolates shared a distinctive signature set of 703 proteins not detected in PAO1. PAO1 expressed many transporters for the uptake of organic nutrients and relatively few biosynthetic pathways. Conversely, the CF isolates expressed a narrower range of transporters and a broader set of metabolic pathways for the biosynthesis of amino acids, carbohydrates, nucleotides and polyamines. The proteomic data suggests that in a common laboratory medium PAO1 may transport a diverse set of “ready-made” nutrients from the rich medium, whereas the CF isolates may only utilize a limited number of nutrients from the medium relying mainly on their own metabolism for synthesis of essential nutrients. These variations indicate significant differences between the metabolism and physiology of P. aeruginosa CF isolates and PAO1 that cannot be detected at the genome level alone. The widening gap between the increasing genomic data and the lack of phenotypic data means that researchers are increasingly reliant on extrapolating from genomic comparisons using experimentally characterized model organisms such as PAO1. While comparative genomics can provide valuable information, our data suggests that such extrapolations may be fraught with peril.     

Feedback Valence Affects Auditory Perceptual Learning Independently of Feedback Probability

Previous studies have suggested that negative feedback is more effective in driving learning than positive feedback. We investigated the effect on learning of providing varying amounts of negative and positive feedback while listeners attempted to discriminate between three identical tones; an impossible task that nevertheless produces robust learning. Four feedback conditions were compared during training: 90% positive feedback or 10% negative feedback informed the participants that they were doing equally well, while 10% positive or 90% negative feedback informed them they were doing equally badly. In all conditions the feedback was random in relation to the listeners’ responses (because the task was to discriminate three identical tones), yet both the valence (negative vs. positive) and the probability of feedback (10% vs. 90%) affected learning. Feedback that informed listeners they were doing badly resulted in better post-training performance than feedback that informed them they were doing well, independent of valence. In addition, positive feedback during training resulted in better post-training performance than negative feedback, but only positive feedback indicating listeners were doing badly on the task resulted in learning. As we have previously speculated, feedback that better reflected the difficulty of the task was more effective in driving learning than feedback that suggested performance was better than it should have been given perceived task difficulty. But contrary to expectations, positive feedback was more effective than negative feedback in driving learning. Feedback thus had two separable effects on learning: feedback valence affected motivation on a subjectively difficult task, and learning occurred only when feedback probability reflected the subjective difficulty. To optimize learning, training programs need to take into consideration both feedback valence and probability.