Population genetic structure in the endemic cyprinid fish Microphysogobio alticorpus in Taiwan: Evidence for a new phylogeographical area

The Formosa Bank is a significant zoogeographical barrier for the freshwater fish in Southern Taiwan. Here, we present population genetic structure analyses on Microphysogobio alticorpus populations in Taiwan, and biogeographic pattern to infer the relationship between historic dispersal geological dynamics. A total of 24 haplotypes were genotyped for mitochondrial CR + cyt b region in 134 specimens collected from 9 populations. Relatively high levels of haplotype diversity (h = 0.896) and low levels of nucleotide diversity (π = 0.005) were detected in M. alticorpus. Two major phylogenetic haplotype groups, Clade N1+N2 and S, were revealed and estimated to diverge about 0.121 myr (million years) ago. Haplotype network and population genetic analyses indicated significant genetic structure (F_ST = 0.876), largely concordant with the geographical location of the populations. According to SAMOVA analysis, M. alticorpus populations were divided into five units that can be explained by episodes of dispersal and population expansion followed by population fragmentation and restricted gene flow. The results of the topological pattern and network suggested that the Kaoping River was the first habitat colonized by the ancestor of M. alticorpus, before the northward dispersal.     

The discovery of potent and selective non-steroidal glucocorticoid receptor modulators,suitable for inhalation

We report the discovery of highly potent and selective non-steroidal glucocorticoid receptor modulators with PK properties suitable for inhalation. A high throughput screen of the AstraZeneca compound collection identified sulfonamide 3 as a potent non-steroidal glucocorticoid receptor ligand. Further optimization of this lead generated indazoles 30 and 48 that were progressed to characterization in in vivo models. X-ray crystallography was used to gain further insight into the binding mode of selected ligands.     

Inhibition of microbial β-N-acetylhexosaminidases by 4-deoxy- and galacto-analogues of NAG-thiazoline

NAG-thiazoline is a well-established competitive inhibitor of two physiologically relevant glycosidase families—β-N-acetylhexosaminidases (GH20) and β-N-acetylglucosaminidases (GH84). Based on the different substrate flexibilities of these enzyme groups, we designed and synthesized the 4-deoxy derivative of NAG-thiazoline aiming at the selective inhibition of GH20 β-N-acetylhexosaminidases. One GH84 and two GH20 microbial glycosidases were employed as model enzymes for the inhibition assays. Surprisingly, the new compound 4-deoxy-thiazoline exhibited no activity inhibition with either of the enzyme families of interest. Unlike with the substrates, the 4-hydroxyl group of the inhibitor’s sugar ring seems to be crucial for binding the inhibitor to the active sites of these enzymes.     

HSP70 induction by bleomycin metal core analogs

Induction of heat shock protein 70 (HSP70) is known to be effective against various diseases. We are interested in HSP70 induction capability of an antitumor antibiotic bleomycin which produces oxidative stress by iron chelate formation and oxygen activation in a cell. The HSP70 induction activity of bleomycin and its six metal core analogs was examined, and a compound HPH-1Trt of 10 μM was found to induce this protein in a pheochromocytoma cell line and some T cell and monocytic cell lines. Its mechanism is increase of HSP70 mRNA, but higher concentration of this compound showed toxicity. Two new derivatives were then synthesized, and one of them named DHPH-1Trt was shown to have less toxicity and higher HSP70 induction activity. This study would lead to a clue for new HSP70 inducer clinically used in near future.     

Characterization of recombinant L-ribose isomerase acquired from Cryobacterium sp. N21 with potential application in L-ribulose production

l-Ribose isomerase (lRI) is an enzyme that can catalyze the reversible isomerization between l-ribose and l-ribulose. It can also perform the conversion between many aldoses into their corresponding ketoses. l-RI was produced from Cryobacterium sp. N21 (CrL-RIse), and l-ribose was utilized as a substrate. The recombinant l-RI gene was cloned and overexpressed from Cryobacterium sp. N21. The purification of CrL-RIse was performed by metal-affinity chromatography. The enzyme displayed a corresponding band with an approximate size of 35 kDa on the SDS-PAGE analysis. The protein for this gene contains 266 amino acids with an expected molecular weight (M_w) of 29.6 kDa. The measured M_w of CrL-RIse calculated by HPLC was 125 kDa. CrL-RIse was extremely active in glycine buffer at 35 °C, pH 9.0, showing a specific activity of 54.96 U mg⁻¹. CrL-RIse displayed no major increase in activity with metal ions, excluding Mn²⁺. The estimated Km, K_cat, K_cat/Km and V_max values of CrL-RIse were 37.8 mM, 10,416 min⁻¹, 275.43 min⁻¹ mM⁻¹, and 250 U mg⁻¹, respectively. The rate of l-ribulose production was 31 % (6.24, 12.11, and 20.89 g L⁻¹) at equilibrium by utilizing 20, 40, and 70 g L⁻¹ of the substrate, respectively. The results indicated that CrL-RIse has the capability to manufacture l-ribulose from l-ribose.     

Stronger together: Combining automated classifiers with manual post-validation optimizes the workload vs reliability trade-off of species identification in bat acoustic surveys

Owing to major technological advances, bioacoustics has become a burgeoning field in ecological research worldwide. Autonomous passive acoustic recorders are becoming widely used to monitor aerial insectivorous bats, and automatic classifiers have emerged to aid researchers in the daunting task of analysing the resulting massive acoustic datasets. However, the scarcity of comprehensive reference call libraries still hampers their wider application in highly diverse tropical assemblages. Capitalizing on a unique acoustic dataset of >650,000 bat call sequences collected over a 3-year period in the Brazilian Amazon, the aims of this study were (a) to assess how pre-identified recordings of free-flying and hand-released bats could be used to train an automatic classification algorithm (random forest), and (b) to optimize acoustic analysis protocols by combining automatic classification with visual post-validation, whereby we evaluated the proportion of sound files to be post-validated for different thresholds of classification accuracy. Classifiers were trained at species or sonotype (group of species with similar calls) level. Random forest models confirmed the reliability of using calls of both free-flying and hand-released bats to train custom-built automatic classifiers. To achieve a general classification accuracy of ~85%, random forest had to be trained with at least 500 pulses per species/sonotype. For seven out of 20 sonotypes, the most abundant in our dataset, we obtained high classification accuracy (>90%). Adopting a desired accuracy probability threshold of 95% for the random forest classifier, we found that the percentage of sound files required for manual post-validation could be reduced by up to 75%, a significant saving in terms of workload. Combining automatic classification with manual ID through fully customizable classifiers implemented in open-source software as demonstrated here shows great potential to help overcome the acknowledged risks and biases associated with the sole reliance on automatic classification.     

Distribution models of estuarine fish species: The effect of sampling bias,species ecology and threshold selection on models' accuracy

Species distribution models (SDMs) relate presence/absence data to environmental variables, allowing to predict species environmental requirements and potential distribution. They have been increasingly used in fields such as ecology, biogeography and evolution, and often support conservation priorities and strategies. Thus, it becomes crucial to understand how trustworthy and reliable their predictions are. Different approaches, such as using ensemble methods (combining forecasts of different single models), or applying the most suitable threshold to transform continuous probability maps into species presences or absences, have been used to reduce model-based uncertainty. Taking into account the influence of biased sampling imprecision in species location, small datasets and species ecological characteristics, may also help to detect and compensate for uncertainty in the model building process. To investigate the effect of applying an ensemble approach, several threshold selection criteria and different datasets representing seasonal and spatial sampling bias, on models' accuracy, SDMs were built for four estuarine fish species with distinct use of the estuarine systems. Overall, predictions obtained with the ensemble approach were more accurate. Variability in accuracy metrics obtained with the nine threshold selection criteria applied was more pronounced for species with low prevalence and when sensitivity was calculated. Higher values of accuracy measures were registered with the threshold that maximizes the sum of sensitivity and specificity, and the threshold where the predicted prevalence equals the observed, whereas the 0.5 cut-off was unreliable, originating the lowest values for these metrics. Accuracy of models created from a spatially biased sampling was overall higher than accuracy of models created with a seasonally biased sampling or with the multi-year database created and this pattern was consistently obtained for marine migrant species, which use estuaries as nursery areas, presenting a seasonally and regular use of these ecosystems. The ecological dependence between these fish species and estuaries may add difficulties in the model building process, and needs to be taken into account, to improve their accuracy. The present study highlights the need for a thorough analysis of the critical underlying issues of the complete model building process to predict the distribution of estuarine fish species, due to the particular and dynamic nature of these ecosystems.