Assessing watershed-scale impacts of best management practices and elevated atmospheric carbon dioxide concentrations on water yield.

Changes in water yield are influenced by many intersecting biophysical elements, including climate, on-land best management practices, and landcover. Large-scale reductions in water yield may present a significant threat to water supplies globally. Many of these intersecting factors are intercorrelated and confounded, making it challenging to separate the factors' individual contributions to shaping local streamflow dynamics. Comprehensive hydrological models constructed based on a well-established understanding of biophysical processes are often employed to address these matters. However, these models rarely incorporate all relevant factors influencing local hydrological processes, due to the reliance of these models on the latest, albeit limited, state-of-the-art research. For instance, complexities inherent in watershed hydrology, which involve multilayered interactions among potentially many biophysical factors, leave the direct analysis of subtle impacts on water yields measured in-situ largely intractable. Therefore, we propose an innovative approach to assess impacts of elevated atmospheric CO2 concentrations and flow diversion terraces (FDTs) on stream discharge rates at the watershed scale. Initially, we use a comprehensive hydrological model to account for the impacts of major climatic and landuse/landcover factors on changes in field-acquired measurements of water yield. Next, we employ conventional and advanced statistical methods to decompose the residuals of model predictions to facilitate the identification of subtle influences promoted by increases in atmospheric CO2 concentrations and the application of FDTs in an agriculture-dominated watershed. Through this innovative approach, we find that FDTs contributed to a watershed-wide, net water-yield reduction of 188.0 mm (or 28.9 %) from 1992 to 2014. Ongoing increases in ambient CO2 concentrations, which are responsible for an overall reduction in a watershed-level assessment of stomatal conductance, have led to a minor increase in stream discharge rates during the same 23-year period, i.e., 0.45 mm of water yield per year, or 1.6 % overall. Streamflow reductions explicitly caused by regional warming in the area alone, on account of increased evapotranspiration, may be overestimated due to the opposing, synergistic effects on water yield associated with CO2-enrichment of the lower atmosphere and the annual application of FDTs.

Effects of iodine intake on gut microbiota and gut metabolites in Hashimoto thyroiditis-diseased humans and mice.

Hashimoto thyroiditis (HT) is an organ-specific autoimmune disease linked to iodine intake. Emerging evidence highlights the gut microbiota's role in HT pathogenesis via the microbiota-gut-thyroid axis. However, the process through which iodine intake modifies the microbiota and triggers HT remains unclear. This study examines how iodine affects gut dysbiosis and HT, recruiting 23 patients with HT and 25 healthy individuals to assess gut microbiota composition and metabolic features. Furthermore, we establish a spontaneously developed thyroiditis mouse model using NOD.H-2h4 mice highlighting the influence of iodine intake on HT progression. The butanoate metabolism significantly differs between these two groups according to the enrichment results, and butyric acid is significantly decreased in patients with HT compared with those in healthy individuals. Gut dysbiosis, driven by excessive iodine intake, disrupts TH17/Treg balance by reducing butyric acid. In summary, iodine intake alters intestinal microbiota composition and metabolic changes influencing the microbiota-gut-thyroid axis.

Newcastle disease virus expressing an angiogenic inhibitor exerts an enhanced therapeutic efficacy in colon cancer model.

Newcastle disease virus (NDV)-mediated gene therapy is a promising new approach for treatment of cancer but shows limited anti-angiogenesis. VEGF-Trap plays a vital role in anti-angiogenesis. To enhance the anti-tumor effect of NDV, VEGF-Trap gene was incorporated into the genome of rNDV in this study (named rNDV-VEGF-Trap). Results showed that rNDV-VEGF-Trap reduced cell growth ratio by 85.37% and migration ratio by 87.9% in EA.hy926 cells. In vivo studies, rNDV-VEGF-Trap reduced tumor volume and weight of CT26-bearing mice by more than 3 folds. Immunohistochemistry analysis of CD34 showed rNDV-VEGF-Trap significantly decreased the number of vascular endothelial cells in the tumor tissues. Moreover, Western blot analysis demonstrated that treatment with rNDV-VEGF-Trap significantly decreased the phosphorylation levels of AKT, ERK1/2 and STAT3 and increased the expression levels of P53, BAX and cleaved caspase-3 in the tumor tissue. In addition, to evaluate the toxicity of rNDV-VEGF-Trap, serum chemistries were analyzed. The results showed that rNDV-VEGF-Trap caused insignificant changes of creatinine levels, alanine aminotransferase and aspartate transaminase. Furthermore, administration of rNDV-VEGF-Trap did not cause the diarrhoea, decreased appetite, weight decrease and haemorrhage of the experimental mice. These data suggest that rNDV-VEGF-Trap exhibits an enhanced inhibition of CT26-bearing mice by enhancing anti-angiogenesis and apoptosis and may be a potential candidate for carcinoma therapy especially for colon cancer.

High-throughput measurement of plant fitness traits with an object detection method using Faster R-CNN.

Revealing the contributions of genes to plant phenotype is frequently challenging because loss-of-function effects may be subtle or masked by varying degrees of genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana. An image segmentation-based method using the software ImageJ and an object detection-based method using the Faster Region-based Convolutional Neural Network (R-CNN) algorithm were used for measuring two Arabidopsis fitness traits: seed and fruit counts. The segmentation-based method was error-prone (correlation between true and predicted seed counts, r2 = 0.849) because seeds touching each other were undercounted. By contrast, the object detection-based algorithm yielded near perfect seed counts (r2 = 0.9996) and highly accurate fruit counts (r2 = 0.980). Comparing seed counts for wild-type and 12 mutant lines revealed fitness effects for three genes; fruit counts revealed the same effects for two genes. Our study provides analysis pipelines and models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits when studying gene functions.

Purification and extraction of fibroblast growth factor 21 (FGF-21) protein by sumo fusion in Escherichia coli.

Fibroblast growth factor 21 has recently discovered its pivotal role in glucose, lipid metabolism and regulation of energy homeostasis. Further, it has helped in forming great strides for treatment of chronic diseases like diabetes and inflammation. FGF-21 was sub-cloned into the SUMO vector and was induced for expression in Escherichia coli Rosetta. The recombinant plasmid was transformed into Escherichia coli strain. FGF-21 was induced by IPTG and purified by Ni-NTA agarose (Nickel-nitrilotriacetic acid) column. The purified fusion protein was cleavaged by SUMO protease I to obtain recombinant FGF-21 with high purity. The purified protein was tested for its biological activity of FGF-21. HepG2 cell model was used to detect the regulation of glucose uptake activity of FGF-21 and were further treated with different concentrations of FGF-21.The residual glucose content in medium was measured using the glucose oxidase-peroxidase method. The results indicated that FGF-21 protein had a role in regulating the glucose uptake on HepG2 cells and the effect was significantly dose-dependent manner. In order to further verify whether purified FGF-21 protein obtained has biological activity in diabetic model. Studies have demonstrated that FGF-21 had a greater efficacy in dropping blood glucose in streptozotocin induced diabetic mice.

Association Between Gut Microbiota and Autoimmune Thyroid Disease: A Systematic Review and Meta-Analysis.

Background: Autoimmune thyroid disease (AITD) is characterized by thyroid dysfunction and deficits in the autoimmune system. Growing attention has been paid toward the field of gut microbiota over the last few decades. Several recent studies have found that gut microbiota composition in patients with AITD has altered, but no studies have conducted systematic reviews on the association between gut microbiota and ATID. Methods: We searched PubMed, Web of Science, Embase, and Cochrane databases without language restrictions and conducted a systematic review and meta-analysis of eight studies, including 196 patients with AITD. Results: The meta-analysis showed that the alpha diversity and abundance of certain gut microbiota were changed in patients with AITD compared to the controls. Chao1,the index of the microflora richness, was increased in the Hashimoto's thyroiditis group compared to controls (SMD, 0.68, 95%CI: 0.16 to 1.20), while it was decreased in the Graves' disease group (SMD, -0.87, 95%CI: -1.46 to -0.28). In addition, we found that some beneficial bacteria like Bifidobacterium and Lactobacillus were decreased in the AITD group, and harmful microbiota like Bacteroides fragilis was significantly increased compared with the controls. Furthermore, the percentage of relevant abundance of other commensal bacteria such as Bacteroidetes, Bacteroides, and Lachnospiraceae was increased compared with the controls. Conclusions: This meta-analysis indicates an association between AITD and alteration of microbiota composition at the family, genus, and species levels. Systematic Review Registration: PROSPERO, identifier CRD42021251557.

Therapeutic effect and mechanism of combined use of FGF21 and insulin on diabetic nephropathy.

Although FGF21 ameliorates diabetic nephropathy (DN), the efficacy is not satisfactory. Studies demonstrate that FGF21 combined with Insulin exhibits reciprocal sensitization on glucose and lipid metabolism in mice with type 2 diabetes. However, therapeutic effect of combined use of FGF21 and Insulin on DN has not been reported. Therefore, this study explored therapeutic effect and mechanism of combined use of FGF21 and Insulin on DN. Our results showed that compared with Insulin or FGF21 alone, FGF21 combined with Insulin further ameliorated blood glucose, HbAlc, OGTT, renal function, liver function, blood lipid, histopathological changes, oxidative stress and AGEs in the mice of DN (BKS-Leprem2Cd479/Gpt). Moreover, FGF21 combined with Insulin further reduced expressions of IL-1ß, IL-6, TNF-α via promoting M1 type macrophage into M2 type macrophage. Results of real-time PCR and Western blot showed that FGF21 combined with Insulin upregulated the expressions of autophagy related genes LC3-Ⅱ and BCL-1. Mesangial cells play an important role in the pathological changes of DN mice. However, the effect of FGF21 on mesangial cells has not been reported. In this study, d-glucose was used in high glucose (HG) model in mesangial cells. The results showed that FGF21 significantly reduced the levels of OS, AGEs and cell overproliferation. Meanwhile, FGF21 significantly ameliorated autophagy level via upregulating the phosphorylation of AMPK and downregulating phosphorylation of mTOR. These effects were reversed in siRNA-ß-klotho transfected mesangial cells. In conclusion, our results demonstrate that combination FGF21 with Insulin exhibits a better therapeutic effect on DN compared with FGF21 or Insulin alone. This study provides a theoretical basis for combined used of FGF21 and Insulin as a new treatment for DN and further provides theoretical support for application of FGF21 in treatment of DN.

FGF21 ameliorates hepatic fibrosis by multiple mechanisms.

BACKGROUND: Previous study reports that fibroblast growth factor 21 (FGF21) could ameliorate hepatic fibrosis, but its mechanisms have not been fully investigated. METHODS AND RESULTS: In this study, three models were used to investigate the mechanism by which FGF21 alleviates liver fibrosis. Hepatic fibrosis animal models were respectively induced by CCL4 and dimethylnitrosamine. Our results demonstrated that liver index and liver function were deteriorated in both models. Hematoxylin and eosin and Masson's staining showed that the damaged tissue architectonics were observed in the mice of both models. Treatment with FGF21 significantly ameliorated these changes. ELISA analysis showed that the serum levels of IL-1ß, IL-6 and TNF-α were significantly elevated in both models. However, administration of FGF21 significantly reduced these inflammatory cytokines. Real-time PCR and Western blot analysis showed that treatment with FGF21 significantly decreased mRNA and protein expressions of collagenI, α-SMA and TGF-ß. Platelet-derived growth factor-BB (PDGF-BB) stimulant was used to establish the experimental cell model in hepatic stellate cells (HSCs). Real-time PCR and Western blot analysis demonstrated that the expression of collagenI and α-SMA were significantly upregulated by this stimulant in model group. Interestingly, our results showed that mRNA and protein expressions of leptin were also significantly induced in PDGF-BB treated HSCs. Administration of FGF21 significantly reduced leptin expression in a dose dependent manner and these effects were reversed in siRNA (against ß-klotho) transfected HSCs. Furthermore, the leptin signaling pathways related protein p-ERK/t-ERK, p-STAT3/STAT3 and TGF-ß were significantly downregulated by FGF21 treatment in a dose dependent manner. The expressions of SOCS3 and Nrf-2 were enhanced by treatment with FGF21. The underlying mechanism may be that FGF21 regulates leptin-STAT3 axis via Nrf-2 and SOCS3 pathway in activated HSCs. CONCLUSIONS: FGF21 ameliorates hepatic fibrosis by multiple mechanisms.

Conditional inference trees in the assessment of tree mortality rates in the transitional mixed forests of Atlantic Canada.

Long-term predictions of forest dynamics, including forecasts of tree growth and mortality, are central to sustainable forest-management planning. Although often difficult to evaluate, tree mortality rates under different abiotic and biotic conditions are vital in defining the long-term dynamics of forest ecosystems. In this study, we have modeled tree mortality rates using conditional inference trees (CTREE) and multi-year permanent sample plot data sourced from an inventory with coverage of New Brunswick (NB), Canada. The final CTREE mortality model was based on four tree- and three stand-level terms together with two climatic terms. The correlation coefficient (R2) between observed and predicted mortality rates was 0.67. High cumulative annual growing degree-days (GDD) was found to lead to increased mortality in 18 tree species, including Betula papyrifera, Picea mariana, Acer saccharum, and Larix laricina. In another ten species, including Abies balsamea, Tsuga canadensis, Fraxinus americana, and Fagus grandifolia, mortality rates tended to be higher in areas with high incident solar radiation. High amounts of precipitation in NB's humid maritime climate were also found to contribute to heightened tree mortality. The relationship between high GDD, solar radiation, and high mortality rates was particularly strong when precipitation was also low. This would suggest that although excessive soil water can contribute to heightened tree mortality by reducing the supply of air to the roots, occasional drought in NB can also contribute to increased mortality events. These results would have significant implications when considered alongside regional climate projections which generally entail both components of warming and increased precipitation.

Predictive Models of Genetic Redundancy in Arabidopsis thaliana.

Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Previous studies have identified some characteristics common among redundant gene pairs, but a predictive model of genetic redundancy incorporating a wide variety of features derived from accumulating omics and mutant phenotype data is yet to be established. In addition, the relative importance of these features for genetic redundancy remains largely unclear. Here, we establish machine learning models for predicting whether a gene pair is likely redundant or not in the model plant Arabidopsis thaliana based on six feature categories: functional annotations, evolutionary conservation including duplication patterns and mechanisms, epigenetic marks, protein properties including posttranslational modifications, gene expression, and gene network properties. The definition of redundancy, data transformations, feature subsets, and machine learning algorithms used significantly affected model performance based on holdout, testing phenotype data. Among the most important features in predicting gene pairs as redundant were having a paralog(s) from recent duplication events, annotation as a transcription factor, downregulation during stress conditions, and having similar expression patterns under stress conditions. We also explored the potential reasons underlying mispredictions and limitations of our studies. This genetic redundancy model sheds light on characteristics that may contribute to long-term maintenance of paralogs, and will ultimately allow for more targeted generation of functionally informative double mutants, advancing functional genomic studies.

FGF21 alleviates chronic inflammatory injury in the aging process through modulating polarization of macrophages.

Previous studies reported that FGF21 prolongs life span and delays the body senescence, but the mechanism is not clear. The present study was designed to investigate the effects of FGF21 on hepatic senescence in aging mice and further research the mechanism. The 14-month-old male mice were administered with PBS, FGF21 or metformin once daily for 6 months. Results showed that FGF21 alleviated liver injury and inhibited accumulation of senescence markers SASP, P53 and P16 in the livers of aging mice. Subsequently we found that the aging mice treated by FGF21 showed transition of type 1 macrophages (M1) to type 2 macrophages (M2) in the livers. Next, we used THP-1 macrophages triggered by LPS to study effects of FGF21 on macrophages. Macrophages triggered by LPS exhibited features of M1, but the addition of FGF21 decreased the expression of M1 markers, and promoted the macrophages to exhibit features of M2. Results showed that the effects of FGF21 on macrophages were associated with the AMPK pathway. After adding AMPK inhibitor, the effects of FGF21 were inhibited, which was associated with the NF-κB signaling pathway. Finally, co-culturing differentiated macrophages and hepatocytes, we found that the large amount of pro-inflammatory factors such as IL-6 promoted hepatocyte senescence, which exhibited enhanced P53, P16 and ß-galactosidase. This was contrary to hepatocytes co-cultured with macrophages treated by FGF21. These results indicate that FGF21 alleviates hepatic senescence injury by modulating the polarization of macrophages through the AMPK /NF-κB signaling pathway.

Impact of short-read sequencing on the misassembly of a plant genome.

BACKGROUND: Availability of plant genome sequences has led to significant advances. However, with few exceptions, the great majority of existing genome assemblies are derived from short read sequencing technologies with highly uneven read coverages indicative of sequencing and assembly issues that could significantly impact any downstream analysis of plant genomes. In tomato for example, 0.6% (5.1 Mb) and 9.7% (79.6 Mb) of short-read based assembly had significantly higher and lower coverage compared to background, respectively. RESULTS: To understand what the causes may be for such uneven coverage, we first established machine learning models capable of predicting genomic regions with variable coverages and found that high coverage regions tend to have higher simple sequence repeat and tandem gene densities compared to background regions. To determine if the high coverage regions were misassembled, we examined a recently available tomato long-read based assembly and found that 27.8% (1.41 Mb) of high coverage regions were potentially misassembled of duplicate sequences, compared to 1.4% in background regions. In addition, using a predictive model that can distinguish correctly and incorrectly assembled high coverage regions, we found that misassembled, high coverage regions tend to be flanked by simple sequence repeats, pseudogenes, and transposon elements. CONCLUSIONS: Our study provides insights on the causes of variable coverage regions and a quantitative assessment of factors contributing to plant genome misassembly when using short reads and the generality of these causes and factors should be tested further in other species.

Photosynthetic parameters and stomatal conductance in attached and detached balsam fir foliage.

Leaf level gas-exchange measurements can be made on detached foliage to address the challenge of access to the crown of tall trees. However, detachment may impact leaf gas exchange. This necessitates the study of gas-exchange characteristics of foliage on detached branches to assess the feasibility of using detached branches for gas-exchange analysis. We compared photosynthetic parameters and stomatal conductance in foliage of attached and detached branches of balsam fir [Abies balsamea (L.) Mill.] during the growing season. Data were analyzed using a linear mixed-effect model, with fixed and random effects (branch status and measurement month, and tree number, respectively). Branch detachment had no significant effects on: (i) photosynthesis at the current ambient CO2 concentration (400 µmol mol-1, A 400); (ii) maximum rates of Ribulose-1,5-bisphosphate (RuBP) carboxylation (V cmax) and regeneration (J max); (iii) the ratio of J max to V cmax (i.e., J max:V cmax), and (iv) stomatal conductance (g s) during the study period (p = 0.120-0.335). There was a strong seasonal effect on all gas-exchange variables (p ≤ 0.001-0.015). Gas-exchange measurements made on detached foliage during the warm summer months should be performed with care. Reliable gas-exchange measurements can be obtained using balsam fir foliage on detached branches 50-80 cm in length, in cooler growing-season months, up to 30 min after detachment.

Characterizing the impacts of land use on nitrate load and water yield in an agricultural watershed in Atlantic Canada.

Excessive nitrate loading from agricultural non-point source is threatening the health of receiving water bodies at the global scale. Quantifying the drivers/sources of water and nitrate flux in watersheds and relating them to spatial and temporal land uses is essential for developing effective mitigation strategies. This study investigated the impact of land use on water yield and nitrate loading to surface water in a typical agricultural watershed in Prince Edward Island (PEI), Canada. We used historical streamflow and water quality records to calibrate the comprehensive hydrological model Soil and Water Assessment Tool (SWAT), which was setup with detailed annual land use records. The SWAT model performed well in predicting both daily streamflow and nitrate load. Land use demonstrated little impact on water yield but affected nitrate load significantly. Annual nitrate load ranged from 5.6 to 44.4 kg N ha-1 yr-1 for forest and soybean, respectively. Potato rotated land contributed 84.5% of annual nitrate load to the watershed. Source of water yield demonstrated high variability between the growing season and non-growing season. About 90% of water yield was contributed by groundwater during growing season, while runoff contributed over 60% of water yield during the non-growing season. Groundwater was the dominant source of nitrate loading for both seasons. The watershed estuary faced the highest threats from subbasins in the south western area due to the high nitrate load and proximity to the watershed outlet. Results by the machine learning algorithm random Forest analysis indicated that the climatic variables of temperature and precipitation were the top two factors affecting water yield, with a combined relative importance of 61%. Land use was the dominant factor affecting nitrate load, the relative importance of land use alone was ~50%. The results of this study provided critical insights for watershed management in Atlantic Canada.

Evaluation of the suitability of six drought indices in naturally growing, transitional vegetation zones in Inner Mongolia (China).

Naturally growing vegetation often suffers from the effects of drought. There exists a vast number of drought indices (DI's) to assess the impact of drought on the growth of crops and naturally occurring vegetation. However, assessing the fitness of these indices for large areas with variable vegetation cover is often problematic because of the absence of adequate spatial information. In this study, we compared six DI's to NDVI (the normalized difference vegetation index), a common indicator of vegetation occurrence and health based on satellite-acquired reflectance data. The study area covers an aridity gradient from forests to deserts along a 2,400-km-long section across the Inner Mongolia Autonomous Region of China. On an annual timescale, standardized precipitation index (SPI) was the most appropriate in assessing drought in steppes and deserts. On a seasonal timescale, the self-calibrated Palmer drought severity index (scPDSI) displayed the greatest sensitivity during the summer, but not during the other seasons. On a monthly timescale, scPDSI demonstrated the greatest sensitivity to the various vegetation zones (i.e., forests, steppes, and deserts) in June and July. Further analysis indicated that summer drought had a lag-effect on vegetation growth, which varied from one to six months according to the specific vegetation cover. The mixed response of DI's to NDVI and the lag-effect in transitional vegetation on annual, seasonal, and monthly timescales were ascribed to differences in DI definition and the dominant plant species within the transitional cover. The current study has the potential to inform the drafting of selection criteria of DI's for the study of drought-related impact on naturally growing vegetation at timescales from month to year.

Caveolin-1 Y14 phosphorylation suppresses tumor growth while promoting invasion.

Caveolin-1 is a transmembrane protein with both tumor promoter and suppressor functions that remain poorly understood. Cav1 phosphorylation by Src kinase on tyrosine 14 is closely associated with focal adhesion dynamics and tumor cell migration, however the role of pCav1 in vivo in tumor progression remains poorly characterized. Herein, we expressed phosphomimetic Y14D, wild type, and non-phosphorylatable Y14F forms of Cav1 in MDA-MB-435 cancer cells. Expression of Cav1Y14D reduced cell proliferation and induced the TP53 tumor suppressor. Ectopic expression in MDA-MB-435 cells of Y14 phosphorylatable Cav1 was required for induction of TP53 in response to oxidative stress. Cav1Y14D promotes an apparent reversal of the Warburg effect and markedly inhibited tumor growth in vivo. However, Cav1 induced pseudopodial recruitment of glycolytic enzymes, and time-lapse intravital imaging showed increased invadopodia protrusion and extravasation into blood vessels for Cav1WT and Y14D but not for Y14F. Our results suggest that Cav1 Y14 phosphorylation levels play a role in the conflicting demands on metabolic resources associated with cancer cell proliferation versus motility.

Caveolae and scaffold detection from single molecule localization microscopy data using deep learning.

Caveolae are plasma membrane invaginations whose formation requires caveolin-1 (Cav1), the adaptor protein polymerase I, and the transcript release factor (PTRF or CAVIN1). Caveolae have an important role in cell functioning, signaling, and disease. In the absence of CAVIN1/PTRF, Cav1 forms non-caveolar membrane domains called scaffolds. In this work, we train machine learning models to automatically distinguish between caveolae and scaffolds from single molecule localization microscopy (SMLM) data. We apply machine learning algorithms to discriminate biological structures from SMLM data. Our work is the first that is leveraging machine learning approaches (including deep learning models) to automatically identifying biological structures from SMLM data. In particular, we develop and compare three binary classification methods to identify whether or not a given 3D cluster of Cav1 proteins is a caveolae. The first uses a random forest classifier applied to 28 hand-crafted/designed features, the second uses a convolutional neural net (CNN) applied to a projection of the point clouds onto three planes, and the third uses a PointNet model, a recent development that can directly take point clouds as its input. We validate our methods on a dataset of super-resolution microscopy images of PC3 prostate cancer cells labeled for Cav1. Specifically, we have images from two cell populations: 10 PC3 and 10 CAVIN1/PTRF-transfected PC3 cells (PC3-PTRF cells) that form caveolae. We obtained a balanced set of 1714 different cellular structures. Our results show that both the random forest on hand-designed features and the deep learning approach achieve high accuracy in distinguishing the intrinsic features of the caveolae and non-caveolae biological structures. More specifically, both random forest and deep CNN classifiers achieve classification accuracy reaching 94% on our test set, while the PointNet model only reached 83% accuracy. We also discuss the pros and cons of the different approaches.

MicroRNA-93 promotes the tumorigenesis of osteosarcoma by targeting TIMP2.

Osteosarcoma (OS) is the most frequent primary bone malignancy and affects adolescents and young adults. Recently dysregulation of miRNAs has received more attention because of its extensive role in OS carcinogenesis. This research was designed to verify how microRNA-93 (miR-93) and tissue inhibitor of matrix metalloproteinase 2 (TIMP2) be involved in OS development. At first, the levels of miR-93 and its predictive target gene TIMP2 were detected in OS and osteoblast cell lines, and 62 pairs OS and adjacent non-OS specimens by real-time PCR and western blot. Then, viability, invasion, and epithelial mesenchymal transition (EMT) of OS cell lines were examined when overexpressed or knocked down miR-93, or overexpressed TIMP2. Finally, the interaction between miR-93 and TIMP2 was evaluated using mutation, gain, and loss experiment. Our data indicated that miR-93 was increased while TIMP2 was decreased in both OS cell lines and tissues. MiR-93 high-expression and TIMP2 low-expression were related with poor overall survival and prognosis of OS patients. Overexpression or knockdown experiment indicated that miR-93 enhanced OS cell viability, invasion, and EMT expression. TIMP2 could inhibit OS cell viability, invasion, and EMT expression. Further, miR-93 directly targeted TIMP2 and negatively regulated TIMP2 level in OS cells. And up-regulation of TIMP2 reversed the effects of miR-93 in OS. Finally, miR-93 regulated the oncogenic functions in OS cells by regulating the expression of TIMP2. In conclusion, our study demonstrates that miR-93 may exert an oncogenic function while TIMP2 may act as a tumor suppressor on OS.

Identification of caveolin-1 domain signatures via machine learning and graphlet analysis of single-molecule super-resolution data.

MOTIVATION: Network analysis and unsupervised machine learning processing of single-molecule localization microscopy of caveolin-1 (Cav1) antibody labeling of prostate cancer cells identified biosignatures and structures for caveolae and three distinct non-caveolar scaffolds (S1A, S1B and S2). To obtain further insight into low-level molecular interactions within these different structural domains, we now introduce graphlet decomposition over a range of proximity thresholds and show that frequency of different subgraph (k = 4 nodes) patterns for machine learning approaches (classification, identification, automatic labeling, etc.) effectively distinguishes caveolae and scaffold blobs. RESULTS: Caveolae formation requires both Cav1 and the adaptor protein CAVIN1 (also called PTRF). As a supervised learning approach, we applied a wide-field CAVIN1/PTRF mask to CAVIN1/PTRF-transfected PC3 prostate cancer cells and used the random forest classifier to classify blobs based on graphlet frequency distribution (GFD). GFD of CAVIN1/PTRF-positive (PTRF+) and -negative Cav1 clusters showed poor classification accuracy that was significantly improved by stratifying the PTRF+ clusters by either number of localizations or volume. Low classification accuracy (<50%) of large PTRF+ clusters and caveolae blobs identified by unsupervised learning suggests that their GFD is specific to caveolae. High classification accuracy for small PTRF+ clusters and caveolae blobs argues that CAVIN1/PTRF associates not only with caveolae but also non-caveolar scaffolds. At low proximity thresholds (50-100 nm), the caveolae groups showed reduced frequency of highly connected graphlets and increased frequency of completely disconnected graphlets. GFD analysis of single-molecule localization microscopy Cav1 clusters defines changes in structural organization in caveolae and scaffolds independent of association with CAVIN1/PTRF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Factors Influencing Gene Family Size Variation Among Related Species in a Plant Family, Solanaceae.

Gene duplication and loss contribute to gene content differences as well as phenotypic divergence across species. However, the extent to which gene content varies among closely related plant species and the factors responsible for such variation remain unclear. Here, using the Solanaceae family as a model and Pfam domain families as a proxy for gene families, we investigated variation in gene family sizes across species and the likely factors contributing to the variation. We found that genes in highly variable families have high turnover rates and tend to be involved in processes that have diverged between Solanaceae species, whereas genes in low-variability families tend to have housekeeping roles. In addition, genes in high- and low-variability gene families tend to be duplicated by tandem and whole genome duplication, respectively. This finding together with the observation that genes duplicated by different mechanisms experience different selection pressures suggest that duplication mechanism impacts gene family turnover. We explored using pseudogene number as a proxy for gene loss but discovered that a substantial number of pseudogenes are actually products of pseudogene duplication, contrary to the expectation that most plant pseudogenes are remnants of once-functional duplicates. Our findings reveal complex relationships between variation in gene family size, gene functions, duplication mechanism, and evolutionary rate. The patterns of lineage-specific gene family expansion within the Solanaceae provide the foundation for a better understanding of the genetic basis underlying phenotypic diversity in this economically important family.