PTP1B regulates leptin signal transduction in vivo

Mice lacking the protein-tyrosine phosphatase PTP1B are hypersensitive to insulin and resistant to obesity. However, the molecular basis for resistance to obesity has been unclear. Here we show that PTP1B regulates leptin signaling. In transfection studies, PTP1B dephosphorylates the leptin receptor-associated kinase, Jak2. PTP1B is expressed in hypothalamic regions harboring leptin-responsive neurons. Compared to wild-type littermates, PTP1B(-/-) mice have decreased leptin/body fat ratios, leptin hypersensitivity, and enhanced leptin-induced hypothalamic Stat3 tyrosyl phosphorylation. Gold thioglucose treatment, which ablates leptin-responsive hypothalamic neurons, partially overcomes resistance to obesity in PTP1B(-/-) mice. Our data indicate that PTP1B regulates leptin signaling in vivo, likely by targeting Jak2. PTP1B may be a novel target to treat leptin resistance in obesity.     

Mito‐nuclear discord in six congeneric lineages of Holarctic ducks (genus Anas)

Many species have Holarctic distributions that extend across Europe, Asia and North America. Most genetics research on these species has examined only mitochondrial (mt) DNA, which has revealed wide variance in divergence between Old World (OW) and New World (NW) populations, ranging from shallow, unstructured genealogies to deeply divergent lineages. In this study, we sequenced 20 nuclear introns to test for concordant patterns of OW–NW differentiation between mtDNA and nuclear (nu) DNA for six lineages of Holarctic ducks (genus Anas). Genetic differentiation for both marker types varied widely among these lineages (idiosyncratic population histories), but mtDNA and nuDNA divergence within lineages was not significantly correlated. Moreover, compared with the association between mtDNA and nuDNA divergence observed among different species, OW–NW nuDNA differentiation was generally lower than mtDNA divergence, at least for lineages with deeply divergent mtDNA. Furthermore, coalescent estimates indicated significantly higher rates of gene flow for nuDNA than mtDNA for four of the six lineages. Thus, Holarctic ducks show prominent mito‐nuclear discord between OW and NW populations, and we reject differences in sorting rates as the sole cause of the within‐species discord. Male‐mediated intercontinental gene flow is likely a leading contributor to this discord, although selection could also cause increased mtDNA divergence relative to weak nuDNA differentiation. The population genetics of these ducks contribute to growing evidence that mtDNA can be an unreliable indicator of stage of speciation and that more holistic approaches are needed for species delimitation.     

A Foundation for Reliable Spatial Proteomics Data Analysis

Quantitative mass-spectrometry-based spatial proteomics involves elaborate, expensive, and time-consuming experimental procedures, and considerable effort is invested in the generation of such data. Multiple research groups have described a variety of approaches for establishing high-quality proteome-wide datasets. However, data analysis is as critical as data production for reliable and insightful biological interpretation, and no consistent and robust solutions have been offered to the community so far. Here, we introduce the requirements for rigorous spatial proteomics data analysis, as well as the statistical machine learning methodologies needed to address them, including supervised and semi-supervised machine learning, clustering, and novelty detection. We present freely available software solutions that implement innovative state-of-the-art analysis pipelines and illustrate the use of these tools through several case studies involving multiple organisms, experimental designs, mass spectrometry platforms, and quantitation techniques. We also propose sound analysis strategies for identifying dynamic changes in subcellular localization by comparing and contrasting data describing different biological conditions. We conclude by discussing future needs and developments in spatial proteomics data analysis.     

Long-Term Citizen-Collected Data Reveal Geographical Patterns and Temporal Trends in Lake Water Clarity

We compiled a lake-water clarity database using publically available, citizen volunteer observations made between 1938 and 2012 across eight states in the Upper Midwest, USA. Our objectives were to determine (1) whether temporal trends in lake-water clarity existed across this large geographic area and (2) whether trends were related to the lake-specific characteristics of latitude, lake size, or time period the lake was monitored. Our database consisted of >140,000 individual Secchi observations from 3,251 lakes that we summarized per lake-year, resulting in 21,020 summer averages. Using Bayesian hierarchical modeling, we found approximately a 1% per year increase in water clarity (quantified as Secchi depth) for the entire population of lakes. On an individual lake basis, 7% of lakes showed increased water clarity and 4% showed decreased clarity. Trend direction and strength were related to latitude and median sample date. Lakes in the southern part of our study-region had lower average annual summer water clarity, more negative long-term trends, and greater inter-annual variability in water clarity compared to northern lakes. Increasing trends were strongest for lakes with median sample dates earlier in the period of record (1938–2012). Our ability to identify specific mechanisms for these trends is currently hampered by the lack of a large, multi-thematic database of variables that drive water clarity (e.g., climate, land use/cover). Our results demonstrate, however, that citizen science can provide the critical monitoring data needed to address environmental questions at large spatial and long temporal scales. Collaborations among citizens, research scientists, and government agencies may be important for developing the data sources and analytical tools necessary to move toward an understanding of the factors influencing macro-scale patterns such as those shown here for lake water clarity.     

Wavelet-Based 3D Reconstruction of Microcalcification Clusters from Two Mammographic Views: New Evidence That Fractal Tumors Are Malignant and Euclidean Tumors Are Benign

The 2D Wavelet-Transform Modulus Maxima (WTMM) method was used to detect microcalcifications (MC) in human breast tissue seen in mammograms and to characterize the fractal geometry of benign and malignant MC clusters. This was done in the context of a preliminary analysis of a small dataset, via a novel way to partition the wavelet-transform space-scale skeleton. For the first time, the estimated 3D fractal structure of a breast lesion was inferred by pairing the information from two separate 2D projected mammographic views of the same breast, i.e. the cranial-caudal (CC) and mediolateral-oblique (MLO) views. As a novelty, we define the “CC-MLO fractal dimension plot”, where a “fractal zone” and “Euclidean zones” (non-fractal) are defined. 118 images (59 cases, 25 malignant and 34 benign) obtained from a digital databank of mammograms with known radiologist diagnostics were analyzed to determine which cases would be plotted in the fractal zone and which cases would fall in the Euclidean zones. 92% of malignant breast lesions studied (23 out of 25 cases) were in the fractal zone while 88% of the benign lesions were in the Euclidean zones (30 out of 34 cases). Furthermore, a Bayesian statistical analysis shows that, with 95% credibility, the probability that fractal breast lesions are malignant is between 74% and 98%. Alternatively, with 95% credibility, the probability that Euclidean breast lesions are benign is between 76% and 96%. These results support the notion that the fractal structure of malignant tumors is more likely to be associated with an invasive behavior into the surrounding tissue compared to the less invasive, Euclidean structure of benign tumors. Finally, based on indirect 3D reconstructions from the 2D views, we conjecture that all breast tumors considered in this study, benign and malignant, fractal or Euclidean, restrict their growth to 2-dimensional manifolds within the breast tissue.     

Extraction and Analysis of Cortisol from Human and Monkey Hair

The stress hormone cortisol (CORT) is slowly incorporated into the growing hair shaft of humans, nonhuman primates, and other mammals. We developed and validated a method for CORT extraction and analysis from rhesus monkey hair and subsequently adapted this method for use with human scalp hair. In contrast to CORT "point samples" obtained from plasma or saliva, hair CORT provides an integrated measure of hypothalamic-pituitary-adrenocortical (HPA) system activity, and thus physiological stress, during the period of hormone incorporation. Because human scalp hair grows at an average rate of 1 cm/month, CORT levels obtained from hair segments several cm in length can potentially serve as a biomarker of stress experienced over a number of months.In our method, each hair sample is first washed twice in isopropanol to remove any CORT from the outside of the hair shaft that has been deposited from sweat or sebum. After drying, the sample is ground to a fine powder to break up the hair''s protein matrix and increase the surface area for extraction. CORT from the interior of the hair shaft is extracted into methanol, the methanol is evaporated, and the extract is reconstituted in assay buffer. Extracted CORT, along with standards and quality controls, is then analyzed by means of a sensitive and specific commercially available enzyme immunoassay (EIA) kit. Readout from the EIA is converted to pg CORT per mg powdered hair weight. This method has been used in our laboratory to analyze hair CORT in humans, several species of macaque monkeys, marmosets, dogs, and polar bears. Many studies both from our lab and from other research groups have demonstrated the broad applicability of hair CORT for assessing chronic stress exposure in natural as well as laboratory settings.     

Complete viral RNA genome sequencing of ultra-low copy samples by sequence-independent amplification

RNA viruses are the causative agents for AIDS, influenza, SARS, and other serious health threats. Development of rapid and broadly applicable methods for complete viral genome sequencing is highly desirable to fully understand all aspects of these infectious agents as well as for surveillance of viral pandemic threats and emerging pathogens. However, traditional viral detection methods rely on prior sequence or antigen knowledge. In this study, we describe sequence-independent amplification for samples containing ultra-low amounts of viral RNA coupled with Illumina sequencing and de novo assembly optimized for viral genomes. With 5 million reads, we capture 96 to 100% of the viral protein coding region of HIV, respiratory syncytial and West Nile viral samples from as little as 100 copies of viral RNA. The methods presented here are scalable to large numbers of samples and capable of generating full or near full length viral genomes from clone and clinical samples with low amounts of viral RNA, without prior sequence information and in the presence of substantial host contamination.