Bacterial composition of soils of the Lake Wellman area, Darwin Mountains, Antarctica

The aim of this study was to examine the bacterial composition of high latitude soils from the Darwin–Hatherton glacier region of Antarctica. Four soil pits on each of four glacial drift sheets were sampled for chemical and microbial analyses. The four drifts—Hatherton, Britannia, Danum, and Isca—ranged, respectively, from early Holocene (10 ky) to mid-Quaternary (ca 900 ky). Numbers of culturable bacteria were low, with highest levels detected in soils from the younger Hatherton drift. DNA was extracted and 16S rRNA gene clone libraries prepared from samples below the desert pavement for each of the four drift sheets. Between 31 and 262 clones were analysed from each of the Hatherton, Britannia, and Danum drifts. Bacterial sequences were dominated by members of the phyla Deinococcus-Thermus, Actinobacteria, and Bacteroidetes. Culturable bacteria, including some that clustered with soil clones (e.g., members of the genera Arthrobacter, Adhaeribacter, and Pontibacter), belonged to Actinobacteria and Bacteroidetes. The isolated bacteria are ideal model organisms for genomic and phenotypic investigations of those attributes that allow bacteria to survive and/or grow in Antarctic soils because they have close relatives that are not tolerant of these conditions.     

Legacies of Prehistoric Agricultural Practices Within Plant and Soil Properties Across an Arid Ecosystem

Closely integrated research between archaeologists and ecologists provides a long-term view of human land use that is rare in the ecological literature, allowing for investigation of activities that lead to enduring environmental outcomes. This extended temporal perspective is particularly important in aridlands where succession occurs slowly and ecosystem processes are mediated by abiotic, geomorphic factors. Numerous studies show that impacts from ancient human actions can persist, but few have explored the types of practices or mechanisms that lead to either transient or long-term environmental change. We compared plant and soil properties and processes from a range of landscape patch types in the Sonoran Desert of the US Southwest that supported different, well-documented prehistoric farming practices from AD 750–1300. Our results show that the types of ancient human activities that leave long-term ecological legacies in aridlands are those that fundamentally alter “slow variables” such as soil properties that regulate the timing and supply of water. Prehistoric Hohokam floodwater-irrigation practices, but not dryland farming techniques, substantially altered soil texture, which was strongly associated with desert plant community and functional composition. However, prehistoric agriculture did not consistently alter long-term nutrient availability and thus had no impact on “fast variables” such as production of seasonal annual plants that are restricted to periods of ample rainfall. In this arid ecosystem, the inverse texture model explained patterns in plant functional composition at large scales, but is less predictive of production of short-lived desert annuals that experience a more mesic precipitation regime.     

Avoiding pitfalls in bone marrow engraftment analysis: a case study highlighting the weakness of using buccal cells for determining a patient's constitutional genotype after hematopoietic stem cell transplantation

Background aimsAn accurate and reliable assessment of bone marrow engraftment (BME) after hematopoietic stem cell transplantation (HSCT) is based on the ability to distinguish between recipient and donor cells at selected polymorphic short tandem repeat (STR) DNA loci. Buccal cells are an important source of DNA for determining the recipient's constitutional genotype, particularly in patients transplanted before the STR evaluation.MethodsGenomic DNA was extracted from the recipient buccal cells and from isolated CD3⁺ (T-cell lymphocyte) and CD33⁺ (myelocyte) cells after HSCT. BME analysis was performed using a STR-based polymerase chain reaction amplification method followed by fragment-size analysis for assessing the recipient-derived or donor-derived composition of cell lineage-specific peripheral blood DNA.ResultsWe identified three cases of complete buccal epithelial cell engraftment after HSCT detected by BME analysis, potentially leading to misinterpretation of testing results if these cells were used as the sole source for determining the recipient's genotype.ConclusionsThese cases suggest that complete engraftment of buccal epithelial cells may be a common finding in patients receiving HSCT, drawing attention to important issues such as the type of samples used for determining a patient's constitutional genotype that may confound testing results. This study also highlights the need for careful interpretation of the BME testing results in the context of the clinical findings.     

Trypsin Potentiates Human Fibrocyte Differentiation

Trypsin-containing topical treatments can be used to speed wound healing, although the mechanism of action is unknown. To help form granulation tissue and heal wounds, monocytes leave the circulation, enter the wound tissue, and differentiate into fibroblast-like cells called fibrocytes. We find that 20 to 200 ng/ml trypsin (concentrations similar to those used in wound dressings) potentiates the differentiation of human monocytes to fibrocytes in cell culture. Adding trypsin inhibitors increases the amount of trypsin needed to potentiate fibrocyte differentiation, suggesting that the potentiating effect is dependent on trypsin proteolytic activity. Proteases with other site specificities such as pepsin, endoprotease GluC, and chymotrypsin do not potentiate fibrocyte differentiation. This potentiation requires the presence of albumin in the culture medium, and tryptic fragments of human or bovine albumin also potentiate fibrocyte differentiation. These results suggest that topical trypsin speeds wound healing by generating tryptic fragments of albumin, which in turn potentiate fibrocyte differentiation.     

A Hybrid Likelihood Model for Sequence-Based Disease Association Studies

In the past few years, case-control studies of common diseases have shifted their focus from single genes to whole exomes. New sequencing technologies now routinely detect hundreds of thousands of sequence variants in a single study, many of which are rare or even novel. The limitation of classical single-marker association analysis for rare variants has been a challenge in such studies. A new generation of statistical methods for case-control association studies has been developed to meet this challenge. A common approach to association analysis of rare variants is the burden-style collapsing methods to combine rare variant data within individuals across or within genes. Here, we propose a new hybrid likelihood model that combines a burden test with a test of the position distribution of variants. In extensive simulations and on empirical data from the Dallas Heart Study, the new model demonstrates consistently good power, in particular when applied to a gene set (e.g., multiple candidate genes with shared biological function or pathway), when rare variants cluster in key functional regions of a gene, and when protective variants are present. When applied to data from an ongoing sequencing study of bipolar disorder (191 cases, 107 controls), the model identifies seven gene sets with nominal p-values0.05, of which one MAPK signaling pathway (KEGG) reaches trend-level significance after correcting for multiple testing.     

Distance-based analysis of variance for brain connectivity

The field of neuroimaging dedicated to mapping connections in the brain is increasingly being recognized as key for understanding neurodevelopment and pathology. Networks of these connections are quantitatively represented using complex structures, including matrices, functions, and graphs, which require specialized statistical techniques for estimation and inference about developmental and disorder-related changes. Unfortunately, classical statistical testing procedures are not well suited to high-dimensional testing problems. In the context of global or regional tests for differences in neuroimaging data, traditional analysis of variance (ANOVA) is not directly applicable without first summarizing the data into univariate or low-dimensional features, a process that might mask the salient features of high-dimensional distributions. In this work, we consider a general framework for two-sample testing of complex structures by studying generalized within-group and between-group variances based on distances between complex and potentially high-dimensional observations. We derive an asymptotic approximation to the null distribution of the ANOVA test statistic, and conduct simulation studies with scalar and graph outcomes to study finite sample properties of the test. Finally, we apply our test to our motivating study of structural connectivity in autism spectrum disorder.