Expression and DNA-binding activity of MYCN/Max and Mnt/Max during induced differentiation of human neuroblastoma cells

Amplification of MYCN is one of the most important prognostic markers for neuroblastoma and is correlated with rapid tumor progression and poor prognosis. MYCN belongs to the Myc/Max/Mad/Mnt network of proteins that regulate proliferation, apoptosis, and differentiation. It is well established that MYCN is downregulated during induced differentiation of neuroblastoma cells carrying an amplified MYCN gene, but very little is known about other components of the network, i.e., the Max, Mad, and Mnt proteins, during this process. In this study we show that Mad and Mnt expression was only modestly regulated in differentiating SK-N-BE(2) neuroblastoma cells, while MYCN was rapidly downregulated. This downregulation was reflected in a decreased MYCN/Max DNA-binding activity while the Mnt/Max binding did not change during differentiation. In parallel experiments we also analyzed the Myc/Max/Mad expression and DNA binding capacity during induced differentiation in the MYCN single copy neuroblastoma cell line SH-SY5Y. In this cell line only modest changes in expression of the components of the MYCN/Max/Mad/Mnt network was detected, but since the cell line expresses relatively low levels of MYCN and c-Myc, these changes might be of functional significance. Cell cycle analyses of SK-N-BE(2) demonstrated an increase in the G1-phase fraction after RA-treatment. These data show that the decreased MYCN expression and MYCN DNA-binding is correlated with retarded cell cycle progression. Furthermore, when Mad1 or Mnt was overexpressed in SK-N-BE(2) cells they retained the capacity to differentiate, underscoring the notion that MYCN downregulation, and not changes in Mad/Mnt expression, is essential for neuroblastoma cell differentiation.     

Influence of Whey Protein Concentrate on the Production of Antibacterial Peptides Derived from Fermented Milk by Lactic Acid Bacteria

In the study, growth, proteolysis and antimicrobial activity of lactic acid bacteria were evaluated in skim milk medium supplemented with different concentration of whey protein concentrate (WPC 70). Lactobacillus helveticus (V3) showed maximum pH reduction with 1% WPC. Lactobacillus rhamnosus (NS4) also produced maximum lactic acid production and viable cells counts at 1 and 1.5% WPC, respectively. However, V3 showed maximum proteolytic activity with 1.5% WPC. Streptococcus thermophilus (MD2) was found to exhibit maximum antimicrobial activity with 1.5% WPC. Peptides formed during fermentation were purified by RP-HPLC and identified using RP-LC/MS analysis. Antimicrobial peptide was identified as lactoferrin, which was found in fermented milk supplemented with 1.5% WPC by NS4.     

Simple algorithms for the estimation of the initial number of individuals in commingled skeletal remains

The determination of the number of individuals represented within commingled remains is based on two types of estimators, those assessing the minimum number of individuals and those assessing the most likely number of individuals. Much as the latter produce improved results, they still exhibit significant drawbacks, which are related to the misidentification of the number of pairs between the existing bilateral elements. This article addresses these problems through the use of two computer algorithms. One algorithm produces a number of potential pairs between bilateral elements and the other estimates the number of individuals in a commingled sample by incorporating the percentages of lost and altered bones into the analysis. These algorithms were validated using hypothetical and actual skeletal samples, and are more effective in comparison to any conventional estimators, particularly in cases, where the elements are poorly preserved.     

Mild Concussion,but Not Moderate Traumatic Brain Injury,Is Associated with Long-Term Depression-Like Phenotype in Mice

Mild traumatic brain injuries can lead to long-lasting cognitive and motor deficits, increasing the risk of future behavioral, neurological, and affective disorders. Our study focused on long-term behavioral deficits after repeated injury in which mice received either a single mild CHI (mCHI), a repeated mild CHI (rmCHI) consisting of one impact to each hemisphere separated by 3 days, or a moderate controlled cortical impact injury (CCI). Shams received only anesthesia. Behavioral tests were administered at 1, 3, 5, 7, and 90 days post-injury (dpi). CCI animals showed significant motor and sensory deficits in the early (1–7 dpi) and long-term (90 dpi) stages of testing. Interestingly, sensory and subtle motor deficits in rmCHI animals were found at 90 dpi. Most importantly, depression-like behaviors and social passiveness were observed in rmCHI animals at 90 dpi. These data suggest that mild concussive injuries lead to motor and sensory deficits and affective disorders that are not observed after moderate TBI.     

An Evaluation of High-Throughput Approaches to QTL Mapping in Saccharomyces cerevisiae

Dissecting the molecular basis of quantitative traits is a significant challenge and is essential for understanding complex diseases. Even in model organisms, precisely determining causative genes and their interactions has remained elusive, due in part to difficulty in narrowing intervals to single genes and in detecting epistasis or linked quantitative trait loci. These difficulties are exacerbated by limitations in experimental design, such as low numbers of analyzed individuals or of polymorphisms between parental genomes. We address these challenges by applying three independent high-throughput approaches for QTL mapping to map the genetic variants underlying 11 phenotypes in two genetically distant Saccharomyces cerevisiae strains, namely (1) individual analysis of >700 meiotic segregants, (2) bulk segregant analysis, and (3) reciprocal hemizygosity scanning, a new genome-wide method that we developed. We reveal differences in the performance of each approach and, by combining them, identify eight polymorphic genes that affect eight different phenotypes: colony shape, flocculation, growth on two nonfermentable carbon sources, and resistance to two drugs, salt, and high temperature. Our results demonstrate the power of individual segregant analysis to dissect QTL and address the underestimated contribution of interactions between variants. We also reveal confounding factors like mutations and aneuploidy in pooled approaches, providing valuable lessons for future designs of complex trait mapping studies.     

Biogeography of microbial communities in high-latitude ecosystems: Contrasting drivers for methanogens,methanotrophs and global prokaryotes

Methane-cycling is becoming more important in high-latitude ecosystems as global warming makes permafrost organic carbon increasingly available. We explored 387 samples from three high-latitudes regions (Siberia, Alaska and Patagonia) focusing on mineral/organic soils (wetlands, peatlands, forest), lake/pond sediment and water. Physicochemical, climatic and geographic variables were integrated with 16S rDNA amplicon sequences to determine the structure of the overall microbial communities and of specific methanogenic and methanotrophic guilds. Physicochemistry (especially pH) explained the largest proportion of variation in guild composition, confirming species sorting (i.e., environmental filtering) as a key mechanism in microbial assembly. Geographic distance impacted more strongly beta diversity for (i) methanogens and methanotrophs than the overall prokaryotes and, (ii) the sediment habitat, suggesting that dispersal limitation contributed to shape the communities of methane-cycling microorganisms. Bioindicator taxa characterising different ecological niches (i.e., specific combinations of geographic, climatic and physicochemical variables) were identified, highlighting the importance of Methanoregula as generalist methanogens. Methylocystis and Methylocapsa were key methanotrophs in low pH niches while Methylobacter and Methylomonadaceae in neutral environments. This work gives insight into the present and projected distribution of methane-cycling microbes at high latitudes under climate change predictions, which is crucial for constraining their impact on greenhouse gas budgets.