Feulgen Cytophotometry of Pine Nuclei. II. Effect of Pectinase Used in Cell Sfparation

Pectinase used for cell separation prior to cytophotometry contains a DNase that is able to penetrate the cells of pine root tips and attack nuclear DNA. When pine root tips were exposed to 1% pectinase (pH 6.0), there was a decrease in nuclear DNA content at every sample point and a sharp drop between 16 and 20 hr. The effect of the DNase was eliminated by preparing the enzyme solution in 0.01 M sodium citrate or 0.001 M EDTA. It is suggested that hut denaturation of the DNase should also be effective and might be used in combination with the magnesium chelators.     

Differential Declines in DNA of Aging Leaf Tissues

Leaves from tobacco and peanut were sampled for cytophotometricstudy from young to senescent stages. Differences in nucleibetween tissues developed during aging. Tobacco epidermal cellsshowed an average loss of approximately one-third of their DNAand peanut epidermis lost more than one-fourth. Mesophyll nuclearDNA declined more slowly than epidermal. These results implythat leaf aging may begin in the epidermis. (Received March 6, 1982; Accepted August 23, 1982)     

Genetically identified protein kinases in yeast. I: Transcription, translation, transport and mating.

Studies from a wide array of different fields using Saccharomyces cerevisiae as an experimental organism have uncovered protein phosphorylation as a recurrent theme in the regulation of diverse cellular activities. Protein kinases in yeast regulate a variety of processes; this article discusses several genetically identified protein kinases and the roles that these kinases play in cell growth and development.     

Proteomic analysis of human osteoarthritis synovial fluid

Background

Osteoarthritis is a chronic musculoskeletal disorder characterized mainly by progressive degradation of the hyaline cartilage. Patients with osteoarthritis often postpone seeking medical help, which results in the diagnosis being made at an advanced stage of cartilage destruction. Sustained efforts are needed to identify specific markers that might help in early diagnosis, monitoring disease progression and in improving therapeutic outcomes. We employed a multipronged proteomic approach, which included multiple fractionation strategies followed by high resolution mass spectrometry analysis to explore the proteome of synovial fluid obtained from osteoarthritis patients. In addition to the total proteome, we also enriched glycoproteins from synovial fluid using lectin affinity chromatography.

Results

We identified 677 proteins from synovial fluid of patients with osteoarthritis of which 545 proteins have not been previously reported. These novel proteins included ADAM-like decysin 1 (ADAMDEC1), alanyl (membrane) aminopeptidase (ANPEP), CD84, fibulin 1 (FBLN1), matrix remodelling associated 5 (MXRA5), secreted phosphoprotein 2 (SPP2) and spondin 2 (SPON2). We identified 300 proteins using lectin affinity chromatography, including the glycoproteins afamin (AFM), attractin (ATRN), fibrillin 1 (FBN1), transferrin (TF), tissue inhibitor of metalloproteinase 1 (TIMP1) and vasorin (VSN). Gene ontology analysis confirmed that a majority of the identified proteins were extracellular and are mostly involved in cell communication and signaling. We also confirmed the expression of ANPEP, dickkopf WNT signaling pathway inhibitor 3 (DKK3) and osteoglycin (OGN) by multiple reaction monitoring (MRM) analysis of osteoarthritis synovial fluid samples.

Conclusions

We present an in-depth analysis of the synovial fluid proteome from patients with osteoarthritis. We believe that the catalog of proteins generated in this study will further enhance our knowledge regarding the pathophysiology of osteoarthritis and should assist in identifying better biomarkers for early diagnosis.     

BAsE-Seq: a method for obtaining long viral haplotypes from short sequence reads

Epidemiologists aim to inform the design of public health interventions with evidence on the evolution, emergence and spread of infectious diseases. Sequencing of pathogen genomes, together with date, location, clinical manifestation and other relevant data about sample origins, can contribute to describing nearly every aspect of transmission dynamics, including local transmission and global spread. The analyses of these data have implications for all levels of clinical and public health practice, from institutional infection control to policies for surveillance, prevention and treatment. This review highlights the range of epidemiological questions that can be addressed from the combination of genome sequence and traditional ‘line lists’ (tables of epidemiological data where each line includes demographic and clinical features of infected individuals). We identify opportunities for these data to inform interventions that reduce disease incidence and prevalence. By considering current limitations of, and challenges to, interpreting these data, we aim to outline a research agenda to accelerate the genomics-driven transformation in public health microbiology.     

The landscape of transposable elements in the finished genome of the fungal wheat pathogen Mycosphaerella graminicola

Background

In addition to gene identification and annotation, repetitive sequence analysis has become an integral part of genome sequencing projects. Identification of repeats is important not only because it improves gene prediction, but also because of the role that repetitive sequences play in determining the structure and evolution of genes and genomes. Several methods using different repeat-finding strategies are available for whole-genome repeat sequence analysis. Four independent approaches were used to identify and characterize the repetitive fraction of the Mycosphaerella graminicola (synonym Zymoseptoria tritici) genome. This ascomycete fungus is a wheat pathogen and its finished genome comprises 21 chromosomes, eight of which can be lost with no obvious effects on fitness so are dispensable.

Results

Using a combination of four repeat-finding methods, at least 17% of the M. graminicola genome was estimated to be repetitive. Class I transposable elements, that amplify via an RNA intermediate, account for about 70% of the total repetitive content in the M. graminicola genome. The dispensable chromosomes had a higher percentage of repetitive elements as compared to the core chromosomes. Distribution of repeats across the chromosomes also varied, with at least six chromosomes showing a non-random distribution of repetitive elements. Repeat families showed transition mutations and a CpA → TpA dinucleotide bias, indicating the presence of a repeat-induced point mutation (RIP)-like mechanism in M. graminicola. One gene family and two repeat families specific to subtelomeres also were identified in the M. graminicola genome. A total of 78 putative clusters of nested elements was found in the M. graminicola genome. Several genes with putative roles in pathogenicity were found associated with these nested repeat clusters. This analysis of the transposable element content in the finished M. graminicola genome resulted in a thorough and highly curated database of repetitive sequences.

Conclusions

This comprehensive analysis will serve as a scaffold to address additional biological questions regarding the origin and fate of transposable elements in fungi. Future analyses of the distribution of repetitive sequences in M. graminicola also will be able to provide insights into the association of repeats with genes and their potential role in gene and genome evolution.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1132) contains supplementary material, which is available to authorized users.     

Robust computational analysis of rRNA hypervariable tag datasets

Next-generation DNA sequencing is increasingly being utilized to probe microbial communities, such as gastrointestinal microbiomes, where it is important to be able to quantify measures of abundance and diversity. The fragmented nature of the 16S rRNA datasets obtained, coupled with their unprecedented size, has led to the recognition that the results of such analyses are potentially contaminated by a variety of artifacts, both experimental and computational. Here we quantify how multiple alignment and clustering errors contribute to overestimates of abundance and diversity, reflected by incorrect OTU assignment, corrupted phylogenies, inaccurate species diversity estimators, and rank abundance distribution functions. We show that straightforward procedural optimizations, combining preexisting tools, are effective in handling large (10(5)-10(6)) 16S rRNA datasets, and we describe metrics to measure the effectiveness and quality of the estimators obtained. We introduce two metrics to ascertain the quality of clustering of pyrosequenced rRNA data, and show that complete linkage clustering greatly outperforms other widely used methods.