Ultrastructural changes induced by juvenile hormone analogue in oöcyte membranes of apterous4Drosophila melanogaster

Egg chambers from apterous⁴ (ap⁴), a female sterile mutant of Drosophila melanogaster, show none of the microvilli or pinocytotic vesicles which are a prominent feature of the membrane of the wild-type vitellogenic oöcyte. The studies reported here show that a juvenile hormone analogue (ZR515) stimulates formation of microvilli and pinocytotic vesicles in oöcytes of ap⁴ flies. Within 12 hr after topical application of ZR515 to homozygous ap⁴ females the oöcyte membranes exhibit extensive microvilli and pinocytotic activity. The follicle-cell surface adjoining the oöcyte also shows some changes. In vitro studies in which ap⁴ ovaries were incubated in Schneider's Drosophila tissue-culture medium in the presence of ZR515 with or without female haemolymph, or in the absence of ZR515, showed that the analogue acts alone directly on the ovary to cause formation of microvilli and pinocytotic vesicles on the oöcyte membrane.     

Identification of function for CD44 intracytoplasmic domain (CD44-ICD): modulation of matrix metalloproteinase 9 (MMP-9) transcription via novel promoter response element

CD44 is a multifunctional cell receptor that conveys a cancer phenotype, regulates macrophage inflammatory gene expression and vascular gene activation in proatherogenic environments, and is also a marker of many cancer stem cells. CD44 undergoes sequential proteolytic cleavages that produce an intracytoplasmic domain called CD44-ICD. However, the role of CD44-ICD in cell function is unknown. We take a major step toward the elucidation of the CD44-ICD function by using a CD44-ICD-specific antibody, a modification of a ChIP assay to detect small molecules, and extensive computational analysis. We show that CD44-ICD translocates into the nucleus, where it then binds to a novel DNA consensus sequence in the promoter region of the MMP-9 gene to regulate its expression. We also show that the expression of many other genes that contain this novel response element in their promoters is up- or down-regulated by CD44-ICD. Furthermore, hypoxia-inducible factor-1α (Hif1α)-responsive genes also have the CD44-ICD consensus sequence and respond to CD44-ICD induction under normoxic conditions and therefore independent of Hif1α expression. Additionally, CD44-ICD early responsive genes encode for critical enzymes in the glycolytic pathway, revealing how CD44 could be a gatekeeper of the Warburg effect (aerobic glycolysis) in cancer cells and possibly cancer stem cells. The link of CD44 to metabolism is novel and opens a new area of research not previously considered, particularly in the study of obesity and cancer. In summary, our results finally give a function to the CD44-ICD and will accelerate the study of the regulation of many CD44-dependent genes.     

Motif mining: an assessment and perspective for amyloid fibril prediction tool

Amyloid fibril forming regions in protein sequences are associated with a number of diseases. Experimental evidences compel in favor of the hypothesis that short motif regions are responsible for its amyloidogenic behavior. Thus, identifying these short peptides is critical in understanding the cause of diseases associated with aggregation of proteins and developing sequencetargeted anti-aggregation drugs. Owing to the constraints of wet lab molecular techniques for the identification of amyloid fibril forming targets, computational methods are implemented to offer better and affordable in silico predictions. The present study takes into consideration an assessment and perspective of the recent tools available for predicting a peptide status: amyloidogenic or non-amyloidogenic. To the best of our knowledge, the existing review articles on amyloidogenic prediction tools have not touched upon their effectiveness in terms of true positive rates or false positive rates. In this work, we compare few tools such as Aggrescan, Amylpred and FoldAmyloid to evaluate the performance of their predictability based on the experimentally proved data in terms of specificity, sensitivity, Matthews Correlation Coefficient and Balanced accuracy. As evident from the results, a significant reduction of sensitivity associated with a gain in specificity is noted in all the tools considered under the present study.     

Machine learning study of classifiers trained with biophysiochemical properties of amino acids to predict fibril forming Peptide motifs

It is important to understand the cause of amyloid illnesses by predicting the short protein fragments capable of forming amyloid-like fibril motifs aiding in the discovery of sequence-targeted anti-aggregation drugs. It is extremely desirable to design computational tools to provide affordable in silico predictions owing to the limitations of molecular techniques for their identification. In this research article, we tried to study, from a machine learning perspective, the performance of several machine learning classifiers that use heterogenous features based on biochemical and biophysical properties of amino acids to discriminate between amyloidogenic and non-amyloidogenic regions in peptides. Four conventional machine learning classifiers namely Support Vector Machine, Neural network, Decision tree and Random forest were trained and tested to find the best classifier that fits the problem domain well. Prior to classification, novel implementations of two biologically-inspired feature optimization techniques based on evolutionary algorithms and methodologies that mimic social life and a multivariate method based on projection are utilized in order to remove the unimportant and uninformative features. Among the dimenionality reduction algorithms considered under the study, prediction results show that algorithms based on evolutionary computation is the most effective. SVM best suits the problem domain in its fitment among the classifiers considered. The best classifier is also compared with an online predictor to evidence the equilibrium maintained between true positive rates and false positive rates in the proposed classifier. This exploratory study suggests that these methods are promising in providing amyloidogenity prediction and may be further extended for large-scale proteomic studies.     

Crystal structure of trehalose-6-phosphate phosphatase-related protein: biochemical and biological implications

We report here the crystal structure of a trehalose-6-phosphate phosphatase-related protein (T6PP) from Thermoplasma acidophilum, TA1209, determined by the dual-wavelength anomalous diffraction (DAD) method. T6PP is a member of the haloacid dehalogenase (HAD) superfamily with significant sequence homology with trehalose-6-phosphate phosphatase, phosphoserine phosphatase, P-type ATPases and other members of the family. T6PP possesses a core domain of known alpha/beta-hydrolase fold, characteristic of the HAD family, and a cap domain, with a tertiary fold consisting of a four-stranded beta-sheet with two alpha-helices on one side of the sheet. An active-site magnesium ion and a glycerol molecule bound at the interface between the two domains provide insight into the mode of substrate binding by T6PP. A trehalose-6-phosphate molecule modeled into a cage formed by the two domains makes favorable interactions with the protein molecule. We have confirmed that T6PP is a trehalose phosphatase from amino acid sequence, three-dimensional structure, and biochemical assays.     

Chemical shift optimization in multidimensional NMR spectra by AUREMOL-SHIFTOPT

A problem often encountered in multidimensional NMR-spectroscopy is that an existing chemical shift list of a protein has to be used to assign an experimental spectrum but does not fit sufficiently well for a safe assignment. A similar problem occurs when temperature or pressure series of n-dimensional spectra are to be evaluated automatically. We have developed two different algorithms, AUREMOL-SHIFTOPT1 and AUREMOL-SHIFTOPT2 that fulfill this task. In the present contribution their performance is analyzed employing a set of simulated and experimental two-dimensional and three-dimensional spectra obtained from three different proteins. A new z-score based on atom and amino acid specific chemical shift distributions is introduced to weight the chemical shift contributions in different dimensions properly.     

Isolation and identification of an anticancer drug,taxol from <Emphasis Type="Italic">Phyllosticta tabernaemontanae</Emphasis>, a leaf spot fungus of an angiosperm, <Emphasis Type="Italic">Wrightia tinctoria</Emphasis>

Phyllosticta tabernaemontanae, a leaf spot fungus isolated from the diseased leaves of Wrightia tinctoria, showed the production of taxol, an anticancer drug, on modified liquid medium (MID) and potato dextrose broth (PDB) medium in culture for the first time. The presence of taxol was confirmed by spectroscopic and chromatographic methods of analysis. The amount of taxol produced by this fungus was quantified using high performance liquid chromatography (HPLC). The maximum amount of taxol production was recorded in the fungus grown on MID medium (461 μg/L) followed by PDB medium (150 μg/L). The production rate was increased to 9.2 × 10³ fold than that found in the culture broth of earlier reported fungus, Taxomyces andreanae. The results designate that P. tabernaemontanae is an excellent candidate for taxol production. The fungal taxol extracted also showed a strong cytotoxic activity in the in vitro culture of tested human cancer cells by apoptotic assay.