Tunicamycin-resistant mutants of Bacillus amyloliquefaciens are deficient in amylase, protease and penicillinase synthesis and have altered sensitivity to antibiotics and autolysis

Mutants of Bacillus amyloliquefaciens resistant to at least 10 micrograms/ml of tunicamycin were isolated and shown to be pleiotropic. The mutants were more resistant to streptomycin, chloramphenicol, kanamycin and neomycin than was the parent strain but less resistant to penicillin G and tetracycline. They were more autolytic, presumably due to an altered cell wall. The mutants produced reduced levels of amylase, penicillinase and both metal and serine protease besides having an enhanced sporulation frequency and being more motile.     

Hydroxylation of benzoate and its chlorinated derivatives in Aspergillus niger

Comparison of the properties of a purified benzoate-4-hydroxylase, using benzoic acid and its chlorinated derivatives as substrate, suggests that the same enzyme operates during the initial hydroxylation of benzoate, 2-chlorobenzoate (2-cba), and 3-chlorobenzoate (3-cba) in A.niger.     

Partial purification of a competence factor from Rhizobium japonicum

A competence factor (CF) from Rhizobium japonicum was partially purified to 43 fold on Sephadex G-100. This CF preparation was sensitive to heat, trypsin and pronase, was resistant to DNase 1, RNase A and lysozyme. It had an approximate mol. wt. of 82,000. Osmotic shock treatment of competent cells revealed that the CF is located in the periplasmic region of the cell.Abbreviations CF competence factor - BSA bovine serum albumin - YM yeast mannitol medium     

Regulation of glutamine synthetase I fromRhizobium meliloti

Glutamine synthetase I fromRhizobium meliloti was found to be inhibited by adenosine 5-monophosphate, alanine, glycine, carbamyl phosphate, cytidine 5-triphosphate, tryptophan, histidine, and glucosamine-6-phosphate. Each inhibitor was independent in its action and the effect was cumulative when more than one inhibitor was added.     

Studies on transducing phage M-1 for Rhizobium japonicum D211

Phage M-1 produced clear plaques with a halo in the lawn of Rhizobium japonicum D211. A one step growth curve of phage M-1 showed a latent period of 3 h, burst size of 55 and rise period of 2 h. The inactivation of phage M-1 was found to be dependent upon the concentraion of d-glucosomanine. The neutralization kinetics of phage M-1 by antiphage serum gave a K value (velocity constant) of 83.1 min^–1. Transduction of str and kan was studied in the presence of antiphage serum and d-glucosamine. Cotransduction of different antibiotic resistance markers suggested that the system can be further explored for high resolution mapping in R. japonicum.Abbreviations YM yeast mannitol medium - PFU plaque forming unit - moi multiplicity of infection - EOP efficiency of plating     

Regulation of Neuronal Cell Cycle and Apoptosis by MicroRNA 34a

The cell cycle of neurons remains suppressed to maintain the state of differentiation and aberrant cell cycle reentry results in loss of neurons, which is a feature in neurodegenerative disorders like Alzheimer''s disease (AD). Present studies revealed that the expression of microRNA 34a (miR-34a) needs to be optimal in neurons, as an aberrant increase or decrease in its expression causes apoptosis. miR-34a keeps the neuronal cell cycle under check by preventing the expression of cyclin D1 and promotes cell cycle arrest. Neurotoxic amyloid β_1–42 peptide (Aβ₄₂) treatment of cortical neurons suppressed miR-34a, resulting in unscheduled cell cycle reentry, which resulted in apoptosis. The repression of miR-34a was a result of degradation of TAp73, which was mediated by aberrant activation of the MEK extracellular signal-regulated kinase (ERK) pathway by Aβ₄₂. A significant decrease in miR-34a and TAp73 was observed in the cortex of a transgenic (Tg) mouse model of AD, which correlated well with cell cycle reentry observed in the neurons of these animals. Importantly, the overexpression of TAp73α and miR-34a reversed cell cycle-related neuronal apoptosis (CRNA). These studies provide novel insights into how modulation of neuronal cell cycle machinery may lead to neurodegeneration and may contribute to the understanding of disorders like AD.     

RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks

Inferring gene co-expression networks is a useful process for understanding gene regulation and pathway activity. The networks are usually undirected graphs where genes are represented as nodes and an edge represents a significant co-expression relationship. When expression data of multiple (p) genes in multiple (K) conditions (e.g., treatments, tissues, strains) are available, joint estimation of networks harnessing shared information across them can significantly increase the power of analysis. In addition, examining condition-specific patterns of co-expression can provide insights into the underlying cellular processes activated in a particular condition. Condition adaptive fused graphical lasso (CFGL) is an existing method that incorporates condition specificity in a fused graphical lasso (FGL) model for estimating multiple co-expression networks. However, with computational complexity of O(p²K log K), the current implementation of CFGL is prohibitively slow even for a moderate number of genes and can only be used for a maximum of three conditions. In this paper, we propose a faster alternative of CFGL named rapid condition adaptive fused graphical lasso (RCFGL). In RCFGL, we incorporate the condition specificity into another popular model for joint network estimation, known as fused multiple graphical lasso (FMGL). We use a more efficient algorithm in the iterative steps compared to CFGL, enabling faster computation with complexity of O(p²K) and making it easily generalizable for more than three conditions. We also present a novel screening rule to determine if the full network estimation problem can be broken down into estimation of smaller disjoint sub-networks, thereby reducing the complexity further. We demonstrate the computational advantage and superior performance of our method compared to two non-condition adaptive methods, FGL and FMGL, and one condition adaptive method, CFGL in both simulation study and real data analysis. We used RCFGL to jointly estimate the gene co-expression networks in different brain regions (conditions) using a cohort of heterogeneous stock rats. We also provide an accommodating C and Python based package that implements RCFGL.