A numerical model for the oxygenation of blood in lung capillaries--effect of nth order one-step kinetics of oxygen uptake by haemoglobin

A numerical model is described for the oxygenation of blood in lung capillaries by considering the transport mechanisms of molecular diffusion, convection and the facilitated diffusion due to the presence of haemoglobin. In order to represent the oxygen dissociation curve accurately in the model, the nth order one-step kinetics of oxygen uptake by haemoglobin has been used. The resulting system of coupled, non-linear partial differential equations is solved numerically. It is shown that the blood is required to traverse a larger distance in the capillary before becoming fully oxygenated with nth order one-step kinetics in comparison to first-order one-step kinetics.     

Translation efficiency in humans: tissue specificity,global optimization and differences between developmental stages

Various studies in unicellular and multicellular organisms have shown that codon bias plays a significant role in translation efficiency (TE) by co-adaptation to the tRNA pool. Yet, in humans and other mammals the role of codon bias is still an open question, with contradictory results from different studies. Here we address this question, performing a large-scale tissue-specific analysis of TE in humans, using the tRNA Adaptation Index (tAI) as a direct measure for TE. We find tAI to significantly correlate with expression levels both in tissue-specific and in global expression measures, testifying to the TE of human tissues. Interestingly, we find significantly higher correlations in adult tissues as opposed to fetal tissues, suggesting that the tRNA pool is more adjusted to the adult period. Optimization based analysis suggests that the tRNA pool—codon bias co-adaptation is globally (and not tissue-specific) driven. Additionally, we find that tAI correlates with several measures related to the protein functionally importance, including gene essentiality. Using inferred tissue-specific tRNA pools lead to similar results and shows that tissue-specific genes are more adapted to their tRNA pool than other genes and that related sets of functional gene groups are translated efficiently in each tissue. Similar results are obtained for other mammals. Taken together, these results demonstrate the role of codon bias in TE in humans, and pave the way for future studies of tissue-specific TE in multicellular organisms.     

Occurrence of Horizontal Gene Transfer of PIB-type ATPase Genes among Bacteria Isolated from the Uranium Rich Deposit of Domiasiat in North East India

Uranium (U) tolerant aerobic heterotrophs were isolated from the subsurface soils of one of the pre-mined U-rich deposits at Domiasiat located in the north-eastern part of India. On screening of genomic DNA from 62 isolates exhibiting superior U and heavy metal tolerance, 32 isolates were found to be positive for P_IB-type ATPase genes. Phylogenetic incongruence and anomalous DNA base compositions revealed the acquisition of P_IB-type ATPase genes by six isolates through horizontal gene transfer (HGT). Three of these instances of HGT appeared to have occurred at inter-phylum level and the other three instances indicated to have taken place at intra-phylum level. This study provides an insight into one of the possible survival strategies that bacteria might employ to adapt to environments rich in uranium and heavy metals.     

Network-Free Inference of Knockout Effects in Yeast

Perturbation experiments, in which a certain gene is knocked out and the expression levels of other genes are observed, constitute a fundamental step in uncovering the intricate wiring diagrams in the living cell and elucidating the causal roles of genes in signaling and regulation. Here we present a novel framework for analyzing large cohorts of gene knockout experiments and their genome-wide effects on expression levels. We devise clustering-like algorithms that identify groups of genes that behave similarly with respect to the knockout data, and utilize them to predict knockout effects and to annotate physical interactions between proteins as inhibiting or activating. Differing from previous approaches, our prediction approach does not depend on physical network information; the latter is used only for the annotation task. Consequently, it is both more efficient and of wider applicability than previous methods. We evaluate our approach using a large scale collection of gene knockout experiments in yeast, comparing it to the state-of-the-art SPINE algorithm. In cross validation tests, our algorithm exhibits superior prediction accuracy, while at the same time increasing the coverage by over 25-fold. Significant coverage gains are obtained also in the annotation of the physical network.     

Handling class imbalance problem in miRNA dataset associated with cancer

MiRNAs are small (~22nt long) non-coding RNA sequences; binds to the complementarity target sites in 3'' Untranslated Region (UTR) of mRNA sequences but not restricted to other mRNA regions viz., 5'' UTR and Coding sequences (CDS). Complementarity binding of miRNA to mRNA target sites either results in complete degradation of the mRNA itself or it may regulate the mRNA as an oncogene or as a tumor suppressor gene. However, the exact mechanism involved in identifying a miRNA to be associated with cancer is still unclear. Further, with the outburst in the number of miRNAs sequences recorded every year in miRBase, the gap is still widening mainly due to the laborious and economically unfavorable experimental procedures associated with the functional annotation. Motivated by the fact, we constructed a two-step support vector machine-based predictive model - miRSEQ and miRINT. However, the major pitfall during the construction of the model is the class imbalance problem. Hence, in order to overcome class imbalance problem, in the present study we empirically compare the effectiveness of two different methods viz., Synthetic Minority Oversampling Technique (SMOTE) and cost-senstive learning method. Performance measures were evaluated in terms of Precision and Recall. Based on our result, it was observed that for miRNA dataset with high class imbalance utilized for predicting association of cancer, cost-sensitive method outperformed the oversampling method.     

Identification of UDP-linked murein precursors as contaminants in recombinant proteins of low molecular weight.

The A(280)/A(260) ratio of a purified protein is frequently used as an indication of the purity of the preparation with respect to nucleic acids. We show here that for low-molecular-weight recombinant proteins purified from Escherichia coli, a low A(280)/A(260) ratio can also result from contamination with UDP-linked murein precursors derived from bacterial cell wall metabolism. Although these precursors are small molecules of molecular weight 1000-1200, they comigrate in gel filtration with recombinant human FKBP (MW 11,820). This gel filtration behavior, which is distinct from that of unmodified mononucleotides, does not reflect binding interactions with FKBP, but is an intrinsic property of these precursors. Therefore, these molecules would be expected to copurify with other low-molecular-weight proteins, especially in the abbreviated purification protocols made possible by freeze-thaw release of recombinant proteins from E. coli (Johnson, B. H., and Hecht, M. H. (1994) BioTechnology 12, 1357-1360). Several alternative strategies are discussed for integrating these findings into the design of improved purification procedures for low-molecular-weight recombinant proteins.