NAF-1 and mitoNEET are central to human breast cancer proliferation by maintaining mitochondrial homeostasis and promoting tumor growth

Mitochondria are emerging as important players in the transformation process of cells, maintaining the biosynthetic and energetic capacities of cancer cells and serving as one of the primary sites of apoptosis and autophagy regulation. Although several avenues of cancer therapy have focused on mitochondria, progress in developing mitochondria-targeting anticancer drugs nonetheless has been slow, owing to the limited number of known mitochondrial target proteins that link metabolism with autophagy or cell death. Recent studies have demonstrated that two members of the newly discovered family of NEET proteins, NAF-1 (CISD2) and mitoNEET (mNT; CISD1), could play such a role in cancer cells. NAF-1 was shown to be a key player in regulating autophagy, and mNT was proposed to mediate iron and reactive oxygen homeostasis in mitochondria. Here we show that the protein levels of NAF-1 and mNT are elevated in human epithelial breast cancer cells, and that suppressing the level of these proteins using shRNA results in significantly reduced cell proliferation and tumor growth, decreased mitochondrial performance, uncontrolled accumulation of iron and reactive oxygen in mitochondria, and activation of autophagy. Our findings highlight NEET proteins as promising mitochondrial targets for cancer therapy

Maximum Likelihood of Phylogenetic Networks

Horizontal gene transfer (HGT) is believed to be ubiquitous among bacteria, and plays a major role in their genome diversification as well as their ability to develop resistance to antibiotics. In light of its evolutionary significance and implications in human health, developing accurate and efficient methods for detecting and reconstructing HGT is imperative. In this paper we provide a first likelihood framework for phylogeny-based HGT detection and reconstruction. Beside the formulation of various likelihood criteria, we offer novel hardness results and heuristics for efficient and accurate reconstruction of HGT under these criteria. We implemented our heuristics and used them to analyze biological as well as synthetic data. Our methods exhibited very good performance on the biological data, and preliminary results on the synthetic data show a similar trend. Further, preliminary results on synthetic data show good promise for detecting chimeric (partial) HGT, which has been mostly ignored by existing computational methods. Implementation of the criteria as well as heuristics is available from the authors upon request

A new linear-time heuristic algorithm for computing the parsimony score of phylogenetic networks: theoretical bounds and empirical performance

Phylogenies play a major role in representing the interrelationships among biological entities. Many methods for reconstructing and studying such phylogenies have been proposed, almost all of which assume that the underlying history of a given set of species can be represented by a binary tree. Although many biological processes can be effectively modeled and summarized in this fashion, others cannot: recombination, hybrid speciation, and horizontal gene transfer result in networks, rather than trees, of relationships. In a series of papers, we have extended the maximum parsimony (MP) criterion to phylogenetic networks, demonstrated its appropriateness, and established the intractability of the problem of scoring the parsimony of a phylogenetic network. In this work we show the hardness of approximation for the general case of the problem, devise a very fast (linear-time) heuristic algorithm for it, and implement it on simulated as well as biological data

in Fig. 1.

Input: A formula F over a set U of variables, collection C of clauses over U such that each clause c ∈ C has |c | = 3. Question: Is there a truth assignment for U that simultaneously satisfies all clauses in C? Given such a formula F we construct a network N(F) as follows. In the sequel, we use x is connected to y to indicate that x is a child of y. 1. A root R with a 1-leaf child. 2. For every variable we crate a node connected to a diamond loop as depicte