Judith Klein-Seetharaman

Carbon nanotubes degraded by neutrophil myeloperoxidase induce less pulmonary inflammation

We have shown previously that single-walled carbon nanotubes can be catalytically biodegraded over several weeks by the plant-derived enzyme, horseradish peroxidase. However, whether peroxidase intermediates generated inside human cells or biofluids are involved in the biodegradation of carbon nanotubes has not been explored. Here, we show that hypochlorite and reactive radical intermediates of the human neutrophil enzyme myeloperoxidase catalyse the biodegradation of single-walled carbon nanotubes in vitro, in neutrophils and to a lesser degree in macrophages. Molecular modelling suggests that interactions of basic amino acids of the enzyme with the carboxyls on the carbon nanotubes position the nanotubes near the catalytic site. Importantly, the biodegraded nanotubes do not generate an inflammatory response when aspirated into the lungs of mice. Our findings suggest that the extent to which carbon nanotubes are biodegraded may be a major determinant of the scale and severity of the associated inflammatory responses in exposed individuals.

Cardiolipin externalization to the outer mitochondrial membrane acts as an elimination signal for mitophagy in neuronal cells

Recognition of injured mitochondria for degradation by macroautophagy is essential for cellular health, but the mechanisms remain poorly understood. Cardiolipin is an inner mitochondrial membrane phospholipid. We found that rotenone, staurosporine, 6-hydroxydopamine and other pro-mitophagy stimuli caused externalization of cardiolipin to the mitochondrial surface in primary cortical neurons and SH-SY5Y cells. RNAi knockdown of cardiolipin synthase or of phospholipid scramblase-3, which transports cardiolipin to the outer mitochondrial membrane, decreased the delivery of mitochondria to autophagosomes. Furthermore, we found that the autophagy protein microtubule-associated-protein-1 light chain 3 (LC3), which mediates both autophagosome formation and cargo recognition, contains cardiolipin-binding sites important for the engulfment of mitochondria by the autophagic system. Mutation of LC3 residues predicted as cardiolipin-interaction sites by computational modelling inhibited its participation in mitophagy. These data indicate that redistribution of cardiolipin serves as an ‘eat-me’ signal for the elimination of damaged mitochondria from neuronal cells.

Evaluation of different biological data and computational classification methods for use in protein interaction prediction

Protein–protein interactions play a key role in many biological systems. High‐throughput methods can directly detect the set of interacting proteins in yeast, but the results are often incomplete and exhibit high false‐positive and false‐negative rates. Recently, many different research groups independently suggested using supervised learning methods to integrate direct and indirect biological data sources for the protein interaction prediction task. However, the data sources, approaches, and implementations varied. Furthermore, the protein interaction prediction task itself can be subdivided into prediction of (1) physical interaction, (2) co‐complex relationship, and (3) pathway co‐membership. To investigate systematically the utility of different data sources and the way the data is encoded as features for predicting each of these types of protein interactions, we assembled a large set of biological features and varied their encoding for use in each of the three prediction tasks. Six different classifiers were used to assess the accuracy in predicting interactions, Random Forest (RF), RF similarity‐based k‐Nearest‐Neighbor, Naïve Bayes, Decision Tree, Logistic Regression, and Support Vector Machine. For all classifiers, the three prediction tasks had different success rates, and co‐complex prediction appears to be an easier task than the other two. Independently of prediction task, however, the RF classifier consistently ranked as one of the top two classifiers for all combinations of feature sets. Therefore, we used this classifier to study the importance of different biological datasets. First, we used the splitting function of the RF tree structure, the Gini index, to estimate feature importance. Second, we determined classification accuracy when only the top‐ranking features were used as an input in the classifier. We find that the importance of different features depends on the specific prediction task and the way they are encoded. Strikingly, gene expression is consistently the most important feature for all three prediction tasks, while the protein interactions identified using the yeast‐2‐hybrid system were not among the top‐ranking features under any condition. Proteins 2006.

A critical assessment of Mus musculus gene function prediction using integrated genomic evidence

Background:Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to this task in model organisms; however, the performance of such approaches in mammals has not yet been evaluated.Results:In this study, a standardized collection of mouse functional genomic data was assembled; nine bioinformatics teams used this data set to independently train classifiers and generate predictions of function, as defined by Gene Ontology (GO) terms, for 21,603 mouse genes; and the best performing submissions were combined in a single set of predictions. We identified strengths and weaknesses of current functional genomic data sets and compared the performance of function prediction algorithms. This analysis inferred functions for 76% of mouse genes, including 5,000 currently uncharacterized genes. At a recall rate of 20%, a unified set of predictions averaged 41% precision, with 26% of GO terms achieving a precision better than 90%.Conclusion:We performed a systematic evaluation of diverse, independently developed computational approaches for predicting gene function from heterogeneous data sources in mammals. The results show that currently available data for mammals allows predictions with both breadth and accuracy. Importantly, many highly novel predictions emerge for the 38% of mouse genes that remain uncharacterized.

The Enzymatic Oxidation of Graphene Oxide

Two-dimensional graphitic carbon is a new material with many emerging applications, and studying its chemical properties is an important goal. Here, we reported a new phenomenon--the enzymatic oxidation of a single layer of graphitic carbon by horseradish peroxidase (HRP). In the presence of low concentrations of hydrogen peroxide (∼40 μM), HRP catalyzed the oxidation of graphene oxide, which resulted in the formation of holes on its basal plane. During the same period of analysis, HRP failed to oxidize chemically reduced graphene oxide (RGO). The enzymatic oxidation was characterized by Raman, ultraviolet-visible, electron paramagnetic resonance, Fourier transform infrared spectroscopy, transmission electron microscopy, atomic force microscopy, sodium dodecyl sulfate-polyacrylamide gel electrophoresis, and gas chromatography-mass spectrometry. Computational docking studies indicated that HRP was preferentially bound to the basal plane rather than the edge for both graphene oxide and RGO. Owing to the more dynamic nature of HRP on graphene oxide, the heme active site of HRP was in closer proximity to graphene oxide compared to RGO, thereby facilitating the oxidation of the basal plane of graphene oxide. We also studied the electronic properties of the reduced intermediate product, holey reduced graphene oxide (hRGO), using field-effect transistor (FET) measurements. While RGO exhibited a V-shaped transfer characteristic similar to a single layer of graphene that was attributed to its zero band gap, hRGO demonstrated a p-type semiconducting behavior with a positive shift in the Dirac points. This p-type behavior rendered hRGO, which can be conceptualized as interconnected graphene nanoribbons, as a potentially attractive material for FET sensors.

Mechanistic Investigations of Horseradish Peroxidase-Catalyzed Degradation of Single-Walled Carbon Nanotubes

Single-walled carbon nanotubes (SWNTs) have been investigated for a variety of applications including composite materials, electronics, and drug delivery. However, these applications may be compromised depending on the negative effects of SWNTs to living systems. While reports of toxicity induced by SWNTs vary, means to alleviate or quell these effects are in small abundance. We have reported recently the degradation of carboxylated SWNTs through enzymatic catalysis with horseradish peroxidase (HRP). In this full Article, we investigated the degradation of both carboxylated and pristine SWNTs with HRP and compared these results with chemical degradation by hemin and FeCl(3). The interaction between pristine and carboxylated SWNTs with HRP was further studied by computer modeling, and the products of the enzymatic degradation were identified. By examining these factors with both pristine and carboxylated SWNTs through a variety of techniques including atomic force microscopy (AFM), transmission electron microscopy (TEM), Raman spectroscopy, ultraviolet-visible-near-infrared (UV-vis-NIR) spectroscopy, gas chromatography-mass spectrometry (GC-MS), high-performance liquid chromatography (HPLC), and liquid chromatography-mass spectrometry (LC-MS), degradation pathways were elucidated. It was observed that pristine SWNTs demonstrate no degradation with HRP incubation but display significant degradation when incubated with either hemin or FeCl(3). Such data signify a heterolytic cleavage of H(2)O(2) with HRP as pristine nanotubes do not degrade, whereas Fenton catalysis results in the homolytic cleavage of H(2)O(2) producing free radicals that oxidize pristine SWNTs. Product analysis shows complete degradation produces CO(2) gas. Conversely, incomplete degradation results in the formation of different oxidized aromatic hydrocarbons.

Random forest similarity for protein-protein interaction prediction from multiple sources.

One of the most important, but often ignored, parts of any clustering and classification algorithm is the computation of the similarity matrix. This is especially important when integrating high throughput biological data sources because of the high noise rates and the many missing values. In this paper we present a new method to compute such similarities for the task of classifying pairs of proteins as interacting or not. Our method uses direct and indirect information about interaction pairs to constructs a random forest (a collection of decision tress) from a training set. The resulting forest is used to determine the similarity between protein pairs and this similarity is used by a classification algorithm (a modified kNN) to classify protein pairs. Testing the algorithm on yeast data indicates that it is able to improve coverage to 20% of interacting pairs with a false positive rate of 50%. These results compare favorably with all previously suggested methods for this task indicating the importance of robust similarity estimates.

Protein classification based on text document classification techniques.

The need for accurate, automated protein classification methods continues to increase as advances in biotechnology uncover new proteins. G‐protein coupled receptors (GPCRs) are a particularly difficult superfamily of proteins to classify due to extreme diversity among its members. Previous comparisons of BLAST, k‐nearest neighbor (k‐NN), hidden markov model (HMM) and support vector machine (SVM) using alignment‐based features have suggested that classifiers at the complexity of SVM are needed to attain high accuracy. Here, analogous to document classification, we applied Decision Tree and Naïve Bayes classifiers with chi‐square feature selection on counts of n‐grams (i.e. short peptide sequences of length n) to this classification task. Using the GPCR dataset and evaluation protocol from the previous study, the Naïve Bayes classifier attained an accuracy of 93.0 and 92.4% in level I and level II subfamily classification respectively, while SVM has a reported accuracy of 88.4 and 86.3%. This is a 39.7 and 44.5% reduction in residual error for level I and level II subfamily classification, respectively. The Decision Tree, while inferior to SVM, outperforms HMM in both level I and level II subfamily classification. For those GPCR families whose profiles are stored in the Protein FAMilies database of alignments and HMMs (PFAM), our method performs comparably to a search against those profiles. Finally, our method can be generalized to other protein families by applying it to the superfamily of nuclear receptors with 94.5, 97.8 and 93.6% accuracy in family, level I and level II subfamily classification respectively. Proteins 2005.

Oxidized arachidonic and adrenic PEs navigate cells to ferroptosis

Enigmatic lipid peroxidation products have been claimed as the proximate executioners of ferroptosis-a specialized death program triggered by insufficiency of glutathione peroxidase 4 (GPX4). Using quantitative redox lipidomics, reverse genetics, bioinformatics and systems biology, we discovered that ferroptosis involves a highly organized oxygenation center, wherein oxidation in endoplasmic-reticulum-associated compartments occurs on only one class of phospholipids (phosphatidylethanolamines (PEs)) and is specific toward two fatty acyls-arachidonoyl (AA) and adrenoyl (AdA). Suppression of AA or AdA esterification into PE by genetic or pharmacological inhibition of acyl-CoA synthase 4 (ACSL4) acts as a specific antiferroptotic rescue pathway. Lipoxygenase (LOX) generates doubly and triply-oxygenated (15-hydroperoxy)-diacylated PE species, which act as death signals, and tocopherols and tocotrienols (vitamin E) suppress LOX and protect against ferroptosis, suggesting a homeostatic physiological role for vitamin E. This oxidative PE death pathway may also represent a target for drug discovery.

Mitochondrial targeting of electron scavenging antioxidants: Regulation of selective oxidation vs random chain reactions

Effective regulation of highly compartmentalized production of reactive oxygen species and peroxidation reactions in mitochondria requires targeting of small molecule antioxidants and antioxidant enzymes into the organelles. This review describes recently developed approaches to mitochondrial targeting of small biologically active molecules based on: (i) preferential accumulation in mitochondria because of their hydrophobicity and positive charge (hydrophobic cations), (ii) binding with high affinity to an intra-mitochondrial constituent, and (iii) metabolic conversions by specific mitochondrial enzymes to reveal an active entity. In addition, targeted delivery of antioxidant enzymes via expression of leader sequences directing the proteins into mitochondria is considered. Examples of successful antioxidant and anti-apoptotic protection based on the ability of targeted cargoes to inhibit cytochrome c-catalyzed peroxidation of a mitochondria-specific phospholipid cardiolipin, in vitro and in vivo are presented. Particular emphasis is placed on the employment of triphenylphosphonium- and hemi-gramicidin S-moieties as two effective vehicles for mitochondrial delivery of antioxidants.