Andrew Wong

Toward an Understanding of Changes in Diversity Associated with Fecal Microbiome Transplantation Based on 16S rRNA Gene Deep Sequencing

ABSTRACT Fecal microbiome transplantation by low-volume enema is an effective, safe, and inexpensive alternative to antibiotic therapy for patients with chronic relapsing Clostridium difficile infection (CDI). We explored the microbial diversity of pre- and posttransplant stool specimens from CDI patients (n = 6) using deep sequencing of the 16S rRNA gene. While interindividual variability in microbiota change occurs with fecal transplantation and vancomycin exposure, in this pilot study we note that clinical cure of CDI is associated with an increase in diversity and richness. Genus- and species-level analysis may reveal a cocktail of microorganisms or products thereof that will ultimately be used as a probiotic to treat CDI. IMPORTANCE Antibiotic-associated diarrhea (AAD) due to Clostridium difficile is a widespread phenomenon in hospitals today. Despite the use of antibiotics, up to 30% of patients are unable to clear the infection and suffer recurrent bouts of diarrheal disease. As a result, clinicians have resorted to fecal microbiome transplantation (FT). Donor stool for this type of therapy is typically obtained from a spouse or close relative and thoroughly tested for various pathogenic microorganisms prior to infusion. Anecdotal reports suggest a very high success rate of FT in patients who fail antibiotic treatment (>90%). We used deep-sequencing technology to explore the human microbial diversity in patients with Clostridium difficile infection (CDI) disease after FT. Genus- and species-level analysis revealed a cocktail of microorganisms in the Bacteroidetes and Firmicutes phyla that may ultimately be used as a probiotic to treat CDI. Antibiotic-associated diarrhea (AAD) due to Clostridium difficile is a widespread phenomenon in hospitals today. Despite the use of antibiotics, up to 30% of patients are unable to clear the infection and suffer recurrent bouts of diarrheal disease. As a result, clinicians have resorted to fecal microbiome transplantation (FT). Donor stool for this type of therapy is typically obtained from a spouse or close relative and thoroughly tested for various pathogenic microorganisms prior to infusion. Anecdotal reports suggest a very high success rate of FT in patients who fail antibiotic treatment (>90%). We used deep-sequencing technology to explore the human microbial diversity in patients with Clostridium difficile infection (CDI) disease after FT. Genus- and species-level analysis revealed a cocktail of microorganisms in the Bacteroidetes and Firmicutes phyla that may ultimately be used as a probiotic to treat CDI.

Mutations in calpain 3 associated with limb girdle muscular dystrophy: analysis by molecular modeling and by mutation in m-calpain.

Limb-girdle muscular dystrophy type 2A (LGMD2A) is an autosomal recessive disorder characterized by selective atrophy of the proximal limb muscles. Its occurrence is correlated, in a large number of patients, with defects in the human CAPN3 gene, a gene that encodes the skeletal muscle-specific member of the calpain family, calpain 3 (or p94). Because calpain 3 is difficult to study due to its rapid autolysis, we have developed a molecular model of calpain 3 based on the recently reported crystal structures of m-calpain and on the high-sequence homology between p94 and m-calpain (47% sequence identity). On the basis of this model, it was possible to explain many LGMD2A point mutations in terms of calpain 3 inactivation, supporting the idea that loss of calpain 3 activity is responsible for the disease. The majority of the LGMD2A mutations appear to affect domain/domain interaction, which may be critical in the assembly and the activation of the multi-domain calpain 3. In particular, we suggest that the flexibility of protease domain I in calpain 3 may play a critical role in the functionality of calpain 3. In support of the model, some clinically observed calpain 3 mutations were generated and analyzed in recombinant m-calpain. Mutations of residues forming intramolecular domain contacts caused the expected loss of activity, but mutations of some surface residues had no effect on activity, implying that these residues in calpain 3 may interact in vivo with other target molecules. These results contribute to an understanding of structure-function relationships and of pathogenesis in calpain 3.

Protein Function Prediction using Text-based Features extracted from the Biomedical Literature: The CAFA Challenge

BackgroundAdvances in sequencing technology over the past decade have resulted in an abundance of sequenced proteins whose function is yet unknown. As such, computational systems that can automatically predict and annotate protein function are in demand. Most computational systems use features derived from protein sequence or protein structure to predict function. In an earlier work, we demonstrated the utility of biomedical literature as a source of text features for predicting protein subcellular location. We have also shown that the combination of text-based and sequence-based prediction improves the performance of location predictors. Following up on this work, for the Critical Assessment of Function Annotations (CAFA) Challenge, we developed a text-based system that aims to predict molecular function and biological process (using Gene Ontology terms) for unannotated proteins. In this paper, we present the preliminary work and evaluation that we performed for our system, as part of the CAFA challenge.ResultsWe have developed a preliminary system that represents proteins using text-based features and predicts protein function using a k-nearest neighbour classifier (Text-KNN). We selected text features for our classifier by extracting key terms from biomedical abstracts based on their statistical properties. The system was trained and tested using 5-fold cross-validation over a dataset of 36,536 proteins. System performance was measured using the standard measures of precision, recall, F-measure and overall accuracy. The performance of our system was compared to two baseline classifiers: one that assigns function based solely on the prior distribution of protein function (Base-Prior) and one that assigns function based on sequence similarity (Base-Seq). The overall prediction accuracy of Text-KNN, Base-Prior, and Base-Seq for molecular function classes are 62%, 43%, and 58% while the overall accuracy for biological process classes are 17%, 11%, and 28% respectively. Results obtained as part of the CAFA evaluation itself on the CAFA dataset are reported as well.ConclusionsOur evaluation shows that the text-based classifier consistently outperforms the baseline classifier that is based on prior distribution, and typically has comparable performance to the baseline classifier that uses sequence similarity. Moreover, the results suggest that combining text features with other types of features can potentially lead to improved prediction performance. The preliminary results also suggest that while our text-based classifier can be used to predict both molecular function and biological process in which a protein is involved, the classifier performs significantly better for predicting molecular function than for predicting biological process. A similar trend was observed for other classifiers participating in the CAFA challenge.

ADP-dependent 6-phosphofructokinase from Pyrococcus horikoshii OT3: structure determination and biochemical characterization of PH1645.

Some hyperthermophilic archaea use a modified glycolytic pathway that employs an ADP-dependent glucokinase (ADP-GK) and an ADP-dependent phosphofructokinase (ADP-PFK) or, in the case of Methanococcus jannaschii, a bifunctional ADP-dependent glucophosphofructokinase (ADP-GK/PFK). The crystal structures of three ADP-GKs have been determined. However, there is no structural information available for ADP-PFKs or the ADP-GK/PFK. Here, we present the first crystal structure of an ADP-PFK from Pyrococcus horikoshii OT3 (PhPFK) in both apo- and AMP-bound forms determined to 2.0-Å and 1.9-Å resolution, respectively, along with biochemical characterization of the enzyme. The overall structure of PhPFK maintains a similar large and small α/β domain structure seen in the ADP-GK structures. A large conformational change accompanies binding of phosphoryl donor, acceptor, or both, in all members of the ribokinase superfamily characterized thus far, which is believed to be critical to enzyme function. Surprisingly, no such conformational change was observed in the AMP-bound PhPFK structure compared with the apo structure. Through comprehensive site-directed mutagenesis of the substrate binding pocket we identified residues that were critical for both substrate recognition and the phosphotransfer reaction. The catalytic residues and many of the substrate binding residues are conserved between PhPFK and ADP-GKs; however, four key residues differ in the sugar-binding pocket, which we have shown determine the sugar-binding specificity. Using these results we were able to engineer a mutant PhPFK that mimics the ADP-GK/PFK and is able to phosphorylate both fructose 6-phosphate and glucose.

A linear classifier based on entity recognition tools and a statistical approach to method extraction in the protein-protein interaction literature

BackgroundWe participated, as Team 81, in the Article Classification and the Interaction Method subtasks (ACT and IMT, respectively) of the Protein-Protein Interaction task of the BioCreative III Challenge. For the ACT, we pursued an extensive testing of available Named Entity Recognition and dictionary tools, and used the most promising ones to extend our Variable Trigonometric Threshold linear classifier. Our main goal was to exploit the power of available named entity recognition and dictionary tools to aid in the classification of documents relevant to Protein-Protein Interaction (PPI). For the IMT, we focused on obtaining evidence in support of the interaction methods used, rather than on tagging the document with the method identifiers. We experimented with a primarily statistical approach, as opposed to employing a deeper natural language processing strategy. In a nutshell, we exploited classifiers, simple pattern matching for potential PPI methods within sentences, and ranking of candidate matches using statistical considerations. Finally, we also studied the benefits of integrating the method extraction approach that we have used for the IMT into the ACT pipeline.ResultsFor the ACT, our linear article classifier leads to a ranking and classification performance significantly higher than all the reported submissions to the challenge in terms of Area Under the Interpolated Precision and Recall Curve, Mathew’s Correlation Coefficient, and F-Score. We observe that the most useful Named Entity Recognition and Dictionary tools for classification of articles relevant to protein-protein interaction are: ABNER, NLPROT, OSCAR 3 and the PSI-MI ontology. For the IMT, our results are comparable to those of other systems, which took very different approaches. While the performance is not very high, we focus on providing evidence for potential interaction detection methods. A significant majority of the evidence sentences, as evaluated by independent annotators, are relevant to PPI detection methods.ConclusionsFor the ACT, we show that the use of named entity recognition tools leads to a substantial improvement in the ranking and classification of articles relevant to protein-protein interaction. Thus, we show that our substantially expanded linear classifier is a very competitive classifier in this domain. Moreover, this classifier produces interpretable surfaces that can be understood as “rules” for human understanding of the classification. We also provide evidence supporting certain named entity recognition tools as beneficial for protein-interaction article classification, or demonstrating that some of the tools are not beneficial for the task. In terms of the IMT task, in contrast to other participants, our approach focused on identifying sentences that are likely to bear evidence for the application of a PPI detection method, rather than on classifying a document as relevant to a method. As BioCreative III did not perform an evaluation of the evidence provided by the system, we have conducted a separate assessment, where multiple independent annotators manually evaluated the evidence produced by one of our runs. Preliminary results from this experiment are reported here and suggest that the majority of the evaluators agree that our tool is indeed effective in detecting relevant evidence for PPI detection methods. Regarding the integration of both tasks, we note that the time required for running each pipeline is realistic within a curation effort, and that we can, without compromising the quality of the output, reduce the time necessary to extract entities from text for the ACT pipeline by pre-selecting candidate relevant text using the IMT pipeline.

Structural and functional characterization of the TYW3/Taw3 class of SAM-dependent methyltransferases

S-adenosylmethionine (SAM)-dependent methyltransferases regulate a wide range of biological processes through the modification of proteins, nucleic acids, polysaccharides, as well as various metabolites. TYW3/Taw3 is a SAM-dependent methyltransferase responsible for the formation of a tRNA modification known as wybutosine and its derivatives that are required for accurate decoding in protein synthesis. Here, we report the crystal structure of Taw3, a homolog of TYW3 from Sulfolobus solfataricus, which revealed a novel α/β fold. The sequence motif (S/T)xSSCxGR and invariant aspartate and histidine, conserved in TYW3/Taw3, cluster to form the catalytic center. These structural and sequence features indicate that TYW3/Taw3 proteins constitute a distinct class of SAM-dependent methyltransferases. Using site-directed mutagenesis along with in vivo complementation assays combined with mass spectrometry as well as ligand docking and cofactor binding assays, we have identified the active site of TYW3 and residues essential for cofactor binding and methyltransferase activity.