ASU Regents' Professors Open Access Works
The title “Regents’ Professor” is the highest faculty honor awarded at Arizona State University. It is conferred on ASU faculty who have made pioneering contributions in their areas of expertise, who have achieved a sustained level of distinction, and who enjoy national and international recognition for these accomplishments. This collection contains primarily open access works by ASU Regents' Professors.
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- Creators: Kumar, Sudhir
- Creators: Krajmalnik-Brown, Rosa
Background:
Many pharmaceutical drugs are known to be ineffective or have negative side effects in a substantial proportion of patients. Genomic advances are revealing that some non-synonymous single nucleotide variants (nsSNVs) may cause differences in drug efficacy and side effects. Therefore, it is desirable to evaluate nsSNVs of interest in their ability to modulate the drug response.
Results:
We found that the available data on the link between drug response and nsSNV is rather modest. There were only 31 distinct drug response-altering (DR-altering) and 43 distinct drug response-neutral (DR-neutral) nsSNVs in the whole Pharmacogenomics Knowledge Base (PharmGKB). However, even with this modest dataset, it was clear that existing bioinformatics tools have difficulties in correctly predicting the known DR-altering and DR-neutral nsSNVs. They exhibited an overall accuracy of less than 50%, which was not better than random diagnosis. We found that the underlying problem is the markedly different evolutionary properties between positions harboring nsSNVs linked to drug responses and those observed for inherited diseases. To solve this problem, we developed a new diagnosis method, Drug-EvoD, which was trained on the evolutionary properties of nsSNVs associated with drug responses in a sparse learning framework. Drug-EvoD achieves a TPR of 84% and a TNR of 53%, with a balanced accuracy of 69%, which improves upon other methods significantly.
Conclusions:
The new tool will enable researchers to computationally identify nsSNVs that may affect drug responses. However, much larger training and testing datasets are needed to develop more reliable and accurate tools.
Syngas fermentation, the bioconversion of CO, CO[subscript 2], and H[subscript 2] to biofuels and chemicals, has undergone considerable optimization for industrial applications. Even more, full-scale plants for ethanol production from syngas fermentation by pure cultures are being built worldwide. The composition of syngas depends on the feedstock gasified and the gasification conditions. However, it remains unclear how different syngas mixtures affect the metabolism of carboxidotrophs, including the ethanol/acetate ratios. In addition, the potential application of mixed cultures in syngas fermentation and their advantages over pure cultures have not been deeply explored. In this work, the effects of CO[subscript 2] and H[subscript 2] on the CO metabolism by pure and mixed cultures were studied and compared. For this, a CO-enriched mixed culture and two isolated carboxidotrophs were grown with different combinations of syngas components (CO, CO:H[subscript 2], CO:CO[subscript 2], or CO:CO[subscript 2]:H[subscript 2]).
Results
The CO metabolism of the mixed culture was somehow affected by the addition of CO[subscript 2] and/or H[subscript 2], but the pure cultures were more sensitive to changes in gas composition than the mixed culture. CO[subscript 2] inhibited CO oxidation by the Pleomorphomonas-like isolate and decreased the ethanol/acetate ratio by the Acetobacterium-like isolate. H[subscript 2] did not inhibit ethanol or H[subscript 2] production by the Acetobacterium and Pleomorphomonas isolates, respectively, but decreased their CO consumption rates. As part of the mixed culture, these isolates, together with other microorganisms, consumed H[subscript 2] and CO[subscript 2] (along with CO) for all conditions tested and at similar CO consumption rates (2.6 ± 0.6 mmol CO L[superscript −1] day[superscript −1]), while maintaining overall function (acetate production). Providing a continuous supply of CO by membrane diffusion caused the mixed culture to switch from acetate to ethanol production, presumably due to the increased supply of electron donor. In parallel with this change in metabolic function, the structure of the microbial community became dominated by Geosporobacter phylotypes, instead of Acetobacterium and Pleomorphomonas phylotypes.
Conclusions
These results provide evidence for the potential of mixed-culture syngas fermentation, since the CO-enriched mixed culture showed high functional redundancy, was resilient to changes in syngas composition, and was capable of producing acetate or ethanol as main products of CO metabolism.
Inhibition by ammonium at concentrations above 1000 mgN/L is known to harm the methanogenesis phase of anaerobic digestion. We anaerobically digested swine waste and achieved steady state COD-removal efficiency of around 52% with no fatty-acid or H[subscript 2] accumulation. As the anaerobic microbial community adapted to the gradual increase of total ammonia-N (NH[subscript 3]-N) from 890 ± 295 to 2040 ± 30 mg/L, the Bacterial and Archaeal communities became less diverse. Phylotypes most closely related to hydrogenotrophic Methanoculleus (36.4%) and Methanobrevibacter (11.6%), along with acetoclastic Methanosaeta (29.3%), became the most abundant Archaeal sequences during acclimation. This was accompanied by a sharp increase in the relative abundances of phylotypes most closely related to acetogens and fatty-acid producers (Clostridium, Coprococcus, and Sphaerochaeta) and syntrophic fatty-acid Bacteria (Syntrophomonas, Clostridium, Clostridiaceae species, and Cloacamonaceae species) that have metabolic capabilities for butyrate and propionate fermentation, as well as for reverse acetogenesis. Our results provide evidence countering a prevailing theory that acetoclastic methanogens are selectively inhibited when the total ammonia-N concentration is greater than ~1000 mgN/L. Instead, acetoclastic and hydrogenotrophic methanogens coexisted in the presence of total ammonia-N of ~2000 mgN/L by establishing syntrophic relationships with fatty-acid fermenters, as well as homoacetogens able to carry out forward and reverse acetogenesis.
Methods: Using next-generation sequence data, we assembled the plastid genome of saguaro cactus and probed the nuclear genome for transferred plastid genes and functionally related nuclear genes. We combined these results with available data across Cactaceae and seed plants more broadly to infer the history of gene loss and to assess the strength of phylogenetic association between gene loss and loss of the inverted repeat (IR).
Key results: The saguaro plastid genome is the smallest known for an obligately photosynthetic angiosperm (∼113 kb), having lost the IR and plastid ndh genes. This loss supports a statistically strong association across seed plants between the loss of ndh genes and the loss of the IR. Many nonplastid copies of plastid ndh genes were found in the nuclear genome, but none had intact reading frames; nor did three related nuclear-encoded subunits. However, nuclear pgr5, which functions in a partially redundant pathway, was intact.
Conclusions: The existence of an alternative pathway redundant with the function of the plastid NADH dehydrogenase-like complex (NDH) complex may permit loss of the plastid ndh gene suite in photoautotrophs like saguaro. Loss of these genes may be a recurring mechanism for overall plastid genome size reduction, especially in combination with loss of the IR.
Background:
Drosophila gene expression pattern images document the spatiotemporal dynamics of gene expression during embryogenesis. A comparative analysis of these images could provide a fundamentally important way for studying the regulatory networks governing development. To facilitate pattern comparison and searching, groups of images in the Berkeley Drosophila Genome Project (BDGP) high-throughput study were annotated with a variable number of anatomical terms manually using a controlled vocabulary. Considering that the number of available images is rapidly increasing, it is imperative to design computational methods to automate this task.
Results:
We present a computational method to annotate gene expression pattern images automatically. The proposed method uses the bag-of-words scheme to utilize the existing information on pattern annotation and annotates images using a model that exploits correlations among terms. The proposed method can annotate images individually or in groups (e.g., according to the developmental stage). In addition, the proposed method can integrate information from different two-dimensional views of embryos. Results on embryonic patterns from BDGP data demonstrate that our method significantly outperforms other methods.
Conclusion:
The proposed bag-of-words scheme is effective in representing a set of annotations assigned to a group of images, and the model employed to annotate images successfully captures the correlations among different controlled vocabulary terms. The integration of existing annotation information from multiple embryonic views improves annotation performance.
“Stoichioproteomics” relates the elemental composition of proteins and proteomes to variation in the physiological and ecological environment. To help harness and explore the wealth of hypotheses made possible under this framework, we introduce GRASP (http://www.graspdb.net), a public bioinformatic knowledgebase containing information on the frequencies of 20 amino acids and atomic composition of their side chains. GRASP integrates comparative protein composition data with annotation data from multiple public databases. Currently, GRASP includes information on proteins of 12 sequenced Drosophila (fruit fly) proteomes, which will be expanded to include increasingly diverse organisms over time. In this paper we illustrate the potential of GRASP for testing stoichioproteomic hypotheses by conducting an exploratory investigation into the composition of 12 Drosophila proteomes, testing the prediction that protein atomic content is associated with species ecology and with protein expression levels.
Results
Elements varied predictably along multivariate axes. Species were broadly similar, with the D. willistoni proteome a clear outlier. As expected, individual protein atomic content within proteomes was influenced by protein function and amino acid biochemistry. Evolution in elemental composition across the phylogeny followed less predictable patterns, but was associated with broad ecological variation in diet. Using expression data available for D. melanogaster, we found evidence consistent with selection for efficient usage of elements within the proteome: as expected, nitrogen content was reduced in highly expressed proteins in most tissues, most strongly in the gut, where nutrients are assimilated, and least strongly in the germline.
Conclusions
The patterns identified here using GRASP provide a foundation on which to base future research into the evolution of atomic composition in Drosophila and other taxa.
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.