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- All Subjects: proteomics
- Creators: Lake, Douglas
- Resource Type: Text
Description
Infertility has become an increasing problem in developed countries and in many cases can be attributed to compromised sperm quality. Assessment of male fertility typically utilizes semen analysis which mainly examines sperm morphology, however many males whose sperm appear normal are sub- or infertile, suggesting that sperm from these males may be deficient in a protein or suite of proteins. To date, very little is known about the composition of sperm or the complex maturation process that confers motility and fertilization competency to sperm. Chapter 1 discusses the use of whole cell mass spectrometry to identify 1247 proteins comprising the Rhesus macaque (Macaca mulatta) sperm proteome, a commonly used model of human reproduction. This study provides a more robust proxy of human sperm composition than was previously available and facilitates studies of sperm using the rhesus macaque as a model. Chapters 2 & 3 provide a systems level overview of changes in sperm proteome composition that occurs during epididymal transit. Chapter 2 reports the proteomes of sperm collected from the caput, corpus and cauda segments of the mouse epididymis, identifying 1536, 1720 and 1234 proteins respectively. Chapter 3 reports the sperm proteome from four distinct segments of the Rhesus macaque epididymis, including the caput, proximal corpus, distal corpus and cauda, identifying 1951, 2014, 1764 and 1423 proteins respectively. These studies identify a number of proteins that are added and removed from sperm during epididymal transit which likely play an important role in the sperm maturation process. To date no comparative evolutionary studies of sperm proteomes have been undertaken. Chapter 4 compares four mammalian sperm proteomes including the human, macaque, mouse and rat. This study identified 98 proteins common to all four sperm proteomes, 82 primate and 90 rodent lineage-specific proteins and 494, 467, 566, and 193 species specific proteins in the human, macaque, mouse and rat sperm proteomes respectively and discusses how differences in sperm composition may ultimately lead to functional differences across species. Finally, chapter 5 uses sperm proteome data to inform the preliminary design of a rodent contraceptive vaccine delivered orally using recombinant attenuated Salmonella vaccine vectors.
ContributorsSkerget, Sheri Jo (Author) / Karr, Timothy L. (Thesis advisor) / Lake, Douglas (Committee member) / Petritis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2013
Description
Valley Fever, also known as coccidioidomycosis, is a respiratory disease that affects 10,000 people annually, primarily in Arizona and California. Due to a lack of gene annotation, diagnosis and treatment of Valley Fever is severely limited. In turn, gene annotation efforts are also hampered by incomplete genome sequencing. We intend to use proteogenomic analysis to reannotate the Coccidioides posadasii str. Silveira genome from protein-level data. Protein samples extracted from both phases of Silveira were fragmented into peptides, sequenced, and compared against databases of known and predicted proteins sequences, as well as a de novo six-frame translation of the genome. 288 unique peptides were located that did not match a known Silveira annotation, and of those 169 were associated with another Coccidioides strain. Additionally, 17 peptides were found at the boundary of, or outside of, the current gene annotation comprising four distinct clusters. For one of these clusters, we were able to calculate a lower bound and an estimate for the size of the gap between two Silveira contigs using the Coccidioides immitis RS transcript associated with that cluster's peptides \u2014 these predictions were consistent with the current annotation's scaffold structure. Three peptides were associated with an actively translated transposon, and a putative active site was located within an intact LTR retrotransposon. We note that gene annotation is necessarily hindered by the quality and level of detail in prior genome sequencing efforts, and recommend that future studies involving reannotation include additional sequencing as well as gene annotation via proteogenomics or other methods.
ContributorsSherrard, Andrew (Author) / Lake, Douglas (Thesis director) / Grys, Thomas (Committee member) / Mitchell, Natalie (Committee member) / Computing and Informatics Program (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
While the entire human genome has been sequenced, the understanding of its functional elements remains unclear. The Encyclopedia of DNA Elements (ENCODE) project analyzed 1% of the human genome and found that the majority of the human genome is transcribed, including non-protein coding regions. The hypothesis is that some of the "non-coding" sequences are translated into peptides and small proteins. Using mass spectrometry numerous peptides derived from the ENCODE transcriptome were identified. Peptides and small proteins were also found from non-coding regions of the 1% of the human genome that the ENCODE did not find transcripts for. A large portion of these peptides mapped to the intronic regions of known genes, thus it is suspected that they may be undiscovered exons present in alternative spliceoforms of certain genes. Further studies proved the existence of polyadenylated RNAs coding for these peptides. Although their functional significance has not been determined, I anticipate the findings will lead to the discovery of new splice variants of known genes and possibly new transcriptional and translational mechanisms.
ContributorsWang, Lulu (Author) / Lake, Douglas (Thesis advisor) / Chang, Yung (Committee member) / Touchman, Jeffery (Committee member) / Arizona State University (Publisher)
Created2010
Description
Cancer researchers have traditionally used a handful of markers to understand the origin of tumors and to predict therapeutic response. Additionally, performing machine learning activities on disparate data sources of varying quality is fraught with inherent bias. The Caris Life Sciences Molecular Database (CMD) is an immense resource for discovery as it contains over 215,000 molecular profiles of tumors with consistently gathered clinical grade molecular data along with immense amounts of clinical outcomes data. This resource was leveraged to generate two artificial intelligence algorithms aiding in diagnosis and one for therapy selection.
The Molecular Disease Classifier (MDC) was trained on 34,352 cases and tested on 15,473 unambiguously diagnosed cases. The MDC predicted the correct tumor type out of thirteen possibilities in the labeled data set with sensitivity, specificity, PPV, and NPV of 90.5%, 99.2%, 90.5% and 99.2% respectively when considering up to 5 predictions for a case.
The availability of whole transcriptome data in the CMD prompted its inclusion into a new platform called MI GPSai (MI Genomic Prevalence Score). The algorithm trained on genomic data from 34,352 cases and genomic and transcriptomic data from 23,137 cases and was validated on 19,555 cases. MI GPSai can predict the correct tumor type out of 21 possibilities on 93% of cases with 94% accuracy. When considering the top two predictions for a case, the accuracy increases to 97%.
Finally, a 67 gene molecular signature predictive of efficacy of oxaliplatin-based chemotherapy in patients with metastatic colorectal cancer was developed - FOLFOXai. The signature was predictive of survival in an independent real-world evidence (RWE) dataset of 412 patients who had received FOLFOX/BV in 1st line and inversely predictive of survival in RWE data from 55 patients who had received 1st line FOLFIRI. Blinded analysis of TRIBE2 samples confirmed that FOLFOXai was predictive of OS in both oxaliplatin-containing arms (FOLFOX HR=0.629, p=0.04 and FOLFOXIRI HR=0.483, p=0.02).
The Molecular Disease Classifier (MDC) was trained on 34,352 cases and tested on 15,473 unambiguously diagnosed cases. The MDC predicted the correct tumor type out of thirteen possibilities in the labeled data set with sensitivity, specificity, PPV, and NPV of 90.5%, 99.2%, 90.5% and 99.2% respectively when considering up to 5 predictions for a case.
The availability of whole transcriptome data in the CMD prompted its inclusion into a new platform called MI GPSai (MI Genomic Prevalence Score). The algorithm trained on genomic data from 34,352 cases and genomic and transcriptomic data from 23,137 cases and was validated on 19,555 cases. MI GPSai can predict the correct tumor type out of 21 possibilities on 93% of cases with 94% accuracy. When considering the top two predictions for a case, the accuracy increases to 97%.
Finally, a 67 gene molecular signature predictive of efficacy of oxaliplatin-based chemotherapy in patients with metastatic colorectal cancer was developed - FOLFOXai. The signature was predictive of survival in an independent real-world evidence (RWE) dataset of 412 patients who had received FOLFOX/BV in 1st line and inversely predictive of survival in RWE data from 55 patients who had received 1st line FOLFIRI. Blinded analysis of TRIBE2 samples confirmed that FOLFOXai was predictive of OS in both oxaliplatin-containing arms (FOLFOX HR=0.629, p=0.04 and FOLFOXIRI HR=0.483, p=0.02).
ContributorsAbraham, Jim (Author) / Spetzler, David (Thesis advisor) / Frasch, Wayne (Thesis advisor) / Lake, Douglas (Committee member) / Compton, Carolyn (Committee member) / Arizona State University (Publisher)
Created2020