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Biomedical subjects

Jennifer Listgarten

Publications and source records attributed to Jennifer Listgarten.

6 recordsLinked to original sources

Write and Read: Harnessing Synthetic DNA Modifications for Nanopore Sequencing.

An exciting feature of nanopore sequencing is its ability to record multi-omic information on the same sequenced DNA molecule. Well-trained models allow the detection of nucleotide-specific molecular signatures through changes in ionic current as DNA molecules translocate through the nanopore. Thus, naturally occurring DNA modifications, such as DNA methylation and hydroxymethylation, may be recorded simultaneously with the genetic sequence. Additional genomic information, such as chromatin state or the locations of bound transcription factors, may also be recorded if their locations are chemically encoded into the DNA. Here, we present a versatile "write-and-read" framework, where chemo-enzymatic DNA labeling with unnatural synthetic tags results in predictable electrical fingerprints in nanopore sequencing. As a proof-of-concept, we explore a DNA glucosylation approach that selectively modifies 5-hydroxymethylcytosine (5hmC) with glucose or glucose-azide adducts. We demonstrate that these modifications generate distinct and reproducible electrical shifts, enabling the direct detection of chemically altered nucleotides. We further demonstrate that enzymatic alkylation, such as the enzymatic transfer of azide residues to the N6 position of adenines, also produces characteristic nanopore signal shifts relative to the native adenine and 6-methyladenine. Beyond direct nucleotide detection, this approach introduces new possibilities for bio-orthogonal DNA labeling, enabling an extended alphabet of sequence-specific detectable moieties. The future use of programmable chemical modifications for simultaneous analysis of multiple omics features on individual molecules opens new avenues for genetic research and discovery.

5-hydroxymethylcytosine (5hmC)↗

Lymphovascular invasion is associated with poor survival in gastric cancer: an application of gene-expression and tissue array techniques.

OBJECTIVES: To examine a population-based cohort for the association between clinicopathologic predictors of survival and immunohistochemical markers (IHC), and to assess changes in gene expression that are associated with lymphovascular invasion (LVI). SUMMARY BACKGROUND DATA: LVI has been associated with poor survival and aggressive tumor behavior. The molecular changes responsible for the behavior of gastric cancer have yet to be determined. Characterization of IHC markers and gene expression profiles may identify molecular alterations governing tumor behavior. METHODS: : Clinicopathologic and survival data of 114 patients were reviewed. Archival specimens were used to construct a multitumor tissue array that was subjected to IHC of selected protein targets. Correlation of IHC with tumor thickness (T status), LVI and prognosis was studied. Microarray analysis of fresh gastric cancer tissue was conducted to examine the gene expression profile with respect to LVI. RESULTS: In a multivariate analysis, nodal status (N), metastasis (M), and LVI were independent predictors of survival. LVI was associated with a 5-year survival of 13.9% versus 55.9% in patients in whom it was absent. LVI correlated with advancing T status (P = 0.001) and N status (P < 0.001). IHC staining of cyclooxygenase-2 (COX-2) correlated with T status, tumor grade, lymph node positivity, and IHC staining of matrix metalloproteinase-2 (MMP-2) and matrix metalloproteinase-9 (MMP-9). Microarray analyses suggested differential expression of oligophrenin-1 (OPHN1) and ribophorin-II (RPNII) with respect to LVI. CONCLUSION: LVI was an independent predictor of survival in gastric cancer. Expression of COX-2 may facilitate tumor invasion through MMP-2 and MMP-9 activation. OPHN1 and RPN II appeared to be differentially expressed in gastric cancers exhibiting LVI. The reported function of OPHN1 and RPN II makes these gene products promising candidates for future studies involving LVI in gastric cancer.

Adenocarcinoma↗

Statistical and computational methods for comparative proteomic profiling using liquid chromatography-tandem mass spectrometry.

The combined method of LC-MS/MS is increasingly being used to explore differences in the proteomic composition of complex biological systems. The reliability and utility of such comparative protein expression profiling studies is critically dependent on an accurate and rigorous assessment of quantitative changes in the relative abundance of the myriad of proteins typically present in a biological sample such as blood or tissue. In this review, we provide an overview of key statistical and computational issues relevant to bottom-up shotgun global proteomic analysis, with an emphasis on methods that can be applied to improve the dependability of biological inferences drawn from large proteomic datasets. Focusing on a start-to-finish approach, we address the following topics: 1) low-level data processing steps, such as formation of a data matrix, filtering, and baseline subtraction to minimize noise, 2) mid-level processing steps, such as data normalization, alignment in time, peak detection, peak quantification, peak matching, and error models, to facilitate profile comparisons; and, 3) high-level processing steps such as sample classification and biomarker discovery, and related topics such as significance testing, multiple testing, and choice of feature space. We report on approaches that have recently been developed for these steps, discussing their merits and limitations, and propose areas deserving of further research.

Animals↗

Practical proteomic biomarker discovery: taking a step back to leap forward.

There is a pressing need for radically improved proteomic screening methods that allow for earlier diagnosis of disease, for systematic monitoring of physiological responses and for uncovering the fundamental mechanisms of drug action. Recent developments in proteomic technology offer tremendous, yet untapped, potential to yield novel biomarkers that are translatable to routine clinical use. Despite the significant conceptual promise of comparative proteomic profiling as a research platform for biomarker discovery, however, major hurdles remain for practical and clinical implementation. In particular, there is growing recognition that rigorous experimental design principles are urgently required to validate conclusively the unproven methodologies currently being touted. Debate and confusion persist about where the burden of proof lies: statistically, biologically or clinically? Moreover, there is no consensus about what constitutes a meaningful benchmark. An important question is how to achieve a scientifically rigorous, and therefore convincing, proof-of-concept that can be accepted by the field. Key analytical challenges related to these issues that must be addressed by the burgeoning biomarker community are discussed here.

Animals↗

Predictive models for breast cancer susceptibility from multiple single nucleotide polymorphisms.

Hereditary predisposition and causative environmental exposures have long been recognized in human malignancies. In most instances, cancer cases occur sporadically, suggesting that environmental influences are critical in determining cancer risk. To test the influence of genetic polymorphisms on breast cancer risk, we have measured 98 single nucleotide polymorphisms (SNPs) distributed over 45 genes of potential relevance to breast cancer etiology in 174 patients and have compared these with matched normal controls. Using machine learning techniques such as support vector machines (SVMs), decision trees, and naïve Bayes, we identified a subset of three SNPs as key discriminators between breast cancer and controls. The SVMs performed maximally among predictive models, achieving 69% predictive power in distinguishing between the two groups, compared with a 50% baseline predictive power obtained from the data after repeated random permutation of class labels (individuals with cancer or controls). However, the simpler naïve Bayes model as well as the decision tree model performed quite similarly to the SVM. The three SNP sites most useful in this model were (a) the +4536T/C site of the aldosterone synthase gene CYP11B2 at amino acid residue 386 Val/Ala (T/C) (rs4541); (b) the +4328C/G site of the aryl hydrocarbon hydroxylase CYP1B1 at amino acid residue 293 Leu/Val (C/G) (rs5292); and (c) the +4449C/T site of the transcription factor BCL6 at amino acid 387 Asp/Asp (rs1056932). No single SNP site on its own could achieve more than 60% in predictive accuracy. We have shown that multiple SNP sites from different genes over distant parts of the genome are better at identifying breast cancer patients than any one SNP alone. As high-throughput technology for SNPs improves and as more SNPs are identified, it is likely that much higher predictive accuracy will be achieved and a useful clinical tool developed.

Algorithms↗

Clinically validated benchmarking of normalisation techniques for two-colour oligonucleotide spotted microarray slides.

Acquisition of microarray data is prone to systematic errors. A correction, called normalisation, must be applied to the data before further analysis is performed. With many normalisation techniques published and in use, the best way of executing this correction remains an open question. In this study, a variety of single-slide normalisation techniques, and different parameter settings for these techniques, were compared over many replicated microarray experiments. Different normalisation techniques were assessed through the distribution of the standard deviation of replicates from one biological sample across different slides. It is shown that local normalisation outperformed global normalisation, and intensity-based 'LOWESS' outperformed trimmed mean and median normalisation techniques. Overall, the top performing normalisation technique was a print-tip-based LOWESS with zero robust iterations. Lastly, we validated this evaluation methodology by examining the ability to predict oestrogen receptor-positive and -negative breast cancer samples with data that had been normalised using different techniques.

Algorithms↗