Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets

BACKGROUND: Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. However, interpreting genome-wide microarray results can be overwhelming due to the vast output of gene expression data combined with off-target transcriptional responses many times induced by a drug treatment. This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism. RESULTS: S. cerevisiae cells were treated with the antifungal, fluconazole, and expression profiling was conducted under different biological conditions using Affymetrix genome-wide microarrays. Transcripts were filtered with a formal network-based method, sparse simultaneous equation models and Lasso regression (SSEM-Lasso), under different network training conditions. Gene expression results were evaluated using both gene set and single gene target analyses, and the drug’s transcriptional effects were narrowed first by pathway and then by individual genes. Variables included: (i) Testing conditions – exposure time and concentration and (ii) Network training conditions – training compendium modifications. Two analyses of SSEM-Lasso output – gene set and single gene – were conducted to gain a better understanding of how SSEM-Lasso predicts perturbation targets. CONCLUSIONS: This study demonstrates that genome-wide microarrays can be optimized using a two-stage strategy for a more in-depth understanding of how a cell manifests biological reactions to a drug treatment at the transcription level. Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved.Published versio

Perturbation Detection Through Modeling of Gene Expression on a Latent Biological Pathway Network: A Bayesian hierarchical approach

Cellular response to a perturbation is the result of a dynamic system of biological variables linked in a complex network. A major challenge in drug and disease studies is identifying the key factors of a biological network that are essential in determining the cell's fate. Here our goal is the identification of perturbed pathways from high-throughput gene expression data. We develop a three-level hierarchical model, where (i) the first level captures the relationship between gene expression and biological pathways using confirmatory factor analysis, (ii) the second level models the behavior within an underlying network of pathways induced by an unknown perturbation using a conditional autoregressive model, and (iii) the third level is a spike-and-slab prior on the perturbations. We then identify perturbations through posterior-based variable selection. We illustrate our approach using gene transcription drug perturbation profiles from the DREAM7 drug sensitivity predication challenge data set. Our proposed method identified regulatory pathways that are known to play a causative role and that were not readily resolved using gene set enrichment analysis or exploratory factor models. Simulation results are presented assessing the performance of this model relative to a network-free variant and its robustness to inaccuracies in biological databases

Network-based methods to identify mechanisms of action in disease and drug perturbation profiles using high-throughput genomic data

In the past decade it has become increasingly clear that a biological response is rarely caused by a single gene or protein. Rather, it is a result of a myriad of biological factors, constituting a systematic network of biological variables that span multiple granularities of biology from gene transcription to cell metabolism. Therefore it has become a significant challenge in the field of bioinformatics to integrate different levels of biology and to think of biological problems from a network perspective. In my thesis, I will discuss three projects that address this challenge. First, I will introduce two novel methods that integrate quantitative and qualitative biological data in a network approach. My aim in chapters two and three is to combine high-throughput data with biological databases to identify the causal mechanisms of action (MoA), in the form of canonical biological pathways, underlying the data for a given phenotype. In the second chapter, I will introduce an algorithm called Latent Pathway Identification Analysis (LPIA). This algorithm looks for statistically significant evidence of dysregulation in a network of pathways constructed in a manner that explicitly links pathways through their common function in the cell. In chapter three, I will introduce a new method that focuses on the identification of perturbed pathways from high-throughput gene expression data, which we approach as a task in statistical modeling and inference. We develop a two-level statistical model, where (i) the first level captures the relationship between high-throughput gene expression and biological pathways, and (ii) the second level models the behavior within an underlying network of pathways induced by an unknown perturbation. In the fourth chapter, I will focus on the integration of high throughput data on two distinct levels of biology to elucidate associations and causal relationships amongst genotype, gene expression and glycemic traits relevant to Type 2 Diabetes. I use the Framingham heart study as well as its extension, the SABRe initiative, to identify genes whose expression may be causally linked to fasting glucose

Lung cancer screening: what do long-term smokers know and believe?

Objective To explore knowledge and beliefs of long-term smokers about lung cancer, associated risk factors and lung cancer screening. Design Qualitative study theoretically framed by the expanded Health Belief Model based on four focus group discussions. Content analysis was performed to identify themes of knowledge and beliefs about lung cancer, associated risk factors and lung cancer screening among long-term smokers' who had and had not been screened for lung cancer. Methods Twenty-six long-term smokers were recruited; two groups (n = 9; n = 3) had recently been screened and two groups (n = 7; n = 7) had never been screened. Results While most agreed lung cancer is deadly, confusion or inaccurate information exists regarding the causes and associated risk factors. Knowledge related to lung cancer screening and how it is performed was low; awareness of long-term smoking's association with lung cancer risk remains suboptimal. Perceived benefits of screening identified include: (i) finding lung cancer early; (ii) giving peace of mind; and (iii) motivation to quit smoking. Perceived barriers to screening identified include: (i) inconvenience; (ii) distrust; and (iii) stigma. Conclusions Perceived barriers to lung cancer screening, such as distrust and stigma, must be addressed as lung cancer screening becomes more widely implemented. Heightened levels of health-care system distrust may impact successful implementation of screening programmes. Perceived smoking-related stigma may lead to low levels of patient engagement with medical care and decreased cancer screening participation. It is also important to determine modifiable targets for intervention to enhance the shared decision-making process between health-care providers and their high-risk patients

Detection of EpCAM-Negative and Cytokeratin-Negative Circulating Tumor Cells in Peripheral Blood

Enrichment of rare circulating tumor cells (CTCs) in blood is typically achieved using antibodies to epithelial cell adhesion molecule (EpCAM), with detection using cytokeratin (CK) antibodies. However, EpCAM and CK are not expressed in some tumors and can be downregulated during epithelial-to-mesenchymal transition. A micro-fluidic system, not limited to EpCAM or CK, was developed to use multiple antibodies for capture followed by detection using CEE-Enhanced (CE), a novel in situ staining method that fluorescently labels the capture antibodies bound to CTCs. Higher recovery of CTCs was demonstrated using antibody mixtures compared to anti-EpCAM. In addition, CK-positive breast cancer cells were found in 15 of 24 samples (63%; range 1–60 CTCs), while all samples contained additional CE-positive cells (range 1–41; median = 11; P = .02). Thus, antibody mixtures against a range of cell surface antigens enables capture of more CTCs than anti-EpCAM alone and CE staining enables the detection of CK-negative CTCs

What interventions are effective in improving uptake and retention of HIV-positive pregnant and breastfeeding women and their infants in prevention of mother to child transmission care programmes in low-income and middle-income countries? A systematic review and meta-analysis

Objective This review was conducted to identify interventions effective in improving uptake and retention of HIV-positive mothers and their infants in prevention of mother to child transmission (PMTCT) services in low-income and middle-income countries (LMICs) in order to inform programme planning. Methods We conducted a systematic review of studies comparing usual care with any intervention to improve uptake and retention of HIV-positive pregnant or breastfeeding women and their children from birth to 2 years of age in PMTCT services in LMICs. Twenty-two electronic databases were searched from inception to 15 January 2018, for randomised, quasi-randomised and non-randomised controlled trials, and interrupted time series studies; reference lists of included articles were searched for relevant articles. Risk of bias was assessed using the Cochrane Effective Practice and Organisation of Care group criteria. Random-effects meta-analysis was conducted for studies reporting similar interventions and outcomes. Results We identified 29 837 articles, of which 18 studies were included in our review. Because of heterogeneity in interventions and outcome measures, only one meta-analysis of two studies and one outcome was conducted; we found a statistically significant increase in antiretroviral therapy (ART) use during pregnancy for integration of HIV and antenatal care relative to standard non-integrated care (pooled AOR=2.69; 95% CI 1.25 to 5.78, p=0.0113). The remaining studies assessing other patient, provider or health system interventions were synthesised narratively, with small effects seen across intervention categories for both maternal and infant PMTCT outcomes based predominately on evidence with moderate to high risk of bias. Conclusions Evidence on the effectiveness of interventions to improve uptake and retention of mothers and infants in PMTCT care is lacking. Our findings suggest that integration of HIV and antenatal care may improve ART use during pregnancy. Future studies to replicate promising approaches are needed. Improved reporting of key methodological criteria will facilitate interpretation of findings and improve the utility of evidence to PMTCT programme planners

Benchmark Acetylene Binding Affinity and Separation through Induced Fit in a Flexible Hybrid Ultramicroporous Material

Structural changes at the active site of an enzyme induced by binding to a substrate molecule can result in enhanced activity in biological systems. Herein, we report that the new hybrid ultramicroporous material sql-SIFSIX-bpe-Zn exhibits an induced fit binding mechanism when exposed to acetylene, C₂H₂. The resulting phase change affords exceptionally strong C₂H₂ binding that in turn enables highly selective C₂H₂/C₂H₄ and C₂H₂/CO₂ separation demonstrated by dynamic breakthrough experiments. sql-SIFSIX-bpe-Zn was observed to exhibit at least four phases: as-synthesised (α); activated (β); and C₂H₂ induced phases (β' and γ). sql-SIFSIX-bpe-Zn-β exhibited strong affinity for C₂H₂ at ambient conditions as demonstrated by benchmark isosteric heat of adsorption (Qst ) of 67.5 kJ mol⁻¹ validated through in situ pressure gradient differential scanning calorimetry (PG-DSC). Further, in situ characterisation and DFT calculations provide insight into the mechanism of the C₂H₂ induced fit transformation, binding positions and the nature of host-guest and guest-guest interactions

Identification of protein biomarkers for prediction of response to platinum-based treatment regimens in patients with non-small cell lung cancer

The majority of patients with resected stage II-IIIA non-small cell lung cancer (NSCLC) are treated with platinum-based adjuvant chemotherapy (ACT) in a one-size-fits-all approach. However, a significant number of patients do not derive clinical benefit, and no predictive patient selection biomarker is currently available. Using mass spectrometry-based proteomics, we have profiled tumour resection material of 2 independent, multi-centre cohorts of in total 67 patients with NSCLC who underwent ACT. Unsupervised cluster analysis of both cohorts revealed a poor response/survival sub-cluster composed of ~ 25% of the patients, that displayed a strong epithelial-mesenchymal transition signature and stromal phenotype. Beyond this stromal sub-population, we identified and validated platinum response prediction biomarker candidates involved in pathways relevant to the mechanism of action of platinum drugs, such as DNA damage repair, as well as less anticipated processes such as those related to the regulation of actin cytoskeleton. Integration with pre-clinical proteomics data supported a role for several of these candidate proteins in platinum response prediction. Validation of one of the candidates (HMGB1) in a third independent patient cohort using immunohistochemistry highlights the potential of translating these proteomics results to clinical practice.</p

A Risk Assessment Tool for Predicting Fragility Fractures and Mortality in the Elderly

Existing fracture risk assessment tools are not designed to predict fracture-associated consequences, possibly contributing to the current undermanagement of fragility fractures worldwide. We aimed to develop a risk assessment tool for predicting the conceptual risk of fragility fractures and its consequences. The study involved 8965 people aged >= 60 years from the Dubbo Osteoporosis Epidemiology Study and the Canadian Multicentre Osteoporosis Study. Incident fracture was identified from X-ray reports and questionnaires, and death was ascertained though contact with a family member or obituary review. We used a multistate model to quantify the effects of the predictors on the transition risks to an initial and subsequent incident fracture and mortality, accounting for their complex interrelationships, confounding effects, and death as a competing risk. There were 2364 initial fractures, 755 subsequent fractures, and 3300 deaths during a median follow-up of 13 years (interquartile range [IQR] 7-15). The prediction model included sex, age, bone mineral density, history of falls within 12 previous months, prior fracture after the age of 50 years, cardiovascular diseases, diabetes mellitus, chronic pulmonary diseases, hypertension, and cancer. The model accurately predicted fragility fractures up to 11 years of follow-up and post-fracture mortality up to 9 years, ranging from 7 years after hip fractures to 15 years after non-hip fractures. For example, a 70-year-old woman with aT-score of -1.5 and without other risk factors would have 10% chance of sustaining a fracture and an 8% risk of dying in 5 years. However, after an initial fracture, her risk of sustaining another fracture or dying doubles to 33%, ranging from 26% after a distal to 42% post hip fracture. A robust statistical technique was used to develop a prediction model for individualization of progression to fracture and its consequences, facilitating informed decision making about risk and thus treatment for individuals with different risk profiles. (c) 2020 American Society for Bone and Mineral Research

Multicenter Collaborative Study to Optimize Mass Spectrometry Workflows of Clinical Specimens

The foundation for integrating mass spectrometry (MS)-based proteomics into systems medicine is the development of standardized start-to-finish and fit-for-purpose workflows for clinical specimens. An essential step in this pursuit is to highlight the common ground in a diverse landscape of different sample preparation techniques and liquid chromatography-mass spectrometry (LC-MS) setups. With the aim to benchmark and improve the current best practices among the proteomics MS laboratories of the CLINSPECT-M consortium, we performed two consecutive round-robin studies with full freedom to operate in terms of sample preparation and MS measurements. The six study partners were provided with two clinically relevant sample matrices: plasma and cerebrospinal fluid (CSF). In the first round, each laboratory applied their current best practice protocol for the respective matrix. Based on the achieved results and following a transparent exchange of all lab-specific protocols within the consortium, each laboratory could advance their methods before measuring the same samples in the second acquisition round. Both time points are compared with respect to identifications (IDs), data completeness, and precision, as well as reproducibility. As a result, the individual performances of participating study centers were improved in the second measurement, emphasizing the effect and importance of the expert-driven exchange of best practices for direct practical improvements