Ming-Chih J. Kao

Transitive functional annotation by shortest-path analysis of gene expression data.

Current methods for the functional analysis of microarray gene expression data make the implicit assumption that genes with similar expression profiles have similar functions in cells. However, among genes involved in the same biological pathway, not all gene pairs show high expression similarity. Here, we propose that transitive expression similarity among genes can be used as an important attribute to link genes of the same biological pathway. Based on large-scale yeast microarray expression data, we use the shortest-path analysis to identify transitive genes between two given genes from the same biological process. We find that not only functionally related genes with correlated expression profiles are identified but also those without. In the latter case, we compare our method to hierarchical clustering, and show that our method can reveal functional relationships among genes in a more precise manner. Finally, we show that our method can be used to reliably predict the function of unknown genes from known genes lying on the same shortest path. We assigned functions for 146 yeast genes that are considered as unknown by the Saccharomyces Genome Database and by the Yeast Proteome Database. These genes constitute around 5% of the unknown yeast ORFome.

Functional annotation and network reconstruction through cross-platform integration of microarray data

The rapid accumulation of microarray data translates into a need for methods to effectively integrate data generated with different platforms. Here we introduce an approach, 2nd-order expression analysis, that addresses this challenge by first extracting expression patterns as meta-information from each data set (1st-order expression analysis) and then analyzing them across multiple data sets. Using yeast as a model system, we demonstrate two distinct advantages of our approach: we can identify genes of the same function yet without coexpression patterns and we can elucidate the cooperativities between transcription factors for regulatory network reconstruction by overcoming a key obstacle, namely the quantification of activities of transcription factors. Experiments reported in the literature and performed in our lab support a significant number of our predictions.

GoSurfer: a graphical interactive tool for comparative analysis of large gene sets in Gene Ontology space.

UNLABELLED The analysis of complex patterns of gene regulation is central to understanding the biology of cells, tissues and organisms. Patterns of gene regulation pertaining to specific biological processes can be revealed by a variety of experimental strategies, particularly microarrays and other highly parallel methods, which generate large datasets linking many genes. Although methods for detecting gene expression have improved substantially in recent years, understanding the physiological implications of complex patterns in gene expression data is a major challenge. This article presents GoSurfer, an easy-to-use graphical exploration tool with built-in statistical features that allow a rapid assessment of the biological functions represented in large gene sets. GoSurfer takes one or two list(s) of gene identifiers (Affymetrix probe set ID) as input and retrieves all the Gene Ontology (GO) terms associated with the input genes. GoSurfer visualises these GO terms in a hierarchical tree format. With GoSurfer, users can perform statistical tests to search for the GO terms that are enriched in the annotations of the input genes. These GO terms can be highlighted on the GO tree. Users can manipulate the GO tree in various ways and interactively query the genes associated with any GO term. The user-generated graphics can be saved as graphics files, and all the GO information related to the input genes can be exported as text files. AVAILABILITY GoSurfer is a Windows-based program freely available for noncommercial use and can be downloaded at http://www.gosurfer.org. Datasets used to construct the trees shown in the figures in this article are available at http://www.gosurfer.org/download/GoSurfer.zip.

Chemical Genetic Modifier Screens: Small Molecule Trichostatin Suppressors as Probes of Intracellular Histone and Tubulin Acetylation

Histone deacetylase (HDAC) inhibitors are being developed as new clinical agents in cancer therapy, in part because they interrupt cell cycle progression in transformed cell lines. To examine cell cycle arrest induced by HDAC inhibitor trichostatin A (TSA), a cytoblot cell-based screen was used to identify small molecule suppressors of this process. TSA suppressors (ITSAs) counteract TSA-induced cell cycle arrest, histone acetylation, and transcriptional activation. Hydroxamic acid-based HDAC inhibitors like TSA and suberoylanilide hydroxamic acid (SAHA) promote acetylation of cytoplasmic alpha-tubulin as well as histones, a modification also suppressed by ITSAs. Although tubulin acetylation appears irrelevant to cell cycle progression and transcription, it may play a role in other cellular processes. Small molecule suppressors such as the ITSAs, available from chemical genetic suppressor screens, may prove to be valuable probes of many biological processes.

An integrative approach to characterize disease-specific pathways and their coordination: a case study in cancer

BackgroundThe most common application of microarray technology in disease research is to identify genes differentially expressed in disease versus normal tissues. However, it is known that, in complex diseases, phenotypes are determined not only by genes, but also by the underlying structure of genetic networks. Often, it is the interaction of many genes that causes phenotypic variations.ResultsIn this work, using cancer as an example, we develop graph-based methods to integrate multiple microarray datasets to discover disease-related co-expression network modules. We propose an unsupervised method that take into account both co-expression dynamics and network topological information to simultaneously infer network modules and phenotype conditions in which they are activated or de-activated. Using our method, we have discovered network modules specific to cancer or subtypes of cancers. Many of these modules are consistent with or supported by their functional annotations or their previously known involvement in cancer. In particular, we identified a module that is predominately activated in breast cancer and is involved in tumor suppression. While individual components of this module have been suggested to be associated with tumor suppression, their coordinated function has never been elucidated. Here by adopting a network perspective, we have identified their interrelationships and, particularly, a hub gene PDGFRL that may play an important role in this tumor suppressor network.ConclusionUsing a network-based approach, our method provides new insights into the complex cellular mechanisms that characterize cancer and cancer subtypes. By incorporating co-expression dynamics information, our approach can not only extract more functionally homogeneous modules than those based solely on network topology, but also reveal pathway coordination beyond co-expression.

Does physical activity influence the relationship between low back pain and obesity

BACKGROUND CONTEXT Evidence supporting an association between obesity and low back pain (LBP) continues to grow; yet little is known about the cause and effect of this relationship. Even less is known about the mechanisms linking the two. Physical activity is a logical suspect, but no study has demonstrated its role. PURPOSE This study was designed to examine the interrelationship between physical activity, obesity, and LBP. The specific aims were to determine if obesity is a risk factor for LBP in the U.S. population, measure the strength of any observed association, and evaluate the role of physical activity in modulating this association. STUDY DESIGN/SETTING A cross-sectional U.S. population-based study. PATIENT SAMPLE A cohort of 6,796 adults from the 2003-2004 National Health and Nutrition Examination Survey. OUTCOME MEASURES Demographic information, an in-depth health questionnaire, physical examination details, and 7-day free-living physical activity monitoring using accelerometry (ActiGraph AM-7164; ActiGraph, Pensacola, FL, USA). METHODS LBP status was determined by questionnaire response. Body mass index (BMI) was calculated during physical examination and divided here into four groups (normal weight <25, overweight 25-30, obese 31-35, and ultraobese 36+). Summary measures of physical activity were computed based on intensity cutoffs, percentile intensities, and bout. Demographics, social history, and comorbid health conditions were used to build adjusted weighted logistic regression models constructed using Akaike Information Criterion. All displayed estimates are significant at level <.05. No external funding was received to support this study. None of the authors report conflicts of interest directly related to the specific subject matter of this manuscript. RESULTS In the U.S. population, the risk of low LBP increases in step with BMI from 2.9% for normal BMI (20-25) to 5.2% for overweight (26-30), 7.7% for obese (31-35), and 11.6% for ultraobese (36+). Smoking is consistently the strongest predictor of LBP across the BMI spectrum (odds ratio 1.6-2.9). Physical activity also modulates these risks. In the overall model, the best physical activity predictors of LBP are in the moderate and high intensity ranges with small effects (odds ratio 0.98 and 0.996 per standard deviation increase, respectively). When broken down by BMI, time spent in sedentary and moderate activity ranges demonstrate more robust influences on LBP status in the overweight, obese, and ultraobese groups. CONCLUSIONS Increased BMI is a risk factor for back pain in Americans. More important, the role of physical activity in mitigating back pain risk is shown to be of greater consequence in the overweight and obese populations.

Determination of local statistical significance of patterns in Markov sequences with application to promoter element identification.

High-level eukaryotic genomes present a particular challenge to the computational identification of transcription factor binding sites (TFBSs) because of their long noncoding regions and large numbers of repeat elements. This is evidenced by the noisy results generated by most current methods. In this paper, we present a p-value-based scoring scheme using probability generating functions to evaluate the statistical significance of potential TFBSs. Furthermore, we introduce the local genomic context into the model so that candidate sites are evaluated based both on their similarities to known binding sites and on their contrasts against their respective local genomic contexts. We demonstrate that our approach is advantageous in the prediction of myogenin and MEF2 binding sites in the human genome. We also apply LMM to large-scale human binding site sequences in situ and found that, compared to current popular methods, LMM analysis can reduce false positive errors by more than 50% without compromising sensitivity. This improvement will be of importance to any subsequent algorithm that aims to detect regulatory modules based on known PSSMs.

Duration of Fluoroscopic-Guided Spine Interventions and Radiation Exposure Is Increased in Overweight Patients

The impact of patient body mass index (BMI) on image‐guided spine interventions remains unknown. Higher BMI is known to complicate the acquisition of radiographic images. Therefore it can be hypothesized that the patients body habitus can influence the delivery of a spinal injection.

From Catastrophizing to Recovery: a pilot study of a single-session treatment for pain catastrophizing

Background Pain catastrophizing (PC) – a pattern of negative cognitive-emotional responses to real or anticipated pain – maintains chronic pain and undermines medical treatments. Standard PC treatment involves multiple sessions of cognitive behavioral therapy. To provide efficient treatment, we developed a single-session, 2-hour class that solely treats PC entitled “From Catastrophizing to Recovery” [FCR]. Objectives To determine 1) feasibility of FCR; 2) participant ratings for acceptability, understandability, satisfaction, and likelihood to use the information learned; and 3) preliminary efficacy of FCR for reducing PC. Design and methods Uncontrolled prospective pilot trial with a retrospective chart and database review component. Seventy-six patients receiving care at an outpatient pain clinic (the Stanford Pain Management Center) attended the class as free treatment and 70 attendees completed and returned an anonymous survey immediately post-class. The Pain Catastrophizing Scale (PCS) was administered at class check-in (baseline) and at 2, and 4 weeks post-treatment. Within subjects repeated measures analysis of variance (ANOVA) with Student’s t-test contrasts were used to compare scores across time points. Results All attendees who completed a baseline PCS were included as study participants (N=57; F=82%; mean age =50.2 years); PCS was completed by 46 participants at week 2 and 35 participants at week 4. Participants had significantly reduced PC at both time points (P<0001) and large effect sizes were found (Cohen’s d=0.85 and d=1.15). Conclusion Preliminary data suggest that FCR is an acceptable and effective treatment for PC. Larger, controlled studies of longer duration are needed to determine durability of response, factors contributing to response, and the impact on pain, function and quality of life.

Physical and psychological correlates of fatigue and physical function: a Collaborative Health Outcomes Information Registry (CHOIR) study.

UNLABELLED Fatigue is a multidimensional construct that has significant implications for physical function in chronic noncancer pain populations but remains relatively understudied. The current study characterized the independent contributions of self-reported ratings of pain intensity, sleep disturbance, depression, and fatigue to ratings of physical function and pain-related interference in a diverse sample of treatment-seeking individuals with chronic pain. These relationships were examined as a path modeling analysis of self-report scores obtained from 2,487 individuals with chronic pain from a tertiary care outpatient pain clinic. Our analyses revealed unique relationships of pain intensity, sleep disturbance, and depression with self-reported fatigue. Further, fatigue scores accounted for significant proportions of the relationships of both pain intensity and depression with physical function and pain-related interference and accounted for the entirety of the unique statistical relationship between sleep disturbance and both physical function and pain-related interference. Fatigue is a complex construct with relationships to both physical and psychological factors that has significant implications for physical functioning in chronic noncancer pain. The current results identify potential targets for future treatment of fatigue in chronic pain and may provide directions for future clinical and theoretical research in the area of chronic noncancer pain. PERSPECTIVE Fatigue is an important physical and psychological variable that factors prominently in the deleterious consequences of pain intensity, sleep disturbance, and depression for physical function in chronic noncancer pain.