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Melanocortin Receptor Selective Ligands

<h3 class="red">Melanocortin Receptor&nbsp;Selective Ligands</h3><p>The melanocortin receptor (MCR) system consists of endogenous agonists, antagonists, G-protein coupled receptors, and auxiliary proteins that are involved in the regulation of complex physiological functions such as energy and weight homeostasis, feeding behavior, inflammation, sexual function, pigmentation, and exocrine gland function. The five melanocortin receptors (MC1-5R) are member of a G-protein coupled receptors (GPCRs) that activate the cAMP signal transduction pathway and are stimulated by the melanocortin agonists α, β, γ-melanocyte stimulating hormone (MSH) and adrenocorticotropin (ACTH). These receptors are antagonized by agouti (ASP) and agouti-related protein (AGRP), which are the only known endogenous antagonists of GPCRs identified to date.</p><p>Obesity continues to be a major health problem worldwide. Obesity-related conditions include heart disease, stroke, type 2 diabetes, and certain types of cancer. The human melanocortin-4 receptor (MC4R) has been identified by genetic studies as well as in individual morbidly obese humans to be a locus connected to obesity. More than 100 single nucleotide polymorphisms (SNPs) have been identified so far in obese and non-obese human adults and children. The MC4R is expressed primarily in the brain and regulates obesity, feeding behavior, and satiety, making it an attractive target for antiobesity drugs. The MC3R, which is expressed both centrally (including the hypothalamus) and peripherally, has been postulated to play fundamental roles in metabolism and energy homeostasis, as well as food intake. However, the physiological mechanism(s) of action for the MC3 receptor in the different tissues and the periphery are still unclear.</p><p>Selective and potent ligands on these receptors are highly desirable. But designing MC3R versus MC4R selective ligands have been proven a difficult task without the knowledge of structural and conformational features that differentiate SAR between the MC3 and MC4 receptor subtypes. One strategy to address this issue is to probe ligand structural features that can differentiate the ligand&minus;receptor pharmacological profiles of the melanocortin receptors (MC1, MC3&minus;5R).</p><p>Return to this PI&#39;s <a href="">main page</a>.</p><p>&nbsp;</p>
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Proteomics in Lung

<h3 class="red">Proteomics in Lung</h3><p>Collectively, lung cancer and chronic obstructive pulmonary disease (COPD) are the leading causes of death related to lung disease in the US. Lung cancer is now the primary cause of cancer death in both men and women, and increasingly occurs in people who have never smoked and in former smokers. Published reports indicate that the presence of COPD independently increases the risk of lung cancer 3- to 10-fold. COPD is estimated to affect nearly 25 million people in the US, but less than half of these individuals have been diagnosed and more than four million have never smoked. COPD currently is the fourth leading cause of mortality in the US and is projected to be the third in five years. In a substantial proportion of patients with lung cancer, chronic airflow obstruction precedes the development of lung cancer, but whether it actually lies on the causal pathway remains unknown. Thus while smoking causes both COPD and lung cancer, COPD itself provides a fertile substrate for lung carcinogenesis by either harboring or nurturing oncogenic lesions. Many smokers quit at the onset of COPD, but remain at risk for often-lethal lung cancer. New knowledge combining comprehensive genome-wide information with systems biology approaches has high potential to identify which COPD patients are at risk of lung cancer, so that promising approaches to chemo-prevention and early interdiction can be tested.</p><p>This project aims to develop such new predictive and diagnostic strategies. These researchers hypothesize that linking precise clinical phenotypes (magnitude and pattern of airflow limitation and emphysematous tissue damage) to specific germ line and somatic genetic alterations will enable improved prediction of lung cancer risk and early diagnosis; provide opportunities to test innovative preventive strategies; and identify new molecular targets for the treatment of COPD and lung cancer.</p><p>Return to this PI&rsquo;s <a href="">main page</a>.</p>
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Use of Fecal Microbiota Transplantation to Cure Clostridium difficile Disease

<h3 class="red">Use of Fecal Microbiota Transplantation to Cure <em>Clostridium difficile</em> Disease</h3><p>Fecal microbiota transplantation (FMT) is an effective treatment for recurrent <em>Clostridium difficile</em> infection (CDI) that often fails standard antibiotic therapy. Despite its widespread recent use, however, little is known about the stability of the fecal microbiota following FMT. These researchers are studying short- and long-term changes and provide kinetic visualization of fecal microbiota composition in patients with multiply recurrent CDI that were refractory to antibiotic therapy and treated using FMT. Fecal samples were collected from four patients before and up to 151 days after FMT, with daily collections until 28 days and weekly collections until 84 days post-FMT. The composition of fecal bacteria was characterized using high throughput 16S rRNA gene sequence analysis, compared to microbiota across body sites in the Human Microbiome Project (HMP) database, and visualized in a movie-like, kinetic format. FMT resulted in rapid normalization of bacterial fecal sample composition from a markedly dysbiotic state to one representative of normal fecal microbiota. While the microbiome appeared most similar to the donor implant material one day post-FMT, the composition diverged variably at later time points. The donor microbiota composition also varied over time. However, both post-FMT and donor samples remained within the larger cloud of fecal microbiota characterized as healthy by the HMP. Dynamic behavior appaers to be an intrinsic property of normal fecal microbiota and should be accounted for in comparing microbial communities among normal individuals and those with disease states. This also suggests that more frequent sample analyses are needed in order to properly assess success of FMT procedures.</p><p>This PI&rsquo;s research was featured in an <a href="">MSI Research Spotlight</a> in April 2015.</p><p>Return to this PI&rsquo;s <a href="">main page</a>.</p>
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Genetic Differences in Osteosarcoma Subtypes

Osteosarcoma is a bone cancer that affects both humans and dogs. Researchers at the University of Minnesota, including MSI users, are studying this disease. In a recent paper that appeared in The Journal of Biological Chemistry , MSI Principal Investigators Associate Professor Subbaya Subramanian...

Structural Comparisons of Two Receptors for Mouse Coronavirus

Coronaviruses are a large group of viruses that can cause illness in humans, other mammals, and birds. They use cell surface receptors to attach themselves to host cells. While most of them are not dangerous, causing minor illnesses such as colds, the group includes such deadly diseases as SARS and...

MSI Research Exhibition 2016 - Biological and Medical Sciences Posters

The titles for the posters submitted in the Biological and Medical Sciences category for the 2016 MSI Research Exhibition are listed are shown below. See posters in the Physical Sciences and Engineering category . Return to Research Exhibition 2016 page . Adaptive Gene- and Pathway-Trait...

Translational Informatics

Application of Informatics to Transcription of Ancient Papyri While computers can do many things, there are still a few areas in which humans excel such as the discriminatory power of the eye and the natural human ability to quickly classify objects. The visual ability of recognizing patterns is at...

MSI Users Bulletin – December 2016

The Users Bulletin provides a summary of new policies, procedures, and events of interest to MSI users. It is published quarterly.

To request technical assistance with your MSI account, please contact

1. MSI Primary File System: On November 9, 2016, Director of Research Computing Claudia Neuhauser sent an email to MSI users concerning issues with MSI systems. The information below repeats some of that information, plus provides some updates:

Starting in late August, we experienced an unusually high number of outages and you felt the impact. We immediately started to work with the vendor to figure why the system was behaving differently. After extensive testing, our storage vendor was able to replicate in their labs the conditions that led to our outages. A bug in the file system software is triggered when the following conditions occur: a certain type of hardware failure occurs, new storage shelves are being integrated into the current system, and the file system is under a heavy load. These conditions are not unusual at MSI, because we are frequently adding new storage shelves to keep up with demand, we expect parts to fail in a system as large as ours, and our systems are at least at 90% load all of the time. 

Our storage vendor recognizes the criticality of this issue and has made creating a patch for this bug a top priority. On December's maintenance day, we fully migrated the one volume which is needed across all of MSI to a different bladeset. That migration allows us to now work with the vendor on more debugging. There no significant risk of an MSI-site outage at this time. The vendor is continuing to test and is developing a patch.

We continue to address system availability by addressing other issues as well. For example, contractors will soon begin work to upgrade the cooling zones in Walter Library, so that we are much less susceptible to general building outages. We’re also actively working on new types of systems that offer a higher level of availability by leveraging some of the features of a cloud-based infrastructure. We’ll provide more details on these developments in the weeks and months to come. In the meantime, please don’t hesitate to contact me ( if you have any questions or comments about what we are doing to support your research requirements.

2. Account Renewal Reminders:

a. The MSI 2016 allocation renewal period ended on December 9, 2016. MSI user accounts must be renewed if you wish to continue using MSI during 2017. PIs and Group Administrators who still wish to renew but who have not submitted a renewal request should contact as soon as possible. Non-renewed accounts will be locked in early 2017.

b. Accounts for Non-UMN MSI Users: MSI is transitioning from using sponsored accounts for non-UMN affiliated users to a “Person of Interest (POI)” designation. This change will create a greater level of security for accounts.

MSI is no longer accepting sponsored accounts as valid UMN Internet IDs for new users. As of January 1, 2017, sponsored accounts will no longer be allowed to log in to MSI resources. PI groups who have users with sponsored accounts must convert the accounts to POI. PI groups will need to get POI status for any external user they wish to add to their group. See the FAQ for more information.

The MSI Tech Support staff ( will assist non-University affiliated PIs with creating POIs. University-affiliated PIs are authorized to set up POIs with the University.

3. Printing at MSI: MSI has removed the printers from the Scientific Development and Visualization Laboratory in Walter Library. We will no longer provide printing capability for MSI users. There are multiple places available on campus where you can print, including:


Printing Services Digital Copy Centers

4. Spring Tutorials: MSI will resume tutorials at the start of the spring semester. They will be posted on the Events page of the MSI website when the schedule is finalized. 

5. 2017 MSI Research Exhibition: Save the Date! MSI will host the annual Research Exhibition on April 25, 2017, in Walter Library. The event includes a judged poster session with prizes awarded to the finalists. We will post information on our website and send out a Call for Posters in January 2017.

6. Jobs Available at MSI:

Student Customer Service

User Engagement Developer

7. Useful Webpages: Looking for help with using MSI? One of these pages may have the information you need:

a. Services available at MSI

b. Getting Started (includes Quickstart Guides)

c. MSI Systems

d. Help and Documentation

e. Staff Listing and Areas of Expertise

f. Upcoming Events and Tutorials

g. Proposal Support


Comparing the performance of MPI vector datatypes


Performance of MPI vector datatypes



1. write a program to send a vector of 1000 elements of type MPI_DOUBLE, with a stride of 24 between each element. You may want to use these MPI routines in your solution:
2. Time routines and do several interations to get a good average and repeat the test 10 times.

Use the following example and complete the arguments of the calling routines for MPI_Type_vector 
#include < stdio.h>
#include < stdlib.h>
#include "mpi.h"

#define NUMBER_OF_TESTS 10

int main( argc, argv )
int argc;
char **argv;
    MPI_Datatype vec1, vec_n;
    int          blocklens[2];
    MPI_Datatype old_types[2];

    double       *buf, *lbuf;
    register double *in_p, *out_p;
    int          rank;
    int          n, stride;
    double       t1, t2, tmin;
    int          i, j, k, nloop;
    MPI_Status   status;

    MPI_Init( &argc, &argv );

    MPI_Comm_rank( MPI_COMM_WORLD, &rank );

    n      = 1000;
    stride = 24;
    nloop  = 100000/n;

    buf = (double *) malloc( n * stride * sizeof(double) );
    if (!buf) {
        fprintf( stderr, "Could not allocate send/recv buffer of size %d
                 n * stride );
        MPI_Abort( MPI_COMM_WORLD, 1 );
    lbuf = (double *) malloc( n * sizeof(double) );
    if (!lbuf) {
        fprintf( stderr, "Could not allocated send/recv lbuffer of size %d
                 n );
        MPI_Abort( MPI_COMM_WORLD, 1 );

    if (rank == 0) 
        printf( "Kind	n	stride	time (sec)	Rate (MB/sec)
" );

    /* Use a fixed vector type */
    MPI_Type_vector( ******************************* );
    MPI_Type_commit( ***** );

    tmin = 1000;
    for (k=0; k < NUMBER_OF_TESTS; k++) {
        if (rank == 0) {
            t1 = MPI_Wtime();
            for (j=0; j < nloop; j++) {
                MPI_Send(*********************************** );
                MPI_Recv( ******************************************* );
            t2 = (MPI_Wtime() - t1) / nloop;
            if (t2 < tmin) tmin = t2;
        else if (rank == 1) {
            for (j=0; j < nloop; j++) {
                MPI_Recv( buf, 1, vec1, 0, k, MPI_COMM_WORLD, &status );
                MPI_Send( buf, 1, vec1, 0, k, MPI_COMM_WORLD );
    /* Convert to half the round-trip time */
    tmin = tmin / 2.0;
    if (rank == 0) {
        printf( "Vector	%d	%d	%f	%f
                n, stride, tmin, n * sizeof(double) * 1.0e-6 / tmin );
    MPI_Type_free( &vec1 );

    MPI_Finalize( );
    return 0;

Galaxy-P Case Study (Proteomics)

Professor Tim Griffin collaborated with MSI to develop and deploy tools to automate proteomics analysis tasks. The result was a set of tools incorporated into the Galaxy framework, and additional funding to expand his group’s work into related fields. The first objective was to provide an...
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