Posts Tagged ‘antimicrobial resistance’

Unlocking the Microbiome

Larry H. Bernstein, MD, FCAP, Curator



3.3.11   Unlocking the Microbiome, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair

Machine-learning technique uncovers unknown features of multi-drug-resistant pathogen

Relatively simple “unsupervised” learning system reveals important new information to microbiologists
January 29, 201   http://www.kurzweilai.net/machine-learning-technique-uncovers-unknown-features-of-pathogen


According to the CDC, Pseudomonas aeruginosa is a common cause of healthcare-associated infections, including pneumonia, bloodstream infections, urinary tract infections, and surgical site infections. Some strains of P. aeruginosa have been found to be resistant to nearly all or all antibiotics. (illustration credit: CDC)

A new machine-learning technique can uncover previously unknown features of organisms and their genes in large datasets, according to researchers from the Perelman School of Medicine at the University of Pennsylvania and the Geisel School of Medicine at Dartmouth University.

For example, the technique learned to identify the characteristic gene-expression patterns that appear when a bacterium is exposed in different conditions, such as low oxygen and the presence of antibiotics.

The technique, called “ADAGE” (Analysis using Denoising Autoencoders of Gene Expression), uses a “denoising autoencoder” algorithm, which learns to identify recurring features or patterns in large datasets — without being told what specific features to look for (that is, “unsupervised.”)*

Last year,  Casey Greene, PhD, an assistant professor of Systems Pharmacology and Translational Therapeutics at Penn, and his team published, in an open-access paper in the American Society for Microbiology’s mSystems, the first demonstration of ADAGE in a biological context: an analysis of two gene-expression datasets of breast cancers.

Tracking down gene patterns of a multi-drug-resistant bacterium

The new study, published Jan. 19 in an open-access paper in mSystems, was more ambitious. It applied ADAGE to a dataset of 950 gene-expression arrays publicly available at the time for the multi-drug-resistant bacteriumPseudomonas aeruginosa. This bacterium is a notorious pathogen in the hospital and in individuals with cystic fibrosis and other chronic lung conditions; it’s often difficult to treat due to its high resistance to standard antibiotic therapies.

The data included only the identities of the roughly 5,000 P. aeruginosa genes and their measured expression levels in each published experiment. The goal was to see if this “unsupervised” learning system could uncover important patterns in P. aeruginosa gene expression and clarify how those patterns change when the bacterium’s environment changes — for example, when in the presence of an antibiotic.

Even though the model built with ADAGE was relatively simple — roughly equivalent to a brain with only a few dozen neurons — it had no trouble learning which sets of P. aeruginosa genes tend to work together or in opposition. To the researchers’ surprise, the ADAGE system also detected differences between the main laboratory strain of P. aeruginosa and strains isolated from infected patients. “That turned out to be one of the strongest features of the data,” Greene said.

“We expect that this approach will be particularly useful to microbiologists researching bacterial species that lack a decades-long history of study in the lab,” said Greene. “Microbiologists can use these models to identify where the data agree with their own knowledge and where the data seem to be pointing in a different direction … and to find completely new things in biology that we didn’t even know to look for.”

Support for the research came from the Gordon and Betty Moore Foundation, the William H. Neukom Institute for Computational Science, the National Institutes of Health, and the Cystic Fibrosis Foundation.

* In 2012, Google-sponsored researchers applied a similar method to randomly selected YouTube images; their system learned to recognize major recurring features of those images — including cats of course.

Abstract of ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa Gene Expression Data with Denoising Autoencoders Illuminates Microbe-Host Interactions

The increasing number of genome-wide assays of gene expression available from public databases presents opportunities for computational methods that facilitate hypothesis generation and biological interpretation of these data. We present an unsupervised machine learning approach, ADAGE (analysis using denoising autoencoders of gene expression), and apply it to the publicly available gene expression data compendium for Pseudomonas aeruginosa. In this approach, the machine-learned ADAGE model contained 50 nodes which we predicted would correspond to gene expression patterns across the gene expression compendium. While no biological knowledge was used during model construction, cooperonic genes had similar weights across nodes, and genes with similar weights across nodes were significantly more likely to share KEGG pathways. By analyzing newly generated and previously published microarray and transcriptome sequencing data, the ADAGE model identified differences between strains, modeled the cellular response to low oxygen, and predicted the involvement of biological processes based on low-level gene expression differences. ADAGE compared favorably with traditional principal component analysis and independent component analysis approaches in its ability to extract validated patterns, and based on our analyses, we propose that these approaches differ in the types of patterns they preferentially identify. We provide the ADAGE model with analysis of all publicly available P. aeruginosa GeneChip experiments and open source code for use with other species and settings. Extraction of consistent patterns across large-scale collections of genomic data using methods like ADAGE provides the opportunity to identify general principles and biologically important patterns in microbial biology. This approach will be particularly useful in less-well-studied microbial species.

Abstract of Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders

Big data bring new opportunities for methods that efficiently summarize and automatically extract knowledge from such compendia. While both supervised learning algorithms and unsupervised clustering algorithms have been successfully applied to biological data, they are either dependent on known biology or limited to discerning the most significant signals in the data. Here we present denoising autoencoders (DAs), which employ a data-defined learning objective independent of known biology, as a method to identify and extract complex patterns from genomic data. We evaluate the performance of DAs by applying them to a large collection of breast cancer gene expression data. Results show that DAs successfully construct features that contain both clinical and molecular information. There are features that represent tumor or normal samples, estrogen receptor (ER) status, and molecular subtypes. Features constructed by the autoencoder generalize to an independent dataset collected using a distinct experimental platform. By integrating data from ENCODE for feature interpretation, we discover a feature representing ER status through association with key transcription factors in breast cancer. We also identify a feature highly predictive of patient survival and it is enriched by FOXM1 signaling pathway. The features constructed by DAs are often bimodally distributed with one peak near zero and another near one, which facilitates discretization. In summary, we demonstrate that DAs effectively extract key biological principles from gene expression data and summarize them into constructed features with convenient properties.

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Antimicrobial Resistance

Larry H. Bernstein, MD, FCAP, Curator



NIH Funds Nine Anti-Microbial Resistance Diagnostic Projects to Deal with ‘Super Bugs’ and Give Clinical Laboratories New Diagnostic Tools to Improve Patient Care

Lab-on-a-chip technology could reduce the time needed to identify infection-causing bacteria and for physicians to prescribe correct antibiotics 

Pathology groups and medical laboratories may see their role in the patient-care process grow if researchers succeed in developing culture-independent diagnostic tools that quickly identify bacterial infections as well as pinpoint the antibiotics needed to treat them.

In the battle against antibiotic-resistant infections (AKA “super bugs”) the National Institutes of Health (NIH) is funding nine research projects aimed at thwarting the growing problem of life-threatening infections that no longer are controlled or killed by today’s arsenal of drugs.

Common Practices in Hospitals Leading to Super Bugs

Currently, when infections are suspected in hospitals or other settings where illness can quickly spread, samples are sent to a central medical laboratory where it may take up to three days to determine what germ is causing the infection. Because of that delay, physicians often prescribe broad-spectrum antibiotics based on a patient’s symptoms rather than lab test results, a practice that can lead to the growth of antibiotic-resistant microbes.

“Antimicrobial resistance is a serious global health threat that is undermining our ability to effectively detect, treat, and prevent infections,” said National Institute of Allergy and Infectious Diseases (NIAID) Director Anthony S. Fauci, MD, in a news release. “One way we can combat drug resistance is by developing enhanced diagnostic tests that rapidly identify the bacteria causing an infection and their susceptibility to various antimicrobials. This will help physicians determine the most effective treatments for infected individuals and thereby reduce the use of broad-spectrum antibiotics that can contribute to the drug resistance problem.”

The Centers for Disease Control and Prevention (CDC) estimates that preventing infections and improving antibiotic prescribing could save 37,000 lives from drug-resistant infections over five years.

Click here to see image

As Director of the National Institute of Allergy and Infectious Diseases (NIAID), part of the National Institutes of Health (NIH), Anthony S. Fauci, MD, (above) leads research to prevent, diagnose, and treat infectious diseases, such as HIV/AIDS, influenza, tuberculosis, malaria, and illness from potential agents of bioterrorism. He serves as one of the key advisors to the White House and U.S. Department of Health and Human Services (HHS) on global AIDS issues. (Photo and caption copyright: NIH Medline Plus.)

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The Challenge of Antimicrobial Resistance

Writer and Curator: Larry H. Bernstein, MD, FCAP


Antibiotic resistance has become a major challenge of our time.  Common microorganisms that inhabit the skin, mouth and nares, and fecal organisms are transmitted in the hospital setting. Handwashing procedures have had limited benefit. Operating rooms are ventilated and environmentally engineered to minimize transmission intraoperatively. The patient may be immune-compromized. The organisms that are encountered have genetically adapted to the most effective antibiotics at our disposal. even with some risk of secondary toxicity in some cases.

What is Drug Resistance?

Antimicrobial resistance is the ability of microbes, such as bacteria, viruses,
parasites, or fungi, to grow in the presence of a chemical (drug) that would
normally kill it or limit its growth.

Diagram showing the difference between non-resistant bacteria and drug
resistant bacteria.

Drug Resistance difference between non-resistant bacteria and drug resistant bacteria

Drug Resistance difference between non-resistant bacteria and drug resistant bacteria

Credit: NIAID


Diagram showing the difference between non-resistant bacteria and drug
resistant bacteria. Non-resistant bacteria multiply, and upon drug treatment,
the bacteria die. Drug resistant bacteria multiply as well, but upon drug
treatment, the bacteria continue to spread.

Many infectious diseases are increasingly difficult to treat because of
antimicrobial-resistant organisms, including HIV infection, staphylococcal
infection, tuberculosis, influenza, gonorrhea, candida infection, and malaria.

Between 5 and 10 percent of all hospital patients develop an infection. About
90,000 of these patients die each year as a result of their infection, up from
13,300 patient deaths in 1992.

According to the Centers for Disease Control and Prevention (April 2011),
antibiotic resistance in the United States costs an estimated $20 billion a year
in excess health care costs, $35 million in other societal costs and more than 8
million additional days that people spend in the hospital.

World Health Organization – 2014 Report

Antimicrobial resistance (AMR) is an increasingly serious threat to
global public health. AMR develops when a microorganism (bacteria,
fungus, virus or parasite) no longer responds to a drug to which it
was originally sensitive. This means that standard treatments no
longer work; infections are harder or impossible to control; the risk
of the spread of infection to others is increased; illness and hospital
stays are prolonged, with added economic and social costs; and the
risk of death is greater—in some cases, twice that of patients who
have infections caused by non-resistant bacteria. The problem is so
serious that it threatens the achievements of modern medicine. A
post-antibiotic era—in which common infections and minor
injuries can kill—is a very real possibility for the 21st century.

WHO is developing a global action plan for AMR that
will include:
• development of tools and standards for harmonized
surveillance of ABR in humans, and for integrated
surveillance in food-producing animals and the
food chain;
• elaboration of strategies for population-based
surveillance of AMR and its health and economic
impact; and
• collaboration between AMR surveillance networks
and centres to create or strengthen coordinated
regional and global surveillance.
AMR is a global health security threat that requires
action across government sectors and society as a
whole. Surveillance that generates reliable data is the
essential foundation of global strategies and public
health actions to contain AMR.

Resistance to Antibiotics: Are We in the Post-Antibiotic Era?
Alfonso J. Alanis
Archives of Medical Research 36 (2005) 697–705

Serious infections caused by bacteria that have become resistant
to commonly used antibiotics have become a major global healthcare
problem in the 21st century. They not only are more severe and
require longer and more complex treatments, but they are also
significantly more expensive to diagnose and to treat. Antibiotic
resistance, initially a problem of the hospital setting associated
with an increased number of hospital acquired infections usually
in critically ill and immunosuppressed patients, has now extended
into the community causing severe infections difficult to diagnose
and treat. The molecular mechanisms by which bacteria have
become resistant to antibiotics are diverse and complex. Bacteria
have developed resistance to all different classes of antibiotics
discovered to date. The most frequent type of resistance is
acquired and transmitted horizontally via the conjugation
of a plasmid. In recent times new mechanisms of resistance
have resulted in the simultaneous development of resistance
to several antibiotic classes creating very dangerous multidrug
-resistant (MDR) bacterial strains, some also known as
‘‘superbugs’’. The indiscriminate and inappropriate use of
antibiotics in outpatient clinics, hospitalized patients and
in the food industry is the single largest factor leading to
antibiotic resistance. In recent years, the number of new
antibiotics licensed for human use in different parts of the
world has been lower than in the recent past. In addition,
there has been less innovation in the field of antimicrobial
discovery research and development. The pharmaceutical
industry, large academic institutions or the government are
not investing the necessary resources to produce the next
generation of newer safe and effective antimicrobial drugs.
In many cases, large pharmaceutical companies have terminated
their anti-infective research programs altogether due to economic
reasons. The potential negative consequences of all these events
are relevant because they put society at risk for the spread of
potentially serious MDR bacterial infections.

Structural and biological studies on bacterial nitric oxide synthase
JK Holden,  H Li, Q Jing, S Kang, J Richo, RB Silverman, TL Poulos

Significance: Nitric oxide (NO) produced by bacterial nitric oxide
synthase has recently been shown to protect the Gram-positive
pathogens Bacillus anthracis and Staphylococcus aureus from
antibiotics and oxidative stress. Using Bacillus subtilis as a model
system, we identified two NOS inhibitors that work in conjunction
with an antibiotic to kill B. subtilis. Moreover, comparison of inhibitor-bound crystal structures between the bacterial NOS and mammalian
NOS revealed an unprecedented mode of binding to the bacterial NOS
that can be further exploited for future structure-based drug design.
Overall, this work is an important advance in developing inhibitors
against gram-positive pathogens.

Summary: Nitric oxide (NO) produced by bacterial NOS functions as a
cytoprotective agent against oxidative stress in Staphylococcus aureus,
Bacillus anthracis, and Bacillus subtilis. The screening of several NOS-selective inhibitors uncovered two inhibitors with potential antimicrobial
properties. These two compounds impede the growth of B. subtilis under
oxidative stress, and crystal structures show that each compound exhibits
a unique binding mode. Both compounds serve as excellent leads for the
future development of antimicrobials against bacterial NOS-containing
bacteria.  http://dx.doi.org/10.1073/pnas.1314080110

Speciation of clinically significant coagulase negative Staphylococci
and their antibiotic resistant patterns in a tertiary care hospital
PR Vysakh, S Kandasamy and RM Prabhavathi
Int.J.Curr.Microbiol.App.Sci (2015) 4(1): 704-709

Human skin and mucus membrane has Coagulase Negative Staphylococci
(CoNS) as the indigenous flora. CoNS had become an important agent for
nosocomial infections accounting for about 9%. These infections are
difficult to treat because of the risk factors and the multiple drug resistance
nature of these organisms. The study was undertaken to identify the
prevalence of clinical isolates of CoNS, their speciation and to determine
the antibiotic sensitivity/resistant patterns of CoNS. A total of 490 isolates
were collected from different samples and subjected to biochemical
characterization and antimicrobial screening using conventional
microbiological methods. 165 isolates were identified as CoNS. 23% of
CoNS were isolated from blood, 30% from post-operative wound infections,
23% from pus, 18% from urine, 3% from body fluids (CSF, ascitic fluid etc)
and 3% from CVP tips. The antibiotic sensitivity revealed 81% resistance
to Penicillin,32% resistance to Cefoxitin, 27% resistance to Cefazolin,
55% resistance to Erythromycin, 22% to Clindamycin and 35% to
Cotrimoxazole and with no resistance to Vancomycin, Linezolid and
Ciprofloxacin. The increased recognition of CoNS and emergence of
drug resistance among them demonstrates the need to consider them
as a potent pathogen and to devise laboratory procedure to identify
and to determine the prevalence and antibiotic resistant patterns of CoNS.

Resistance to rifampicin: a review
Beth P Goldstein
The Journal of Antibiotics (2014) 67, 625–630

Resistance to rifampicin (RIF) is a broad subject covering not just the
mechanism of clinical resistance, nearly always due to a genetic change
in the b subunit of bacterial RNA polymerase (RNAP), but also how
studies of resistant polymerases have helped us understand the structure
of the enzyme, the intricacies of the transcription process and its role
in complex physiological pathways. This review can only scratch the
surface of these phenomena. The identification, in strains of
Escherichia coli, of the positions within b of the mutations determining
resistance is discussed in some detail, as are mutations in organisms
that are therapeutic targets of RIF, in particular Mycobacterium
tuberculosis. Interestingly, changes in the same three codons of
the consensus sequence occur repeatedly in unrelated RIF-resistant
(RIFr) clinical isolates of several different single mutation
predominates in mycobacteria. The utilization of our knowledge of
these mutations to develop rapid screening tests for detecting resistance
is briefly discussed. Cross-resistance among rifamycins has been a topic
of controversy; current thinking is that there is no difference in the
susceptibility of RNAP mutants to RIF, rifapentine and rifabutin.
Also summarized are intrinsic RIF resistance and other resistance

Multi-drug resistance, inappropriate initial antibiotic therapy and
mortality in Gram negative severe sepsis and septic shock: A
retrospective cohort study
MD Zilberberg, AF Shorr, ST Micek, C Vazquez-Guillamet, MH Kollef
Critical Care 2014, 18:596 http://dx.doi.org:/10.1186/s13054-014-0596-8

The impact of in vitro resistance on initially appropriate antibiotic therapy
(IAAT) remains unclear. We elucidated the relationship between non-IAAT
and mortality, and between IAAT and multi-drug resistance (MDR) in
sepsis due to Gram-negative bacteremia (GNS).
We conducted a single-center retrospective cohort study of adult intensive
care unit patients with bacteremia and severe sepsis/septic shock caused by
a gram-negative (GN) organism. We identified the following MDR pathogens:
MDR P. aeruginosa, extended spectrum beta lactamase and carbapenemase-
producing organisms. IAAT was defined as exposure within 24 hours of
infection onset to antibiotics active against identified pathogens based on
in vitro susceptibility testing. We derived logistic regression models to
examine a) predictors of hospital mortality and b) impact of MDR on
non-IAAT. Proportions are presented for categorical variables, and
median values with interquartile ranges (IQR) for continuous

Out of 1,064 patients with GNS, 351 (29.2%) did not survive
hospitalization. Non-survivors were older (66.5 (55, 73.5)
versus 63 (53, 72) years, P =0.036), sicker (Acute Physiology and
Chronic Health Evaluation II (19 (15, 25) versus 16 (12, 19),
P <0.001), and more likely to be on pressors (odds ratio (OR) 2.79,
95% confidence interval (CI) 2.12 to 3.68), mechanically ventilated
(OR 3.06, 95% CI 2.29 to 4.10) have MDR (10.0% versus 4.0%,
P <0.001) and receive non-IAAT (43.4% versus 14.6%, P <0.001).
In a logistic regression model, non-IAAT was an independent
predictor of hospital mortality (adjusted OR 3.87, 95% CI 2.77 to
5.41). In a separate model, MDR was strongly associated with
the receipt of non-IAAT (adjusted OR 13.05, 95% CI 7.00 to 24.31).
MDR, an important determinant of non-IAAT, is associated with
a three-fold increase in the risk of hospital mortality. Given the
paucity of therapies to cover GN MDRs, prevention and
development of new agents are critical.

Phenotypic and molecular characteristics of methicillin-resistant
Staphylococcus aureus isolates from Ekiti State, Nigeria
OA Olowe, OO Kukoyi, SS Taiwo, O Ojurongbe, OO Opaleye, et al.
Infection and Drug Resistance 2013:6 87–92

Introduction: The characteristics and antimicrobial resistance profiles
of Staphylococcus aureus differs according to geographical regions and
in relation to antibiotic usage. The aim of this study was to determine
the biochemical characteristics of the prevalent S. aureus from Ekiti State,
Nigeria, and to evaluate three commonly used disk diffusion methods
(cefoxitin, oxacillin, and methicillin) for the detection of methicillin
resistance in comparison with mecA gene detection by polymerase chain
Materials and methods: A total of 208 isolates of S. aureus recovered
from clinical specimens were included in this study. Standard
microbiological procedures were employed in isolating the strains.
Susceptibility of each isolate to methicillin (5 μg), oxacillin (1 μg),
and cefoxitin (30 μg) was carried out using the modified Kirby–Bauer/
Clinical and Laboratory Standard Institute disk diffusion technique.
They were also tested against panels of antibiotics including vancomycin.
The conventional polymerase chain reaction method was used to detect
the presence of the mecA gene.
Results: Phenotypic resistance to methicillin, oxacillin, and cefoxitin
were 32.7%, 40.3%, and 46.5%, respectively. The mecA gene was detected
in 40 isolates, giving a methicillin-resistant S. aureus (MRSA) prevalence
of 19.2%. The S. aureus isolates were resistant to penicillin (82.7%) and
tetracycline (65.4%), but largely susceptible to erythromycin (78.8%
sensitive), pefloxacin (82.7%), and gentamicin (88.5%). When compared
to the mecA gene as the gold standard for MRSA detection, methicillin,
oxacillin, and cefoxitin gave sensitivity rates of 70%, 80%, and 100%,
and specificity rates of 76.2%, 69.1%, and 78.5% respectively.
Conclusion: When compared with previous studies employing mecA
polymerase chain reaction for MRSA detection, the prevalence of 19.2%
reported in Ekiti State, Nigeria in this study is an indication of gradual rise
in the prevalence of MRSA in Nigeria. A cefoxitin (30 μg) disk diffusion test
is recommended above methicillin and oxacillin for the phenotypic detection
of MRSA in clinical laboratories.

Direct sequencing for rapid detection of multidrug resistant Mycobacterium
tuberculosis strains in Morocco
F Zakham, I Chaoui, AH Echchaoui, F Chetioui, M Driss Elmessaoudi, et al.
Infection and Drug Resistance 2013:6 207–213

Background: Tuberculosis (TB) is a major public health problem with high
mortality and morbidity rates, especially in low-income countries.
Disturbingly, the emergence of multidrug resistant (MDR) and extensively
drug resistant (XDR) TB cases has worsened the situation, raising concerns
of a future epidemic of virtually untreatable TB. Indeed, the rapid diagnosis
of MDR TB is a critical issue for TB management. This study is an attempt to
establish a rapid diagnosis of MDR TB by sequencing the target fragments of
the rpoB gene which linked to resistance against rifampicin and the katG gene
and inhA promoter region, which are associated with resistance to isoniazid.
Methods: For this purpose, 133 sputum samples of TB patients from Morocco
were enrolled in this study. One hundred samples were collected from new
cases, and the remaining 33 were from previously treated patients (drug
relapse or failure, chronic cases) and did not respond to anti-TB drugs after
a sufficient duration of treatment. All samples were subjected to rpoB, katG
and pinhA mutation analysis by polymerase chain reaction and DNA sequencing.
Results: Molecular analysis showed that seven strains were isoniazid-
monoresistant and 17 were rifampicin-monoresistant. MDR TB strains were
identified in nine cases (6.8%). Among them, eight were traditionally
diagnosed as critical cases, comprising four chronic and four drug-relapse
cases. The last strain was isolated from a new case. The most recorded
mutation in the rpoB gene was the substitution TCG . TTG at codon 531
(Ser531 Leu), accounting for 46.15%. Significantly, the only mutation found
in the katG gene was at codon 315 (AGC to ACC) with a Ser315Thr amino acid
change. Only one sample harbored mutation in the inhA promoter region
and was a point mutation at the −15p position (C . T). Conclusion: The
polymerase chain reaction sequencing approach is an accurate and rapid
method for detection of drug-resistant TB in clinical specimens, and could
be of great interest in the management of TB in critical cases to adjust the
treatment regimen and limit the emergence of MDR and XDR strains.

Limiting and controlling carbapenem-resistant Klebsiella pneumoniae
L Saidel-Odes, A Borer.
Infection and Drug Resistance 2014:7 9–14

Carbapenem-resistant Klebsiella pneumoniae (CRKP) is resistant to
almost all antimicrobial agents, is associated with substantial morbidity
and mortality, and poses a serious threat to public health. The ongoing
worldwide spread of this pathogen emphasizes the need for immediate
intervention. This article reviews the global spread and risk factors for
CRKP colonization/infection, and provides an overview of the strategy
to combat CRKP dissemination.

Staphylococcus aureus – antimicrobial resistance and the immuno-
compromised child
J Chase McNeil
Infection and Drug Resistance 2014:7 117–127

Children with immunocompromising conditions represent a unique
group for the acquisition of antimicrobial resistant infections due to
their frequent encounters with the health care system, need for empiric
antimicrobials, and immune dysfunction. These infections are further
complicated in that there is a relative paucity of literature on the clinical
features and management of Staphylococcus aureus infections in
immunocompromised children. The available literature on the clinical
features, antimicrobial susceptibility, and management of S. aureus
infections in immunocompromised children is reviewed. S. aureus
infections in children with human immunodeficiency virus (HIV) are
associated with higher HIV viral loads and a greater degree of CD4 T-cell
suppression. In addition, staphylococcal infections in children with HIV
often exhibit a multidrug resistant phenotype. Children with cancer have
a high rate of S. aureus bacteremia and associated complications. Increased
tolerance to antiseptics among staphylococcal isolates from pediatric
oncology patients is an emerging area of research. The incidence of S. aureus
infections among pediatric solid organ transplant recipients varies
considerably by the organ transplanted; in general however, staphylococci
figure prominently among infections in the early post-transplant period.
Staphylococcal infections are also prominent pathogens among children
with a number of immunodeficiencies, notably chronic granulomatous
disease. Significant gaps in knowledge exist regarding the epidemiology
and management of S. aureus infection in these vulnerable children.

selected Staphylococcus aureus mechanisms for immune evasion.

selected Staphylococcus aureus mechanisms for immune evasion.

Figure 1 A schematic depiction of selected Staphylococcus aureus
mechanisms for immune evasion.
Notes: Cna interacts with C1q preventing formation of the C1qrs complex.
ClfA and SdrE each promote Factor I mediated conversion of C3b to iC3b.
Protein A is depicted binding to the Fc region of IgG preventing immunoglobulin
Abbreviations: ClfA, staphylococcal clumping factor A; Cna, collagen adhesin;
IgG, immunoglobulin G; PVL, Panton–Valentine leukocidin; SdrE, S. aureus
surface protein.

The Future of Antibiotics and Resistance
B Spellberg, JG Bartlett, and DN Gilbert
N Engl J Med Jan 24, 2013; 368(4): 299-302
http://dx.doi.org:/ 10.1056/NEJMp1215093

In its recent annual report on global risks, the World Economic
Forum (WEF) concluded that “arguably the greatest
risk . . . to human health comes in the form of antibiotic-resistant
bacteria. We live in a bacterial world where we will never be able
to stay ahead of the mutation curve. A test of our resilience is
how far behind the curve we allow ourselves to fall.”

The WEF report underscores the facts that antibiotic resistance
and the collapse of the antibiotic research and-development
pipeline continue to worsen despite our ongoing efforts on
current fronts. If we’re to develop countermeasures that
have lasting effects, new ideas that complement traditional
approaches will be needed.

Resistance is primarily the result of bacterial adaptation to eons
of antibiotic exposure. What are the fundamental implications of
this reality? First, in addition to antibiotics’ curative power, their
use naturally selects for preexisting resistant populations of bacteria
in nature. Second, it is not just “inappropriate” antibiotic use
that selects for resistance. Rather, the speed with which resistance
spreads is driven by microbial exposure to all antibiotics, whether
appropriately prescribed or not. Thus, even if all inappropriate
antibiotic use were eliminated, antibiotic-resistant infections
would still occur (albeit at lower frequency). Third, after billions
of years of evolution, microbes have most likely invented
antibiotics against every biochemical target that can be attacked
— and, of necessity, developed resistance mechanisms
to protect all those biochemical targets.

Remarkably, resistance was found even to synthetic antibiotics
that did not exist on earth until the 20th century. These results
underscore a critical reality: antibiotic resistance already exists,
widely disseminated in nature, to drugs we have not yet invented.

Table **

Interventions to Address the Antibiotic-Resistance Crisis.*

Intervention Status                                                   Preventing infection
and resistance

“Self-cleaning” hospital rooms;                                Some commercially available
automated disinfectant application                         but require clinical validation;
through misting, vapor, radiation, etc.                    more needed

Novel drug-delivery systems to replace                  Basic science and
IV catheters; regenerative-tissue technology        conceptual stages
to replace prosthetics; superior, noninvasive
ventilation strategies

Improvement of population health and                 Implementation
health care systems to reduce admissions             research stage
to hospitals and skilled nursing facilities

Niche vaccines to prevent resistant                        Basic and clinical
bacterial infections                                                    development stage

Refilling antibiotic pipeline by aligning
economic and regulatory approaches

Models in place, expansion needed in number    Government or nonprofit grants
and scope; new nonprofit corporations                 and contracts to defray R&D costs
needed                                                                          and establish nonprofits
to develop antibiotics

Institution of novel approval pathways                 Proposed, legislative
(e.g., Limited Population Antibiotic                        and regulatory
Drug proposal)                                                            action needed

Preserving available antibiotics,
slowing resistance

Public reporting of antibiotic-use data as a         Policy action needed to
basis for benchmarking and reimbursement      develop and implement

Development of and reimbursement for            Basic and applied research
rapid diagnostic and biomarker tests to              and policy action and
enable appropriate use of antibiotics                   policy action needed

Elimination of use of antibiotics to                       Legislation proposed
promote livestock growth

New waste-treatment strategies;                       One strategy approaching
targeted chemical or biologic                              clinical trials
degradation of antibiotics in waste

Studies to define shortest effective                    Some trials completed
courses of antibiotics for infections

Developing microbe-attacking                            Preclinical, proof-of-
treatments with diminished                                principle stage
potential to drive resistance

Immune-based therapies, such
as infusion of monoclonal antibodies
and white cells that kill microbes

Antibiotics or biologic agents that
don’t kill bacteria but alter their ability
to trigger inflammation or cause disease

Developing treatments attacking host             Preclinical, proof-of-principle stage
targets rather than microbial targets to
avoid selective pressure driving resistance

Direct moderation of host inflammation
in response to infection (e.g., cytokine
agonists or antagonists, PAMP receptor

Sequestration of host nutrients to
prevent microbial access to nutrients

Probiotics that compete with microbial

* IV denotes intravenous, PAMP pathogen-associated molecular
pattern, and R&D research and development

Antibiotic-Resistant Bugs Appear to Use Universal Ribosome-Stalling Mechanism

GEN News  Jan 26, 2015

Researchers at St. Louis University say they have discovered new information
about how antibiotics like azithromycin stop staph infections, and why staph
sometimes becomes resistant to drugs. The team, led by Mee-Ngan F. Yap, Ph.D.,
believe their evidence suggests a universal, evolutionary mechanism by which
the bacteria elude this kind of drug, offering scientists a way to improve the
effectiveness of antibiotics to which bacteria have become resistant.  Their
study (“Sequence selectivity of macrolide-induced translational attenuation”)
was published in PNAS.

Staphylococcus aureus  is a strain of bacteria that frequently has become
resistant to antibiotics, a development that has been challenging for doctors
and dangerous for patients with severe infections. Dr. Yap and her research
team studied staph that had been treated with the antibiotic azithromycin and
learned two things: One, it turns out that the antibiotic isn’t as effective as was
previously thought. And two, the process that the bacteria use to evade the
antibiotic appears to be an evolutionary mechanism that the bacteria developed
in order to delay genetic replication when beneficial.

Genomic epidemiology of a protracted hospital outbreak caused by multidrug-
resistant Acinetobacter baumannii in Birmingham, England
MR Halachev, J Z-M Chan, CI Constantinidou, N Cumley, C Bradley, et al.
Genome Medicine 2014, 6:70 http://genomemedicine.com/content/6/11/70

Background: Multidrug-resistant Acinetobacter baumannii commonly causes
hospital outbreaks. However, within an outbreak, it can be difficult to identify
the routes of cross-infection rapidly and accurately enough to inform infection
control. Here, we describe a protracted hospital outbreak of multidrug-resistant
A. baumannii, in which whole-genome sequencing (WGS) was used to obtain
a high-resolution view of the relationships between isolates.
Methods: To delineate and investigate the outbreak, we attempted to genome-
sequence 114 isolates that had been assigned to the A. baumannii complex
by the Vitek2 system and obtained informative draft genome sequences from
102 of them. Genomes were mapped against an outbreak reference sequence
to identify single nucleotide variants (SNVs).
Results: We found that the pulsotype 27 outbreak strain was distinct from all
other genome-sequenced strains. Seventy-four isolates from 49 patients
could be assigned to the pulsotype 27 outbreak on the basis of genomic
similarity, while WGS allowed 18 isolates to be ruled out of the outbreak.
Among the pulsotype 27 outbreak isolates, we identified 31 SNVs and seven
major genotypic clusters. In two patients, we documented within-host diversity,
including mixtures of unrelated strains and within-strain clouds of SNV diversity.
By combining WGS and epidemiological data, we reconstructed potential
transmission events that linked all but 10 of the patients and confirmed links
between clinical and environmental isolates. Identification of a contaminated
bed and a burns theatre as sources of transmission led to enhanced
environmental decontamination procedures.
Conclusions: WGS is now poised to make an impact on hospital infection
prevention and control, delivering cost-effective identification of routes of
infection within a clinically relevant timeframe and allowing infection control
teams to track, and even prevent, the spread of drug-resistant hospital pathogens.

Discovery of β-lactam-resistant variants in diverse pneumococcal populations
Regine Hakenbeck
Genome Medicine 2014, 6:72  http://genomemedicine.com/content/6/9/72

Understanding of antibiotic resistance in Streptococcus pneumoniae has been
hindered by the low frequency of recombination events in bacteria and thus the
presence of large linked haplotype blocks, which preclude identification of
causative variants. A recent study combining a large number of genomes of
resistant phenotypes has given an insight into the evolving resistance to
β-lactams, providing the first large-scale identification of candidate variants
underlying resistance.

Additional sources:

A Simple Method for Assessment of MDR Bacteria for Over-Expressed
Efflux Pumps
M Martins, MP McCusker, M Viveiros, I Couto, S Fanning, .., L Amaral
The Open Microbiology Journal, 2013, 7, 1-5

Identification of Efflux Pump-mediated Multidrug-resistant
Bacteria by the Ethidium Bromide-agar Cartwheel Method
in vivo 25: 171-178 (2011)

Efflux Pumps that Bestow Multi-Drug Resistance of Pathogenic
Gram negative Bacteria
Amaral L, Spengler G, Martins A and Molnar J
Biochem Pharmacol 2013; 2(3):119

graphical abstract

graphical abstract

An Instrument-free Method for the Demonstration
of Efflux Pump Activity of Bacteria
in vivo 20: 657-664 (2006)

Potential Therapy of Multidrug-resistant and Extremely
Drug-resistant Tuberculosis with Thioridazine
in vivo 26: 231-236 (2012)

Inhibitors of efflux pumps of Gram-negative bacteria
inhibit Quorum Sensing
Leonard Amaral, Joseph Molnar
Open Journal of Pharmacology, 2012, 2-2

An Overview of Clinical Microbiology, Classification,
and Antimicrobial Resistance
Larry H. Bernstein

New protein detonates bacteria from within

By Tim Sandle     in Science

Tel Aviv – By sequencing the DNA of bacteria resistant to viral toxins, scientists have identified novel proteins capable of stymieing growth in pathogenic, antibiotic-resistant bacteria.

Today’s arsenal of antibiotics is ineffective against some emerging strains of antibiotic-resistant pathogens. Novel inhibitors of bacterial growth therefore need to be found. One way is looking into the viruses that infect bacteria.

Key to the new initiative is the concept of fighting bacteria from within, rather than using an external chemical to batter through the bacterial cell wall. the basis of the new weapon is viral. In order to select an appropriate viral protein, researchers undertook a comprehensive screening exercise in order to identify proteins in viruses that are known to infect bacteria (bacteriophages). Bacteriophages occur abundantly in the biosphere, with different virions, genomes and lifestyles. The review was so comprehensive that it took almost three years to complete.

The screening was achieved through the use of high-throughput DNA sequencing. This is the process of determining the precise order of nucleotides within a DNA molecule. By using this advanced genetic method, the scientists identified mutations in bacterial genes that resisted the toxicity of growth inhibitors produced by bacterial viruses. Through this, a new, tiny protein was found. The protein is termed “growth inhibitor gene product (Gp) 0.6”.

Later testing found that the protein specifically targets and inhibits the activity of a protein essential to bacterial cells. The bacterial protein affected has the function of holding the microbe’s cell wall together. Without this protein functioning correctly, the cell bursts open from within and the bacterium dies.

For the next wave of research, the Israeli science group are looking further at bacterial viruses with the aim of finding compounds that facilitate improved treatment of antibiotic-resistant bacteria.
Read more: http://www.digitaljournal.com/science/new-protein-detonates-bacteria-from-within/article/424747#ixzz3QJN0uo1d

Revealing bacterial targets of growth inhibitors encoded by bacteriophage T7

Shahar Molshanski-Mora, Ido Yosefa, Ruth Kiroa, Rotem Edgara, Miriam Manora, Michael Gershovitsb, Mia Lasersonb, Tal Pupkob, and Udi Qimrona,1

Author Affiliations

Edited* by Sankar Adhya, National Institutes of Health, National Cancer Institute, Bethesda, MD, and approved November 24, 2014 (received for review July 13, 2014)


Antibiotic resistance of pathogens is a growing threat to human health, requiring immediate action. Identifying new gene products of bacterial viruses and their bacterial targets may provide potent tools for fighting antibiotic-resistant strains. We show that a significant number of phage proteins are inhibitory to their bacterial host. DNA sequencing was used to map the targets of these proteins. One particular target was a key cytoskeleton protein whose function is impaired following the phage protein’s expression, resulting in bacterial death. Strikingly, in over 70 y of extensive research into the tested bacteriophage, this inhibition had never been characterized. We believe that the presented approach may be broadened to identify novel, clinically relevant bacteriophage growth inhibitors and to characterize their targets.


Today’s arsenal of antibiotics is ineffective against some emerging strains of antibiotic-resistant pathogens. Novel inhibitors of bacterial growth therefore need to be found. The target of such bacterial-growth inhibitors must be identified, and one way to achieve this is by locating mutations that suppress their inhibitory effect. Here, we identified five growth inhibitors encoded by T7 bacteriophage. High-throughput sequencing of genomic DNA of resistant bacterial mutants evolving against three of these inhibitors revealed unique mutations in three specific genes. We found that a nonessential host gene, ppiB, is required for growth inhibition by one bacteriophage inhibitor and another nonessential gene, pcnB, is required for growth inhibition by a different inhibitor. Notably, we found a previously unidentified growth inhibitor, gene product (Gp) 0.6, that interacts with the essential cytoskeleton protein MreB and inhibits its function. We further identified mutations in two distinct regions in the mreB gene that overcome this inhibition. Bacterial two-hybrid assay and accumulation of Gp0.6 only in MreB-expressing bacteria confirmed interaction of MreB and Gp0.6. Expression of Gp0.6 resulted in lemon-shaped bacteria followed by cell lysis, as previously reported for MreB inhibitors. The described approach may be extended for the identification of new growth inhibitors and their targets across bacterial species and in higher organisms.

New funding to fight antibiotic resistance SPECIAL

By Tim Sandle

This week the White House stated that it will double the amount of federal funding put aside to combat and preventing antibiotic resistance. The sum stands at greater than $1.2 billion.

Read more: http://www.digitaljournal.com/life/health/new-funding-to-fight-antibiotic-resistance/article/424745#ixzz3QJSBRxLU

U.S. Senator Sherrod Brown has been campaigning across the U.S. about the risks related to antibiotic-resistant infections for several years. Such infections affect more than two million U.S. citizens each year. The issue is not only of importance in one country for the growing menace of antibiotic resistance is, arguably, the single biggest threat faced by the world’s population. Moreover, emerging antimicrobial resistance and the growing shortage of effective antibiotic drugs is widely regarded as a crisis that jeopardizes patient safety and public health.

Senator Brown has welcomed the increased spending, although he also feels that more action is required. “To combat antibiotic resistance, it’s important that we leverage the best in medical expertise, stewardship, and technological innovation,” Brown has told Digital Journal.

He went on to add: “This unprecedented proposal underscores the importance of taking a comprehensive, wide-ranging approach to tackle this issue. I look forward to continuing to work with federal agencies, research institutions, and health care providers to combat this threat to America’s health.”

In 2014, Brown proposed the Strategies to Address Antimicrobial Resistance (STAAR) Act. The aim of this legislation was to boost the federal response to antibiotic resistance through promoting prevention and control. Other measures included: tracking drug-resistant bacteria; supporting enhanced research efforts; and improving the development, use, and stewardship of antibiotics. The Act would have provided an opportunity to bring multiple federal and non-governmental partners together to protect the public health from these drug-resistant bugs.

The Act, reported by Digital Journal, did not get through, despite the recent announcement of increased federal spending. Senator Brown argues that more preventative measures are needed. For this reason he plans to reintroduce similar legislation this year.

The STAAR Act would:

Promote prevention through public health partnerships at the U.S. Centers for Disease Control and Prevention (CDC) and local health departments;

Track resistant bacteria by making data collection better and requiring better reporting;

Improve the use of antibiotics by educating health care facilities on appropriate antibiotic use;

Enhance leadership and accountability in antibiotic resistance by reauthorizing a task force and coordinating agency efforts;

Support research by directing the National Institutes of Health (NIH) to work with other agencies and experts to create a strategic plan to address the problem.

Read more: http://www.digitaljournal.com/life/health/new-funding-to-fight-antibiotic-resistance/article/424745#ixzz3QJSliTXy

Senator takes on antibiotic resistant organisms SPECIAL

By Tim Sandle     Apr 16, 2014 in Science

Washington – With so-called “super bugs” on the rise, U.S. Sen. Sherrod Brown (D-OH) has introduced a bill aimed at slowing down the rate of antibiotic resistant microorganisms.

Read more: http://www.digitaljournal.com/science/senator-takes-on-antibiotic-resistant-organisms/article/381328#ixzz3QJT1jbOk

Senator Brown has introduced the Strategies to Address Antimicrobial Resistance (STAAR) Act. This is legislation aimed at combating antimicrobial resistance. In presenting the Act, Brown called for greater Federal attention to the growth of antibiotic-resistant infections, which affect more than two million Americans each year.

Brown is aiming for the STAAR Act to provide an opportunity to bring multiple federal and non-governmental partners together to protect the public health from these drug-resistant bugs.

Senator Brown contacted Digital Journal to explain more. In explaining the basis to the Act, Brown said: “Each year more than 23,000 Americans die from bacterial infections that are resistant to antibiotics.”

Antimicrobial resistance describes the ability of a microorganism to resist the action of antimicrobial drugs. In some instances some microorganisms are naturally resistant to particular antimicrobial agents; in other instances, the genes of non-disease-causing bacteria can be transferred to pathogenic bacteria, leading to patterns of clinically significant antibiotic resistance. Since the 1990s antibiotic resistance has been of concern for scientists and health policy makers.

Looking at the reasons for this, Brown explained that: “Antibiotics and other antimicrobial drugs have been a victim of their own success. We have used these drugs so widely and for so long that the microbes they are designed to kill have adapted to them, making the drugs less effective.”

Considering this in the context of his Act, Brown added: “We need a comprehensive strategy to address antimicrobial resistance. That is why I am introducing the STAAR Act, which would revitalize efforts to combat super bugs.”

Emerging antimicrobial resistance and the growing shortage of effective antibiotic drugs is widely regarded as a crisis that jeopardizes patient safety and public health. Once confined to hospitals, drug-resistant microbes, such as multi-drug-resistant Staphylococcus aureus (MRSA), are now striking down healthy, non-hospitalized citizens. This includes both the young and old, adults and children. These infections are painful, difficult to treat, and have become a silent epidemic in communities and hospitals across the U.S. (according to CDC).

Brown hopes that the STAAR Act will help strengthen the federal response to antimicrobial resistance by placing more of an emphasis on federal antimicrobial resistance surveillance, prevention and control, and research efforts.

In addition the Senator hopes that the Act will strengthen coordination within both Department of Health and Human Services (HHS) agencies as well as across other federal departments that are important to addressing antimicrobial resistance and enable opportunities to address this issue globally.

By providing for a more comprehensive and coordinated approach to the antimicrobial resistance crisis, it would seem that the STAAR Act represents a critical first step toward resolving what has become a major public health crisis.

Read more: http://www.digitaljournal.com/science/senator-takes-on-antibiotic-resistant-organisms/article/381328#ixzz3QJTWUxTB

H.R. 2285 (113th): Strategies to Address Antimicrobial Resistance Act

Jun 6, 2013 (113th Congress, 2013–2015)

Died (Referred to Committee) in a previous session of Congress

See Instead:
S. 2236 (same title)

Referred to Committee — Apr 10, 2014

  • Vaccination -how is vaccination important in preventing resistance?
  • Bioterrorism – what are the risks of resistance associated with bioterrorism
  • Antibacterials – do they cause resistance?
  • Food & Farming – why are antimicrobials used in farming?

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Evolution and Medicine

Reporter and Curator: Larry H. Bernstein, MD, FCAP 



Excerpt of article

Cancer is an inescapable fact of life. All of us with either die from it or know someone who will. Cancer is so prevalent because it isn’t a disease in the way a flu or a cold is. No outside force or germ is needed to cause cancer (although it can). It arises from the very way we are put together.  Most of the genes that are needed for multicellular life have been found to be associated with cancer. Cancer is a result of our natural genetic machinery that has been built up over billions of years breaking down over time.


Cancer is not only a result of evolutionary processes, cancer itself follows evolutionary theory as it grows. The immune system places a selective pressure on cancer cells, keeping it in check until the cancer evolves a way to avoid it and surpass it in a process known as immunoediting. Cancers face selective pressures in the microenvironments in which they grow. Due to the fast growth of cancer cells, they suck up oxygen in the tissues, causing wildly fluctuating oxygen levels as the body tries to get oxygen to the tissues. This sort of situation is bad for normal tissues and so it is for cancer, at least until they evolve and adapt. At some point, some cancer cells will develop the ability to use what is called aerobic glycolysis to make the ATP we use for energy. Ordinarily, our cells only use glycolysis when they run out of oxygen because aerobic respiration (aka oxidative phosphorylation) is far more efficient. Cancer cells, on the other hand, learn to use glycolysis all the time, even in the presence of abundant oxygen. They may not grow as quickly when there is plenty of oxygen, but they are far better than normal cells at hypoxic, or low oxygen, conditions, which they create by virtue of their metabolism. Moreover, they are better at taking up nutrients because many of the metabolic pathways for aerobic respiration also influence nutrient uptake, so shifting those pathways to nutrient uptake rather than metabolism ensures cancer cells get first pick of any nutrients in the area. The Warburg Effect, as this is called, works by selective pressures hindering those cells that can’t do so and favoring those that can. Because cancer cells have loose genetic controls and they are constantly dividing, the cancer population can evolve, whereas the normal cells cannot.

Evolutionary theory can also be used to track cancer as it metastasizes. If a person has several tumors, it is possible to take biopsies of each one and use standard cladistic programs that are normally used to determine evolutionary relationships between organisms to find which tumor is the original tumor. If the original tumor is not one of those biopsied, it will tell you where the cancer originated within the body. You can thus track the progression of cancer throughout a person’s body. Expanding on this, one can even track the effect of cancer through its effects on how organisms interact within ecosystems, creating its own evolutionary stamp on the environment as its effects radiate throughout the ecosystem.

I’ve talked about cancer at decent length (although I could easily go one for many more pages) because it is less well publicly known than some of the other ways that evolutionary theory helps us out in medicine. The increasing resistance of bacteria and viruses to antibiotics is well known. Antibiotic resistance follows standard evolutionary processes, with the result that antibiotic resistant bacteria are expected to kill 10 million people a year by 2050.  People have to get a new flu shot every year because the flu viruses are legion and they evolve rapidly to bypass old vaccinations.  If we are to accurately predict how the viruses may adapt and properly prepare vaccines for the coming year, evolutionary theory must be taken into account. Without it, the vaccines are much less likely to be effective. Evolutionary studies have pointed out important changes in the Ebola virus and how those changes areaffecting its lethality, which will need to be taken into account for effective treatments. Tracking the origins of viruses, like the avian flu or swine flu, gives us information that will be useful in combating them or even stopping them at their source before they become a problem.




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Classification of Microbiota –

An Overview of Clinical Microbiology, Classification, and Antimicrobial Resistance

Author and Curator: Larry H. Bernstein, MD, FCAP

Classification of Microbiota

Introduction to Overview of Microbiology

This is a contribution to a series of pieces on the history of biochemistry, molecular biology, physiology and medicine in the 20th century.  Here I describe the common microbial organisms encountered in the clinical laboratory, the method of their collection, plating, culture and identification, and antibiotic sensitivity testing and resistant strains.

I may begin with the recognition that there are common strains in the environment that are not pathogenic, and there are pathogenic bacteria.
In addition, there are bacteria that coexist in the body habitat under specific conditions so that we are able to map the types expected to location, such as, skin, mouth and nasal cavities, the colon, the vagina and urinary system.  Meningitides occur as a result of extension from the nasal cavity to the brain.  When bacteria invade the circulation, it is referred to as septicemia, and the bacteria can cause valvular heart damage.

Bacteriology can be traced to origins in the 19th century.  The clinical features of localized infection are classically referred to as redness, heat, a raised lesion (pustule), and exudate (serous or purulent – watery or cellular).  This not only holds for a focal lesion (as skin), but also for pneumonia, urinary infection, and genital. It may be accompanied by cough, or bloody cough and wheezing, or by an unclear urine. In the case of septicemia, there is fever, and there may be seizures or delirium.

Collection and handling of specimens

Specimens are collected by sterile technique by a nurse or physician and sent to a lab as a swab, or as a blood specimen.  In the case of a febrile illness, blood cultures may be obtained from opposite arms, and another an hour later.  This is related to the possible cyclical seeding of bacteria into the circulation.  If the specimen is collected from a site of infection, a swab may be put onto a glass slide for gram staining.  The specimen collected is sent to the laboratory.

We may consider syphilis and tuberculosis special cases that I’ll set aside.  I shall not go into virology either, although I may referred to smallpox, influenza, polio, HIV under epidemic.  The first step in identification is the Gram stain, developed in the 19th century.  Organisms of the skin are Gram positive and appear blue on staining.  They are cocci, or circular, organized in characteristic clusters (staphylococcus, streptococcus) or in pairs (diplococci, eg. Pneumococcus), and if from the intestine (enterococcus).  If they are elongated rods, they might be coliform.  If they stain red, they are Gram negative.  Gram negative rods are coliform, and are enterobacteriaceae. Meningococci are Gram negative cocci.  So we have certain information about these organisms before we plate them for growth.

Laboratory growth characteristics

The specimen is applied to an agar plate with a metal rod applicator, or perhaps onto more than one agar plate.  The agar plate contains a growth media or a growth inhibitor that is more favorable to certain species than to others.  The bacteria are grown at 37o C in an incubator and colonies develop that are white or nonwhite, and they are smooth or wrinkled.  The appearance of the colonies is characteristic for certain strains.  If there is no contamination, all of the colonies look the same.  The next step is to:

  • Gram stain from a colony
  • Transfer samples from the colony to a series of growth media that identify presence or absence of specific nutrient requirements for growth (which is presumed from the prior findings).

In addition, the colony samples are grown on an agar to which is applied antibiotic tabs.  The tabs either allow or repress growth.  It wa some 50 years ago that the infectious disease physician and microbiologist Abraham Braude would culture the bacteria on agar plates that had a gradient of antibiotic to check for concentration that would inhibit growth.

Principles of Diagnosis (Extracts)

By John A. Washington

The clinical presentation of an infectious disease reflects the interaction between the host and the microorganism. This interaction is affected by the host immune status and microbial virulence factors. Signs and symptoms vary according to the site and severity of infection. Diagnosis requires a composite of information, including history, physical examination, radiographic findings, and laboratory data.

Microbiologic Examination

Direct Examination and Techniques: Direct examination of specimens reveals gross pathology. Microscopy may identify microorganisms. Immunofluorescence, immuno-peroxidase staining, and other immunoassays may detect specific microbial antigens. Genetic probes identify genus- or species-specific DNA or RNA sequences.

Culture: Isolation of infectious agents frequently requires specialized media. Nonselective (noninhibitory) media permit the growth of many microorganisms. Selective media contain inhibitory substances that permit the isolation of specific types of microorganisms.

Microbial Identification: Colony and cellular morphology may permit preliminary identification. Growth characteristics under various conditions, utilization of carbohydrates and other substrates, enzymatic activity, immunoassays, and genetic probes are also used.

Serodiagnosis: A high or rising titer of specific IgG antibodies or the presence of specific IgM antibodies may suggest or confirm a diagnosis.

Antimicrobial Susceptibility: Microorganisms, particularly bacteria, are tested in vitro to determine whether they are susceptible to antimicrobial agents.

Diagnostic medical microbiology is the discipline that identifies etiologic agents of disease. The job of the clinical microbiology laboratory is to test specimens from patients for microorganisms that are, or may be, a cause of the illness and to provide information (when appropriate) about the in vitro activity of antimicrobial drugs against the microorganisms identified (Fig. 1).

Laboratory procedures used in confirming a clinical diagnosis of infectious disease with a bacterial etiology


A variety of microscopic, immunologic, and hybridization techniques have been developed for rapid diagnosis

techniques have been developed for rapid diagnosis

techniques have been developed for rapid diagnosis

From: Chapter 10, Principles of Diagnosis
Medical Microbiology. 4th edition.
Baron S, editor.
Galveston (TX): University of Texas Medical Branch at Galveston; 1996.

For immunologic detection of microbial antigens, latex particle agglutination, coagglutination, and enzyme-linked immunosorbent assay (ELISA) are the most frequently used techniques in the clinical laboratory. Antibody to a specific antigen is bound to latex particles or to a heat-killed and treated protein A-rich strain of Staphylococcus aureus to produce agglutination (Fig. 10-2). There are several approaches to ELISA; the one most frequently used for the detection of microbial antigens uses an antigen-specific antibody that is fixed to a solid phase, which may be a latex or metal bead or the inside surface of a well in a plastic tray. Antigen present in the specimen binds to the antibody as inFig. 10-2. The test is then completed by adding a second antigen-specific antibody bound to an enzyme that can react with a substrate to produce a colored product. The initial antigen antibody complex forms in a manner similar to that shown inFigure 10-2. When the enzyme-conjugated antibody is added, it binds to previously unbound antigenic sites, and the antigen is, in effect, sandwiched between the solid phase and the enzyme-conjugated antibody. The reaction is completed by adding the enzyme substrate.

agglutination test ch10f2

agglutination test ch10f2

Figure 2 Agglutination test in which inert particles (latex beads or heat-killed S aureus Cowan 1 strain with protein A) are coated with antibody to any of a variety of antigens and then used to detect the antigen in specimens or in isolated bacteria


Genetic probes are based on the detection of unique nucleotide sequences with the DNA or RNA of a microorganism. Once such a unique nucleotide sequence, which may represent a portion of a virulence gene or of chromosomal DNA, is found, it is isolated and inserted into a cloning vector (plasmid), which is then transformed into Escherichia coli to produce multiple copies of the probe. The sequence is then reisolated from plasmids and labeled with an isotope or substrate for diagnostic use. Hybridization of the sequence with a complementary sequence of DNA or RNA follows cleavage of the double-stranded DNA of the microorganism in the specimen.

The use of molecular technology in the diagnoses of infectious diseases has been further enhanced by the introduction of gene amplication techniques, such as the polymerase chain reaction (PCR) in which DNA polymerase is able to copy a strand of DNA by elongating complementary strands of DNA that have been initiated from a pair of closely spaced oligonucleotide primers. This approach has had major applications in the detection of infections due to microorganisms that are difficult to culture (e.g. the human immunodeficiency virus) or that have not as yet been successfully cultured (e.g. the Whipple’s disease bacillus).

Solid media, although somewhat less sensitive than liquid media, provide isolated colonies that can be quantified if necessary and identified. Some genera and species can be recognized on the basis of their colony morphologies.

In some instances one can take advantage of differential carbohydrate fermentation capabilities of microorganisms by incorporating one or more carbohydrates in the medium along with a suitable pH indicator. Such media are called differential media (e.g., eosin methylene blue or MacConkey agar) and are commonly used to isolate enteric bacilli. Different genera of the Enterobacteriaceae can then be presumptively identified by the color as well as the morphology of colonies.

Culture media can also be made selective by incorporating compounds such as antimicrobial agents that inhibit the indigenous flora while permitting growth of specific microorganisms resistant to these inhibitors. One such example is Thayer-Martin medium, which is used to isolate Neisseria gonorrhoeae. This medium contains vancomycin to inhibit Gram-positive bacteria, colistin to inhibit most Gram-negative bacilli, trimethoprim-sulfamethoxazole to inhibit Proteus species and other species that are not inhibited by colistin and anisomycin to inhibit fungi. The pathogenic Neisseria species, N gonorrhoeae and N meningitidis, are ordinarily resistant to the concentrations of these antimicrobial agents in the medium.

Infection of the bladder (cystitis) or kidney (pyelone-phritis) is usually accompanied by bacteriuria of about ≥ 104 CFU/ml. For this reason, quantitative cultures (Fig. 10-3) of urine must always be performed. For most other specimens a semiquantitative streak method (Fig. 10-3) over the agar surface is sufficient. For quantitative cultures, a specific volume of specimen is spread over the agar surface and the number of colonies per milliliter is estimated.

Identification of bacteria (including mycobacteria) is based on growth characteristics (such as the time required for growth to appear or the atmosphere in which growth occurs), colony and microscopic morphology, and biochemical, physiologic, and, in some instances, antigenic or nucleotide sequence characteristics. The selection and number of tests for bacterial identification depend upon the category of bacteria present (aerobic versus anaerobic, Gram-positive versus Gram-negative, cocci versus bacilli) and the expertise of the microbiologist examining the culture. Gram-positive cocci that grow in air with or without added CO2 may be identified by a relatively small number of tests. The identification of most Gram-negative bacilli is far more complex and often requires panels of 20 tests for determining biochemical and physiologic characteristics.

Antimicrobial susceptibility tests are performed by either disk diffusion or a dilution method. In the former, a standardized suspension of a particular microorganism is inoculated onto an agar surface to which paper disks containing various antimicrobial agents are applied. Following overnight incubation, any zone diameters of inhibition about the disks are measured. An alternative method is to dilute on a log2 scale each antimicrobial agent in broth to provide a range of concentrations and to inoculate each tube or, if a microplate is used, each well containing the antimicrobial agent in broth with a standardized suspension of the microorganism to be tested. The lowest concentration of antimicrobial agent that inhibits the growth of the microorganism is the minimal inhibitory concentration.

Classification Principles

This Week’s Citation Classic®_______ Sneath P H A & Sokal R R.
Numerical taxonomy: the principles and practice of
numerical classification. San Francisco: Freeman, 1973. 573 p.
[Medical Research Council Microbial Systematics Unit, Univ. Leicester, England
and Dept. Ecology and Evolution, State Univ. New York, Stony Brook, NY]
Numerical taxonomy establishes classification
of organisms based on their similarities. It utilizes
many equally weighted characters and employs
clustering and similar algorithms to yield
objective groupings. It can beextended to give
phylogenetic or diagnostic systems and can be
applied to many other fields of endeavour.

Mathematical Foundations of Computer Science 1998
Lecture Notes in Computer Science Volume 1450, 1998, pp 474-482
Date: 28 May 2006
Positive Turing and truth-table completeness for NEXP are incomparable 1998
Levke Bentzien

The truth-table method [matrix method] is one of the decision procedures for sentence logic (q.v., §3.2). The method is based on the fact that the truth value of a compound formula of sentence logic, construed as a truth-function, is determined by the truth values of its arguments (cf. “Sentence logic” §2.2). To decide whether a formula A is a tautology or not, we list all possible combinations of truth values to the variables in A: A is a tautology if it takes the value truth under each assignment.

Using ideas introduced by Buhrman et al. ([2], [3]) to separate various completeness notions for NEXP = NTIME (2poly), positive Turing complete sets for NEXP are studied. In contrast to many-one completeness and bounded truth-table completeness with norm 1 which are known to coincide on NEXP ([3]), whence any such set for NEXP is positive Turing complete, we give sets A and B such that

A is ≤ bT(2) P -complete but not ≤ posT P -complete for NEXP

B is ≤ posT P -complete but not ≤ tt P -complete for NEXP. These results come close to optimality since a further strengthening of (1), as was done by Buhrman in [1] for EXP = DTIME(2poly), seems to require the assumption NEXP = co-NEXP.

Computability and Models
The University Series in Mathematics 2003, pp 1-10
Truth-Table Complete Computably Enumerable Sets
Marat M. Arslanov

We prove a truth-table completeness criterion for computably enumerable sets.
The authors research was partially supported by Russian Foundation of Basic Research, Project 99-01-00830, and RFBR-INTAS, Project 97-91-71991.

Department of Microbiology, Lovelace Foundation for Medical Education and Research,
Albuquerque, N.M. 87108, U.S.A.
Space life sciences 1971-12-1; 3(2): pp 135-156
(Received 15 July, 1971)
Abstract. A logical basis for classification is that elements grouped together and higher categories of elements should have a high degree of similarity with the provision that all groups and categories be disjoint to some degree. A methodology has been developed for constructing classifications automatically that gives
nearly instantaneous correlations of character patterns of organisms with time and clusters with apparent similarity. This means that automatic numerical identification will always construct schemes from which disjoint answers can be obtained if test sensitivities for characters are correct. Unidentified organisms are recycled through continuous classification with reconstruction of identification schemes. This process is
cyclic and self-correcting. The method also accumulates and analyzes data which updates and presents a more accurate biological picture.

Syndromic classification: A process for amplifying information using S-clustering

Eugene W. Rypka, PHD


Optimal classification/Rypka < Optimal classification>


1 Rypka’s Method

1.1 Equations

1.2 Examples

2 Notes and References

Rypka’s Method

Rypka’s[1] method[2] utilizes the theoretical and empirical separatory equations shown below to perform the task of optimal classification. The method finds the optimal order of the fewest attributes, which in combination define a bounded class of elements.

Application of the method begins with construction of an attribute-valued system in truth table[3] or spreadsheet form with elements listed in the left most column beginning in the second row. Characteristics[4] are listed in the first row beginning in the second column with the code name of the data in the upper left most cell. The values which connect each characteristic with each element are placed in the intersecting cells. Selecting appropriate characteristics to universally define the class of elements may be the most difficult part for the classifier of utilizing this method.

The elements are first sorted in descending order according to their truth table value, which is calculated from the existing sequence and value of characteristics for each element. Duplicate truth table values or multisets for the entire bounded class reveal either the need to eliminate duplicate elements or the need to include additional characteristics.

An empirical separatory value is calculated for each characteristic in the set and the characteristic with the greatest empirical separatory value is exchanged with the characteristic which occupies the most significant attribute position.

Next the second most significant characteristic is found by calculating an empirical separatory value for each remaining characteristic in combination with the first characteristic. The characteristic which produces the greatest separatory value is then exchanged with the characteristic which occupies the second most significant attribute position.

Next the third most significant characteristic is found by calculating an empirical separatory value for each remaining characteristic in combination with the first and second characteristics. The characteristic which produces the greatest empirical separatory value is then exchanged with the characteristic which occupies the third most significant attribute position. This procedure may continue until all characteristics have been processed or until one hundred percent separation of the elements has been achieved.

A larger radix will allow faster identification by excluding a greater percentage of elements per characteristic. A binary radix for instance excludes only fifty percent of the elements per characteristic whereas a five-valued radix excludes eighty percent of the elements per characteristic.[5] What follows is an elucidation of the matrix and separatory equations.[6]

Computational Example
Bounded Class Data

bounded class data

Bounded Class Dimensions

G = 28 – 28 elements – i = 0…G-1[1]

C = 10 – 10 characteristics or attributes – j = 0…C-1

V = 5 – 5 valued logic – l = 0…V-1

Order of Elements

order of elements

Count multisets

count multisets

Squared multiset Counts

squared multiset counts

Separatory Values

separatory values


max(T) = 309 = S8 = highest initial separatory value


Mathcad’s ORIGIN function applies to all arrays such that if more than one array is being used and one array requires a zero origin then the other arrays must use a zero origin with all variables being adapted as well.

Rypka’s Method Edit

Rypka’s[1] method[2] utilizes the theoretical and empirical separatory equations shown below to perform the task of optimal classification. The method finds the optimal order of the fewest attributes, which in combination define a bounded class of elements.

Application of the method begins with construction of an attribute-valued system in truth table[3] or spreadsheet form with elements listed in the left most column beginning in the second row. Characteristics[4] are listed in the first row beginning in the second column with the title of the attributes in the upper left most cell. Normally the file name of the data is given the title of the element class. The values which connect each characteristic with each element are placed in the intersecting cells. Selecting characteristics which all elements share may be the most difficult part of creating a database which can utilizing this method.

The elements are first sorted in descending order according to their truth table value, which is calculated from the existing sequence and value of characteristics for each element. Duplicate truth table values or multisets for the entire bounded class reveal either the need to eliminate duplicate elements or the need to include additional characteristics.

An empirical separatory value is calculated for each characteristic in the set and the characteristic with the greatest empirical separatory value is exchanged with the characteristic which occupies the most significant attribute position.

Next the second most significant characteristic is found by calculating an empirical separatory value for each remaining characteristic in combination with the first characteristic. The characteristic which produces the greatest separatory value is then exchanged with the characteristic which occupies the second most significant attribute position.

Next the third most significant characteristic is found by calculating an empirical separatory value for each remaining characteristic in combination with the first and second characteristics. The characteristic which produces the greatest empirical separatory value is then exchanged with the characteristic which occupies the third most significant attribute position. This procedure may continue until all characteristics have been processed or until one hundred percent separation of the elements has been achieved.

A larger radix will allow faster identification by excluding a greater percentage of elements per characteristic. A binary radix for instance excludes only fifty percent of the elements per characteristic whereas a five-valued radix excludes eighty percent of the elements per characteristic.[5] What follows is an elucidation of the matrix and separatory equations.[6]

Syndromic Classification: A Process for Amplifying Information Using S-Clustering

Eugene W. Rypka, PhD
University of New Mexico, Albuquerque, New Mexico, USA
Statistics Editor: Marcello Pagano, PhD
Harvard School of Public Health, Boston, Massachusetts, USA
Nutrition 1996; 12(11/12): 827-829

In a previous issue of Nutrition, Drs. Bernstein and Pleban’ use the method of S-clustering to aid in nutritional classification of patients directly on-line. Classification of this type is called primary or syndromic classification.* It is created by a process called separatory (S-) clustering (E. Rypka, unpublished observations). The authors use S-clustering in Table I.  S-clustering extracts features (analytes, variables) from endogenous data that amplify or maximize structural information to create classes of patients (pathophysiologic events) which are the most disjointed or separable. S-clustering differs from other classificatory methods because it finds in a database a theoretic- or more- number of variables with the required variety that map closest to an ideal, theoretic, or structural information standard. In Table I of their article, Bernstein and Pleban’ indicate there would have to be 3 ’ = 243 rows to show all possible patterns. In Table II of this article, I have used a 33 = 27 row truth table to convey the notion of mapping amplified information to an ideal, theoretic standard using just the first three columns. Variables are scaled for use in S-clustering.

A Survey of Binary Similarity and Distance Measures
Seung-Seok Choi, Sung-Hyuk Cha, Charles C. Tappert
The binary feature vector is one of the most common
representations of patterns and measuring similarity and
distance measures play a critical role in many problems
such as clustering, classification, etc. Ever since Jaccard
proposed a similarity measure to classify ecological
species in 1901, numerous binary similarity and distance
measures have been proposed in various fields. Applying
appropriate measures results in more accurate data
analysis. Notwithstanding, few comprehensive surveys
on binary measures have been conducted. Hence we
collected 76 binary similarity and distance measures used
over the last century and reveal their correlations through
the hierarchical clustering technique.

This paper is organized as follows. Section 2 describes
the definitions of 76 binary similarity and dissimilarity
measures. Section 3 discusses the grouping of those
measures using hierarchical clustering. Section 4
concludes this work.

Historically, all the binary measures observed above have
had a meaningful performance in their respective fields.
The binary similarity coefficients proposed by Peirce,
Yule, and Pearson in 1900s contributes to the evolution
of the various correlation based binary similarity
measures. The Jaccard coefficient proposed at 1901 is
still widely used in the various fields such as ecology and
biology. The discussion of inclusion or exclusion of
negative matches was actively arisen by Sokal & Sneath
in during 1960s and by Goodman & Kruskal in 1970s.

Polyphasic Taxonomy of the Genus Vibrio: Numerical Taxonomy of Vibrio cholerae, Vibrio
parahaemolyticus, and Related Vibrio Species
JOURNAL OF BACTERIOLOGY, Oct. 1970;  104(1): 410-433
A set of 86 bacterial cultures, including 30 strains of Vibrio cholerae, 35 strains of
V. parahaemolyticus, and 21 representative strains of Pseudomonas, Spirillum,
Achromobacter, Arthrobacter, and marine Vibrio species were tested for a total of 200
characteristics. Morphological, physiological, and biochemical characteristics were
included in the analysis. Overall deoxyribonucleic acid (DNA) base compositions
and ultrastructure, under the electron microscope, were also examined. The taxonomic
data were analyzed by computer by using numerical taxonomy programs
designed to sort and cluster strains related phenetically. The V. cholerae strains
formed an homogeneous cluster, sharing overall S values of >75%. Two strains,
V. cholerae NCTC 30 and NCTC 8042, did not fall into the V. cholerae species
group when tested by the hypothetical median organism calculation. No separation
of “classic” V. cholerae, El Tor vibrios, and nonagglutinable vibrios was observed.
These all fell into a single, relatively homogeneous, V. cholerae species cluster.
PJ. parahaemolyticus strains, excepting 5144, 5146, and 5162, designated members
of the species V. alginolyticus, clustered at S >80%. Characteristics uniformly
present in all the Vibrio species examined are given, as are also characteristics and
frequency of occurrence for V. cholerae and V. parahaemolyticus. The clusters formed
in the numerical taxonomy analyses revealed similar overall DNA base compositions,
with the range for the Vibrio species of 40 to 48% guanine plus cytosine. Generic
level of relationship of V. cholerae and V. parahaemolyticus is considered
dubious. Intra- and intergroup relationships obtained from the numerical taxonomy
studies showed highly significant correlation with DNA/DNA reassociation data.

A Numerical Classification of the Genus Bacillus
Journal of General Microbiology (1988), 134, 1847-1882.

Three hundred and sixty-eight strains of aerobic, endospore-forming bacteria which included type and reference cultures of Bacillus and environmental isolates were studied. Overall similarities of these strains for 118 unit characters were determined by the SSMS,, and Dp coefficients and clustering achieved using the UPGMA algorithm. Test error was within acceptable limits. Six cluster-groups were defined at 70% SSM which corresponded to 69% Sp and 48-57% SJ.G roupings obtained with the three coefficients were generally similar but there were some changes in the definition and membership of cluster-groups and clusters, particularly with the SJ coefficient. The Bacillus strains were distributed among 31 major (4 or more strains), 18 minor (2 or 3 strains) and 30 single-member clusters at the 83% SsMle vel. Most of these clusters can be regarded as taxospecies. The heterogeneity of several species, including Bacillus breuis, B. circulans, B. coagulans, B. megateriun, B . sphaericus and B . stearothermophilus, has been indicated  and the species status of several taxa of hitherto uncertain validity confirmed. Thus on the basis of the numerical phenetic and appropriate (published) molecular genetic data, it is proposed
that the following names be recognized; BacillusJlexus (Batchelor) nom. rev., Bacillus fusiformis (Smith et al.) comb. nov., Bacillus kaustophilus (Prickett) nom. rev., Bacilluspsychrosaccharolyticus (Larkin & Stokes) nom. rev. and Bacillus simplex (Gottheil) nom. rev. Other phenetically well-defined taxospecies included ‘ B. aneurinolyticus’, ‘B. apiarius’, ‘B. cascainensis’, ‘B. thiaminolyticus’ and three clusters of environmental isolates related to B . firmus and previously described as ‘B. firmus-B. lentus intermediates’. Future developments in the light of the numerical phenetic data are discussed.

Numerical Classification of Bacteria
Part II. * Computer Analysis of Coryneform Bacteria (2)
Comparison of Group-Formations Obtained on Two
Different Methods of Scoring Data
By Eitaro MASUOan d Toshio NAKAGAWA
[Agr. Biol. Chem., 1969; 33(8): 1124-1133.
Sixty three organisms selected from 12 genera of bacteria were subjected to numerical analysis. The purpose of this work is to examine the relationships among 38 coryneform bacteria included in the test organisms by two coding methods-Sneath’s and Lockhart’s systems-, and to compare the results with conventional classification. In both cases of codification, five groups and one or two single item(s) were found in the resultant classifications. Different codings brought, however, a few distinct differences in some groups , especially in a group of sporogenic bacilli or lactic-acid bacteria. So far as the present work concerns, the result obtained on Lockhart’s coding rather than that obtained on Sneath’s coding resembled the conventional classification. The taxonomic positions of corynebacteria were quite different from those of the conventional classification, regardless
of which coding method was applied.
Though animal corynebacteria have conventionally been considered to occupy the
taxonomic position neighboring to genera Arthrobacter and Cellulornonas and regarded to be the nucleus of so-called “coryneform bacteria,’ the present work showed that many of the corynebacteria are akin to certain mycobacteria rather than to the organisms belonging to the above two genera.

Numerical Classification of Bacteria
Part III. Computer Analysis of “Coryneform Bacteria” (3)
Classification Based on DNA Base Compositions
By EitaroM ASUaOnd ToshioN AKAGAWA
Agr. Biol. Chem., 1969; 33(11): 1570-1576
It has been known that the base compositions of deoxyribonucleic acids (DNA) are
quite different from organism to organism. A pertinent example of this diversity is
found in bacterial species. The base compositions of DNA isolated from a wide variety
of bacteria are distributed in a range from 25 to 75 GC mole-percent (100x(G+C)/
(A+T+G+C)).1) The usefulness of the information of DNA base composition for
the taxonomy of bacteria has been emphasized by several authors. Lee et al.,” Sueoka,” and Freese) have speculated on the evolutionary significance of microbial DNA base composition. They pointed out that closely related microorganisms generally showed similar base compositions of DNA, and suggested that phylogenetic relationship should be reflected in the GC content.
In the present paper are compared the results of numerical classifications of 45
bacteria based on the two different similarity matrices: One representing the overall
similarities of phenotypic properties, the other representing the similarities of GC contents.

Advanced computational algorithms for microbial community analysis using massive 16S rRNA
sequence data
Y Sun, Y Cai, V Mai, W Farmerie, F Yu, J Li and S Goodison
Nucleic Acids Research, 2010; 38(22): e205

With the aid of next-generation sequencing technology, researchers can now obtain millions of microbial signature sequences for diverse applications ranging from human epidemiological studies to global ocean surveys. The development of advanced computational strategies to maximally extract pertinent information from massive nucleotide data has become a major focus of the bioinformatics community. Here, we describe a novel analytical strategy including discriminant and topology analyses that enables researchers to deeply investigate the hidden world of microbial communities, far beyond basic microbial diversity estimation. We demonstrate the utility of our
approach through a computational study performed on a previously published massive human gut 16S rRNA data set. The application of discriminant and
topology analyses enabled us to derive quantitative disease-associated microbial signatures and describe microbial community structure in far more detail than previously achievable. Our approach provides rigorous statistical tools for sequence based studies aimed at elucidating associations between known or unknown organisms and a variety of physiological or environmental conditions.

What is Drug Resistance?

Antimicrobial resistance is the ability of microbes, such as bacteria, viruses, parasites, or fungi, to grow in the presence of a chemical (drug) that would normally kill it or limit its growth.

Diagram showing the difference between non-resistant bacteria and drug resistant bacteria.

Credit: NIAID

DrugResistance difference between non-resistant bacteria and drug resistant bacteria

DrugResistance difference between non-resistant bacteria and drug resistant bacteria


Diagram showing the difference between non-resistant bacteria and drug resistant bacteria. Non-resistant bacteria multiply, and upon drug treatment, the bacteria die. Drug resistant bacteria multiply as well, but upon drug treatment, the bacteria continue to spread.

Between 5 and 10 percent of all hospital patients develop an infection. About 90,000 of these patients die each year as a result of their infection, up from 13,300 patient deaths in 1992.

According to the Centers for Disease Control and Prevention (April 2011), antibiotic resistance in the United States costs an estimated $20 billion a year in excess health care costs, $35 million in other societal costs and more than 8 million additional days that people spend in the hospital.

Resistance to Antibiotics: Are We in the Post-Antibiotic Era?

Alfonso J. Alanis
Archives of Medical Research 36 (2005) 697–705

Serious infections caused by bacteria that have become resistant to commonly used antibiotics have become a major global healthcare problem in the 21st century. They not only are more severe and require longer and more complex treatments, but they are also significantly more expensive to diagnose and to treat. Antibiotic resistance, initially a problem of the hospital setting associated with an increased number of hospital acquired infections usually in critically ill and immunosuppressed patients, has now extended into the community causing severe infections difficult to diagnose and treat. The molecular mechanisms by which bacteria have become resistant to antibiotics are diverse and complex. Bacteria have developed resistance to all different classes of antibiotics discovered to date. The most frequent type of resistance is acquired and transmitted horizontally via the conjugation of a plasmid. In recent times new mechanisms of resistance have resulted in the simultaneous development of resistance to several antibiotic classes creating very dangerous multidrug-resistant (MDR) bacterial strains, some also known as ‘‘superbugs’’. The indiscriminate and inappropriate use of antibiotics in outpatient clinics, hospitalized patients and in the food industry is the single largest factor leading to antibiotic resistance. The pharmaceutical industry, large academic institutions or the government are not investing the necessary resources to produce the next generation of newer safe and effective antimicrobial drugs. In many cases, large pharmaceutical companies have terminated their anti-infective research programs altogether due to economic reasons. The potential negative consequences of all these events are relevant because they put society at risk for the spread of potentially serious MDR bacterial infections.

Targeting the Human Macrophage with Combinations of Drugs and Inhibitors of Ca2+ and K+ Transport to Enhance the Killing of Intracellular Multi-Drug Resistant M. tuberculosis (MDR-TB) – a Novel, Patentable Approach to Limit the Emergence of XDR-TB

Marta Martins
Recent Patents on Anti-Infective Drug Discovery, 2011, 6, 000-000

The emergence of resistance in Tuberculosis has become a serious problem for the control of this disease. For that reason, new therapeutic strategies that can be implemented in the clinical setting are urgently needed. The design of new compounds active against mycobacteria must take into account that Tuberculosis is mainly an intracellular infection of the alveolar macrophage and therefore must maintain activity within the host cells. An alternative therapeutic approach will be described in this review, focusing on the activation of the phagocytic cell and the subsequent killing of the internalized bacteria. This approach explores the combined use of antibiotics and phenothiazines, or Ca2+ and K+ flux inhibitors, in the infected macrophage. Targeting the infected macrophage and not the internalized bacteria could overcome the problem of bacterial multi-drug resistance. This will potentially eliminate the appearance of new multi-drug resistant tuberculosis (MDR-TB) cases and subsequently prevent the emergence of extensively-drug resistant tuberculosis (XDR-TB). Patents resulting from this novel and innovative approach could be extremely valuable if they can be implemented in the clinical setting. Other patents will also be discussed such as the treatment of TB using immunomodulator compounds (for example: betaglycans).

Six Epigenetic Faces of Streptococcus

Kevin Mayer

Medical illustration of Streptococcus pneumonia. [CDC]

Streptococcus pneumonia

Streptococcus pneumonia

It appears that S. pneumoniae has even more personalities, each associated with a different proclivity toward invasive, life-threatening disease. In fact, any of six personalities may emerge depending on the action of a single genetic switch.

To uncover the switch, an international team of scientists conducted a study in genomics, but they looked beyond nucleotide polymorphisms or accessory regions as possible phenotype-shifting mechanisms. Instead, they focused on the potential of restriction-modification (RM) systems to mediate gene regulation via epigenetic changes.

Scientists representing the University of Leicester, Griffith University’s Institute for Glycomics, theUniversity of Adelaide, and Pacific Biosciences realized that the S. pneumoniae genome contains two Type I, three Type II, and one Type IV RM systems. Of these, only the DpnI Type II RM system had been described in detail. Switchable Type I systems had been described previously, but these reports did not provide evidence for differential methylation or for phenotypic impact.

As it turned out, the Type I system embodied a mechanism capable of randomly changing the bacterium’s characteristics into six alternative states. The mechanism’s details were presented September 30 in Nature Communications, in an article entitled, “A random six-phase switch regulates pneumococcal virulence via global epigenetic changes.”

“The underlying mechanism for such phase variation consists of genetic rearrangements in a Type I restriction-modification system (SpnD39III),” wrote the authors. “The rearrangements generate six alternative specificities with distinct methylation patterns, as defined by single-molecule, real-time (SMRT) methylomics.”

Eradication of multidrug-resistant A. baumanniii in burn wounds by antiseptic pulsed electric field.

A Golberg, GF Broelsch, D Vecchio,S Khan, MR Hamblin, WG Austen, Jr, RL Sheridan,  ML Yarmush.

Emerging bacterial resistance to multiple drugs is an increasing problem in burn wound management. New non-pharmacologic interventions are needed for wound disinfection. Here we report on a novel physical method for disinfection: antiseptic pulsed electric field (PEF) applied externally to the infected wounds.  In an animal model, we show that PEF can reduce the load of multidrug resistant Acinetobacter baumannii present in a full thickness burn wound by more than four orders of magnitude, as detected by bioluminescence imaging. Furthermore, using a finite element numerical model, we demonstrate that PEF provides non-thermal, homogeneous, full thickness treatment for the burn wound, thus, overcoming the limitation of treatment depth for many topical antimicrobials. These modeling tools and our in vivo results will be extremely useful for further translation of the PEF technology to the clinical setting. We believe that PEF, in combination with systemic antibiotics, will synergistically eradicate multidrug-resistant burn wound infections, prevent biofilm formation and restore natural skin microbiome. PEF provides a new platform for infection combat in patients, therefore it has a potential to significantly decreasing morbidity and mortality.

Golberg, A. & Yarmush, M. L. Nonthermal irreversible electroporation: fundamentals, applications, and challenges. IEEE Trans Biomed Eng 60, 707-14 (2013).

Mechanisms Of Antibiotic Resistance In Salmonella: Efflux Pumps, Genetics, Quorum Sensing And Biofilm Formation.

Martins M, McCusker M, Amaral L, Fanning S
Perspectives in Drug Discovery and Design 02/2011; 8:114-123.

In Salmonella the main mechanisms of antibiotic resistance are mutations in target genes (such as DNA gyrase and topoisomerase IV) and the over-expression of efflux pumps. However, other mechanisms such as changes in the cell envelope; down regulation of membrane porins; increased lipopolysaccharide (LPS) component of the outer cell membrane; quorum sensing and biofilm formation can also contribute to the resistance seen in this microorganism. To overcome this problem new therapeutic approaches are urgently needed. In the case of efflux-mediated multidrug resistant isolates, one of the treatment options could be the use of efflux pump inhibitors (EPIs) in combination with the antibiotics to which the bacteria is resistant. By blocking the efflux pumps resistance is partly or wholly reversed, allowing antibiotics showing no activity against the MDR strains to be used to treat these infections. Compounds that show potential as an EPI are therefore of interest, as well as new strategies to target the efflux systems. Quorum sensing (QS) and biofilm formation are systems also known to be involved in antibiotic resistance. Consequently, compounds that can disrupt or inhibit these bacterial “communication systems” will be of use in the treatment of these infections.

Role of Phenothiazines and Structurally Similar Compounds of Plant Origin in the Fight against Infections by Drug Resistant Bacteria

SG Dastidar, JE Kristiansen, J Molnar and L Amaral
Antibiotics 2013, 2, 58-71;

Phenothiazines have their primary effects on the plasma membranes of prokaryotes and eukaryotes. Among the components of the prokaryotic plasma membrane affected are efflux pumps, their energy sources and energy providing enzymes, such as ATPase, and genes that regulate and code for the permeability aspect of a bacterium. The response of multidrug and extensively drug resistant tuberculosis to phenothiazines shows an alternative therapy for its treatment. Many phenothiazines have shown synergistic activity with several antibiotics thereby lowering the doses of antibiotics administered for specific bacterial infections. Trimeprazine is synergistic with trimethoprim. Flupenthixol (Fp) has been found to be synergistic with penicillin and chlorpromazine (CPZ); in addition, some antibiotics are also synergistic. Along with the antibacterial action described in this review, many phenothiazines possess plasmid curing activities, which render the bacterial carrier of the plasmid sensitive to antibiotics. Thus, simultaneous applications of a phenothiazine like TZ would not only act as an additional antibacterial agent but also would help to eliminate drug resistant plasmid from the infectious bacterial cells.

Multidrug Efflux Pumps Described for Staphylococcus aureus

Efflux Pump  Family Regulator(s) Substrate Specificity  References 
Chromosomally-encoded Efflux Systems 
NorA MFS MgrA, NorG(?) Hydrophilic fluoroquinolones (ciprofloxacin, norfloxacin)QACs (tetraphenylphosphonium, benzalkonium chloride)

Dyes (e.g. ethidium bromide, rhodamine)

NorB MFS MgrA, NorG Fluoroquinolones (e.g. hydrophilic: ciprofloxacin, norfloxacin and hydrophobic: moxifloxacin,
sparfloxacin)TetracyclineQACs (e.g. tetraphenylphosphonium, cetrimide)Dyes (e.g. ethidium bromide)
NorC MFS MgrA(?), NorG Fluoroquinolones (e.g. hydrophilic: ciprofloxacin and hydrophobic: moxifloxacin)Dyes (e.g. rhodamine) [35,36]
MepA MATE MepR Fluoroquinolones (e.g. hydrophilic: ciprofloxacin, norfloxacin and hydrophobic: moxifloxacin,
sparfloxacin)Glycylcyclines (e.g. tigecycline)QACs (e.g. tetraphenylphosphonium, cetrimide, benzalkonium chloride)Dyes (e.g. ethidium bromide)
MdeA MFS n.i. Hydrophilic fluoroquinolones (e.g. ciprofloxacin, norfloxacin)Virginiamycin, novobiocin, mupirocin, fusidic acid

QACs (e.g. tetraphenylphosphonium, benzalkonium chloride, dequalinium)

Dyes (e.g. ethidium bromide)

SepA n.d. n.i. QACs (e.g. benzalkonium chloride)Biguanidines (e.g. chlorhexidine)

Dyes (e.g. acriflavine)

SdrM MFS n.i. Hydrophilic fluoroquinolones (e.g. norfloxacin)Dyes (e.g. ethidium bromide, acriflavine) [42]
LmrS MFS n.i. Oxazolidinone (linezolid)Phenicols (e.g. choramphenicol, florfenicol)

Trimethoprim, erythromycin, kanamycin, fusidic acid

QACs (e.g. tetraphenylphosphonium)

Detergents (e.g. sodium docecyl sulphate)

Dyes (e.g. ethidium bromide)


Plasmid-encoded Efflux Systems

QacA MFS QacR QACs (e.g. tetraphenylphosphonium, benzalkonium chloride, dequalinium)Biguanidines (e.g. chlorhexidine)

Diamidines (e.g. pentamidine)

Dyes (e.g. ethidium bromide, rhodamine, acriflavine)

QacB MFS QacR QACs (e.g. tetraphenylphosphonium, benzalkonium chloride)Dyes (e.g. ethidium bromide, rhodamine, acriflavine) [53]
Smr SMR n.i. QACs (e.g. benzalkonium chloride, cetrimide)Dyes (e.g. ethidium bromide) [58,61]
QacG SMR n.i. QACs (e.g. benzalkonium chloride, cetyltrymethylammonium)Dyes (e.g. ethidium bromide) [67]
QacH SMR n.i. QACs (e.g. benzalkonium chloride, cetyltrymethylammonium)Dyes (e.g. ethidium bromide) [68]
QacJ SMR n.i. QACs (e.g. benzalkonium chloride, cetyltrymethylammonium)Dyes (e.g. ethidium bromide) [69]

a n.d.: The family of transporters to which SepA belongs is not elucidated to date.
b n.i.: The transporter has no regulator identified to date.
QACs: quaternary ammonium compounds
The importance of efflux pumps in bacterial antibiotic resistance

  1. A. Webber and L. J. V. Piddock
    Journal of Antimicrobial Chemotherapy (2003) 51, 9–11
    http://dx.doi.org:/10.1093/jac/dkg050Efflux pumps are transport proteins involved in the extrusion of toxic substrates (including virtually all classes of clinically relevant antibiotics) from within cells into the external environment. These proteins are found in both Gram-positive and -negative bacteria as well as in eukaryotic organisms. Pumps may be specific for one substrate or may transport a range of structurally dissimilar compounds (including antibiotics of multiple classes); such pumps can be associated with multiple drug resistance (MDR). In the prokaryotic kingdom there are five major families of efflux transporter: MF (major facilitator), MATE (multidrug and toxic efflux), RND (resistance-nodulation-division), SMR (small multidrug resistance) and ABC (ATP binding cassette). All these systems utilize the proton motive force as an energy source. Advances in DNA technology have led to the identification of members of the above families. Transporters that efflux multiple substrates, including antibiotics, have not evolved in response to the stresses of the antibiotic era. All bacterial genomes studied contain efflux pumps that indicate their ancestral origins. It has been estimated that ∼5–10% of all bacterial genes are involved in transport and a large proportion of these encode efflux pumps.
The efflux pump

The efflux pump

Multidrug-resistance efflux pumps — not just for resistance

Laura J. V. Piddock
Nature Reviews | Microbiology | Aug 2006; 4: 629

It is well established that multidrug-resistance efflux pumps encoded by bacteria can confer clinically relevant resistance to antibiotics. It is now understood that these efflux pumps also have a physiological role(s). They can confer resistance to natural substances produced by the host, including bile, hormones and host defense molecules. In addition, some efflux pumps of the resistance nodulation division (RND) family have been shown to have a role in the colonization and the persistence of bacteria in the host. Here, I present the accumulating evidence that multidrug-resistance efflux pumps have roles in bacterial pathogenicity and propose that these pumps therefore have greater clinical relevance than is usually attributed to them.

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