Primary Antibiotic Resistance to Helicobacter pylori Strains Isolated From Children in Northern Iran: A Single Center Study.

BACKGROUND: Initial resistance to antibiotics is the main reason for the failure of Helicobacter pylori (H. pylori) eradication in children. OBJECTIVES: As we commonly face high antibiotic resistance rates in children, we aimed to determine the susceptibility of H. pylori to common antibiotics. PATIENTS AND METHODS: In this cross-sectional in vitro study, 169 children younger than 14 years with clinical diagnosis of peptic ulcer underwent upper gastrointestinal endoscopy. Biopsy specimens from stomach and duodenum were cultured. In isolated colonies, tests of catalase, urease, and oxidase as well as gram staining were performed. After confirming the colonies as H. pylori, the antibiogram was obtained using disk diffusion method. RESULTS: Culture for H. pylori was positive in 12.3% of the specimens, urease test in 21.3%, serological test in 18.9% and stool antigen test was positive in 21.9%. We could show high specificity but moderate sensitivity of both histological and H. pylori stool antigen tests to detect H. pylori. The overall susceptibility to metronidazole was 42.9%, amoxicillin 95.2%, clarithromycin 85.7%, furazolidone 61.9%, azithromycin 81.0%, and tetracycline 76.2% with the highest resistance to metronidazole and the lowest to clarithromycin. CONCLUSIONS: In our region, there is high resistance of H. pylori to some antibiotics including metronidazole and furazolidone among affected children. To reduce the prevalence of this antibiotic resistance, more controlled use of antibiotics should be considered in children.

Ensemble analysis of adaptive compressed genome sequencing strategies.

BACKGROUND: Acquiring genomes at single-cell resolution has many applications such as in the study of microbiota. However, deep sequencing and assembly of all of millions of cells in a sample is prohibitively costly. A property that can come to rescue is that deep sequencing of every cell should not be necessary to capture all distinct genomes, as the majority of cells are biological replicates. Biologically important samples are often sparse in that sense. In this paper, we propose an adaptive compressed method, also known as distilled sensing, to capture all distinct genomes in a sparse microbial community with reduced sequencing effort. As opposed to group testing in which the number of distinct events is often constant and sparsity is equivalent to rarity of an event, sparsity in our case means scarcity of distinct events in comparison to the data size. Previously, we introduced the problem and proposed a distilled sensing solution based on the breadth first search strategy. We simulated the whole process which constrained our ability to study the behavior of the algorithm for the entire ensemble due to its computational intensity. RESULTS: In this paper, we modify our previous breadth first search strategy and introduce the depth first search strategy. Instead of simulating the entire process, which is intractable for a large number of experiments, we provide a dynamic programming algorithm to analyze the behavior of the method for the entire ensemble. The ensemble analysis algorithm recursively calculates the probability of capturing every distinct genome and also the expected total sequenced nucleotides for a given population profile. Our results suggest that the expected total sequenced nucleotides grows proportional to log of the number of cells and proportional linearly with the number of distinct genomes. The probability of missing a genome depends on its abundance and the ratio of its size over the maximum genome size in the sample. The modified resource allocation method accommodates a parameter to control that probability. AVAILABILITY: The squeezambler 2.0 C++ source code is available at http://sourceforge.net/projects/hyda/.

Methods and approaches in the topology-based analysis of biological pathways.

The goal of pathway analysis is to identify the pathways significantly impacted in a given phenotype. Many current methods are based on algorithms that consider pathways as simple gene lists, dramatically under-utilizing the knowledge that such pathways are meant to capture. During the past few years, a plethora of methods claiming to incorporate various aspects of the pathway topology have been proposed. These topology-based methods, sometimes referred to as "third generation," have the potential to better model the phenomena described by pathways. Although there is now a large variety of approaches used for this purpose, no review is currently available to offer guidance for potential users and developers. This review covers 22 such topology-based pathway analysis methods published in the last decade. We compare these methods based on: type of pathways analyzed (e.g., signaling or metabolic), input (subset of genes, all genes, fold changes, gene p-values, etc.), mathematical models, pathway scoring approaches, output (one or more pathway scores, p-values, etc.) and implementation (web-based, standalone, etc.). We identify and discuss challenges, arising both in methodology and in pathway representation, including inconsistent terminology, different data formats, lack of meaningful benchmarks, and the lack of tissue and condition specificity.

Distilled single-cell genome sequencing and de novo assembly for sparse microbial communities.

MOTIVATION: Identification of every single genome present in a microbial sample is an important and challenging task with crucial applications. It is challenging because there are typically millions of cells in a microbial sample, the vast majority of which elude cultivation. The most accurate method to date is exhaustive single-cell sequencing using multiple displacement amplification, which is simply intractable for a large number of cells. However, there is hope for breaking this barrier, as the number of different cell types with distinct genome sequences is usually much smaller than the number of cells. RESULTS: Here, we present a novel divide and conquer method to sequence and de novo assemble all distinct genomes present in a microbial sample with a sequencing cost and computational complexity proportional to the number of genome types, rather than the number of cells. The method is implemented in a tool called Squeezambler. We evaluated Squeezambler on simulated data. The proposed divide and conquer method successfully reduces the cost of sequencing in comparison with the naïve exhaustive approach. AVAILABILITY: Squeezambler and datasets are available at http://compbio.cs.wayne.edu/software/squeezambler/.