Michael Black

Retromer and the sorting nexins Snx4/41/42 mediate distinct retrieval pathways from yeast endosomes

The endocytic pathway in yeast leads to the vacuole, but resident proteins of the late Golgi, and some endocytosed proteins such as the exocytic SNARE Snc1p, are retrieved specifically to the Golgi. Retrieval can occur from both a late pre‐vacuolar compartment and early or ‘post‐Golgi’ endosomes. We show that the endosomal SNARE Pep12p, and a mutant version that reaches the cell surface and is endocytosed, are retrieved from pre‐vacuolar endosomes. As with Golgi proteins, this requires the sorting nexin Grd19p and components of the retromer coat, supporting the view that endosomal and Golgi residents both cycle continuously between the exocytic and endocytic pathways. In contrast, retrieval of Snc1p from post‐Golgi endosomes requires the sorting nexin Snx4p, to which Snc1p can be cross‐linked. Snx4p binds to Snx41p/ydr425w and to Snx42p/ydl113c, both of which are also required for efficient Snc1p sorting. Our findings suggest a general role for yeast sorting nexins in protein retrieval, rather than degradation, and indicate that different sorting nexins operate in different classes of endosomes.

Pyroprinting: a rapid and flexible genotypic fingerprinting method for typing bacterial strains.

Bacterial strain typing is commonly employed in studies involving epidemiology, population ecology, and microbial source tracking to identify sources of fecal contamination. Methods for differentiating strains generally use either a collection of phenotypic traits or rely on some interrogation of the bacterial genotype. This report introduces pyroprinting, a novel genotypic strain typing method that is rapid, inexpensive, and discriminating compared to the most sensitive methods already in use. Pyroprinting relies on the simultaneous pyrosequencing of polymorphic multicopy loci, such as the intergenic transcribed spacer regions of rRNA operons in bacterial genomes. Data generated by sequencing combinations of variable templates are reproducible and intrinsically digitized. The theory and development of pyroprinting in Escherichia coli, including the selection of similarity thresholds to define matches between isolates, are presented. The pyroprint-based strain differentiation limits and phylogenetic relevance compared to other typing methods are also explored. Pyroprinting is unique in its simplicity and, paradoxically, in its intrinsic complexity. This new approach serves as an excellent alternative to more cumbersome or less phylogenetically relevant strain typing methods.

Chronology-Sensitive Hierarchical Clustering of Pyrosequenced DNA Samples of E. coli: A Case Study

Hierarchical clustering is used in computational biology as a method of comparing sequenced bacterial strain DNA and determining bacterial isolates that belong to the same strain. However, the results of the hierarchical clustering are, at times, difficult to read and interpret. This paper is a case study for the use of a modified hierarchical clustering algorithm, which takes into account the underlying structure of the bacterial DNA isolate collection to which it is applied.

Short communication: Typing and tracking Bacillaceae in raw milk and milk powder using pyroprinting

Contamination of fluid and processed milk products with endospore-forming bacteria, such as Bacillaceae, affect milk quality and longevity. Contaminants come from a variety of sources, including the dairy farm environment, transportation equipment, or milk processing machinery. Tracking the origin of bacterial contamination to allow specifically targeted remediation efforts depends on a reliable strain-typing method that is reproducible, fast, easy to use, and amenable to computerized analysis. Our objective was to adapt a recently developed genotype-based Escherichia coli strain-typing method, called pyroprinting, for use in a microbial source-tracking study to follow endospore-forming bacillus bacteria from raw milk to powdered milk. A collection of endospores was isolated from both raw milk and its finished powder, and, after germination, the vegetative cells were subject to the pyroprinting protocol. Briefly, a ribosomal DNA intergenic transcribed spacer present in multiple copies in Bacillaceae genomes was amplified by the PCR. This multicopy locus generated a mixed PCR product that was subsequently subject to pyrosequencing, a quantitative real-time sequencing method. Through a series of enzymatic reactions, each nucleotide incorporation event produces a photon of light that is quantified at each nucleotide dispensation. The pattern of light peaks generated from this mixed template reaction is called a pyroprint. Isolates with pyroprints that match with a Pearson correlation of 0.99 or greater are considered to be in the same group. The pyroprint also contains some sequence data useful for presumptive species-level identification. This method identified groups with isolates from raw milk only, from powdered milk only, or from both sources. This study confirms pyroprinting as a rapid, reproducible, automatically digitized tool that can be used to distinguish bacterial strains into taxonomically relevant groups and, thus, indicate probable origins of bacterial contamination in powdered milk.

Leveraging the k-Nearest Neighbors classification algorithm for Microbial Source Tracking using a bacterial DNA fingerprint library

Fecal contamination in bodies of water is an issue that cities must combat regularly. Often, city governments must restrict access to water sources until the contaminants dissipate. Sourcing the species of the fecal matter helps curb the issue in the future, giving city governments the ability to mitigate the effects before they occur again. Microbial Source Tracking (MST) aims to determine source host species of strains of microbiological lifeforms and library-based MST is one method that can assist in sourcing fecal matter. Recently, the Biology Department in conjunction with the Computer Science Department at California Polytechnic State University San Luis Obispo (Cal Poly) teamed up to build a database called the Cal Poly Library of Pyroprints (CPLOP). Students collect fecal samples, culture and pyrosequence the E. coli in the samples, and insert this data, called pyroprints, into CPLOP. Using two intergenic transcribed spacer regions of DNA, Cal Poly biologists perform studies on strain differentiation. We propose using k-Nearest Neighbors, a straightforward machine learning technique, to classify the host species of a given pyroprint, construct four algorithms to resolve the regions, and investigate classification accuracy.

Pyroprinting sensitivity analysis on the GPU

Microbial Source Tracking (MST) is a field in which microbial strains are identified and associated with a specific host source (e.g., human, canine, avian, etc). Identifying the hosts of microbial strains lies at the heart of many studies of bacterial contamination in the environment. Being able to determine which host species is responsible, e.g., for fecal contamination of a creek, allows the parties involved to develop specific measures for addressing the contamination. The paper presents an in-silico study to investigate the sensitivity of the pyroprinting method. Given a collection of possible DNA sequences that can be found in the sequenced ITS regions, we construct a collection of all possible theoretical combinations. Each such combination represents a theoretically possible strain of E. coli. We construct a pyroprint model of each strain, and then build a matrix of pairwise similarities between the pyroprints.

Microbial source tracking by molecular fingerprinting

To date, microbial source tracking (MST), i.e. determining the source of microbial contamination based on the specific strains observed in environment, is done using methods that are time-consuming, expensive and not always reliable. The biology department at Cal Poly, San Luis Obispo has developed a new method for MST called pyroprinting. Pyroprints are a result of pyrosequencing replicates of intergenic transcribed spacer (ITS) regions in a target bacterial genome. E. coli pyroprints can be used as DNA fngerprints of individual E. coli strains in identifying sources of fecal contamination and studying bacterial patterns in host animals. The MST method consists of two parts: the pyroprinting process and a database of sequenced pyroprints. The actual source tracking is achieved by comparing a newly obtained pyroprint to the pyroprints of known provenance from a database. In this paper, we describe the design and implementation of Cal Poly Library of Pyroprints (CPLOP). The CPLOP database provides storage and essential analysis of pyroprints for strain identification. Our current implementation contains pyroprints of bacterial isolates of E. coli, obtained by students and researchers from known hosts and from the environment. Users of CPLOP are able to organize pyroprints into groups, run analyses to find similarities between bacterial isolates, and cluster isolates into bacterial strains.

Ontological Hierarchical Clustering for Library-Based Microbial Source Tracking

Pyroprinting is a novel library-based microbial source tracking method developed by the Biology department at Cal Poly, San Luis Obispo. Biologists conducting research using pyroprinting rely on methods for partitioning collected bacterial isolates into bacterial strains. Clustering algorithms are often used for bacterial strain analysis of organisms in computational biology. Agglomerative hierarchical clustering, a commonly used clustering algorithm, is inadequate given the nature of data collection for pyroprinting. While the clusters produced are acceptable, pyroprinting requires a method of analysis that is scalable and incorporates useful metadata into the clustering process. We propose an ontology-based hierarchical clustering algorithm OHClust!, a modification of the agglomerative hierarchical clustering algorithm. OHClust! uses metadata associated with the data being clustered to direct the order in which the individual data points and sub clusters are compared to each other. This paper describes OHClust! and compares it to agglomerative hierarchical clustering.

Cloning yeast actin cDNA leads to an investigative approach for the molecular biology laboratory

The emergence of molecular tools in multiple disciplines has elevated the importance of undergraduate laboratory courses that train students in molecular biology techniques. Although it would also be desirable to provide students with opportunities to apply these techniques in an investigative manner, this is generally not possible in the classroom because of the preparation, expense, and logistics involved in independent student projects. The authors have designed a 10‐week lab series that mimics the research environment by tying separate fundamental lab techniques to a common goal: to build a plasmid with yeast actin cDNA cloned in a particular orientation. In the process of completing this goal, a problem arises in that students are unable to obtain the target plasmid and instead only recover the gene cloned in the opposite orientation. To address this problem, students identify four plausible hypotheses and work in teams to address them by designing and executing experiments. This project reinforces the utility and flexibility of techniques covered earlier in the class and serves to develop their skills in experimental design and analysis. As the project is focused on one problem, the diversity of experimental approaches is limited and may be prepared in advance with little additional expense in reagents or technical support.

Library-Based Microbial Source Tracking via Strain Identification

Microbial Source Tracking (MST) aims to classify the source host-species of biological matter, typically fecal matter, using strains of fecal indicator bacteria, often E. coli. This paper continues addressing the MST problem using analysis of a library of bacterial fingerprints started in [9]. The Cal Poly Library of Pyroprints (CPLOP) is a collection of fingerprints of over 6,000 E. coli isolates collected from the fecal matter of a variety of host-species. In prior work [9] we studied the accuracy of the MST process based on k-Nearest Neighbors discovery in CPLOP. This process, while sufficiently accurate, does not scale well with the size of the database. In this paper, we study the accuracy of a clustering-based MST approach which scales significantly better: the bacterial isolate information stored in CPLOP is clustered using an efficient density-based clustering technique. We present our analysis of the accuracy and efficiency of the clustering-based MST methodology for CPLOP.