Andi Dhroso

Short-Stalked Prosthecomicrobium hirschii Cells Have a Caulobacter-Like Cell Cycle

UNLABELLED The dimorphic alphaproteobacterium Prosthecomicrobium hirschii has both short-stalked and long-stalked morphotypes. Notably, these morphologies do not arise from transitions in a cell cycle. Instead, the maternal cell morphology is typically reproduced in daughter cells, which results in microcolonies of a single cell type. In this work, we further characterized the short-stalked cells and found that these cells have a Caulobacter-like life cycle in which cell division leads to the generation of two morphologically distinct daughter cells. Using a microfluidic device and total internal reflection fluorescence (TIRF) microscopy, we observed that motile short-stalked cells attach to a surface by means of a polar adhesin. Cells attached at their poles elongate and ultimately release motile daughter cells. Robust biofilm growth occurs in the microfluidic device, enabling the collection of synchronous motile cells and downstream analysis of cell growth and attachment. Analysis of a draft P. hirschii genome sequence indicates the presence of CtrA-dependent cell cycle regulation. This characterization of P. hirschii will enable future studies on the mechanisms underlying complex morphologies and polymorphic cell cycles. IMPORTANCE Bacterial cell shape plays a critical role in regulating important behaviors, such as attachment to surfaces, motility, predation, and cellular differentiation; however, most studies on these behaviors focus on bacteria with relatively simple morphologies, such as rods and spheres. Notably, complex morphologies abound throughout the bacteria, with striking examples, such as P. hirschii, found within the stalked Alphaproteobacteria. P. hirschii is an outstanding candidate for studies of complex morphology generation and polymorphic cell cycles. Here, the cell cycle and genome of P. hirschii are characterized. This work sets the stage for future studies of the impact of complex cell shapes on bacterial behaviors.

The variation game: Cracking complex genetic disorders with NGS and omics data.

Tremendous advances in Next Generation Sequencing (NGS) and high-throughput omics methods have brought us one step closer towards mechanistic understanding of the complex disease at the molecular level. In this review, we discuss four basic regulatory mechanisms implicated in complex genetic diseases, such as cancer, neurological disorders, heart disease, diabetes, and many others. The mechanisms, including genetic variations, copy-number variations, posttranscriptional variations, and epigenetic variations, can be detected using a variety of NGS methods. We propose that malfunctions detected in these mechanisms are not necessarily independent, since these malfunctions are often found associated with the same disease and targeting the same gene, group of genes, or functional pathway. As an example, we discuss possible rewiring effects of the cancer-associated genetic, structural, and posttranscriptional variations on the protein-protein interaction (PPI) network centered around P53 protein. The review highlights multi-layered complexity of common genetic disorders and suggests that integration of NGS and omics data is a critical step in developing new computational methods capable of deciphering this complexity.

Novel global effector mining from the transcriptome of early life stages of the soybean cyst nematode Heterodera glycines

Soybean cyst nematode (SCN) Heterodera glycines is an obligate parasite that relies on the secretion of effector proteins to manipulate host cellular processes that favor the formation of a feeding site within host roots to ensure its survival. The sequence complexity and co-evolutionary forces acting upon these effectors remain unknown. Here we generated a de novo transcriptome assembly representing the early life stages of SCN in both a compatible and an incompatible host interaction to facilitate global effector mining efforts in the absence of an available annotated SCN genome. We then employed a dual effector prediction strategy coupling a newly developed nematode effector prediction tool, N-Preffector, with a traditional secreted protein prediction pipeline to uncover a suite of novel effector candidates. Our analysis distinguished between effectors that co-evolve with the host genotype and those conserved by the pathogen to maintain a core function in parasitism and demonstrated that alternative splicing is one mechanism used to diversify the effector pool. In addition, we confirmed the presence of viral and microbial inhabitants with molecular sequence information. This transcriptome represents the most comprehensive whole-nematode sequence currently available for SCN and can be used as a tool for annotation of expected genome assemblies.

DOMMINO 2.0: integrating structurally resolved protein-, RNA-, and DNA-mediated macromolecular interactions

Macromolecular interactions are formed between proteins, DNA and RNA molecules. Being a principle building block in macromolecular assemblies and pathways, the interactions underlie most of cellular functions. Malfunctioning of macromolecular interactions is also linked to a number of diseases. Structural knowledge of the macromolecular interaction allows one to understand the interaction’s mechanism, determine its functional implications and characterize the effects of genetic variations, such as single nucleotide polymorphisms, on the interaction. Unfortunately, until now the interactions mediated by different types of macromolecules, e.g. protein–protein interactions or protein–DNA interactions, are collected into individual and unrelated structural databases. This presents a significant obstacle in the analysis of macromolecular interactions. For instance, the homogeneous structural interaction databases prevent scientists from studying structural interactions of different types but occurring in the same macromolecular complex. Here, we introduce DOMMINO 2.0, a structural Database Of Macro-Molecular INteractiOns. Compared to DOMMINO 1.0, a comprehensive database on protein-protein interactions, DOMMINO 2.0 includes the interactions between all three basic types of macromolecules extracted from PDB files. DOMMINO 2.0 is automatically updated on a weekly basis. It currently includes ∼1 040 000 interactions between two polypeptide subunits (e.g. domains, peptides, termini and interdomain linkers), ∼43 000 RNA-mediated interactions, and ∼12 000 DNA-mediated interactions. All protein structures in the database are annotated using SCOP and SUPERFAMILY family annotation. As a result, protein-mediated interactions involving protein domains, interdomain linkers, C- and N- termini, and peptides are identified. Our database provides an intuitive web interface, allowing one to investigate interactions at three different resolution levels: whole subunit network, binary interaction and interaction interface. Database URL: http://dommino.org

The yeast protein interaction network has a capacity for self-organization.

The organization of the cellular interior gives rise to properties including metabolic channeling and micro‐compartmentalization of signaling. Here, we use a lattice model of molecular crowding, together with literature‐derived protein interactions and abundances, to describe the molecular organization and stoichiometry of local cellular regions, showing that physical protein–protein interactions induce emergent structures not seen when random interaction networks are modeled. Specifically, we find that the lattices give rise to micro‐groups of enzymes on the surfaces of protein clusters. These arrangements of proteins are also robust to protein overexpression, while still showing evidence for expression tuning. Our results indicate that some of the complex organization of the cell may derive from simple rules of molecular aggregation and interaction.

Genome-wide prediction of bacterial effectors across six secretion system types using a feature-based supervised learning framework

Gram-negative bacteria are responsible for hundreds of millions infections worldwide, including the emerging hospital-acquired infections and neglected tropical diseases in the third-world countries. Finding a fast and cheap way to understand the molecular mechanisms behind the bacterial infections is critical for efficient diagnostics and treatment. An important step towards understanding these mechanisms is discovering bacterial effectors, the proteins secreted into the host through one of the six common secretion system types. Unfortunately, current effector prediction methods are designed to specifically target one of three secretion systems, and no accurate “secretion system-agnostic” method is available. Here, we present PREFFECTOR, a computational feature-based approach to discover effectors in Gram-negative bacteria without prior knowledge on bacterial secretion system(s) or cryptic secretion signals. Our approach was first evaluated using several assessment protocols on a manually curated, balanced dataset of experimentally determined effectors across all six secretion systems as well as non-effector proteins. The evaluation revealed high accuracy of the top performing classifiers in PREFFECTOR, with the small false positive discovery rate across all six secretion systems. Our method was also applied to four bacteria that had limited knowledge on virulence factors or secreted effectors. PREFFECTOR web-server is freely available at: http://korkinlab.org/preffector.

Systematic Mutagenesis of Serine Hydroxymethyltransferase Reveals an Essential Role in Nematode Resistance

A soybean serine hydroxymethyltransferase has a unique and essential role in soybean cyst nematode resistance. Rhg4 is a major genetic locus that contributes to soybean cyst nematode (SCN) resistance in the Peking-type resistance of soybean (Glycine max), which also requires the rhg1 gene. By map-based cloning and functional genomic approaches, we previously showed that the Rhg4 gene encodes a predicted cytosolic serine hydroxymethyltransferase (GmSHMT08); however, the novel gain of function of GmSHMT08 in SCN resistance remains to be characterized. Using a forward genetic screen, we identified an allelic series of GmSHMT08 mutants that shed new light on the mechanistic aspects of GmSHMT08-mediated resistance. The new mutants provide compelling genetic evidence that Peking-type rhg1 resistance in cv Forrest is fully dependent on the GmSHMT08 gene and demonstrates that this resistance is mechanistically different from the PI 88788-type of resistance that only requires rhg1. We also demonstrated that rhg1-a from cv Forrest, although required, does not exert selection pressure on the nematode to shift from HG type 7, which further validates the bigenic nature of this resistance. Mapping of the identified mutations onto the SHMT structural model uncovered key residues for structural stability, ligand binding, enzyme activity, and protein interactions, suggesting that GmSHMT08 has additional functions aside from its main enzymatic role in SCN resistance. Lastly, we demonstrate the functionality of the GmSHMT08 SCN resistance gene in a transgenic soybean plant.

Tools for annotation and comparison of structural variation

The impact of structural variants (SVs) on a variety of organisms and diseases like cancer has become increasingly evident. Methods for SV detection when studying genomic differences across cells, individuals or populations are being actively developed. Currently, just a few methods are available to compare different SVs callsets, and no specialized methods are available to annotate SVs that account for the unique characteristics of these variant types. Here, we introduce SURVIVOR_ant, a tool that compares types and breakpoints for candidate SVs from different callsets and enables fast comparison of SVs to genomic features such as genes and repetitive regions, as well as to previously established SV datasets such as from the 1000 Genomes Project. As proof of concept we compared 16 SV callsets generated by different SV calling methods on a single genome, the Genome in a Bottle sample HG002 (Ashkenazi son), and annotated the SVs with gene annotations, 1000 Genomes Project SV calls, and four different types of repetitive regions. Computation time to annotate 134,528 SVs with 33,954 of annotations was 22 seconds on a laptop.

Biological classification with RNA-seq data: Can alternatively spliced transcript expression enhance machine learning classifiers?

RNA sequencing (RNA-seq) is becoming a prevalent approach to quantify gene expression and is expected to gain better insights into a number of biological and biomedical questions compared to DNA microarrays. Most importantly, RNA-seq allows us to quantify expression at the gene or transcript levels. However, leveraging the RNA-seq data requires development of new data mining and analytics methods. Supervised learning methods are commonly used approaches for biological data analysis that have recently gained attention for their applications to RNA-seq data. Here, we assess the utility of supervised learning methods trained on RNA-seq data for a diverse range of biological classification tasks. We hypothesize that the transcript-level expression data are more informative for biological classification tasks than the gene-level expression data. Our large-scale assessment utilizes multiple data sets, organisms, lab groups, and RNA-seq analysis pipelines. Overall, we performed and assessed 61 biological classification problems that leverage three independent RNA-seq data sets and include over 2000 samples that come from multiple organisms, lab groups, and RNA-seq analyses. These 61 problems include predictions of the tissue type, sex, or age of the sample, healthy or cancerous phenotypes, and pathological tumor stages for the samples from the cancerous tissue. For each problem, the performance of three normalization techniques and six machine learning classifiers was explored. We find that for every single classification problem, the transcript-based classifiers outperform or are comparable with gene expression-based methods. The top-performing techniques reached a near perfect classification accuracy, demonstrating the utility of supervised learning for RNA-seq based data analysis.

Biological classification with RNA-Seq data: Can alternative splicing enhance machine learning classifier?

The extent to which the genes are expressed in the cell can be simplistically defined as a function of one or more factors of the environment, lifestyle, and genetics. RNA sequencing (RNA-Seq) is becoming a prevalent approach to quantify gene expression, and is expected to gain better insights to a number of biological and biomedical questions, compared to the DNA microarrays. Most importantly, RNA-Seq allows to quantify expression at the gene and alternative splicing isoform levels. However, leveraging the RNA-Seq data requires development of new data mining and analytics methods. Supervised machine learning methods are commonly used approaches for biological data analysis, and have recently gained attention for their applications to the RNA-Seq data. In this work, we assess the utility of supervised learning methods trained on RNA-Seq data for a diverse range of biological classification tasks. We hypothesize that the isoform-level expression data is more informative for biological classification tasks than the gene-level expression data. Our large-scale assessment is done through utilizing multiple datasets, organisms, lab groups, and RNA-Seq analysis pipelines. Overall, we performed and assessed 61 biological classification problems that leverage three independent RNA-Seq datasets and include over 2,000 samples that come from multiple organisms, lab groups, and RNA-Seq analyses. These 61 problems include predictions of the tissue type, sex, or age of the sample, healthy or cancerous phenotypes and, the pathological tumor stage for the samples from the cancerous tissue. For each classification problem, the performance of three normalization techniques and six machine learning classifiers was explored. We find that for every single classification problem, the isoform-based classifiers outperform or are comparable with gene expression based methods. The top-performing supervised learning techniques reached a near perfect classification accuracy, demonstrating the utility of supervised learning for RNA-Seq based data analysis.