Hyunsung John Kim

Acceleration of Emergence of Bacterial Antibiotic Resistance in Connected Microenvironments

Gradients of antibiotics generated in a microfluidic device provoke selection of ciprofloxacin resistance in Escherichia coli. The emergence of bacterial antibiotic resistance is a growing problem, yet the variables that influence the rate of emergence of resistance are not well understood. In a microfluidic device designed to mimic naturally occurring bacterial niches, resistance of Escherichia coli to the antibiotic ciprofloxacin developed within 10 hours. Resistance emerged with as few as 100 bacteria in the initial inoculation. Whole-genome sequencing of the resistant organisms revealed that four functional single-nucleotide polymorphisms attained fixation. Knowledge about the rapid emergence of antibiotic resistance in the heterogeneous conditions within the mammalian body may be helpful in understanding the emergence of drug resistance during cancer chemotherapy.

Whole-transcriptome RNAseq analysis from minute amount of total RNA

RNA sequencing approaches to transcriptome analysis require a large amount of input total RNA to yield sufficient mRNA using either poly-A selection or depletion of rRNA. This feature makes it difficult to miniaturize transcriptome analysis for greater efficiency. To address this challenge, we devised and validated a simple procedure for the preparation of whole-transcriptome cDNA libraries from a minute amount (500 pg) of total RNA. We compared a single-sample library prepared by this Ovation® RNA-Seq system with two available methods of mRNA enrichment (TruSeq™ poly-A enrichment and RiboMinus™ rRNA depletion). Using the Ovation® preparation method for a set of eight mouse tissue samples, the RNA sequencing data obtained from two different next-generation sequencing platforms (SOLiD and Illumina Genome Analyzer IIx) yielded negligible rRNA reads (<3.5%) while retaining transcriptome sequencing fidelity. We further validated the Ovation® amplification technique by examining the resulting library complexity, reproducibility, evenness of transcript coverage, 5′ and 3′ bias and platform-specific biases. Notably, in this side-by-side comparison, SOLiD sequencing chemistry is biased toward higher GC content of transcriptome and Illumina Genome analyzer IIx is biased away from neutral to lower GC content of the transcriptomics regions.

Compartmental Genomics in Living Cells Revealed by Single-Cell Nanobiopsy

The ability to study the molecular biology of living single cells in heterogeneous cell populations is essential for next generation analysis of cellular circuitry and function. Here, we developed a single-cell nanobiopsy platform based on scanning ion conductance microscopy (SICM) for continuous sampling of intracellular content from individual cells. The nanobiopsy platform uses electrowetting within a nanopipette to extract cellular material from living cells with minimal disruption of the cellular milieu. We demonstrate the subcellular resolution of the nanobiopsy platform by isolating small subpopulations of mitochondria from single living cells, and quantify mutant mitochondrial genomes in those single cells with high throughput sequencing technology. These findings may provide the foundation for dynamic subcellular genomic analysis.

Single-cell analyses of transcriptional heterogeneity during drug tolerance transition in cancer cells by RNA sequencing.

Significance Tumor cells are heterogeneous, and much variation occurs at the single-cell level, which may contribute to therapeutic response. Here, we studied drug resistance dynamics in a model of tolerance with a metastatic breast cancer cell line by leveraging the power of single-cell RNA-Seq technology. Drug-tolerant cells within a single clone rapidly express high cell-to-cell transcript variability, with a gene expression profile similar to untreated cells, and the population reacquires paclitaxel sensitivity. Our gene expression and single nucleotide variants analyses suggest that equivalent phenotypes are achieved without relying on a unique molecular event or fixed transcriptional programs. Thus, transcriptional heterogeneity might ensure survival of cancer cells with equivalent combinations of gene expression programs and/or single nucleotide variants. The acute cellular response to stress generates a subpopulation of reversibly stress-tolerant cells under conditions that are lethal to the majority of the population. Stress tolerance is attributed to heterogeneity of gene expression within the population to ensure survival of a minority. We performed whole transcriptome sequencing analyses of metastatic human breast cancer cells subjected to the chemotherapeutic agent paclitaxel at the single-cell and population levels. Here we show that specific transcriptional programs are enacted within untreated, stressed, and drug-tolerant cell groups while generating high heterogeneity between single cells within and between groups. We further demonstrate that drug-tolerant cells contain specific RNA variants residing in genes involved in microtubule organization and stabilization, as well as cell adhesion and cell surface signaling. In addition, the gene expression profile of drug-tolerant cells is similar to that of untreated cells within a few doublings. Thus, single-cell analyses reveal the dynamics of the stress response in terms of cell-specific RNA variants driving heterogeneity, the survival of a minority population through generation of specific RNA variants, and the efficient reconversion of stress-tolerant cells back to normalcy.

NanoStriDE: Normalization and Differential Expression Analysis of NanoString nCounter Data

BackgroundThe nCounter analysis system (NanoString Technologies, Seattle, WA) is a technology that enables the digital quantification of multiplexed target RNA molecules using color-coded molecular barcodes and single-molecule imaging. This system gives discrete counts of RNA transcripts and is capable of providing a high level of precision and sensitivity at less than one transcript copy per cell.ResultsWe have designed a web application compatible with any modern web browser that accepts the raw count data produced by the NanoString nCounter analysis system, normalizes it according to guidelines provided by NanoString Technologies, performs differential expression analysis on the normalized data, and provides a heatmap of the results from the differential expression analysis.ConclusionNanoStriDE allows biologists to take raw data produced by a NanoString nCounter analysis system and easily interpret differential expression analysis of this data represented through a heatmap. NanoStriDE is freely accessible to use on the NanoStriDE website and is available to use under the GPL v2 license.

HLA Haplotyping from RNA-seq Data Using Hierarchical Read Weighting

Correctly matching the HLA haplotypes of donor and recipient is essential to the success of allogenic hematopoietic stem cell transplantation. Current HLA typing methods rely on targeted testing of recognized antigens or sequences. Despite advances in Next Generation Sequencing, general high throughput transcriptome sequencing is currently underutilized for HLA haplotyping due to the central difficulty in aligning sequences within this highly variable region. Here we present the method, HLAforest, that can accurately predict HLA haplotype by hierarchically weighting reads and using an iterative, greedy, top down pruning technique. HLAforest correctly predicts >99% of allele group level (2 digit) haplotypes and 93% of peptide-level (4 digit) haplotypes of the most diverse HLA genes in simulations with read lengths and error rates modeling currently available sequencing technology. The method is very robust to sequencing error and can predict 99% of allele-group level haplotypes with substitution rates as high as 8.8%. When applied to data generated from a trio of cell lines, HLAforest corroborated PCR-based HLA haplotyping methods and accurately predicted 16/18 (89%) major class I genes for a daughter–father-mother trio at the peptide level. Major class II genes were predicted with 100% concordance between the daughter–father-mother trio. In fifty HapMap samples with paired end reads just 37 nucleotides long, HLAforest predicted 96.5% of allele group level HLA haplotypes correctly and 83% of peptide level haplotypes correctly. In sixteen RNAseq samples with limited coverage across HLA genes, HLAforest predicted 97.7% of allele group level haplotypes and 85% of peptide level haplotypes correctly.

Ancient hot and cold genes and chemotherapy resistance emergence

Significance There are two broad components of information dynamics in cancer evolution. One involves permanent changes in which genes are subject to gain or loss-of-function substitutions. This is well established and the main focus of cancer research. The other component is the information in the human genome and preservation of that content. The cancer cell potentially has access to all of this and can upregulate or downregulate any number of strategies used for survival and proliferation during embryogenesis, development, and normal adaptation to environmental stresses. We suggest that nonsubstituted genes may be critical targets for chemotherapy; these nonmutated genes may be the most fundamental ones for preservation of cancer cell fitness, especially if their expression level changes. We use a microfabricated ecology with a doxorubicin gradient and population fragmentation to produce a strong Darwinian selective pressure that drives forward the rapid emergence of doxorubicin resistance in multiple myeloma (MM) cancer cells. RNA sequencing of the resistant cells was used to examine (i) emergence of genes with high de novo substitution densities (i.e., hot genes) and (ii) genes never substituted (i.e., cold genes). The set of cold genes, which were 21% of the genes sequenced, were further winnowed down by examining excess expression levels. Both the most highly substituted genes and the most highly expressed never-substituted genes were biased in age toward the most ancient of genes. This would support the model that cancer represents a revision back to ancient forms of life adapted to high fitness under extreme stress, and suggests that these ancient genes may be targets for cancer therapy.

You cannot tell a book by looking at the cover: Cryptic complexity in bacterial evolution.

Do genetically closely related organisms under identical, but strong selection pressure converge to a common resistant genotype or will they diverge to different genomic solutions? This question gets at the heart of how rough is the fitness landscape in the local vicinity of two closely related strains under stress. We chose a Growth Advantage in Stationary Phase (GASP) E scherichia coli strain to address this question because the GASP strain has very similar fitness to the wild-type (WT) strain in the absence of metabolic stress but in the presence of metabolic stress continues to divide and does not enter into stationary phase. We find that under strong antibiotic selection pressure by the fluoroquinolone antibiotic ciprofloxacin in a complex ecology that the GASP strain rapidly evolves in under 20 h missense mutation in gyrA only 2 amino acids removed from the WT strain indicating a convergent solution, yet does not evolve the other 3 mutations of the WT strain. Further the GASP strain evolves a prophage e14 excision which completely inhibits biofilm formation in the mutant strain, revealing the hidden complexity of E. coli evolution to antibiotics as a function of selection pressure. We conclude that there is a cryptic roughness to fitness landscapes in the absence of stress.

Molecular probe technology detects bacteria without culture

BackgroundOur ultimate goal is to detect the entire human microbiome, in health and in disease, in a single reaction tube, and employing only commercially available reagents. To that end, we adapted molecular inversion probes to detect bacteria using solely a massively multiplex molecular technology. This molecular probe technology does not require growth of the bacteria in culture. Rather, the molecular probe technology requires only a sequence of forty sequential bases unique to the genome of the bacterium of interest. In this communication, we report the first results of employing our molecular probes to detect bacteria in clinical samples.ResultsWhile the assay on Affymetrix GenFlex Tag16K arrays allows the multiplexing of the detection of the bacteria in each clinical sample, one Affymetrix GenFlex Tag16K array must be used for each clinical sample. To multiplex the clinical samples, we introduce a second, independent assay for the molecular probes employing Sequencing by Oligonucleotide Ligation and Detection. By adding one unique oligonucleotide barcode for each clinical sample, we combine the samples after processing, but before sequencing, and sequence them together.ConclusionsOverall, we have employed 192 molecular probes representing 40 bacteria to detect the bacteria in twenty-one vaginal swabs as assessed by the Affymetrix GenFlex Tag16K assay and fourteen of those by the Sequencing by Oligonucleotide Ligation and Detection assay. The correlations among the assays were excellent.