Predicting variable gene content in Escherichia coli using conserved genes

Having the ability to predict the protein-encoding gene content of an incomplete genome or metagenome-assembled genome is important for a variety of bioinformatic tasks. In this study, as a proof of concept, we built machine learning classifiers for predicting variable gene content in Escherichia coli genomes using only the nucleotide k-mers from a set of 100 conserved genes as features. Protein families were used to define orthologs, and a single classifier was built for predicting the presence or absence of each protein family occurring in 10%-90% of all E. coli genomes. The resulting set of 3,259 extreme gradient boosting classifiers had a per-genome average macro F1 score of 0.944 [0.943-0.945, 95% CI]. We show that the F1 scores are stable across multi-locus sequence types and that the trend can be recapitulated by sampling a smaller number of core genes or diverse input genomes. Surprisingly, the presence or absence of poorly annotated proteins, including "hypothetical proteins" was accurately predicted (F1 = 0.902 [0.898-0.906, 95% CI]). Models for proteins with horizontal gene transfer-related functions had slightly lower F1 scores but were still accurate (F1s = 0.895, 0.872, 0.824, and 0.841 for transposon, phage, plasmid, and antimicrobial resistance-related functions, respectively). Finally, using a holdout set of 419 diverse E. coli genomes that were isolated from freshwater environmental sources, we observed an average per-genome F1 score of 0.880 [0.876-0.883, 95% CI], demonstrating the extensibility of the models. Overall, this study provides a framework for predicting variable gene content using a limited amount of input sequence data. IMPORTANCE Having the ability to predict the protein-encoding gene content of a genome is important for assessing genome quality, binning genomes from shotgun metagenomic assemblies, and assessing risk due to the presence of antimicrobial resistance and other virulence genes. In this study, we built a set of binary classifiers for predicting the presence or absence of variable genes occurring in 10%-90% of all publicly available E. coli genomes. Overall, the results show that a large portion of the E. coli variable gene content can be predicted with high accuracy, including genes with functions relating to horizontal gene transfer. This study offers a strategy for predicting gene content using limited input sequence data.

SourceFinder: a Machine-Learning-Based Tool for Identification of Chromosomal, Plasmid, and Bacteriophage Sequences from Assemblies

High-throughput genome sequencing technologies enable the investigation of complex genetic interactions, including the horizontal gene transfer of plasmids and bacteriophages. However, identifying these elements from assembled reads remains challenging due to genome sequence plasticity and the difficulty in assembling complete sequences. In this study, we developed a classifier, using random forest, to identify whether sequences originated from bacterial chromosomes, plasmids, or bacteriophages. The classifier was trained on a diverse collection of 23,211 chromosomal, plasmid, and bacteriophage sequences from hundreds of bacterial species. In order to adapt the classifier to incomplete sequences, each complete sequence was subsampled into 5,000 nucleotide fragments and further subdivided into k-mers. This three-class classifier succeeded in identifying chromosomes, plasmids, and bacteriophages using k-mer distributions of complete and partial genome sequences, including simulated metagenomic scaffolds with minimum performance of 0.939 area under the receiver operating characteristic curve (AUC). This classifier, implemented as SourceFinder, has been made available as an online web service to help the community with predicting the chromosomal, plasmid, and bacteriophage sources of assembled bacterial sequence data (https://cge.food.dtu.dk/services/SourceFinder/). IMPORTANCE Extra-chromosomal genes encoding antimicrobial resistance, metal resistance, and virulence provide selective advantages for bacterial survival under stress conditions and pose serious threats to human and animal health. These accessory genes can impact the composition of microbiomes by providing selective advantages to their hosts. Accurately identifying extra-chromosomal elements in genome sequence data are critical for understanding gene dissemination trajectories and taking preventative measures. Therefore, in this study, we developed a random forest classifier for identifying the source of bacterial chromosomal, plasmid, and bacteriophage sequences.

Classification of bacterial plasmid and chromosome derived sequences using machine learning

Plasmids are important genetic elements that facilitate horizonal gene transfer between bacteria and contribute to the spread of virulence and antimicrobial resistance. Most bacterial genome sequences in the public archives exist in draft form with many contigs, making it difficult to determine if a contig is of chromosomal or plasmid origin. Using a training set of contigs comprising 10,584 chromosomes and 10,654 plasmids from the PATRIC database, we evaluated several machine learning models including random forest, logistic regression, XGBoost, and a neural network for their ability to classify chromosomal and plasmid sequences using nucleotide k-mers as features. Based on the methods tested, a neural network model that used nucleotide 6-mers as features that was trained on randomly selected chromosomal and plasmid subsequences 5kb in length achieved the best performance, outperforming existing out-of-the-box methods, with an average accuracy of 89.38% ± 2.16% over a 10-fold cross validation. The model accuracy can be improved to 92.08% by using a voting strategy when classifying holdout sequences. In both plasmids and chromosomes, subsequences encoding functions involved in horizontal gene transfer-including hypothetical proteins, transporters, phage, mobile elements, and CRISPR elements-were most likely to be misclassified by the model. This study provides a straightforward approach for identifying plasmid-encoding sequences in short read assemblies without the need for sequence alignment-based tools.

SARS-CoV-2 Omicron (B.1.1.529) Infection of Wild White-Tailed Deer in New York City

There is mounting evidence of SARS-CoV-2 spillover from humans into many domestic, companion, and wild animal species. Research indicates that humans have infected white-tailed deer, and that deer-to-deer transmission has occurred, indicating that deer could be a wildlife reservoir and a source of novel SARS-CoV-2 variants. We examined the hypothesis that the Omicron variant is actively and asymptomatically infecting the free-ranging deer of New York City. Between December 2021 and February 2022, 155 deer on Staten Island, New York, were anesthetized and examined for gross abnormalities and illnesses. Paired nasopharyngeal swabs and blood samples were collected and analyzed for the presence of SARS-CoV-2 RNA and antibodies. Of 135 serum samples, 19 (14.1%) indicated SARS-CoV-2 exposure, and 11 reacted most strongly to the wild-type B.1 lineage. Of the 71 swabs, 8 were positive for SARS-CoV-2 RNA (4 Omicron and 4 Delta). Two of the animals had active infections and robust neutralizing antibodies, revealing evidence of reinfection or early seroconversion in deer. Variants of concern continue to circulate among and may reinfect US deer populations, and establish enzootic transmission cycles in the wild: this warrants a coordinated One Health response, to proactively surveil, identify, and curtail variants of concern before they can spill back into humans.

GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics

We seek to transform how new and emergent variants of pandemic-causing viruses, specifically SARS-CoV-2, are identified and classified. By adapting large language models (LLMs) for genomic data, we build genome-scale language models (GenSLMs) which can learn the evolutionary landscape of SARS-CoV-2 genomes. By pre-training on over 110 million prokaryotic gene sequences and fine-tuning a SARS-CoV-2-specific model on 1.5 million genomes, we show that GenSLMs can accurately and rapidly identify variants of concern. Thus, to our knowledge, GenSLMs represents one of the first whole genome scale foundation models which can generalize to other prediction tasks. We demonstrate scaling of GenSLMs on GPU-based supercomputers and AI-hardware accelerators utilizing 1.63 Zettaflops in training runs with a sustained performance of 121 PFLOPS in mixed precision and peak of 850 PFLOPS. We present initial scientific insights from examining GenSLMs in tracking evolutionary dynamics of SARS-CoV-2, paving the path to realizing this on large biological data.

Transmission history of SARS-CoV-2 in humans and white-tailed deer

The emergence of a novel pathogen in a susceptible population can cause rapid spread of infection. High prevalence of SARS-CoV-2 infection in white-tailed deer (Odocoileus virginianus) has been reported in multiple locations, likely resulting from several human-to-deer spillover events followed by deer-to-deer transmission. Knowledge of the risk and direction of SARS-CoV-2 transmission between humans and potential reservoir hosts is essential for effective disease control and prioritisation of interventions. Using genomic data, we reconstruct the transmission history of SARS-CoV-2 in humans and deer, estimate the case finding rate and attempt to infer relative rates of transmission between species. We found no evidence of direct or indirect transmission from deer to human. However, with an estimated case finding rate of only 4.2%, spillback to humans cannot be ruled out. The extensive transmission of SARS-CoV-2 within deer populations and the large number of unsampled cases highlights the need for active surveillance at the human–animal interface.

PlasmidHostFinder: Prediction of Plasmid Hosts Using Random Forest

Plasmids play a major role facilitating the spread of antimicrobial resistance between bacteria. Understanding the host range and dissemination trajectories of plasmids is critical for surveillance and prevention of antimicrobial resistance. Identification of plasmid host ranges could be improved using automated pattern detection methods compared to homology-based methods due to the diversity and genetic plasticity of plasmids. In this study, we developed a method for predicting the host range of plasmids using machine learning-specifically, random forests. We trained the models with 8,519 plasmids from 359 different bacterial species per taxonomic level; the models achieved Matthews correlation coefficients of 0.662 and 0.867 at the species and order levels, respectively. Our results suggest that despite the diverse nature and genetic plasticity of plasmids, our random forest model can accurately distinguish between plasmid hosts. This tool is available online through the Center for Genomic Epidemiology (https://cge.cbs.dtu.dk/services/PlasmidHostFinder/). IMPORTANCE Antimicrobial resistance is a global health threat to humans and animals, causing high mortality and morbidity while effectively ending decades of success in fighting against bacterial infections. Plasmids confer extra genetic capabilities to the host organisms through accessory genes that can encode antimicrobial resistance and virulence. In addition to lateral inheritance, plasmids can be transferred horizontally between bacterial taxa. Therefore, detection of the host range of plasmids is crucial for understanding and predicting the dissemination trajectories of extrachromosomal genes and bacterial evolution as well as taking effective countermeasures against antimicrobial resistance.

Signals of Significantly Increased Vaccine Breakthrough, Decreased Hospitalization Rates, and Less Severe Disease in Patients with Coronavirus Disease 2019 Caused by the Omicron Variant of Severe Acute Respiratory Syndrome Coronavirus 2 in Houston, Texas

Genetic variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continue to dramatically alter the landscape of the coronavirus disease 2019 (COVID-19) pandemic. The recently described variant of concern designated Omicron (B.1.1.529) has rapidly spread worldwide and is now responsible for the majority of COVID-19 cases in many countries. Because Omicron was recognized recently, many knowledge gaps exist about its epidemiology, clinical severity, and disease course. A genome sequencing study of SARS-CoV-2 in the Houston Methodist health care system identified 4468 symptomatic patients with infections caused by Omicron from late November 2021 through January 5, 2022. Omicron rapidly increased in only 3 weeks to cause 90% of all new COVID-19 cases, and at the end of the study period caused 98% of new cases. Compared with patients infected with either Alpha or Delta variants in our health care system, Omicron patients were significantly younger, had significantly increased vaccine breakthrough rates, and were significantly less likely to be hospitalized. Omicron patients required less intense respiratory support and had a shorter length of hospital stay, consistent with on average decreased disease severity. Two patients with Omicron stealth sublineage BA.2 also were identified. The data document the unusually rapid spread and increased occurrence of COVID-19 caused by the Omicron variant in metropolitan Houston, Texas, and address the lack of information about disease character among US patients.

Detection of SARS-CoV-2 Omicron variant (B.1.1.529) infection of white-tailed deer

White-tailed deer ( Odocoileus virginianus ) are highly susceptible to infection by SARS-CoV-2, with multiple reports of widespread spillover of virus from humans to free-living deer. While the recently emerged SARS-CoV-2 B.1.1.529 Omicron variant of concern (VoC) has been shown to be notably more transmissible amongst humans, its ability to cause infection and spillover to non-human animals remains a challenge of concern. We found that 19 of the 131 (14.5%; 95% CI: 0.10-0.22) white-tailed deer opportunistically sampled on Staten Island, New York, between December 12, 2021, and January 31, 2022, were positive for SARS-CoV-2 specific serum antibodies using a surrogate virus neutralization assay, indicating prior exposure. The results also revealed strong evidence of age-dependence in antibody prevalence. A significantly (χ 2 , p < 0.001) greater proportion of yearling deer possessed neutralizing antibodies as compared with fawns (OR=12.7; 95% CI 4-37.5). Importantly, SARS-CoV-2 nucleic acid was detected in nasal swabs from seven of 68 (10.29%; 95% CI: 0.0-0.20) of the sampled deer, and whole-genome sequencing identified the SARS-CoV-2 Omicron VoC (B.1.1.529) is circulating amongst the white-tailed deer on Staten Island. Phylogenetic analyses revealed the deer Omicron sequences clustered closely with other, recently reported Omicron sequences recovered from infected humans in New York City and elsewhere, consistent with human to deer spillover. Interestingly, one individual deer was positive for viral RNA and had a high level of neutralizing antibodies, suggesting either rapid serological conversion during an ongoing infection or a "breakthrough" infection in a previously exposed animal. Together, our findings show that the SARS-CoV-2 B.1.1.529 Omicron VoC can infect white-tailed deer and highlights an urgent need for comprehensive surveillance of susceptible animal species to identify ecological transmission networks and better assess the potential risks of spillback to humans.

KEY FINDINGS

These studies provide strong evidence of infection of free-living white-tailed deer with the SARS-CoV-2 B.1.1.529 Omicron variant of concern on Staten Island, New York, and highlight an urgent need for investigations on human-to-animal-to-human spillovers/spillbacks as well as on better defining the expanding host-range of SARS-CoV-2 in non-human animals and the environment.

Delta Variants of SARS-CoV-2 Cause Significantly Increased Vaccine Breakthrough COVID-19 Cases in Houston, Texas

Genetic variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have repeatedly altered the course of the coronavirus disease 2019 (COVID-19) pandemic. Delta variants are now the focus of intense international attention because they are causing widespread COVID-19 globally and are associated with vaccine breakthrough cases. We sequenced 16,965 SARS-CoV-2 genomes from samples acquired March 15, 2021, through September 20, 2021, in the Houston Methodist hospital system. This sample represents 91% of all Methodist system COVID-19 patients during the study period. Delta variants increased rapidly from late April onward to cause 99.9% of all COVID-19 cases and spread throughout the Houston metroplex. Compared with all other variants combined, Delta caused a significantly higher rate of vaccine breakthrough cases (23.7% for Delta compared with 6.6% for all other variants combined). Importantly, significantly fewer fully vaccinated individuals required hospitalization. Vaccine breakthrough cases caused by Delta had a low median PCR cycle threshold value (a proxy for high virus load). This value was similar to the median cycle threshold value for unvaccinated patients with COVID-19 caused by Delta variants, suggesting that fully vaccinated individuals can transmit SARS-CoV-2 to others. Patients infected with Alpha and Delta variants had several significant differences. The integrated analysis indicates that vaccines used in the United States are highly effective in decreasing severe COVID-19, hospitalizations, and deaths.

Delta Variants of SARS-CoV-2 Cause Significantly Increased Vaccine Breakthrough COVID-19 Cases in Houston, Texas

Genetic variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have repeatedly altered the course of the coronavirus disease 2019 (COVID-19) pandemic. Delta variants are now the focus of intense international attention because they are causing widespread COVID-19 globally and are associated with vaccine breakthrough cases. We sequenced 16,965 SARS-CoV-2 genomes from samples acquired March 15, 2021, through September 20, 2021, in the Houston Methodist hospital system. This sample represents 91% of all Methodist system COVID-19 patients during the study period. Delta variants increased rapidly from late April onward to cause 99.9% of all COVID-19 cases and spread throughout the Houston metroplex. Compared with all other variants combined, Delta caused a significantly higher rate of vaccine breakthrough cases (23.7% for Delta compared with 6.6% for all other variants combined). Importantly, significantly fewer fully vaccinated individuals required hospitalization. Vaccine breakthrough cases caused by Delta had a low median PCR cycle threshold value (a proxy for high virus load). This value was similar to the median cycle threshold value for unvaccinated patients with COVID-19 caused by Delta variants, suggesting that fully vaccinated individuals can transmit SARS-CoV-2 to others. Patients infected with Alpha and Delta variants had several significant differences. The integrated analysis indicates that vaccines used in the United States are highly effective in decreasing severe COVID-19, hospitalizations, and deaths.

Analysis of the ARTIC Version 3 and Version 4 SARS-CoV-2 Primers and Their Impact on the Detection of the G142D Amino Acid Substitution in the Spike Protein

The ARTIC Network provides a common resource of PCR primer sequences and recommendations for amplifying SARS-CoV-2 genomes. The initial tiling strategy was developed with the reference genome Wuhan-01, and subsequent iterations have addressed areas of low amplification and sequence drop out. Recently, a new version (V4) was released, based on new variant genome sequences, in response to the realization that some V3 primers were located in regions with key mutations. Herein, we compare the performance of the ARTIC V3 and V4 primer sets with a matched set of 663 SARS-CoV-2 clinical samples sequenced with an Illumina NovaSeq 6000 instrument. We observe general improvements in sequencing depth and quality, and improved resolution of the SNP causing the D950N variation in the spike protein. Importantly, we also find nearly universal presence of spike protein substitution G142D in Delta-lineage samples. Due to the prior release and widespread use of the ARTIC V3 primers during the initial surge of the Delta variant, it is likely that the G142D amino acid substitution is substantially underrepresented among early Delta variant genomes deposited in public repositories. In addition to the improved performance of the ARTIC V4 primer set, this study also illustrates the importance of the primer scheme in downstream analyses. IMPORTANCE ARTIC Network primers are commonly used by laboratories worldwide to amplify and sequence SARS-CoV-2 present in clinical samples. As new variants have evolved and spread, it was found that the V3 primer set poorly amplified several key mutations. In this report, we compare the results of sequencing a matched set of samples with the V3 and V4 primer sets. We find that adoption of the ARTIC V4 primer set is critical for accurate sequencing of the SARS-CoV-2 spike region. The absence of metadata describing the primer scheme used will negatively impact the downstream use of publicly available SARS-Cov-2 sequencing reads and assembled genomes.

Analysis of the ARTIC Version 3 and Version 4 SARS-CoV-2 Primers and Their Impact on the Detection of the G142D Amino Acid Substitution in the Spike Protein

The ARTIC Network provides a common resource of PCR primer sequences and recommendations for amplifying SARS-CoV-2 genomes. The initial tiling strategy was developed with the reference genome Wuhan-01, and subsequent iterations have addressed areas of low amplification and sequence drop out. Recently, a new version (V4) was released, based on new variant genome sequences, in response to the realization that some V3 primers were located in regions with key mutations. Herein, we compare the performance of the ARTIC V3 and V4 primer sets with a matched set of 663 SARS-CoV-2 clinical samples sequenced with an Illumina NovaSeq 6000 instrument. We observe general improvements in sequencing depth and quality, and improved resolution of the SNP causing the D950N variation in the spike protein. Importantly, we also find nearly universal presence of spike protein substitution G142D in Delta-lineage samples. Due to the prior release and widespread use of the ARTIC V3 primers during the initial surge of the Delta variant, it is likely that the G142D amino acid substitution is substantially underrepresented among early Delta variant genomes deposited in public repositories. In addition to the improved performance of the ARTIC V4 primer set, this study also illustrates the importance of the primer scheme in downstream analyses. IMPORTANCE ARTIC Network primers are commonly used by laboratories worldwide to amplify and sequence SARS-CoV-2 present in clinical samples. As new variants have evolved and spread, it was found that the V3 primer set poorly amplified several key mutations. In this report, we compare the results of sequencing a matched set of samples with the V3 and V4 primer sets. We find that adoption of the ARTIC V4 primer set is critical for accurate sequencing of the SARS-CoV-2 spike region. The absence of metadata describing the primer scheme used will negatively impact the downstream use of publicly available SARS-Cov-2 sequencing reads and assembled genomes.

A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes

Antimicrobial resistance (AMR) is a major global health threat that affects millions of people each year. Funding agencies worldwide and the global research community have expended considerable capital and effort tracking the evolution and spread of AMR by isolating and sequencing bacterial strains and performing antimicrobial susceptibility testing (AST). For the last several years, we have been capturing these efforts by curating data from the literature and data resources and building a set of assembled bacterial genome sequences that are paired with laboratory-derived AST data. This collection currently contains AST data for over 67 000 genomes encompassing approximately 40 genera and over 100 species. In this paper, we describe the characteristics of this collection, highlighting areas where sampling is comparatively deep or shallow, and showing areas where attention is needed from the research community to improve sampling and tracking efforts. In addition to using the data to track the evolution and spread of AMR, it also serves as a useful starting point for building machine learning models for predicting AMR phenotypes. We demonstrate this by describing two machine learning models that are built from the entire dataset to show where the predictive power is comparatively high or low. This AMR metadata collection is freely available and maintained on the Bacterial and Viral Bioinformatics Center (BV-BRC) FTP site ftp://ftp.bvbrc.org/RELEASE_NOTES/PATRIC_genomes_AMR.txt.

Trajectory of Growth of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Variants in Houston, Texas, January through May 2021, Based on 12,476 Genome Sequences

Certain genetic variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are of substantial concern because they may be more transmissible or detrimentally alter the pandemic course and disease features in individual patients. SARS-CoV-2 genome sequences from 12,476 patients in the Houston Methodist health care system diagnosed from January 1 through May 31, 2021 are reported here. Prevalence of the B.1.1.7 (Alpha) variant increased rapidly and caused 63% to 90% of new cases in the latter half of May. Eleven B.1.1.7 genomes had an E484K replacement in spike protein, a change also identified in other SARS-CoV-2 lineages. Compared with non-B.1.1.7-infected patients, individuals with B.1.1.7 had a significantly lower cycle threshold (a proxy for higher virus load) and significantly higher hospitalization rate. Other variants [eg, B.1.429 and B.1.427 (Epsilon), P.1 (Gamma), P.2 (Zeta), and R.1] also increased rapidly, although the magnitude was less than that in B.1.1.7. Twenty-two patients infected with B.1.617.1 (Kappa) or B.1.617.2 (Delta) variants had a high rate of hospitalization. Breakthrough cases (n = 207) in fully vaccinated patients were caused by a heterogeneous array of virus genotypes, including many not currently designated variants of interest or concern. In the aggregate, this study delineates the trajectory of SARS-CoV-2 variants circulating in a major metropolitan area, documents B.1.1.7 as the major cause of new cases in Houston, TX, and heralds the arrival of B.1.617 variants in the metroplex.

Sequence Analysis of 20,453 Severe Acute Respiratory Syndrome Coronavirus 2 Genomes from the Houston Metropolitan Area Identifies the Emergence and Widespread Distribution of Multiple Isolates of All Major Variants of Concern

Since the beginning of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there has been international concern about the emergence of virus variants with mutations that increase transmissibility, enhance escape from the human immune response, or otherwise alter biologically important phenotypes. In late 2020, several variants of concern emerged globally, including the UK variant (B.1.1.7), the South Africa variant (B.1.351), Brazil variants (P.1 and P.2), and two related California variants of interest (B.1.429 and B.1.427). These variants are believed to have enhanced transmissibility. For the South Africa and Brazil variants, there is evidence that mutations in spike protein permit it to escape from some vaccines and therapeutic monoclonal antibodies. On the basis of our extensive genome sequencing program involving 20,453 coronavirus disease 2019 patient samples collected from March 2020 to February 2021, we report identification of all six of these SARS-CoV-2 variants among Houston Methodist Hospital (Houston, TX) patients residing in the greater metropolitan area. Although these variants are currently at relatively low frequency (aggregate of 1.1%) in the population, they are geographically widespread. Houston is the first city in the United States in which active circulation of all six current variants of concern has been documented by genome sequencing. As vaccine deployment accelerates, increased genomic surveillance of SARS-CoV-2 is essential to understanding the presence, frequency, and medical impact of consequential variants and their patterns and trajectory of dissemination.

Molecular Architecture of Early Dissemination and Massive Second Wave of the SARS-CoV-2 Virus in a Major Metropolitan Area

We sequenced the genomes of 5,085 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains causing two coronavirus disease 2019 (COVID-19) disease waves in metropolitan Houston, TX, an ethnically diverse region with 7 million residents. The genomes were from viruses recovered in the earliest recognized phase of the pandemic in Houston and from viruses recovered in an ongoing massive second wave of infections. The virus was originally introduced into Houston many times independently. Virtually all strains in the second wave have a Gly614 amino acid replacement in the spike protein, a polymorphism that has been linked to increased transmission and infectivity. Patients infected with the Gly614 variant strains had significantly higher virus loads in the nasopharynx on initial diagnosis. We found little evidence of a significant relationship between virus genotype and altered virulence, stressing the linkage between disease severity, underlying medical conditions, and host genetics. Some regions of the spike protein-the primary target of global vaccine efforts-are replete with amino acid replacements, perhaps indicating the action of selection. We exploited the genomic data to generate defined single amino acid replacements in the receptor binding domain of spike protein that, importantly, produced decreased recognition by the neutralizing monoclonal antibody CR3022. Our report represents the first analysis of the molecular architecture of SARS-CoV-2 in two infection waves in a major metropolitan region. The findings will help us to understand the origin, composition, and trajectory of future infection waves and the potential effect of the host immune response and therapeutic maneuvers on SARS-CoV-2 evolution.IMPORTANCE There is concern about second and subsequent waves of COVID-19 caused by the SARS-CoV-2 coronavirus occurring in communities globally that had an initial disease wave. Metropolitan Houston, TX, with a population of 7 million, is experiencing a massive second disease wave that began in late May 2020. To understand SARS-CoV-2 molecular population genomic architecture and evolution and the relationship between virus genotypes and patient features, we sequenced the genomes of 5,085 SARS-CoV-2 strains from these two waves. Our report provides the first molecular characterization of SARS-CoV-2 strains causing two distinct COVID-19 disease waves.

Carbapenems drive the collateral resistance to ceftaroline in cystic fibrosis patients with MRSA

Chronic airways infection with methicillin-resistant Staphylococcus aureus (MRSA) is associated with worse respiratory disease cystic fibrosis (CF) patients. Ceftaroline is a cephalosporin that inhibits the penicillin-binding protein (PBP2a) uniquely produced by MRSA. We analyzed 335 S. aureus isolates from CF sputum samples collected at three US centers between 2015-2018. Molecular relationships demonstrated that high-level resistance of preceding isolates to carbapenems were associated with subsequent isolation of ceftaroline resistant CF MRSA. In vitro evolution experiments showed that pre-exposure of CF MRSA to meropenem with further selection with ceftaroline implied mutations in mecA and additional mutations in pbp1 and pbp2, targets of carbapenems; no effects were achieved by other β-lactams. An in vivo pneumonia mouse model showed the potential therapeutic efficacy of ceftaroline/meropenem combination against ceftaroline-resistant CF MRSA infections. Thus, the present findings highlight risk factors and potential therapeutic strategies offering an opportunity to both prevent and address antibiotic resistance in this patient population.

Predicting antimicrobial resistance using conserved genes

A growing number of studies are using machine learning models to accurately predict antimicrobial resistance (AMR) phenotypes from bacterial sequence data. Although these studies are showing promise, the models are typically trained using features derived from comprehensive sets of AMR genes or whole genome sequences and may not be suitable for use when genomes are incomplete. In this study, we explore the possibility of predicting AMR phenotypes using incomplete genome sequence data. Models were built from small sets of randomly-selected core genes after removing the AMR genes. For Klebsiella pneumoniae, Mycobacterium tuberculosis, Salmonella enterica, and Staphylococcus aureus, we report that it is possible to classify susceptible and resistant phenotypes with average F1 scores ranging from 0.80-0.89 with as few as 100 conserved non-AMR genes, with very major error rates ranging from 0.11-0.23 and major error rates ranging from 0.10-0.20. Models built from core genes have predictive power in cases where the primary AMR mechanisms result from SNPs or horizontal gene transfer. By randomly sampling non-overlapping sets of core genes, we show that F1 scores and error rates are stable and have little variance between replicates. Although these small core gene models have lower accuracies and higher error rates than models built from the corresponding assembled genomes, the results suggest that sufficient variation exists in the core non-AMR genes of a species for predicting AMR phenotypes.

Predicting antimicrobial susceptibility from the bacterial genome: A new paradigm for one health resistance monitoring

The laboratory identification of antibacterial resistance is a cornerstone of infectious disease medicine. In vitro antimicrobial susceptibility testing has long been based on the growth response of organisms in pure culture to a defined concentration of antimicrobial agents. By comparing individual isolates to wild-type susceptibility patterns, strains with acquired resistance can be identified. Acquired resistance can also be detected genetically. After many decades of research, the inventory of genes underlying antimicrobial resistance is well known for several pathogenic genera including zoonotic enteric organisms such as Salmonella and Campylobacter and continues to grow substantially for others. With the decline in costs for large scale DNA sequencing, it is now practicable to characterize bacteria using whole genome sequencing, including the carriage of resistance genes in individual microorganisms and those present in complex biological samples. With genomics, we can generate comprehensive, detailed information on the bacterium, the mechanisms of antibiotic resistance, clues to its source, and the nature of mobile DNA elements by which resistance spreads. These developments point to a new paradigm for antimicrobial resistance detection and tracking for both clinical and public health purposes.

Molecular Architecture of Early Dissemination and Massive Second Wave of the SARS-CoV-2 Virus in a Major Metropolitan Area

We sequenced the genomes of 5,085 SARS-CoV-2 strains causing two COVID-19 disease waves in metropolitan Houston, Texas, an ethnically diverse region with seven million residents. The genomes were from viruses recovered in the earliest recognized phase of the pandemic in Houston, and an ongoing massive second wave of infections. The virus was originally introduced into Houston many times independently. Virtually all strains in the second wave have a Gly614 amino acid replacement in the spike protein, a polymorphism that has been linked to increased transmission and infectivity. Patients infected with the Gly614 variant strains had significantly higher virus loads in the nasopharynx on initial diagnosis. We found little evidence of a significant relationship between virus genotypes and altered virulence, stressing the linkage between disease severity, underlying medical conditions, and host genetics. Some regions of the spike protein - the primary target of global vaccine efforts - are replete with amino acid replacements, perhaps indicating the action of selection. We exploited the genomic data to generate defined single amino acid replacements in the receptor binding domain of spike protein that, importantly, produced decreased recognition by the neutralizing monoclonal antibody CR30022. Our study is the first analysis of the molecular architecture of SARS-CoV-2 in two infection waves in a major metropolitan region. The findings will help us to understand the origin, composition, and trajectory of future infection waves, and the potential effect of the host immune response and therapeutic maneuvers on SARS-CoV-2 evolution.

PATRIC as a unique resource for studying antimicrobial resistance

The Pathosystems Resource Integration Center (PATRIC, www.patricbrc.org) is designed to provide researchers with the tools and services that they need to perform genomic and other ‘omic’ data analyses. In response to mounting concern over antimicrobial resistance (AMR), the PATRIC team has been developing new tools that help researchers understand AMR and its genetic determinants. To support comparative analyses, we have added AMR phenotype data to over 15 000 genomes in the PATRIC database, often assembling genomes from reads in public archives and collecting their associated AMR panel data from the literature to augment the collection. We have also been using this collection of AMR metadata to build machine learning-based classifiers that can predict the AMR phenotypes and the genomic regions associated with resistance for genomes being submitted to the annotation service. Likewise, we have undertaken a large AMR protein annotation effort by manually curating data from the literature and public repositories. This collection of 7370 AMR reference proteins, which contains many protein annotations (functional roles) that are unique to PATRIC and RAST, has been manually curated so that it projects stably across genomes. The collection currently projects to 1 610 744 proteins in the PATRIC database. Finally, the PATRIC Web site has been expanded to enable AMR-based custom page views so that researchers can easily explore AMR data and design experiments based on whole genomes or individual genes.

The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities

The PathoSystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center funded by the National Institute of Allergy and Infectious Diseases (https://www.patricbrc.org). PATRIC supports bioinformatic analyses of all bacteria with a special emphasis on pathogens, offering a rich comparative analysis environment that provides users with access to over 250 000 uniformly annotated and publicly available genomes with curated metadata. PATRIC offers web-based visualization and comparative analysis tools, a private workspace in which users can analyze their own data in the context of the public collections, services that streamline complex bioinformatic workflows and command-line tools for bulk data analysis. Over the past several years, as genomic and other omics-related experiments have become more cost-effective and widespread, we have observed considerable growth in the usage of and demand for easy-to-use, publicly available bioinformatic tools and services. Here we report the recent updates to the PATRIC resource, including new web-based comparative analysis tools, eight new services and the release of a command-line interface to access, query and analyze data.

Using Machine Learning To Predict Antimicrobial MICs and Associated Genomic Features for Nontyphoidal Salmonella

Nontyphoidal Salmonella species are the leading bacterial cause of foodborne disease in the United States. Whole-genome sequences and paired antimicrobial susceptibility data are available for Salmonella strains because of surveillance efforts from public health agencies. In this study, a collection of 5,278 nontyphoidal Salmonella genomes, collected over 15 years in the United States, was used to generate extreme gradient boosting (XGBoost)-based machine learning models for predicting MICs for 15 antibiotics. The MIC prediction models had an overall average accuracy of 95% within ±1 2-fold dilution step (confidence interval, 95% to 95%), an average very major error rate of 2.7% (confidence interval, 2.4% to 3.0%), and an average major error rate of 0.1% (confidence interval, 0.1% to 0.2%). The model predicted MICs with no a priori information about the underlying gene content or resistance phenotypes of the strains. By selecting diverse genomes for the training sets, we show that highly accurate MIC prediction models can be generated with less than 500 genomes. We also show that our approach for predicting MICs is stable over time, despite annual fluctuations in antimicrobial resistance gene content in the sampled genomes. Finally, using feature selection, we explore the important genomic regions identified by the models for predicting MICs. To date, this is one of the largest MIC modeling studies to be published. Our strategy for developing whole-genome sequence-based models for surveillance and clinical diagnostics can be readily applied to other important human pathogens.

Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae

Antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to rapidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of Klebsiella pneumoniae to develop a XGBoost-based machine learning model that accurately predicts minimum inhibitory concentrations (MICs) for 20 antibiotics. The overall accuracy of the model, within ±1 two-fold dilution factor, is 92%. Individual accuracies are ≥90% for 15/20 antibiotics. We show that the MICs predicted by the model correlate with known antimicrobial resistance genes. Importantly, the genome-wide approach described in this study offers a way to predict MICs for isolates without knowledge of the underlying gene content. This study shows that machine learning can be used to build a complete in silico MIC prediction panel for K. pneumoniae and provides a framework for building MIC prediction models for other pathogenic bacteria.

Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource Center

The Pathosystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center (https://www.patricbrc.org). Recent changes to PATRIC include a redesign of the web interface and some new services that provide users with a platform that takes them from raw reads to an integrated analysis experience. The redesigned interface allows researchers direct access to tools and data, and the emphasis has changed to user-created genome-groups, with detailed summaries and views of the data that researchers have selected. Perhaps the biggest change has been the enhanced capability for researchers to analyze their private data and compare it to the available public data. Researchers can assemble their raw sequence reads and annotate the contigs using RASTtk. PATRIC also provides services for RNA-Seq, variation, model reconstruction and differential expression analysis, all delivered through an updated private workspace. Private data can be compared by ‘virtual integration’ to any of PATRIC's public data. The number of genomes available for comparison in PATRIC has expanded to over 80 000, with a special emphasis on genomes with antimicrobial resistance data. PATRIC uses this data to improve both subsystem annotation and k-mer classification, and tags new genomes as having signatures that indicate susceptibility or resistance to specific antibiotics.

Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function and regulation is the identification of sets of genes that are always co-expressed. These gene sets, Atomic Regulons (ARs), represent fundamental units of function within a cell and could be used to associate genes of unknown function with cellular processes and to enable rational genetic engineering of cellular systems. Here, we describe an approach for inferring ARs that leverages large-scale expression data sets, gene context, and functional relationships among genes. We computed ARs for Escherichia coli based on 907 gene expression experiments and compared our results with gene clusters produced by two prevalent data-driven methods: Hierarchical clustering and k-means clustering. We compared ARs and purely data-driven gene clusters to the curated set of regulatory interactions for E. coli found in RegulonDB, showing that ARs are more consistent with gold standard regulons than are data-driven gene clusters. We further examined the consistency of ARs and data-driven gene clusters in the context of gene interactions predicted by Context Likelihood of Relatedness (CLR) analysis, finding that the ARs show better agreement with CLR predicted interactions. We determined the impact of increasing amounts of expression data on AR construction and find that while more data improve ARs, it is not necessary to use the full set of gene expression experiments available for E. coli to produce high quality ARs. In order to explore the conservation of co-regulated gene sets across different organisms, we computed ARs for Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus, each of which represents increasing degrees of phylogenetic distance from E. coli. Comparison of the organism-specific ARs showed that the consistency of AR gene membership correlates with phylogenetic distance, but there is clear variability in the regulatory networks of closely related organisms. As large scale expression data sets become increasingly common for model and non-model organisms, comparative analyses of atomic regulons will provide valuable insights into fundamental regulatory modules used across the bacterial domain.

Theranostic Application of Mixed Gold and Superparamagnetic Iron Oxide Nanoparticle Micelles in Glioblastoma Multiforme

The treatment of glioblastoma multiforme, the most prevalent and lethal form of brain cancer in humans, has been limited in part by poor delivery of drugs through the blood-brain barrier and by unclear delineation of the extent of infiltrating tumor margins. Nanoparticles, which selectively accumulate in tumor tissue due to their leaky vasculature and the enhanced permeability and retention effect, have shown promise as both therapeutic and diagnostic agents for brain tumors. In particular, superparamagnetic iron oxide nanoparticles (SPIONs) have been leveraged as T2-weighted MRI contrast agents for tumor detection and imaging; and gold nanoparticles (AuNP) have been demonstrated as radiosensitizers capable of propagating electron and free radical-induced radiation damage to tumor cells. In this study, we investigated the potential applications of novel gold and SPION-loaded micelles (GSMs) coated by polyethylene glycol-polycaprolactone (PEG-PCL) polymer. By quantifying gh2ax DNA damage foci in glioblastoma cell lines, we tested the radiosensitizing efficacy of these GSMs, and found that GSM administration in conjunction with radiation therapy (RT) led to ~2-fold increase in density of double-stranded DNA breaks. For imaging, we used GSMs as a contrast agent for both computed tomography (CT) and magnetic resonance imaging (MRI) studies of stereotactically implanted GBM tumors in a mouse model, and found that MRI but not CT was sufficiently sensitive to detect and delineate tumor borders after administration and accumulation of GSMs. These results suggest that with further development and testing, GSMs may potentially be integrated into both imaging and treatment of brain tumors, serving a theranostic purpose as both an MRI-based contrast agent and a radiosensitizer.

PATtyFams: Protein Families for the Microbial Genomes in the PATRIC Database

The ability to build accurate protein families is a fundamental operation in bioinformatics that influences comparative analyses, genome annotation, and metabolic modeling. For several years we have been maintaining protein families for all microbial genomes in the PATRIC database (Pathosystems Resource Integration Center, patricbrc.org) in order to drive many of the comparative analysis tools that are available through the PATRIC website. However, due to the burgeoning number of genomes, traditional approaches for generating protein families are becoming prohibitive. In this report, we describe a new approach for generating protein families, which we call PATtyFams. This method uses the k-mer-based function assignments available through RAST (Rapid Annotation using Subsystem Technology) to rapidly guide family formation, and then differentiates the function-based groups into families using a Markov Cluster algorithm (MCL). This new approach for generating protein families is rapid, scalable and has properties that are consistent with alignment-based methods.

RASTtk: a modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes

The RAST (Rapid Annotation using Subsystem Technology) annotation engine was built in 2008 to annotate bacterial and archaeal genomes. It works by offering a standard software pipeline for identifying genomic features (i.e., protein-encoding genes and RNA) and annotating their functions. Recently, in order to make RAST a more useful research tool and to keep pace with advancements in bioinformatics, it has become desirable to build a version of RAST that is both customizable and extensible. In this paper, we describe the RAST tool kit (RASTtk), a modular version of RAST that enables researchers to build custom annotation pipelines. RASTtk offers a choice of software for identifying and annotating genomic features as well as the ability to add custom features to an annotation job. RASTtk also accommodates the batch submission of genomes and the ability to customize annotation protocols for batch submissions. This is the first major software restructuring of RAST since its inception.

Enabling comparative modeling of closely related genomes: example genus Brucella

For many scientific applications, it is highly desirable to be able to compare metabolic models of closely related genomes. In this short report, we attempt to raise awareness to the fact that taking annotated genomes from public repositories and using them for metabolic model reconstructions is far from being trivial due to annotation inconsistencies. We are proposing a protocol for comparative analysis of metabolic models on closely related genomes, using fifteen strains of genus Brucella, which contains pathogens of both humans and livestock. This study lead to the identification and subsequent correction of inconsistent annotations in the SEED database, as well as the identification of 31 biochemical reactions that are common to Brucella, which are not originally identified by automated metabolic reconstructions. We are currently implementing this protocol for improving automated annotations within the SEED database and these improvements have been propagated into PATRIC, Model-SEED, KBase and RAST. This method is an enabling step for the future creation of consistent annotation systems and high-quality model reconstructions that will support in predicting accurate phenotypes such as pathogenicity, media requirements or type of respiration.

In search of genome annotation consistency: solid gene clusters and how to use them

Maintaining consistency in genome annotations is important for supporting many computational tasks, particularly metabolic modeling. The SEED project has implemented a process that improves annotation consistencies across microbial genomes for proteins with conserved sequences and genomic context. In this research report, we describe this process and show how this effort has resulted in improvements to microbial genome annotations in the SEED. We also compare SEED annotation consistencies with other commonly used resources such as IMG (the Joint Genome Institute’s Integrated Microbial Genomes system), RefSeq (the National Center for Biotechnology Information’s Reference Sequence Database), Swiss-Prot (the annotated protein sequence database of the Swiss Institute of Bioinformatics, European Molecular Biology Laboratory and the European Bioinformatics Institute) and TrEMBL (Translated European Molecular Biology Laboratory nucleotide sequence data Library). Our analysis indicates that manual and computational efforts are paying off for the databases where consistency is a major goal.

The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST)

In 2004, the SEED (http://pubseed.theseed.org/) was created to provide consistent and accurate genome annotations across thousands of genomes and as a platform for discovering and developing de novo annotations. The SEED is a constantly updated integration of genomic data with a genome database, web front end, API and server scripts. It is used by many scientists for predicting gene functions and discovering new pathways. In addition to being a powerful database for bioinformatics research, the SEED also houses subsystems (collections of functionally related protein families) and their derived FIGfams (protein families), which represent the core of the RAST annotation engine (http://rast.nmpdr.org/). When a new genome is submitted to RAST, genes are called and their annotations are made by comparison to the FIGfam collection. If the genome is made public, it is then housed within the SEED and its proteins populate the FIGfam collection. This annotation cycle has proven to be a robust and scalable solution to the problem of annotating the exponentially increasing number of genomes. To date, >12 000 users worldwide have annotated >60 000 distinct genomes using RAST. Here we describe the interconnectedness of the SEED database and RAST, the RAST annotation pipeline and updates to both resources.

Selective targeting of brain tumors with gold nanoparticle-induced radiosensitization

Successful treatment of brain tumors such as glioblastoma multiforme (GBM) is limited in large part by the cumulative dose of Radiation Therapy (RT) that can be safely given and the blood-brain barrier (BBB), which limits the delivery of systemic anticancer agents into tumor tissue. Consequently, the overall prognosis remains grim. Herein, we report our pilot studies in cell culture experiments and in an animal model of GBM in which RT is complemented by PEGylated-gold nanoparticles (GNPs). GNPs significantly increased cellular DNA damage inflicted by ionizing radiation in human GBM-derived cell lines and resulted in reduced clonogenic survival (with dose-enhancement ratio of ∼1.3). Intriguingly, combined GNP and RT also resulted in markedly increased DNA damage to brain blood vessels. Follow-up in vitro experiments confirmed that the combination of GNP and RT resulted in considerably increased DNA damage in brain-derived endothelial cells. Finally, the combination of GNP and RT increased survival of mice with orthotopic GBM tumors. Prior treatment of mice with brain tumors resulted in increased extravasation and in-tumor deposition of GNP, suggesting that RT-induced BBB disruption can be leveraged to improve the tumor-tissue targeting of GNP and thus further optimize the radiosensitization of brain tumors by GNP. These exciting results together suggest that GNP may be usefully integrated into the RT treatment of brain tumors, with potential benefits resulting from increased tumor cell radiosensitization to preferential targeting of tumor-associated vasculature.

Genomes of the class Erysipelotrichia clarify the firmicute origin of the class Mollicutes

The tree of life is paramount for achieving an integrated understanding of microbial evolution and the relationships between physiology, genealogy and genomics. It provides the framework for interpreting environmental sequence data, whether applied to microbial ecology or to human health. However, there remain many instances where there is ambiguity in our understanding of the phylogeny of major lineages, and/or confounding nomenclature. Here we apply recent genomic sequence data to examine the evolutionary history of members of the classes Mollicutes (phylum Tenericutes) and Erysipelotrichia (phylum Firmicutes). Consistent with previous analyses, we find evidence of a specific relationship between them in molecular phylogenies and signatures of the 16S rRNA, 23S rRNA, ribosomal proteins and aminoacyl-tRNA synthetase proteins. Furthermore, by mapping functions over the phylogenetic tree we find that the erysipelotrichia lineages are involved in various stages of genomic reduction, having lost (often repeatedly) a variety of metabolic functions and the ability to form endospores. Although molecular phylogeny has driven numerous taxonomic revisions, we find it puzzling that the most recent taxonomic revision of the phyla Firmicutes and Tenericutes has further separated them into distinct phyla, rather than reflecting their common roots.

Characterizing the native codon usages of a genome: an axis projection approach

Codon usage can provide insights into the nature of the genes in a genome. Genes that are “native” to a genome (have not been recently acquired by horizontal transfer) range in codon usage from a low-bias “typical” usage to a more biased “high-expression” usage characteristic of genes encoding abundant proteins. Genes that differ from these native codon usages are candidates for foreign genes that have been recently acquired by horizontal gene transfer. In this study, we present a method for characterizing the codon usages of native genes—both typical and highly expressed—within a genome. Each gene is evaluated relative to a half line (or axis) in a 59D space of codon usage. The axis begins at the modal codon usage, the usage that matches the largest number of genes in the genome, and it passes through a point representing the codon usage of a set of genes with expression-related bias. A gene whose codon usage matches (does not significantly differ from) a point on this axis is a candidate native gene, and the location of its projection onto the axis provides a general estimate of its expression level. A gene that differs significantly from all points on the axis is a candidate foreign gene. This automated approach offers significant improvements over existing methods. We illustrate this by analyzing the genomes of Pseudomonas aeruginosa PAO1 and Bacillus anthracis A0248, which can be difficult to analyze with commonly used methods due to their biased base compositions. Finally, we use this approach to measure the proportion of candidate foreign genes in 923 bacterial and archaeal genomes. The organisms with the most homogeneous genomes (containing the fewest candidate foreign genes) are mostly endosymbionts and parasites, though with exceptions that include Pelagibacter ubique and Beutenbergia cavernae. The organisms with the most heterogeneous genomes (containing the most candidate foreign genes) include members of the genera Bacteroides, Corynebacterium, Desulfotalea, Neisseria, Xylella, and Thermobaculum.

Modal codon usage: assessing the typical codon usage of a genome

Most genomes are heterogeneous in codon usage, so a codon usage study should start by defining the codon usage that is typical to the genome. Although this is commonly taken to be the genomewide average, we propose that the mode-the codon usage that matches the most genes-provides a more useful approximation of the typical codon usage of a genome. We provide a method for estimating the modal codon usage, which utilizes a continuous approximation to the number of matching genes and a simplex optimization. In a survey of bacterial and archaeal genomes, as many as 20% more of the genes in a given genome match the modal codon usage than the average codon usage. We use the mode to examine the evolution of the multireplicon genomes of Agrobacterium tumefaciens C58 and Borrelia burgdorferi B31. In A. tumefaciens, the circular and linear chromosomes are characterized by a common "chromosome-like" codon usage, whereas both plasmids share a distinct "plasmid-like" codon usage. In B. burgdorferi, in addition to different codon-usage biases on the leading and lagging strands of DNA replication found by McInerney (McInerney JO. 1998. Replicational and transcriptional selection on codon usage in Borrelia burgdorferi. Proc Natl Acad Sci USA. 95:10698-10703), we also detect a codon-usage similarity between linear plasmid lp38 and the leading strand of the chromosome and a high similarity among the cp32 family of plasmids.

Isolated limb perfusion: a novel delivery system for wild-type p53 and fiber-modified oncolytic adenoviruses to extremity sarcoma

Isolated limb perfusion (ILP) is a limb salvage surgical modality used to deliver chemotherapy and biologic agents to locally advanced and recurrent extremity soft tissue sarcoma (STS), and may be readily tailored for delivery of gene therapy. We set out to test the feasibility of delivering AdFLAGp53 (replication incompetent adenovirus bearing FLAG-tagged wild-type p53) and Ad.hTC.GFP/E1a.RGD (a fiber-modified, replication selective oncolytic adenovirus) into human leiomyosarcoma xenografts by ILP. Nude rats bearing SKLMS-1 tumors in their hind limbs underwent ILP with escalating doses of AdLacZ or AdFLAGp53 (study 1), or with Ad.CMV.GFP.RGD or Ad.hTC.GFP/E1a.RGD (study 2) following in vitro confirmation of therapeutic potential in STS cell lines and strains. Seventy-two hours after delivery, reverse transcription-polymerase chain reaction confirmed FLAGp53 expression, and immunohistochemistry confirmed diffuse upregulation of p21CIP1/WAF1 in ILP-treated tumors. Ad.hTC.GFP/E1a.RGD perfused tumors demonstrated robust macroscopic transgene expression throughout their substance, but not in perfused normal tissues, 21 days after delivery. Intra-tumoral viral replication was confirmed by immunohistochemical staining for early (E1a) and late (hexon) viral protein expression. Terminal deoxynucleotidyl transferase-mediated-digoxigenin nick end-labeling staining identified foci of cell death within regions of viral replication. In conclusion, therapeutic adenoviral gene therapy against limb borne human STS can be successfully delivered by ILP and warrants further investigation.

Monitoring Interfacial Bioelectrochemistry Using a FRET Switch

Generation of functionally active biomolecular monolayers is important in both analytical science and biophysical analyses. Our ability to monitor the redox-active state of immobilized proteins or enzymes at a molecular level, from which stochastic and surface-induced variations would be apparent, is impeded by comparatively slow electron-transfer kinetics and associated signal:noise difficulties. We demonstrate herein that by covalently tethering an appropriate dye to the copper protein azurin a highly oxidation-state-sensitive FRET process can be established which enables redox switching to be optically monitored at protein levels down to the zeptomolar limit. The surface-potential-induced cycling of emission enables the redox potential of clusters of a few hundred molecules to be determined.

Scanning probe technology in metalloprotein and biomolecular electronics

The interfacing of man-made electronic components with specifically-folded biomacromolecules lies central not only to the development of sensory interfaces and potential new molecular-scale devices, but also enables us to analyse processes of great biological importance in a refined and controllable manner. Recent advances in both available technology, most notably optical and scanning probes in nature, and our understanding of suitable methodologies, have led us to the point where the characteristics of single biological molecules can be interrogated with good levels of reproducibility. We review here the application of scanning probe microscopy to the analysis of and experimentation on biological redox systems. Within this paper the tunnel transport characteristics, as assayed by both scanning tunnelling microscopy (STM) and conducting probe atomic force microscopy (AFM), of single metalloproteins are discussed. In a specific case study the electron transfer characteristics of the blue copper metalloprotein, azurin, are reported. The modulation of these properties under the influence of calibratable compressional force has also been examined in some detail. Work such as this enables one to reproducibly establish the conductance, barrier height, environmental sensitivity and electromechanical properties of these molecules.

Oncolytic virotherapy for cancer treatment: challenges and solutions

Advances in gene modification and viral therapy have led to the development of a variety of vectors in several viral families that are capable of replication specifically in tumor cells. Because of the nature of viral delivery, infection, and replication, this technology, oncolytic virotherapy, may prove valuable for treating cancer patients, especially those with inoperable tumors. Current limitations exist, however, for oncolytic virotherapy. They include the body's B and T cell responses, innate inflammatory reactions, host range, safety risks involved in using modified viruses as treatments, and the requirement that most currently available oncolytic viruses require local administration. Another important constraint is that genetically enhanced vectors may or may not adhere to their replication restrictions in long-term applications. Several solutions and strategies already exist, however, to minimize or circumvent many of these limitations, supporting viral oncolytic therapy as a viable option and powerful tool in the fight against cancer.

JNK1-dependent antimitotic activity of thiazolidin compounds in human non-small-cell lung and colon cancer cells

We recently identified two thiazolidin compounds, 5-[(4-methylphenyl)methylene]-2-(phenylamino)-4(5H)-thiazolone (MMPT) and 5-(2,4-dihydroxybenzylidene)-2-(phenylimino)-1,3-thiazolidin (DBPT), that inhibit the growth of human non-small-cell lung and colon cancer cells independent of P-glycoprotein and p53 status. Here we further investigated the mechanism by which these thiazolidin compounds mediate their anticancer effects. Treatment of cancer cells with MMPT and DBPT led to a time-dependent accumulation of cells arrested in the G2/M phase with modulation of the expression of proteins such as cyclin B1, cdc25C, and phosphorylated histone H3. Moreover, treatment with MMPT and DBPT increased M-phase arrest with abnormal spindle formation. DBPT-mediated G2/M phase arrest and phosphorylation of cdc25C and histone H3 were abrogated when JNK activation was blocked either with SP600125, a specific JNK inhibitor, or a dominant-negative JNK1 gene. Moreover, DBPT-mediated microtubule disruption was also blocked by SP600125 treatment. Our results demonstrate that thiazolidin compounds can effectively induce G2/M arrest in cancer cells and that this G2/M arrest requires JNK activation.

The tunnelling conductance of molecularly ordered metalloprotein arrays

Metalloproteins can be self-assembled in molecularly ordered, electrochemically addressable arrays. We report here on a study of the transport characteristics of the blue copper protein, azurin, from Pseudomonas aeruginosa, by a combination of electrochemical and scanning probe techniques (scanning tunnelling microscopy and conducting atomic force microscopy). Redox-switchable chemisorbed molecular arrays can be formed from both wild-type and mutant proteins using the strong affinity of cysteine residue thiolates for pristine gold surfaces. The molecular transconductance of single protein molecules within these arrays has been studied under controllable conditions where it has been additionally possible to resolve the effects of protein mechanical perturbation. Although tunnelling appears to be non-resonant and adequately explained through the use of a square barrier model, under some conditions the contribution of the redox-active copper centre to conductance is resolvable.

Molecular bioelectronics

Biological macromolecules have evolved over many millions of years into structures primed, in some cases, for both specific surface recognition and facile, directional electron tunnelling. The redox-active centres of metalloproteins play a central role in photosynthesis and respiration. The processes by which constructive man-made interfaces to these moieties can be generated have advanced greatly during the past two decades or so. Together with recent advances in molecular manipulation, analyses and lithographic fabrication, this knowledge has led to us to the point where bioelectronic devices can be designed and interrogated with good levels of reproducibility.

High resolution scanning force microscopy of cardiac myocytes

The advent of scanning probe microscopy has introduced a powerful new method of probing the structural features of biological specimens. In this study, high resolution atomic force microscopy micrographs of single, isolated, cardiac myocytes are presented. Significantly, our images show not only the features to be expected of the external sarcolemma, but also resolve sub-surface features, including the striated pattern of the contractile proteins and their associated sarcoplasmic reticulum and mitochondria.

A scanning tunnelling study of immobilised cytochrome P450cam

A site-specifically engineered surface cysteine residue, located in a region where the haem moiety is closest to the surface, is used to anchor cytochrome P450cam enzyme molecules covalently to a gold electrode. More reproducibly ordered adsorption, at high coverage, occurs with this K344C mutant than with the wild-type enzyme. The subsequently formed close-packed monolayer arrays have been probed by scanning tunnelling microscopy under ambient conditions and under aqueous (buffered) solution at high resolution. Initial indications suggest that the immobilised enzyme is both electrochemically addressable and catalytically active.

Ultrastructural organization of amyloid fibrils by atomic force microscopy

Atomic force microscopy has been employed to investigate the structural organization of amyloid fibrils produced in vitro from three very different polypeptide sequences. The systems investigated are a 10-residue peptide derived from the sequence of transthyretin, the 90-residue SH3 domain of bovine phosphatidylinositol-3'-kinase, and human wild-type lysozyme, a 130-residue protein containing four disulfide bridges. The results demonstrate distinct similarities between the structures formed by the different classes of fibrils despite the contrasting nature of the polypeptide species involved. SH3 and lysozyme fibrils consist typically of four protofilaments, exhibiting a left-handed twist along the fibril axis. The substructure of TTR(10-19) fibrils is not resolved by atomic force microscopy and their uniform appearance is suggestive of a regular self-association of very thin filaments. We propose that the exact number and orientation of protofilaments within amyloid fibrils is dictated by packing of the regions of the polypeptide chains that are not directly involved in formation of the cross-beta core of the fibrils. The results obtained for these proteins, none of which is directly associated with any human disease, are closely similar to those of disease-related amyloid fibrils, supporting the concept that amyloid is a generic structure of polypeptide chains. The detailed architecture of an individual fibril, however, depends on the manner in which the protofilaments assemble into the fibrillar structure, which in turn is dependent on the sequence of the polypeptide and the conditions under which the fibril is formed.

Riskier than we think? The relationship between risk statement completeness and perceptions of direct to consumer advertised prescription drugs

Direct to consumer (DTC) prescription drug advertising is one of the fastest growing categories of advertising. Expenditures have increased from about $25 million in 1992 to nearly $2 billion in 1999. Given strong evidence of consumer-driven demand for advertised prescription drugs, research was conducted to assess the extent to which DTC prescription drug advertising provides consumers with the information they need to make an informed evaluation of an advertised drug's relative benefits and risks. Two studies explored the relationship between the completeness of the statement describing drug-associated side effects (the "risk statement") and consumers' perceptions of a drug's safety and appeal. The research manipulated risk statement completeness with regard to the incidence levels of side effects mentioned in the statement (which in turn affected the number of side effects mentioned) and the presence or absence of a numeric indicator of side effect incidence. The research strongly suggests a direct relationship between risk statement completeness and consumers' perceptions of drug safety and appeal. Consumers rate the safety and appeal of drugs described with an incomplete risk statement significantly more positively than comparable drugs described with a more complete risk statement. Implications of the research for the regulation and presentation of DTC prescription drug advertising and advertiser communication practices are discussed.