Vancomycin-resistant Staphylococcus aureus (VRSA) can overcome the cost of antibiotic resistance and may threaten vancomycin's clinical durability.

Vancomycin has proven remarkably durable to resistance evolution by Staphylococcus aureus despite widespread treatment with vancomycin in the clinic. Only 16 cases of vancomycin-resistant S. aureus (VRSA) have been documented in the United States. It is thought that the failure of VRSA to spread is partly due to the fitness cost imposed by the vanA operon, which is the only known means of high-level resistance. Here, we show that the fitness cost of vanA-mediated resistance can be overcome through laboratory evolution of VRSA in the presence of vancomycin. Adaptation to vancomycin imposed a tradeoff such that fitness in the presence of vancomycin increased, while fitness in its absence decreased in evolved lineages. Comparing the genomes of vancomycin-exposed and vancomycin-unexposed lineages pinpointed the D-alanine:D-alanine ligase gene (ddl) as the target of loss-of-function mutations, which were associated with the observed fitness tradeoff. Vancomycin-exposed lineages exhibited vancomycin dependence and abnormal colony morphology in the absence of drug, which were associated with mutations in ddl. However, further evolution of vancomycin-exposed lineages in the absence of vancomycin enabled some evolved lineages to escape this fitness tradeoff. Many vancomycin-exposed lineages maintained resistance in the absence of vancomycin, unlike their ancestral VRSA strains. These results indicate that VRSA might be able to compensate for the fitness deficit associated with vanA-mediated resistance, which may pose a threat to the prolonged durability of vancomycin in the clinic. Our results also suggest vancomycin treatment should be immediately discontinued in patients after VRSA is identified to mitigate potential adaptations.

Applications of machine learning on electronic health record data to combat antibiotic resistance.

There is growing excitement about the clinical use of artificial intelligence and machine learning technologies. Advancements in computing and the accessibility of machine learning frameworks enable researchers to easily train predictive models using electronic health record data. However, there are several practical factors that must be considered when employing machine learning on electronic health record data. We provide a primer on machine learning and approaches commonly taken to address these challenges. To illustrate how these approaches have been applied to address antimicrobial resistance, we review the use of electronic health record data to construct machine learning models for predicting pathogen carriage or infection, optimizing empiric therapy, and aiding antimicrobial stewardship tasks. Machine learning shows promise in promoting the appropriate use of antimicrobials, although clinical deployment is limited. We conclude by describing potential dangers of, and barriers to, implementation of machine learning models in the clinic.

Many purported pseudogenes in bacterial genomes are bona fide genes.

BACKGROUND: Microbial genomes are largely comprised of protein coding sequences, yet some genomes contain many pseudogenes caused by frameshifts or internal stop codons. These pseudogenes are believed to result from gene degradation during evolution but could also be technical artifacts of genome sequencing or assembly. RESULTS: Using a combination of observational and experimental data, we show that many putative pseudogenes are attributable to errors that are incorporated into genomes during assembly. Within 126,564 publicly available genomes, we observed that nearly identical genomes often substantially differed in pseudogene counts. Causal inference implicated assembler, sequencing platform, and coverage as likely causative factors. Reassembly of genomes from raw reads confirmed that each variable affects the number of putative pseudogenes in an assembly. Furthermore, simulated sequencing reads corroborated our observations that the quality and quantity of raw data can significantly impact the number of pseudogenes in an assembler dependent fashion. The number of unexpected pseudogenes due to internal stops was highly correlated (R2 = 0.96) with average nucleotide identity to the ground truth genome, implying relative pseudogene counts can be used as a proxy for overall assembly correctness. Applying our method to assemblies in RefSeq resulted in rejection of 3.6% of assemblies due to significantly elevated pseudogene counts. Reassembly from real reads obtained from high coverage genomes showed considerable variability in spurious pseudogenes beyond that observed with simulated reads, reinforcing the finding that high coverage is necessary to mitigate assembly errors. CONCLUSIONS: Collectively, these results demonstrate that many pseudogenes in microbial genome assemblies are actually genes. Our results suggest that high read coverage is required for correct assembly and indicate an inflated number of pseudogenes due to internal stops is indicative of poor overall assembly quality.

FindNonCoding: rapid and simple detection of non-coding RNAs in genomes.

SUMMARY: Non-coding RNAs are often neglected during genome annotation due to their difficulty of detection relative to protein coding genes. FindNonCoding takes a pattern mining approach to capture the essential sequence motifs and hairpin loops representing a non-coding RNA family and quickly identify matches in genomes. FindNonCoding was designed for ease of use and accurately finds non-coding RNAs with a low false discovery rate. AVAILABILITY AND IMPLEMENTATION: FindNonCoding is implemented within the DECIPHER package (v2.19.3) for R (v4.1) available from Bioconductor. Pre-trained models of common non-coding RNA families are included for bacteria, archaea and eukarya. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multi-factorial examination of amplicon sequencing workflows from sample preparation to bioinformatic analysis.

BACKGROUND: The development of sequencing technologies to evaluate bacterial microbiota composition has allowed new insights into the importance of microbial ecology. However, the variety of methodologies used among amplicon sequencing workflows leads to uncertainty about best practices as well as reproducibility and replicability among microbiome studies. Using a bacterial mock community composed of 37 soil isolates, we performed a comprehensive methodological evaluation of workflows, each with a different combination of methodological factors spanning sample preparation to bioinformatic analysis to define sources of artifacts that affect coverage, accuracy, and biases in the resulting compositional profiles. RESULTS: Of the workflows examined, those using the V4-V4 primer set enabled the highest level of concordance between the original mock community and resulting microbiome sequence composition. Use of a high-fidelity polymerase, or a lower-fidelity polymerase with an increased PCR elongation time, limited chimera formation. Bioinformatic pipelines presented a trade-off between the fraction of distinct community members identified (coverage) and fraction of correct sequences (accuracy). DADA2 and QIIME2 assembled V4-V4 reads amplified by Taq polymerase resulted in the highest accuracy (100%) but had a coverage of only 52%. Using mothur to assemble and denoise V4-V4 reads resulted in a coverage of 75%, albeit with marginally lower accuracy (99.5%). CONCLUSIONS: Optimization of microbiome workflows is critical for accuracy and to support reproducibility and replicability among microbiome studies. These considerations will help reveal the guiding principles of microbial ecology and impact the translation of microbiome research to human and environmental health.

Sample-efficient identification of high-dimensional antibiotic synergy with a normalized diagonal sampling design.

Antibiotic resistance is an important public health problem. One potential solution is the development of synergistic antibiotic combinations, in which the combination is more effective than the component drugs. However, experimental progress in this direction is severely limited by the number of samples required to exhaustively test for synergy, which grows exponentially with the number of drugs combined. We introduce a new metric for antibiotic synergy, motivated by the popular Fractional Inhibitory Concentration Index and the Highest Single Agent model. We also propose a new experimental design that samples along all appropriately normalized diagonals in concentration space, and prove that this design identifies all synergies among a set of drugs while only sampling a small fraction of the possible combinations. We applied our method to screen two- through eight-way combinations of eight antibiotics at 10 concentrations each, which requires sampling only 2,560 unique combinations of antibiotic concentrations.

Evaluating the long-term portrayal of antibiotic resistance in major U.S. newspapers.

BACKGROUND: Popular media play a critical role in informing the public about antibiotic resistance, which has remained a health concern for over seven decades. Media attention increases the notoriety of antibiotic resistance and shapes the public's perception of its severity, causes, and solutions. Therefore, it is critical the media accurately portray scientific knowledge that may shape personal and policy responses to antibiotic resistance. METHODS: We analyzed articles from two major U.S. newspapers, The New York Times and Los Angeles Times, from 1940 to 2019 to assess trends in sentiment and lexicon surrounding antibiotic and antimicrobial resistance. RESULTS: We observed a gradual increase in the number of relevant articles about resistance, although far fewer than other topics with comparable mortality rates. We found a consistently threatening portrayal of antibiotic resistance as a crisis, reflected in the usage of terms such as "superbug" to refer to some pathogens. Governmental agencies responsible for determining antibiotic usage policies were infrequently mentioned in articles. Blame for resistance was almost exclusively attributed to inappropriate antibiotic use, mainly in animals, rather than appropriate uses of antibiotics. CONCLUSIONS: Collectively, our results provide insights into how popular media can more accurately inform the public about antibiotic resistance. Potential changes include increasing news coverage, avoiding fear-mongering, and adequately conveying the multiple uses of antibiotics that can potentiate resistance.

RNAconTest: comparing tools for noncoding RNA multiple sequence alignment based on structural consistency.

The importance of noncoding RNA sequences has become increasingly clear over the past decade. New RNA families are often detected and analyzed using comparative methods based on multiple sequence alignments. Accordingly, a number of programs have been developed for aligning and deriving secondary structures from sets of RNA sequences. Yet, the best tools for these tasks remain unclear because existing benchmarks contain too few sequences belonging to only a small number of RNA families. RNAconTest (RNA consistency test) is a new benchmarking approach relying on the observation that secondary structure is often conserved across highly divergent RNA sequences from the same family. RNAconTest scores multiple sequence alignments based on the level of consistency among known secondary structures belonging to reference sequences in their output alignment. Similarly, consensus secondary structure predictions are scored according to their agreement with one or more known structures in a family. Comparing the performance of 10 popular alignment programs using RNAconTest revealed that DAFS, DECIPHER, LocARNA, and MAFFT created the most structurally consistent alignments. The best consensus secondary structure predictions were generated by DAFS and LocARNA (via RNAalifold). Many of the methods specific to noncoding RNAs exhibited poor scalability as the number or length of input sequences increased, and several programs displayed substantial declines in score as more sequences were aligned. Overall, RNAconTest provides a means of testing and improving tools for comparative RNA analysis, as well as highlighting the best available approaches. RNAconTest is available from the DECIPHER website (http://DECIPHER.codes/Downloads.html).

Balancing Trade-Offs Imposed by Growth Media and Mass Spectrometry for Bacterial Exometabolomics.

The bacterial exometabolome consists of a vast array of specialized metabolites, many of which are only produced in response to specific environmental stimuli. For this reason, it is desirable to control the extracellular environment with a defined growth medium composed of pure ingredients. However, complex (undefined) media are expected to support the robust growth of a greater variety of microorganisms than defined media. Here, we investigate the trade-offs inherent to a range of complex and defined solid media for the growth of soil microorganisms, production of specialized metabolites, and detection of these compounds using direct infusion mass spectrometry. We find that complex media support growth of more soil microorganisms, as well as allowing for the detection of more previously discovered natural products as a fraction of total m/z features detected in each sample. However, the use of complex media often caused mass spectrometer injection failures and poor-quality mass spectra, which in some cases resulted in over a quarter of samples being removed from analysis. Defined media, while more limiting in growth, generated higher quality spectra and yielded more m/z features after background subtraction. These results inform future exometabolomic experiments requiring a medium that supports the robust growth of many soil microorganisms. IMPORTANCE Bacteria are capable of producing and secreting a rich diversity of specialized metabolites. Yet, much of their exometabolome remains hidden due to challenges associated with eliciting specialized metabolite production, labor-intensive sample preparation, and time-consuming analysis techniques. Using our versatile three-dimensional (3D)-printed culturing platform, SubTap, we demonstrate that rapid exometabolomic data collection from a diverse set of environmental bacteria is feasible. We optimized our platform by surveying Streptomyces isolated from soil on a variety of media types to assess viability, degree of specialized metabolite production, and compatibility with downstream LESA-DIMS analysis. Ultimately, this will enable data-rich experimentation, allowing for a better understanding of bacterial exometabolomes.

AssessORF: combining evolutionary conservation and proteomics to assess prokaryotic gene predictions.

MOTIVATION: A core task of genomics is to identify the boundaries of protein coding genes, which may cover over 90% of a prokaryote's genome. Several programs are available for gene finding, yet it is currently unclear how well these programs perform and whether any offers superior accuracy. This is in part because there is no universal benchmark for gene finding and, therefore, most developers select their own benchmarking strategy. RESULTS: Here, we introduce AssessORF, a new approach for benchmarking prokaryotic gene predictions based on evidence from proteomics data and the evolutionary conservation of start and stop codons. We applied AssessORF to compare gene predictions offered by GenBank, GeneMarkS-2, Glimmer and Prodigal on genomes spanning the prokaryotic tree of life. Gene predictions were 88-95% in agreement with the available evidence, with Glimmer performing the worst but no clear winner. All programs were biased towards selecting start codons that were upstream of the actual start. Given these findings, there remains considerable room for improvement, especially in the detection of correct start sites. AVAILABILITY AND IMPLEMENTATION: AssessORF is available as an R package via the Bioconductor package repository. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Stochastic exits from dormancy give rise to heavy-tailed distributions of descendants in bacterial populations.

Variance in reproductive success is a major determinant of the degree of genetic drift in a population. While many plants and animals exhibit high variance in their number of progeny, far less is known about these distributions for microorganisms. Here, we used a strain barcoding approach to quantify variability in offspring number among replicate bacterial populations and developed a Bayesian method to infer the distribution of descendants from this variability. We applied our approach to measure the offspring distributions for five strains of bacteria from the genus Streptomyces after germination and growth in a homogenous laboratory environment. The distributions of descendants were heavy-tailed, with a few cells effectively 'winning the jackpot' to become a disproportionately large fraction of the population. This extreme variability in reproductive success largely traced back to initial populations of spores stochastically exiting dormancy, which provided early-germinating spores with an exponential advantage. In simulations with multiple dormancy cycles, heavy-tailed distributions of descendants decreased the effective population size by many orders of magnitude and led to allele dynamics differing substantially from classical population genetics models with matching effective population size. Collectively, these results demonstrate that extreme variability in reproductive success can occur even in growth conditions that are far more homogeneous than the natural environment. Thus, extreme variability in reproductive success might be an important factor shaping microbial population dynamics with implications for predicting the fate of beneficial mutations, interpreting sequence variability within populations and explaining variability in infection outcomes across patients.

Exclusivity offers a sound yet practical species criterion for bacteria despite abundant gene flow.

BACKGROUND: The question of whether bacterial species objectively exist has long divided microbiologists. A major source of contention stems from the fact that bacteria regularly engage in horizontal gene transfer (HGT), making it difficult to ascertain relatedness and draw boundaries between taxa. A natural way to define taxa is based on exclusivity of relatedness, which applies when members of a taxon are more closely related to each other than they are to any outsider. It is largely unknown whether exclusive bacterial taxa exist when averaging over the genome or are rare due to rampant hybridization. RESULTS: Here, we analyze a collection of 701 genomes representing a wide variety of environmental isolates from the family Streptomycetaceae, whose members are competent at HGT. We find that the presence/absence of auxiliary genes in the pan-genome displays a hierarchical (tree-like) structure that correlates significantly with the genealogy of the core-genome. Moreover, we identified the existence of many exclusive taxa, although individual genes often contradict these taxa. These conclusions were supported by repeating the analysis on 1,586 genomes belonging to the genus Bacillus. However, despite confirming the existence of exclusive groups (taxa), we were unable to identify an objective threshold at which to assign the rank of species. CONCLUSIONS: The existence of bacterial taxa is justified by considering average relatedness across the entire genome, as captured by exclusivity, but is rejected if one requires unanimous agreement of all parts of the genome. We propose using exclusivity to delimit taxa and conventional genome similarity thresholds to assign bacterial taxa to the species rank. This approach recognizes species that are phylogenetically meaningful, while also establishing some degree of comparability across species-ranked taxa in different bacterial clades.

DesignSignatures: a tool for designing primers that yields amplicons with distinct signatures.

UNLABELLED: For numerous experimental applications, PCR primers must be designed to efficiently amplify a set of homologous DNA sequences while giving rise to amplicons with maximally diverse signatures. We developed DesignSignatures to automate the process of designing primers for high-resolution melting (HRM), fragment length polymorphism (FLP) and sequencing experiments. The program also finds the best restriction enzyme to further diversify HRM or FLP signatures. This enables efficient comparison across many experimental designs in order to maximize signature diversity. AVAILABILITY AND IMPLEMENTATION: DesignSignatures is accessible as a web tool at www.DECIPHER.cee.wisc.edu, or as part of the DECIPHER open source software package for R available from BioConductor. CONTACT: kalin@discovery.wisc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Quality filtering of Illumina index reads mitigates sample cross-talk.

BACKGROUND: Multiplexing multiple samples during Illumina sequencing is a common practice and is rapidly growing in importance as the throughput of the platform increases. Misassignments during de-multiplexing, where sequences are associated with the wrong sample, are an overlooked error mode on the Illumina sequencing platform. This results in a low rate of cross-talk among multiplexed samples and can cause detrimental effects in studies requiring the detection of rare variants or when multiplexing a large number of samples. RESULTS: We observed rates of cross-talk averaging 0.24 % when multiplexing 14 different samples with unique i5 and i7 index sequences. This cross-talk rate corresponded to 254,632 misassigned reads on a single lane of the Illumina HiSeq 2500. Notably, all types of misassignment occur at similar rates: incorrect i5, incorrect i7, and incorrect sequence reads. We demonstrate that misassignments can be nearly eliminated by quality filtering of index reads while preserving about 90 % of the original sequences. CONCLUSIONS: Cross-talk among multiplexed samples is a significant error mode on the Illumina platform, especially if samples are only separated by a single unique index. Quality filtering of index sequences offers an effective solution to minimizing cross-talk among samples. Furthermore, we propose a straightforward method for verifying the extent of cross-talk between samples and optimizing quality score thresholds that does not require additional control samples and can even be performed post hoc on previous runs.

Cardiovascular Considerations for Joint Replacement Surgery.

Heart disease is the leading cause of death in the United States. Cardiovascular complications are associated with higher morbidity and mortality rates for patients who undergo orthopaedic surgery. Therefore, the clinical importance of a comprehensive preoperative evaluation and medical clearance is crucial and may substantially improve postoperative outcomes. A thorough knowledge of cardiovascular perioperative planning and management can enable healthcare professionals to identify patients who are potentially at risk for cardiovascular complications, and eventually improve both short- and long-term patient outcomes and satisfaction.

Effect of Smoking on Joint Replacement Outcomes: Opportunities for Improvement Through Preoperative Smoking Cessation.

Because orthopaedic surgeons focus on identifying serious potential complications, such as heart attack, stroke, and deep vein thrombosis, during the preoperative assessment, correctable factors, such as smoking, may be overlooked. Chronic exposure to nicotine has been correlated with perioperative complications that lead to worse outcomes, including decreased patient satisfaction, longer hospitalization periods, and an increased rate of hospital readmission. It has been proven that smoking is a negative risk factor for decreased bone mineral density, which leads to increased fracture risk, heightened pain, postoperative wound and bone healing complications, decreased fusion rates, and postoperative tendon and ligament healing complications. Physician-led preoperative smoking cessation programs that include, but are not limited to, pharmacotherapy plans have been shown to improve primary surgical outcomes and smoking cessation rates. Smoking has detrimental effects on specialty-specific physiology; however, there are many effective options for intervention that can improve primary outcomes.

Effect of Vitamin D on Joint Replacement Outcomes.

Vitamin D is a steroid hormone that affects not only bone metabolism and strength but also a variety of musculoskeletal health and surgical outcomes that are relevant to orthopaedic medicine. Risk factors for vitamin D deficiency include sex, age, skin pigmentation, obesity, and preexisting conditions such as nephritic syndrome and malabsorption syndrome. Furthermore, vitamin D deficiency is associated with the development of postoperative complications, such as an increased risk of infection, morbidity, and mortality. The standardization of vitamin D terminology as well as a thorough understanding of the medical considerations associated with vitamin D deficiency can improve preoperative planning and clearance, and, ultimately, patient outcomes and satisfaction.

DECIPHER: harnessing local sequence context to improve protein multiple sequence alignment.

BACKGROUND: Alignment of large and diverse sequence sets is a common task in biological investigations, yet there remains considerable room for improvement in alignment quality. Multiple sequence alignment programs tend to reach maximal accuracy when aligning only a few sequences, and then diminish steadily as more sequences are added. This drop in accuracy can be partly attributed to a build-up of error and ambiguity as more sequences are aligned. Most high-throughput sequence alignment algorithms do not use contextual information under the assumption that sites are independent. This study examines the extent to which local sequence context can be exploited to improve the quality of large multiple sequence alignments. RESULTS: Two predictors based on local sequence context were assessed: (i) single sequence secondary structure predictions, and (ii) modulation of gap costs according to the surrounding residues. The results indicate that context-based predictors have appreciable information content that can be utilized to create more accurate alignments. Furthermore, local context becomes more informative as the number of sequences increases, enabling more accurate protein alignments of large empirical benchmarks. These discoveries became the basis for DECIPHER, a new context-aware program for sequence alignment, which outperformed other programs on large sequence sets. CONCLUSIONS: Predicting secondary structure based on local sequence context is an efficient means of breaking the independence assumption in alignment. Since secondary structure is more conserved than primary sequence, it can be leveraged to improve the alignment of distantly related proteins. Moreover, secondary structure predictions increase in accuracy as more sequences are used in the prediction. This enables the scalable generation of large sequence alignments that maintain high accuracy even on diverse sequence sets. The DECIPHER R package and source code are freely available for download at DECIPHER.cee.wisc.edu and from the Bioconductor repository.

Exploiting extension bias in polymerase chain reaction to improve primer specificity in ensembles of nearly identical DNA templates.

We describe a semi-empirical framework that combines thermodynamic models of primer hybridization with experimentally determined elongation biases introduced by 3'-end mismatches for improving polymerase chain reaction (PCR)-based sequence discrimination. The framework enables rational and automatic design of primers for optimal targeting of one or more sequences in ensembles of nearly identical DNA templates. In situations where optimal targeting is not feasible, the framework accurately predicts non-target sequences that are difficult to distinguish with PCR alone. Based on the synergistic effects of disparate sources of PCR bias, we used our framework to robustly distinguish between two alleles that differ by a single base pair. To demonstrate the applicability to environmental microbiology, we designed primers specific to all recognized archaeal and bacterial genera in the Ribosomal Database Project, and have made these primers available online. We applied these primers experimentally to obtain genus-specific amplification of 16S rRNA genes representing minor constituents of an environmental DNA sample. Our results demonstrate that inherent PCR biases can be reliably employed in an automatic fashion to maximize sequence discrimination and accurately identify potential cross-amplifications. We have made our framework accessible online as a programme for designing primers targeting one group of sequences in a set with many other sequences (http://DECIPHER.cee.wisc.edu).