SOFA score performs worse than age for predicting mortality in patients with COVID-19

The use of the Sequential Organ Failure Assessment (SOFA) score, originally developed to describe disease morbidity, is commonly used to predict in-hospital mortality. During the COVID-19 pandemic, many protocols for crisis standards of care used the SOFA score to select patients to be deprioritized due to a low likelihood of survival. A prior study found that age outperformed the SOFA score for mortality prediction in patients with COVID-19, but was limited to a small cohort of intensive care unit (ICU) patients and did not address whether their findings were unique to patients with COVID-19. Moreover, it is not known how well these measures perform across races. In this retrospective study, we compare the performance of age and SOFA score in predicting in-hospital mortality across two cohorts: a cohort of 2,648 consecutive adult patients diagnosed with COVID-19 who were admitted to a large academic health system in the northeastern United States over a 4-month period in 2020 and a cohort of 75,601 patients admitted to one of 335 ICUs in the eICU database between 2014 and 2015. We used age and the maximum SOFA score as predictor variables in separate univariate logistic regression models for in-hospital mortality and calculated area under the receiver operator characteristic curves (AU-ROCs) and area under precision-recall curves (AU-PRCs) for each predictor in both cohorts. Among the COVID-19 cohort, age (AU-ROC 0.795, 95% CI 0.762, 0.828) had a significantly better discrimination than SOFA score (AU-ROC 0.679, 95% CI 0.638, 0.721) for mortality prediction. Conversely, age (AU-ROC 0.628 95% CI 0.608, 0.628) underperformed compared to SOFA score (AU-ROC 0.735, 95% CI 0.726, 0.745) in non-COVID-19 ICU patients in the eICU database. There was no difference between Black and White COVID-19 patients in performance of either age or SOFA Score. Our findings bring into question the utility of SOFA score-based resource allocation in COVID-19 crisis standards of care.

From bedside-to-model: Designing clinical prediction rules for implementation

This editorial comments on the article by Herasevich et al.

Disparities Associated With Electronic Behavioral Alerts for Safety and Violence Concerns in the Emergency Department

STUDY OBJECTIVE

Although electronic behavioral alerts are placed as an alert flag in the electronic health record to notify staff of previous behavioral and/or violent incidents in emergency departments (EDs), they have the potential to reinforce negative perceptions of patients and contribute to bias. We provide characterization of ED electronic behavioral alerts using electronic health record data across a large, regional health care system.

METHODS

We conducted a retrospective cross-sectional study of adult patients presenting to 10 adult EDs within a Northeastern United States health care system from 2013 to 2022. Electronic behavioral alerts were manually screened for safety concerns and then categorized by the type of concern. In our patient-level analyses, we included patient data at the time of the first ED visit where an electronic behavioral alert was triggered or, if a patient had no electronic behavioral alerts, the earliest visit in the study period. We performed a mixed-effects regression analysis to identify patient-level risk factors associated with safety-related electronic behavioral alert deployment.

RESULTS

Of the 2,932,870 ED visits, 6,775 (0.2%) had associated electronic behavioral alerts across 789 unique patients and 1,364 unique electronic behavioral alerts. Of the encounters with electronic behavioral alerts, 5,945 (88%) were adjudicated as having a safety concern involving 653 patients. In our patient-level analysis, the median age for patients with safety-related electronic behavioral alerts was 44 years (interquartile range 33 to 55 years), 66% were men, and 37% were Black. Visits with safety-related electronic behavioral alerts had higher rates of discontinuance of care (7.8% vs 1.5% with no alert; P<.001) as defined by the patient-directed discharge, left-without-being-seen, or elopement-type dispositions. The most common topics in the electronic behavioral alerts were physical (41%) or verbal (36%) incidents with staff or other patients. In the mixed-effects logistic analysis, Black non-Hispanic patients (vs White non-Hispanic patients: adjusted odds ratio 2.60; 95% confidence interval [CI] 2.13 to 3.17), aged younger than 45 (vs aged 45-64 years: adjusted odds ratio 1.41; 95% CI 1.17 to 1.70), male (vs female: adjusted odds ratio 2.09; 95% CI 1.76 to 2.49), and publicly insured patients (Medicaid: adjusted odds ratio 6.18; 95% CI 4.58 to 8.36; Medicare: adjusted odds ratio 5.63; 95% CI 3.96 to 8.00 vs commercial) were associated with a higher risk of a patient having at least 1 safety-related electronic behavioral alert deployment during the study period.

CONCLUSION

In our analysis, younger, Black non-Hispanic, publicly insured, and male patients were at a higher risk of having an ED electronic behavioral alert. Although our study is not designed to reflect causality, electronic behavioral alerts may disproportionately affect care delivery and medical decisions for historically marginalized populations presenting to the ED, contribute to structural racism, and perpetuate systemic inequities.

Automating risk stratification for geriatric syndromes in the emergency department

BACKGROUND

Geriatric emergency department (GED) guidelines endorse screening older patients for geriatric syndromes in the ED, but there have been significant barriers to widespread implementation. The majority of screening programs require engagement of a clinician, nurse, or social worker, adding to already significant workloads at a time of record-breaking ED patient volumes, staff shortages, and hospital boarding crises. Automated, electronic health record (EHR)-embedded risk stratification approaches may be an alternate solution for extending the reach of the GED mission by directing human actions to a smaller subset of higher risk patients.

METHODS

We define the concept of automated risk stratification and screening using existing EHR data. We discuss progress made in three potential use cases in the ED: falls, cognitive impairment, and end-of-life and palliative care, emphasizing the importance of linking automated screening with systems of healthcare delivery.

RESULTS

Research progress and operational deployment vary by use case, ranging from deployed solutions in falls screening to algorithmic validation in cognitive impairment and end-of-life care.

CONCLUSIONS

Automated risk stratification offers a potential solution to one of the most pressing problems in geriatric emergency care: identifying high-risk populations of older adults most appropriate for specific GED care. Future work is needed to realize the promise of improved care with less provider burden by creating tools suitable for widespread deployment as well as best practices for their implementation and governance.

Dementia risk analysis using temporal event modeling on a large real-world dataset

The objective of the study is to identify healthcare events leading to a diagnosis of dementia from a large real-world dataset. This study uses a data-driven approach to identify temporally ordered pairs and trajectories of healthcare codes in the electronic health record (EHR). This allows for discovery of novel temporal risk factors leading to an outcome of interest that may otherwise be unobvious. We identified several known (Down syndrome RR = 116.1, thiamine deficiency RR = 76.1, and Parkinson's disease RR = 41.1) and unknown (Brief psychotic disorder RR = 68.6, Toxic effect of metals RR = 40.4, and Schizoaffective disorders RR = 40.0) factors for a specific dementia diagnosis. The associations with the greatest risk for any dementia diagnosis were found to be primarily related to mental health (Brief psychotic disorder RR = 266.5, Dissociative and conversion disorders RR = 169.8), or neurologic conditions or procedures (Dystonia RR = 121.9, Lumbar Puncture RR = 119.0). Trajectory and clustering analysis identified factors related to cerebrovascular disorders, as well as diagnoses which increase the risk of toxic imbalances. The results of this study have the ability to provide valuable insights into potential patient progression towards dementia and improve recognition of patients at risk for developing dementia.

Automatable end-of-life screening for older adults in the emergency department using electronic health records

BACKGROUND

Emergency department (ED) visits are common at the end-of-life, but the identification of patients with life-limiting illness remains a key challenge in providing timely and resource-sensitive advance care planning (ACP) and palliative care services. To date, there are no validated, automatable instruments for ED end-of-life screening. Here, we developed a novel electronic health record (EHR) prognostic model to screen older ED patients at high risk for 6-month mortality and compare its performance to validated comorbidity indices.

METHODS

This was a retrospective, observational cohort study of ED visits from adults aged ≥65 years who visited any of 9 EDs across a large regional health system between 2014 and 2019. Multivariable logistic regression that included clinical and demographic variables, vital signs, and laboratory data was used to develop a 6-month mortality predictive model-the Geriatric End-of-life Screening Tool (GEST) using five-fold cross-validation on data from 8 EDs. Performance was compared to the Charlson and Elixhauser comorbidity indices using area under the receiver-operating characteristic curve (AUROC), calibration, and decision curve analyses. Reproducibility was tested against data from the remaining independent ED within the health system. We then used GEST to investigate rates of ACP documentation availability and code status orders in the EHR across risk strata.

RESULTS

A total of 431,179 encounters by 123,128 adults were included in this study with a 6-month mortality rate of 12.2%. Charlson (AUROC (95% CI): 0.65 (0.64-0.69)) and Elixhauser indices (0.69 (0.68-0.70)) were outperformed by GEST (0.82 (0.82-0.83)). GEST displayed robust performance across demographic subgroups and in our independent validation site. Among patients with a greater than 30% mortality risk using GEST, only 5.0% had ACP documentation; 79.0% had a code status previously ordered, of which 70.7% were full code. In decision curve analysis, GEST provided greater net benefit than the Charlson and Elixhauser scores.

CONCLUSIONS

Prognostic models using EHR data robustly identify high mortality risk older adults in the ED for whom code status, ACP, or palliative care interventions may be of benefit. Although all tested methods identified patients approaching the end-of-life, GEST was most performant. These tools may enable resource-sensitive end-of-life screening in the ED.

Development of a Novel Emergency Department Quality Measure to Reduce Very Low-Risk Syncope Hospitalizations

STUDY OBJECTIVE

Emergency department (ED) evaluations for syncope are common, representing 1.3 million annual US visits and $2 billion in related hospitalizations. Despite evidence supporting risk stratification and outpatient management, variation in syncope hospitalization rates persist. We sought to develop a new quality measure for very low-risk adult ED patients with syncope that could be applied to administrative data.

METHODS

We developed this quality measure in 2 phases. First, we used an existing prospective, observational ED patient data set to identify a very low-risk cohort with unexplained syncope using 2 variables: age less than 50 years and no history of heart disease. We then applied this to the 2019 Nationwide Emergency Department Sample (NEDS) to assess its potential effect, assessing for hospital-level factors associated with hospitalization variation.

RESULTS

Of the 8,647 adult patients in the prospective cohort, 3,292 (38%) patients fulfilled these 2 criteria: age less than 50 years and no history of heart disease. Of these, 15 (0.46%) suffered serious adverse events within 30 days. In the NEDS, there were an estimated 566,031 patients meeting these 2 criteria, of whom 15,507 (2.7%; 95% confidence interval [CI] 2.48% to 3.00%) were hospitalized. We found substantial variation in the hospitalization rates for this very low-risk cohort, with a median rate of 1.7% (range 0% to 100%; interquartile range 0% to 3.9%). Factors associated with increased hospitalization rates included a yearly ED volume of more than 80,000 (odds ratio [OR] 3.14; 95% CI 2.02 to 4.89) and metropolitan teaching status (OR 1.5; 95% CI 1.24 to 1.81).

CONCLUSION

In summary, our novel syncope quality measure can assess variation in low-value hospitalizations for unexplained syncope. The application of this measure could improve the value of syncope care.

Patterns of Care Partner Communication for Persons Living with Dementia in the Emergency Department

Introduction

Nearly half of all persons living with dementia (PLwD) will visit the emergency department (ED) in any given year and ED visits by PLwD are associated with short-term adverse outcomes. Care partner engagement is critical in the care of PLwD, but little is known about their patterns of communication with ED clinicians.

Methods

We performed a retrospective electronic health record (EHR) review of a random sampling of patients ≥ 65 years with a historical diagnosis code of dementia who visited an ED within a large regional health network between 1/2014 and 1/2022. ED notes within the EHRs were coded for documentation of care partner communication and presence of a care partner in the ED. Logistic regression was used to identify patient characteristics associated with the composite outcome of either care partner communication or care partner presence in the ED.

Results

A total of 460 patients were included. The median age was 83.0 years, 59.3% were female, 11.3% were Black, and 7.6% Hispanic. A care partner was documented in the ED for 22.4% of the visits and care partner communication documented for 43.9% of visits. 54.8% of patients had no documentation of care partner communication nor evidence of a care partner at the bedside. In multivariate logistic regression, increasing age (OR, (95% CI): 1.06 (1.04-1.09)), altered mental status (OR: 2.26 (1.01-5.05)), and weakness (OR: 3.38 (1.49-7.65)) significantly increased the probability of having care partner communication documented or a care partner at the bedside.

Conclusion

More than half of PLwD in our sample did not have clinician documentation of communication with a care partner or a care partner in the ED. Further studies are needed to use these insights to improve communication with care partners of PLwD in the ED.

Risk factor identification and predictive models for central line requirements for patients on vasopressors

Vasopressors are ubiquitous in intensive care units. While central venous catheters are the preferred route of infusion, recent evidence suggests peripheral administration may be safe for short, single-agent courses. Here, we identify risk factors and develop a predictive model for patient central venous catheter requirement using the Medical Information Mart for Intensive Care, a single-centre dataset of patients admitted to an intensive care unit between 2008 and 2019. Using prior literature, a composite endpoint of prolonged single-agent courses (>24 hours) or multi-agent courses of any duration was used to identify likely central venous catheter requirement. From a cohort of 69,619 intensive care unit stays, there were 17,053 vasopressor courses involving one or more vasopressors that met study inclusion criteria. In total, 3807 (22.3%) vasopressor courses involved a single vasopressor for less than six hours, 7952 (46.6%) courses for less than 24 hours and 5757 (33.8%) involved multiple vasopressors of any duration. Of these, 3047 (80.0%) less than six-hour and 6423 (80.8%) less than 24-hour single vasopressor courses used a central venous catheter. Logistic regression models identified associations between the composite endpoint and intubation (odds ratio (OR) 2.36, 95% confidence intervals (CI) 2.16 to 2.58), cardiac diagnosis (OR 0.72, CI 0.65 to 0.80), renal impairment (OR 1.61, CI 1.50 to 1.74), older age (OR 1.002, Cl 1.000 to 1.005) and vital signs in the hour before initiation (heart rate, OR 1.006, CI 1.003 to 1.009; oxygen saturation, OR 0.996, CI 0.993 to 0.999). A logistic regression model predicting the composite endpoint had an area under the receiver operating characteristic curve (standard deviation) of 0.747 (0.013) and an accuracy of 0.691 (0.012). This retrospective study reveals a high prevalence of short vasopressor courses in intensive care unit settings, a majority of which were administered using central venous catheters. We identify several important risk factors that may help guide clinicians deciding between peripheral and central venous catheter administration, and present a predictive model that may inform future prospective trials.

Sepsis Fluid Metric Compliance and its Impact on Outcomes of Patients with Congestive Heart Failure, End-Stage Renal Disease or Obesity

BACKGROUND

Emergency physicians express concern administering a 30-cc/kg fluid bolus to septic shock patients with pre-existing congestive heart failure (CHF), end-stage renal disease (ESRD), or obesity, due to the perceived risk of precipitating a fluid overload state.

OBJECTIVE

Our aim was to determine whether there is a difference in fluid administration to septic shock patients with these pre-existing conditions in the emergency department (ED). Secondary objectives focused on whether compliance impacts mortality, need for intubation, and length of stay.

METHODS

We conducted a retrospective chart review of 470,558 ED patient encounters at a single urban academic center during a 5-year period.

RESULTS

Of 847 patients with septic shock, 308 (36.36%) had no pre-existing condition and 199 (23.49%), 17 (2.01%), and 154 (18.18%) had the single pre-existing condition of CHF, ESRD, and obesity, respectively, and 169 (19.95%) had multiple pre-existing conditions. Weight-based fluid compliance was achieved in 460 patients (54.31%). There was a lower likelihood of compliance among patients with CHF (adjusted odds ratio [aOR] 0.35; 95% confidence interval [CI] 0.24-0.52; p < 0.001), ESRD (aOR 0.11, 95% CI 0.04-0.32; p < 0.001), and obesity (aOR 0.29, 95% CI 0.19-0.44; p < 0.001) compared with patients with no pre-existing conditions. Compliance decreased further in patients with multiple pre-existing conditions (aOR 0.49, 95% CI 0.33-0.72; p < 0.001). Compliance was not associated with mortality in patients with CHF and ESRD, but was protective in patients with obesity and those with no pre-existing conditions.

CONCLUSIONS

Septic shock patients with pre-existing CHF, ESRD, or obesity are less likely to achieve compliance with a 30-cc/kg weight-based fluid goal compared with those without these pre-existing conditions.

Analysis of Health Trajectories Leading to Adverse Opioid-Related Events

Identifying patient risk factors leading to adverse opioid-related events (AOEs) may enable targeted risk-based interventions, uncover potential causal mechanisms, and enhance prognosis. In this article, we aim to discover patient diagnosis, procedure, and medication event trajectories associated with AOEs using large-scale data mining methods. The individual temporally preceding factors associated with the highest relative risk (RR) for AOEs were opioid withdrawal therapy agents, toxic encephalopathy, problems related to housing and economic circumstances, and unspecified viral hepatitis, with RR of 33.4, 26.1, 19.9, and 18.7, respectively. Patient cohorts with a socioeconomic or mental health code had a larger RR for over 75% of all identified trajectories compared to the average population. By analyzing health trajectories leading to AOEs, we discover novel, temporally-connected combinations of diagnoses and health service events that significantly increase risk of AOEs, including natural histories marked by socioeconomic and mental health diagnoses.

Development and Validation of the Quick COVID-19 Severity Index: A Prognostic Tool for Early Clinical Decompensation

STUDY OBJECTIVE

The goal of this study is to create a predictive, interpretable model of early hospital respiratory failure among emergency department (ED) patients admitted with coronavirus disease 2019 (COVID-19).

METHODS

This was an observational, retrospective, cohort study from a 9-ED health system of admitted adult patients with severe acute respiratory syndrome coronavirus 2 (COVID-19) and an oxygen requirement less than or equal to 6 L/min. We sought to predict respiratory failure within 24 hours of admission as defined by oxygen requirement of greater than 10 L/min by low-flow device, high-flow device, noninvasive or invasive ventilation, or death. Predictive models were compared with the Elixhauser Comorbidity Index, quick Sequential [Sepsis-related] Organ Failure Assessment, and the CURB-65 pneumonia severity score.

RESULTS

During the study period, from March 1 to April 27, 2020, 1,792 patients were admitted with COVID-19, 620 (35%) of whom had respiratory failure in the ED. Of the remaining 1,172 admitted patients, 144 (12.3%) met the composite endpoint within the first 24 hours of hospitalization. On the independent test cohort, both a novel bedside scoring system, the quick COVID-19 Severity Index (area under receiver operating characteristic curve mean 0.81 [95% confidence interval {CI} 0.73 to 0.89]), and a machine-learning model, the COVID-19 Severity Index (mean 0.76 [95% CI 0.65 to 0.86]), outperformed the Elixhauser mortality index (mean 0.61 [95% CI 0.51 to 0.70]), CURB-65 (0.50 [95% CI 0.40 to 0.60]), and quick Sequential [Sepsis-related] Organ Failure Assessment (0.59 [95% CI 0.50 to 0.68]). A low quick COVID-19 Severity Index score was associated with a less than 5% risk of respiratory decompensation in the validation cohort.

CONCLUSION

A significant proportion of admitted COVID-19 patients progress to respiratory failure within 24 hours of admission. These events are accurately predicted with bedside respiratory examination findings within a simple scoring system.

Patient factors associated with SARS-CoV-2 in an admitted emergency department population

Background

The SARS-CoV-2 (COVID-19) virus has wide community spread. The aim of this study was to describe patient characteristics and to identify factors associated with COVID-19 among emergency department (ED) patients under investigation for COVID-19 who were admitted to the hospital.

Methods

This was a retrospective observational study from 8 EDs within a 9-hospital health system. Patients with COVID-19 testing around the time of hospital admission were included. The primary outcome measure was COVID-19 test result. Patient characteristics were described and a multivariable logistic regression model was used to identify factors associated with a positive COVID-19 test.

Results

During the study period from March 1, 2020 to April 8, 2020, 2182 admitted patients had a test resulted for COVID-19. Of these patients, 786 (36%) had a positive test result. For COVID-19-positive patients, 63 (8.1%) died during hospitalization. COVID-19-positive patients had lower pulse oximetry (0.91 [95% confidence interval, CI], [0.88-0.94]), higher temperatures (1.36 [1.26-1.47]), and lower leukocyte counts than negative patients (0.78 [0.75-0.82]). Chronic lung disease (odds ratio [OR] 0.68, [0.52-0.90]) and histories of alcohol (0.64 [0.42-0.99]) or substance abuse (0.39 [0.25-0.62]) were less likely to be associated with a positive COVID-19 result.

Conclusion

We observed a high percentage of positive results among an admitted ED cohort under investigation for COVID-19. Patient factors may be useful in early differentiation of patients with COVID-19 from similarly presenting respiratory illnesses although no single factor will serve this purpose.

US-Pro: An Application Enabling Efficient, High-Throughput Ultrasound Video Processing

We describe a new graphical user interface-based application, US-Pro, designed to enable customized, high-throughput ultrasound video anonymization and dynamic cropping before output to video or high-efficiency disk storage. This application is distributed in a Docker container environment, which supports facile software installation on the most commonly used operating systems, as well as local processing of data sets, precluding the external transfer of electronic protected health information. The US-Pro application will facilitate the reproducible production of large-scale ultrasound video data sets for varied applications, including machine-learning analysis, educational distribution, and quality assurance review.

Evolution of translation machinery in recoded bacteria enables multi-site incorporation of nonstandard amino acids

Expansion of the genetic code with nonstandard amino acids (nsAAs) has enabled biosynthesis of proteins with diverse new chemistries. However, this technology has been largely restricted to proteins containing a single or few nsAA instances. Here we describe an in vivo evolution approach in a genomically recoded Escherichia coli strain for the selection of orthogonal translation systems capable of multi-site nsAA incorporation. We evolved chromosomal aminoacyl-tRNA synthetases (aaRSs) with up to 25-fold increased protein production for p-acetyl-L-phenylalanine and p-azido-L-phenylalanine (pAzF). We also evolved aaRSs with tunable specificities for 14 nsAAs, including an enzyme that efficiently charges pAzF while excluding 237 other nsAAs. These variants enabled production of elastin-like-polypeptides with 30 nsAA residues at high yields (∼50 mg/L) and high accuracy of incorporation (>95%). This approach to aaRS evolution should accelerate and expand our ability to produce functionalized proteins and sequence-defined polymers with diverse chemistries.

Recoded organisms engineered to depend on synthetic amino acids

Genetically modified organisms (GMOs) are increasingly used in research and industrial systems to produce high-value pharmaceuticals, fuels, and chemicals¹. Genetic isolation and intrinsic biocontainment would provide essential biosafety measures to secure these closed systems and enable safe applications of GMOs in open systems^2,3, which include bioremediation⁴ and probiotics⁵. Although safeguards have been designed to control cell growth by essential gene regulation⁶, inducible toxin switches⁷, and engineered auxotrophies⁸, these approaches are compromised by cross-feeding of essential metabolites, leaked expression of essential genes, or genetic mutations^9,10. Here, we describe the construction of a series of genomically recoded organisms (GROs)¹¹ whose growth is restricted by the expression of multiple essential genes that depend on exogenously supplied synthetic amino acids (sAAs). We introduced a Methanocaldococcus jannaschii tRNA:aminoacyl-tRNA synthetase (aaRS) pair into the chromosome of a GRO that lacks all TAG codons and release factor 1, endowing this organism with the orthogonal translational components to convert TAG into a dedicated sense codon for sAAs. Using multiplex automated genome engineering (MAGE)¹², we introduced in-frame TAG codons into 22 essential genes, linking their expression to the incorporation of synthetic phenylalanine-derived amino acids. Of the 60 sAA-dependent variants isolated, a notable strain harboring 3 TAG codons in conserved functional residues¹³ of MurG, DnaA and SerS and containing targeted tRNA deletions maintained robust growth and exhibited undetectable escape frequencies upon culturing ∼10¹¹ cells on solid media for seven days or in liquid media for 20 days. This is a significant improvement over existing biocontainment approaches^2,3,6-10. We constructed synthetic auxotrophs dependent on sAAs that were not rescued by cross-feeding in environmental growth assays. These auxotrophic GROs possess alternate genetic codes that impart genetic isolation by impeding horizontal gene transfer¹¹ and now depend on the use of synthetic biochemical building blocks, advancing orthogonal barriers between engineered organisms and the environment.

Designed phosphoprotein recognition in Escherichia coli

Protein phosphorylation is a central biological mechanism for cellular adaptation to environmental changes. Dysregulation of phosphorylation signaling is implicated in a wide variety of diseases. Thus, the ability to detect and quantify protein phosphorylation is highly desirable for both diagnostic and research applications. Here we present a general strategy for detecting phosphopeptide–protein interactions in Escherichia coli. We first redesign a model tetratricopeptide repeat (TPR) protein to recognize phosphoserine in a sequence-specific fashion and characterize the interaction with its target phosphopeptide in vitro. We then combine in vivo site-specific incorporation of phosphoserine with split mCherry assembly to observe the designed phosphopeptide–protein interaction specificity in E. coli. This in vivo strategy for detecting and characterizing phosphopeptide–protein interactions has numerous potential applications for the study of natural interactions and the design of novel ones.

Cell-free protein synthesis from a release factor 1 deficient Escherichia coli activates efficient and multiple site-specific nonstandard amino acid incorporation

Site-specific incorporation of nonstandard amino acids (NSAAs) into proteins enables the creation of biopolymers, proteins, and enzymes with new chemical properties, new structures, and new functions. To achieve this, amber (TAG codon) suppression has been widely applied. However, the suppression efficiency is limited due to the competition with translation termination by release factor 1 (RF1), which leads to truncated products. Recently, we constructed a genomically recoded Escherichia coli strain lacking RF1 where 13 occurrences of the amber stop codon have been reassigned to the synonymous TAA codon (rEc.E13.ΔprfA). Here, we assessed and characterized cell-free protein synthesis (CFPS) in crude S30 cell lysates derived from this strain. We observed the synthesis of 190 ± 20 μg/mL of modified soluble superfolder green fluorescent protein (sfGFP) containing a single p-propargyloxy-l-phenylalanine (pPaF) or p-acetyl-l-phenylalanine. As compared to the parent rEc.E13 strain with RF1, this results in a modified sfGFP synthesis improvement of more than 250%. Beyond introducing a single NSAA, we further demonstrated benefits of CFPS from the RF1-deficient strains for incorporating pPaF at two- and five-sites per sfGFP protein. Finally, we compared our crude S30 extract system to the PURE translation system lacking RF1. We observed that our S30 extract based approach is more cost-effective and high yielding than the PURE translation system lacking RF1, ∼1000 times on a milligram protein produced/$ basis. Looking forward, using RF1-deficient strains for extract-based CFPS will aid in the synthesis of proteins and biopolymers with site-specifically incorporated NSAAs.

Genomically recoded organisms expand biological functions

We describe the construction and characterization of a genomically recoded organism (GRO). We replaced all known UAG stop codons in Escherichia coli MG1655 with synonymous UAA codons, which permitted the deletion of release factor 1 and reassignment of UAG translation function. This GRO exhibited improved properties for incorporation of nonstandard amino acids that expand the chemical diversity of proteins in vivo. The GRO also exhibited increased resistance to T7 bacteriophage, demonstrating that new genetic codes could enable increased viral resistance.

Methods, challenges, and promise of next-generation sequencing in cancer biology

It is generally accepted that cancers result from the aggregation of somatic mutations. The emergence of next-generation sequencing (NGS) technologies during the past half-decade has enabled studies of cancer genomes with high sensitivity and resolution through whole-genome and whole-exome sequencing approaches, among others. This saltatory advance introduces the possibility of assembling multiple cancer genomes for analysis in a cost-effective manner. Analytical approaches are now applied to the detection of a number of somatic genome alterations, including nucleotide substitutions, insertions/deletions, copy number variations, and chromosomal rearrangements. This review provides a thorough introduction to the cancer genomics pipeline as well as a case study of these methods put into practice.

Wavelet analysis of DNA walks

A wavelet transform of the DNA "walk" constructed from a genomic sequence offers a direct visualization of short and long-range patterns in nucleotide sequences. We study sequences that encode diverse biological functions, taken from a variety of genomes. Pattern irregularities in the transform are frequently associated with sequences of biological interest. Exonic regions, for example, visualize differently under wavelet analysis than introns, and ribosomal RNA regions display distinct universal signatures. DNA walk wavelet analysis can provide a sensitive and rapid assessment of the putative biological significance of genomic DNA.