Multiple early factors anticipate post-acute COVID-19 sequelae.

Post-acute sequelae of COVID-19 (PASC) represent an emerging global crisis. However, quantifiable risk factors for PASC and their biological associations are poorly resolved. We executed a deep multi-omic, longitudinal investigation of 309 COVID-19 patients from initial diagnosis to convalescence (2-3 months later), integrated with clinical data and patient-reported symptoms. We resolved four PASC-anticipating risk factors at the time of initial COVID-19 diagnosis: type 2 diabetes, SARS-CoV-2 RNAemia, Epstein-Barr virus viremia, and specific auto-antibodies. In patients with gastrointestinal PASC, SARS-CoV-2-specific and CMV-specific CD8+ T cells exhibited unique dynamics during recovery from COVID-19. Analysis of symptom-associated immunological signatures revealed coordinated immunity polarization into four endotypes, exhibiting divergent acute severity and PASC. We find that immunological associations between PASC factors diminish over time, leading to distinct convalescent immune states. Detectability of most PASC factors at COVID-19 diagnosis emphasizes the importance of early disease measurements for understanding emergent chronic conditions and suggests PASC treatment strategies.

Temporal response of the human virome to immunosuppression and antiviral therapy.

There are few substantive methods to measure the health of the immune system, and the connection between immune strength and the viral component of the microbiome is poorly understood. Organ transplant recipients are treated with posttransplant therapies that combine immunosuppressive and antiviral drugs, offering a window into the effects of immune modulation on the virome. We used sequencing of cell-free DNA in plasma to investigate drug-virome interactions in a cohort of organ transplant recipients (656 samples, 96 patients) and find that antivirals and immunosuppressants strongly affect the structure of the virome in plasma. We observe marked virome compositional dynamics at the onset of the therapy and find that the total viral load increases with immunosuppression, whereas the bacterial component of the microbiome remains largely unaffected. The data provide insight into the relationship between the human virome, the state of the immune system, and the effects of pharmacological treatment and offer a potential application of the virome state to predict immunocompetence.

Clinical Validation of a Novel T-Cell Receptor Sequencing Assay for Identification of Recent or Prior Severe Acute Respiratory Syndrome Coronavirus 2 Infection.

BACKGROUND: While diagnostic, therapeutic, and vaccine development in the coronavirus disease 2019 (COVID-19) pandemic has proceeded at unprecedented speed, critical gaps in our understanding of the immune response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain unaddressed by current diagnostic strategies. METHODS: A statistical classifier for identifying prior SARS-CoV-2 infection was trained using >4000 SARS-CoV-2-associated T-cell receptor (TCR) ß sequences identified by comparing 784 cases and 2447 controls from 5 independent cohorts. The T-Detect COVID (Adaptive Biotechnologies) assay applies this classifier to TCR repertoires sequenced from blood samples to yield a binary assessment of past infection. Assay performance was assessed in 2 retrospective (n = 346; n = 69) and 1 prospective cohort (n = 87) to determine positive percent agreement (PPA) and negative percent agreement (NPA). PPA was compared with 2 commercial serology assays, and pathogen cross-reactivity was evaluated. RESULTS: T-Detect COVID demonstrated high PPA in individuals with prior reverse transcription-polymerase chain reaction (RT-PCR)-confirmed SARS-CoV-2 infection (97.1% 15+ days from diagnosis; 94.5% 15+ days from symptom onset), high NPA (∼100%) in presumed or confirmed SARS-CoV-2 negative cases, equivalent or higher PPA than 2 commercial serology tests, and no evidence of pathogen cross-reactivity. CONCLUSIONS: T-Detect COVID is a novel T-cell immunosequencing assay demonstrating high clinical performance for identification of recent or prior SARS-CoV-2 infection from blood samples, with implications for clinical management, risk stratification, surveillance, and understanding of protective immunity and long-term sequelae.

CANDI: an R package and Shiny app for annotating radiographs and evaluating computer-aided diagnosis.

MOTIVATION: Radiologists have used algorithms for Computer-Aided Diagnosis (CAD) for decades. These algorithms use machine learning with engineered features, and there have been mixed findings on whether they improve radiologists' interpretations. Deep learning offers superior performance but requires more training data and has not been evaluated in joint algorithm-radiologist decision systems. RESULTS: We developed the Computer-Aided Note and Diagnosis Interface (CANDI) for collaboratively annotating radiographs and evaluating how algorithms alter human interpretation. The annotation app collects classification, segmentation, and image captioning training data, and the evaluation app randomizes the availability of CAD tools to facilitate clinical trials on radiologist enhancement. AVAILABILITY AND IMPLEMENTATION: Demonstrations and source code are hosted at (https://candi.nextgenhealthcare.org), and (https://github.com/mbadge/candi), respectively, under GPL-3 license. SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

Dynamics of the cytotoxic T cell response to a model of acute viral infection.

UNLABELLED: A detailed characterization of the dynamics and breadth of the immune response to an acute viral infection, as well as the determinants of recruitment to immunological memory, can greatly contribute to our basic understanding of the mechanics of the human immune system and can ultimately guide the design of effective vaccines. In addition to neutralizing antibodies, T cells have been shown to be critical for the effective resolution of acute viral infections. We report the first in-depth analysis of the dynamics of the CD8(+) T cell repertoire at the level of individual T cell clonal lineages upon vaccination of human volunteers with a single dose of YF-17D. This live attenuated yellow fever virus vaccine yields sterile, long-term immunity and has been previously used as a model to understand the immune response to a controlled acute viral infection. We identified and enumerated unique CD8(+) T cell clones specifically induced by this vaccine through a combined experimental and statistical approach that included high-throughput sequencing of the CDR3 variable region of the T cell receptor ß-chain and an algorithm that detected significantly expanded T cell clones. This allowed us to establish that (i) on average, ∼ 2,000 CD8(+) T cell clones were induced by YF-17D, (ii) 5 to 6% of the responding clones were recruited to long-term memory 3 months postvaccination, (iii) the most highly expanded effector clones were preferentially recruited to the memory compartment, and (iv) a fraction of the YF-17D-induced clones could be identified from peripheral blood lymphocytes solely by measuring clonal expansion. IMPORTANCE: The exhaustive investigation of pathogen-induced effector T cells is essential to accurately quantify the dynamics of the human immune response. The yellow fever vaccine (YFV) has been broadly used as a model to understand how a controlled, self-resolving acute viral infection induces an effective and long-term protective immune response. Here, we extend this previous work by reporting the identity of activated effector T cell clones that expand in response to the YFV 2 weeks postvaccination (as defined by their unique T cell receptor gene sequence) and by tracking clones that enter the memory compartment 3 months postvaccination. This is the first study to use high-throughput sequencing of immune cells to characterize the breadth of the antiviral effector cell response and to determine the contribution of unique virus-induced clones to the long-lived memory T cell repertoire. Thus, this study establishes a benchmark against which future vaccines can be compared to predict their efficacy.

Feasibility of computerized adaptive testing for collection of patient-reported outcomes after inpatient rehabilitation.

OBJECTIVE: To evaluate the feasibility of computer adaptive testing (CAT) using an Internet or telephone interface to collect patient-reported outcomes after inpatient rehabilitation and to examine patient characteristics associated with completion of the CAT-administered measure and mode of administration. DESIGN: Prospective cohort study of patients contacted approximately 4 weeks after discharge from inpatient rehabilitation. Patients selected an Internet or telephone interface. SETTING: Rehabilitation hospital. PARTICIPANTS: Patients (N=674) with diagnoses of neurologic, orthopedic, or medically complex conditions. INTERVENTIONS: None. MAIN OUTCOME MEASURE: CAT version of the Community Participation Indicators (CAT-CPI). RESULTS: From an eligible pool of 3221 patients, 674 (21%) agreed to complete the CAT-CPI. Patients who agreed to complete the CAT-CPI were younger and reported slightly higher satisfaction with overall care than those who did not participate. Among these patients, 231 (34%) actually completed the CAT-CPI; 141 (61%) selected telephone administration, and 90 (39%) selected Internet administration. Decreased odds of completing the CAT-CPI were associated with black and other race; stroke, brain injury, or orthopedic and other impairments; and being a Medicaid beneficiary, whereas increased odds of completing the CAT-CPI were associated with longer length of stay and higher discharge FIM cognition measure. Decreased odds of choosing Internet administration were associated with younger age, retirement status, and being a woman, whereas increased odds of choosing Internet administration were associated with higher discharge FIM motor measure. CONCLUSIONS: CAT administration by Internet and telephone has limited feasibility for collecting postrehabilitation outcomes for most rehabilitation patients, but it is feasible for a subset of patients. Providing alternative ways of answering questions helps assure that a larger proportion of patients will respond.

Universal noninvasive detection of solid organ transplant rejection.

It is challenging to monitor the health of transplanted organs, particularly with respect to rejection by the host immune system. Because transplanted organs have genomes that are distinct from the recipient's genome, we used high throughput shotgun sequencing to develop a universal noninvasive approach to monitoring organ health. We analyzed cell-free DNA circulating in the blood of heart transplant recipients and observed significantly increased levels of cell-free DNA from the donor genome at times when an endomyocardial biopsy independently established the presence of acute cellular rejection in these heart transplant recipients. Our results demonstrate that cell-free DNA can be used to detect an organ-specific signature that correlates with rejection, and this measurement can be made on any combination of donor and recipient. This noninvasive test holds promise for replacing the endomyocardial biopsy in heart transplant recipients and may be applicable to other solid organ transplants.

A microfluidic oligonucleotide synthesizer.

De novo gene and genome synthesis enables the design of any sequence without the requirement of a pre-existing template as in traditional genetic engineering methods. The ability to mass produce synthetic genes holds great potential for biological research, but widespread availability of de novo DNA constructs is currently hampered by their high cost. In this work, we describe a microfluidic platform for parallel solid phase synthesis of oligonucleotides that can greatly reduce the cost of gene synthesis by reducing reagent consumption (by 100-fold) while maintaining a approximately 100 pmol synthesis scale so there is no need for amplification before assembly. Sixteen oligonucleotides were synthesized in parallel on this platform and then successfully used in a ligation-mediated assembly method to generate DNA constructs approximately 200 bp in length.

T cell receptor sequencing identifies prior SARS-CoV-2 infection and correlates with neutralizing antibodies and disease severity.

BACKGROUNDMeasuring the immune response to SARS-CoV-2 enables assessment of past infection and protective immunity. SARS-CoV-2 infection induces humoral and T cell responses, but these responses vary with disease severity and individual characteristics.METHODSA T cell receptor (TCR) immunosequencing assay was conducted using small-volume blood samples from 302 individuals recovered from COVID-19. Correlations between the magnitude of the T cell response and neutralizing antibody (nAb) titers or indicators of disease severity were evaluated. Sensitivity of T cell testing was assessed and compared with serologic testing.RESULTSSARS-CoV-2-specific T cell responses were significantly correlated with nAb titers and clinical indicators of disease severity, including hospitalization, fever, and difficulty breathing. Despite modest declines in depth and breadth of T cell responses during convalescence, high sensitivity was observed until at least 6 months after infection, with overall sensitivity ~5% greater than serology tests for identifying prior SARS-CoV-2 infection. Improved performance of T cell testing was most apparent in recovered, nonhospitalized individuals sampled > 150 days after initial illness, suggesting greater sensitivity than serology at later time points and in individuals with less severe disease. T cell testing identified SARS-CoV-2 infection in 68% (55 of 81) of samples with undetectable nAb titers (<1:40) and in 37% (13 of 35) of samples classified as negative by 3 antibody assays.CONCLUSIONThese results support TCR-based testing as a scalable, reliable measure of past SARS-CoV-2 infection with clinical value beyond serology.TRIAL REGISTRATIONSpecimens were accrued under trial NCT04338360 accessible at clinicaltrials.gov.FUNDINGThis work was funded by Adaptive Biotechnologies, Frederick National Laboratory for Cancer Research, NIAID, Fred Hutchinson Joel Meyers Endowment, Fast Grants, and American Society for Transplantation and Cell Therapy.

Longitudinal analysis of T cell receptor repertoires reveals shared patterns of antigen-specific response to SARS-CoV-2 infection.

T cells play a prominent role in orchestrating the immune response to viral diseases, but their role in the clinical presentation and subsequent immunity to SARS-CoV-2 infection remains poorly understood. As part of a population-based survey of the municipality of Vo', Italy, conducted after the initial SARS-CoV-2 outbreak, we sampled the T cell receptor (TCR) repertoires of the population 2 months after the initial PCR survey and followed up positive cases 9 and 15 months later. At 2 months, we found that 97.0% (98 of 101) of cases had elevated levels of TCRs associated with SARS-CoV-2. T cell frequency (depth) was increased in individuals with more severe disease. Both depth and diversity (breadth) of the TCR repertoire were positively associated with neutralizing antibody titers, driven mostly by CD4+ T cells directed against spike protein. At the later time points, detection of these TCRs remained high, with 90.7% (78 of 96) and 86.2% (25 of 29) of individuals having detectable signal at 9 and 15 months, respectively. Forty-three individuals were vaccinated by month 15 and showed a significant increase in TCRs directed against spike protein. Taken together, these results demonstrate the central role of T cells in mounting an immune defense against SARS-CoV-2 that persists out to 15 months.

T-cell receptor sequencing identifies prior SARS-CoV-2 infection and correlates with neutralizing antibody titers and disease severity.

Measuring the adaptive immune response to SARS-CoV-2 can enable the assessment of past infection as well as protective immunity and the risk of reinfection. While neutralizing antibody (nAb) titers are one measure of protection, such assays are challenging to perform at a large scale and the longevity of the SARS-CoV-2 nAb response is not fully understood. Here, we apply a T-cell receptor (TCR) sequencing assay that can be performed on a small volume standard blood sample to assess the adaptive T-cell response to SARS-CoV-2 infection. Samples were collected from a cohort of 302 individuals recovered from COVID-19 up to 6 months after infection. Previously published findings in this cohort showed that two commercially available SARS-CoV-2 serologic assays correlate well with nAb testing. We demonstrate that the magnitude of the SARS-CoV-2-specific T-cell response strongly correlates with nAb titer, as well as clinical indicators of disease severity including hospitalization, fever, or difficulty breathing. While the depth and breadth of the T-cell response declines during convalescence, the T-cell signal remains well above background with high sensitivity up to at least 6 months following initial infection. Compared to serology tests detecting binding antibodies to SARS-CoV-2 spike and nucleoprotein, the overall sensitivity of the TCR-based assay across the entire cohort and all timepoints was approximately 5% greater for identifying prior SARS-CoV-2 infection. Notably, the improved performance of T-cell testing compared to serology was most apparent in recovered individuals who were not hospitalized and were sampled beyond 150 days of their initial illness, suggesting that antibody testing may have reduced sensitivity in individuals who experienced less severe COVID-19 illness and at later timepoints. Finally, T-cell testing was able to identify SARS-CoV-2 infection in 68% (55/81) of convalescent samples having nAb titers below the lower limit of detection, as well as 37% (13/35) of samples testing negative by all three antibody assays. These results demonstrate the utility of a TCR-based assay as a scalable, reliable measure of past SARS-CoV-2 infection across a spectrum of disease severity. Additionally, the TCR repertoire may be useful as a surrogate for protective immunity with additive clinical value beyond serologic or nAb testing methods.

Distinct populations of antigen-specific tissue-resident CD8+ T cells in human cervix mucosa.

The ectocervix is part of the lower female reproductive tract (FRT), which is susceptible to sexually transmitted infections (STIs). Comprehensive knowledge of the phenotypes and T cell receptor (TCR) repertoire of tissue-resident memory T cells (TRMs) in the human FRT is lacking. We took single-cell RNA-Seq approaches to simultaneously define gene expression and TCR clonotypes of the human ectocervix. There were significantly more CD8+ than CD4+ T cells. Unsupervised clustering and trajectory analysis identified distinct populations of CD8+ T cells with IFNGhiGZMBloCD69hiCD103lo or IFNGloGZMBhiCD69medCD103hi phenotypes. Little overlap was seen between their TCR repertoires. Immunofluorescence staining showed that CD103+CD8+ TRMs were preferentially localized in the epithelium, whereas CD69+CD8+ TRMs were distributed evenly in the epithelium and stroma. Ex vivo assays indicated that up to 14% of cervical CD8+ TRM clonotypes were HSV-2 reactive in HSV-2-seropositive persons, reflecting physiologically relevant localization. Our studies identified subgroups of CD8+ TRMs in the human ectocervix that exhibited distinct expression of antiviral defense and tissue residency markers, anatomic locations, and TCR repertoires that target anatomically relevant viral antigens. Optimization of the location, number, and function of FRT TRMs is an important approach for improving host defenses to STIs.

Enhanced functional potential of nucleic acid aptamer libraries patterned to increase secondary structure.

The in vitro selection of nucleic acid libraries has driven the discovery of RNA and DNA receptors (aptamers) and catalysts with tailor-made functional properties. Functional nucleic acids emerging from selections have been observed to possess an unusually high degree of secondary structure. In this study, we experimentally examined the relationship between the degree of secondary structure in a nucleic acid library and its ability to yield aptamers that bind protein targets. We designed a patterned nucleic acid library (denoted R*Y*) to enhance the formation of stem-loop structures without imposing any specific sequence or secondary structural requirement. This patterned library was predicted computationally to contain a significantly higher average folding energy compared to a standard, unpatterned N(60) library of the same length. We performed three different iterated selections for protein binding using patterned and unpatterned libraries competing in the same solution. In all three cases, the patterned R*Y* library was enriched relative to the unpatterned library over the course of the 9- to 10-round selection. Characterization of individual aptamer clones emerging from the three selections revealed that the highest affinity aptamer assayed arose from the patterned library for two protein targets, while in the third case, the highest affinity aptamers from the patterned and random libraries exhibited comparable affinity. We identified the binding motif requirements for the most active aptamers generated against two of the targets. The two binding motifs are 3.4- and 27-fold more likely to occur in the R*Y* library than in the N(60) library. Collectively, our findings suggest that researchers performing selections for nucleic acid aptamers and catalysts should consider patterned libraries rather than commonly used N(m) libraries to increase both the likelihood of isolating functional molecules and the potential activities of the resulting molecules.

Reaction discovery enabled by DNA-templated synthesis and in vitro selection.

Current approaches to reaction discovery focus on one particular transformation. Typically, researchers choose substrates based on their predicted ability to serve as precursors for the target structure, then evaluate reaction conditions for their ability to effect product formation. This approach is ideal for addressing specific reactivity problems, but its focused nature might leave many areas of chemical reactivity unexplored. Here we report a reaction discovery approach that uses DNA-templated organic synthesis and in vitro selection to simultaneously evaluate many combinations of different substrates for bond-forming reactions in a single solution. Watson-Crick base pairing controls the effective molarities of substrates tethered to DNA strands; bond-forming substrate combinations are then revealed using in vitro selection for bond formation, PCR amplification and DNA microarray analysis. Using this approach, we discovered an efficient and mild carbon-carbon bond-forming reaction that generates an enone from an alkyne and alkene using an inorganic palladium catalyst. Although this approach is restricted to conditions and catalysts that are at least partially compatible with DNA, we expect that its versatility and efficiency will enable the discovery of additional reactions between a wide range of substrates.

A large-scale database of T-cell receptor beta (TCRß) sequences and binding associations from natural and synthetic exposure to SARS-CoV-2.

We describe the establishment and current content of the ImmuneCODE™ database, which includes hundreds of millions of T-cell Receptor (TCR) sequences from over 1,400 subjects exposed to or infected with the SARS-CoV-2 virus, as well as over 135,000 high-confidence SARS-CoV-2-specific TCRs. This database is made freely available, and the data contained in it can be downloaded and analyzed online or offline to assist with the global efforts to understand the immune response to the SARS-CoV-2 virus and develop new interventions.

Magnitude and Dynamics of the T-Cell Response to SARS-CoV-2 Infection at Both Individual and Population Levels.

T cells are involved in the early identification and clearance of viral infections and also support the development of antibodies by B cells. This central role for T cells makes them a desirable target for assessing the immune response to SARS-CoV-2 infection. Here, we combined two high-throughput immune profiling methods to create a quantitative picture of the T-cell response to SARS-CoV-2. First, at the individual level, we deeply characterized 3 acutely infected and 58 recovered COVID-19 subjects by experimentally mapping their CD8 T-cell response through antigen stimulation to 545 Human Leukocyte Antigen (HLA) class I presented viral peptides (class II data in a forthcoming study). Then, at the population level, we performed T-cell repertoire sequencing on 1,815 samples (from 1,521 COVID-19 subjects) as well as 3,500 controls to identify shared "public" T-cell receptors (TCRs) associated with SARS-CoV-2 infection from both CD8 and CD4 T cells. Collectively, our data reveal that CD8 T-cell responses are often driven by a few immunodominant, HLA-restricted epitopes. As expected, the T-cell response to SARS-CoV-2 peaks about one to two weeks after infection and is detectable for at least several months after recovery. As an application of these data, we trained a classifier to diagnose SARS-CoV-2 infection based solely on TCR sequencing from blood samples, and observed, at 99.8% specificity, high early sensitivity soon after diagnosis (Day 3-7 = 85.1% [95% CI = 79.9-89.7]; Day 8-14 = 94.8% [90.7-98.4]) as well as lasting sensitivity after recovery (Day 29+/convalescent = 95.4% [92.1-98.3]). These results demonstrate an approach to reliably assess the adaptive immune response both soon after viral antigenic exposure (before antibodies are typically detectable) as well as at later time points. This blood-based molecular approach to characterizing the cellular immune response has applications in clinical diagnostics as well as in vaccine development and monitoring.

Deep learning predicts hip fracture using confounding patient and healthcare variables.

Hip fractures are a leading cause of death and disability among older adults. Hip fractures are also the most commonly missed diagnosis on pelvic radiographs, and delayed diagnosis leads to higher cost and worse outcomes. Computer-aided diagnosis (CAD) algorithms have shown promise for helping radiologists detect fractures, but the image features underpinning their predictions are notoriously difficult to understand. In this study, we trained deep-learning models on 17,587 radiographs to classify fracture, 5 patient traits, and 14 hospital process variables. All 20 variables could be individually predicted from a radiograph, with the best performances on scanner model (AUC = 1.00), scanner brand (AUC = 0.98), and whether the order was marked "priority" (AUC = 0.79). Fracture was predicted moderately well from the image (AUC = 0.78) and better when combining image features with patient data (AUC = 0.86, DeLong paired AUC comparison, p = 2e-9) or patient data plus hospital process features (AUC = 0.91, p = 1e-21). Fracture prediction on a test set that balanced fracture risk across patient variables was significantly lower than a random test set (AUC = 0.67, DeLong unpaired AUC comparison, p = 0.003); and on a test set with fracture risk balanced across patient and hospital process variables, the model performed randomly (AUC = 0.52, 95% CI 0.46-0.58), indicating that these variables were the main source of the model's fracture predictions. A single model that directly combines image features, patient, and hospital process data outperforms a Naive Bayes ensemble of an image-only model prediction, patient, and hospital process data. If CAD algorithms are inexplicably leveraging patient and process variables in their predictions, it is unclear how radiologists should interpret their predictions in the context of other known patient data. Further research is needed to illuminate deep-learning decision processes so that computers and clinicians can effectively cooperate.

Quantifying neurologic disease using biosensor measurements in-clinic and in free-living settings in multiple sclerosis.

Technological advances in passive digital phenotyping present the opportunity to quantify neurological diseases using new approaches that may complement clinical assessments. Here, we studied multiple sclerosis (MS) as a model neurological disease for investigating physiometric and environmental signals. The objective of this study was to assess the feasibility and correlation of wearable biosensors with traditional clinical measures of disability both in clinic and in free-living in MS patients. This is a single site observational cohort study conducted at an academic neurological center specializing in MS. A cohort of 25 MS patients with varying disability scores were recruited. Patients were monitored in clinic while wearing biosensors at nine body locations at three separate visits. Biosensor-derived features including aspects of gait (stance time, turn angle, mean turn velocity) and balance were collected, along with standardized disability scores assessed by a neurologist. Participants also wore up to three sensors on the wrist, ankle, and sternum for 8 weeks as they went about their daily lives. The primary outcomes were feasibility, adherence, as well as correlation of biosensor-derived metrics with traditional neurologist-assessed clinical measures of disability. We used machine-learning algorithms to extract multiple features of motion and dexterity and correlated these measures with more traditional measures of neurological disability, including the expanded disability status scale (EDSS) and the MS functional composite-4 (MSFC-4). In free-living, sleep measures were additionally collected. Twenty-three subjects completed the first two of three in-clinic study visits and the 8-week free-living biosensor period. Several biosensor-derived features significantly correlated with EDSS and MSFC-4 scores derived at visit two, including mobility stance time with MSFC-4 z-score (Spearman correlation -0.546; p = 0.0070), several aspects of turning including turn angle (0.437; p = 0.0372), and maximum angular velocity (0.653; p = 0.0007). Similar correlations were observed at subsequent clinic visits, and in the free-living setting. We also found other passively collected signals, including measures of sleep, that correlated with disease severity. These findings demonstrate the feasibility of applying passive biosensor measurement techniques to monitor disability in MS patients both in clinic and in the free-living setting.

Effects of template sequence and secondary structure on DNA-templated reactivity.

DNA-templated organic synthesis enables the translation, selection, and amplification of DNA sequences encoding synthetic small-molecule libraries. As the size of DNA-templated libraries increases, the possibility of forming intramolecularly base-paired structures within templates that impede templated reactions increases as well. To achieve uniform reactivity across many template sequences and to computationally predict and remove any problematic sequences from DNA-templated libraries, we have systematically examined the effects of template sequence and secondary structure on DNA-templated reactivity. By testing a series of template sequences computationally designed to contain different degrees of internal secondary structure, we observed that high levels of predicted secondary structure involving the reagent binding site within a DNA template interfere with reagent hybridization and impair reactivity, as expected. Unexpectedly, we also discovered that templates containing virtually no predicted internal secondary structure also exhibit poor reaction efficiencies. Further studies revealed that a modest degree of internal secondary structure is required to maximize effective molarities between reactants, possibly by compacting intervening template nucleotides that separate the hybridized reactants. Therefore, ideal sequences for DNA-templated synthesis lie between two undesirable extremes of too much or too little internal secondary structure. The relationship between effective molarity and intervening nucleic acid secondary structure described in this work may also apply to nucleic acid sequences in living systems that separate interacting biological molecules.

Translation of DNA into a library of 13,000 synthetic small-molecule macrocycles suitable for in vitro selection.

DNA-templated organic synthesis enables the translation, selection, and amplification of DNA sequences encoding synthetic small-molecule libraries. Previously we described the DNA-templated multistep synthesis and model in vitro selection of a pilot library of 65 macrocycles. In this work, we report several key developments that enable the DNA-templated synthesis of much larger (>10,000-membered) small-molecule libraries. We developed and validated a capping-based approach to DNA-templated library synthesis that increases final product yields, simplifies the structure and preparation of reagents, and reduces the number of required manipulations. To expand the size and structural diversity of the macrocycle library, we augmented the number of building blocks in each DNA-templated step from 4 to 12, selected 8 different starting scaffolds which result in 4 macrocycle ring sizes and 2 building-block orientations, and confirmed the ability of the 36 building blocks and 8 scaffolds to generate DNA-templated macrocycle products. We computationally generated and experimentally validated an expanded set of codons sufficient to support 1728 combinations of step 1, step 2, and step 3 building blocks. Finally, we developed new high-resolution LC/MS analysis methods to assess the quality of large DNA-templated small-molecule libraries. Integrating these four developments, we executed the translation of 13,824 DNA templates into their corresponding small-molecule macrocycles. Analysis of the resulting libraries is consistent with excellent (>90%) representation of desired macrocycle products and a stringent test of sequence specificity suggests a high degree of sequence fidelity during translation. The quality and structural diversity of this expanded DNA-templated library provides a rich starting point for the discovery of functional synthetic small-molecule macrocycles.