Incorporating mutational heterogeneity to identify genes that are enriched for synonymous mutations in cancer.

BACKGROUND: Synonymous mutations, which change the DNA sequence but not the encoded protein sequence, can affect protein structure and function, mRNA maturation, and mRNA half-lives. The possibility that synonymous mutations might be enriched in cancer has been explored in several recent studies. However, none of these studies control for all three types of mutational heterogeneity (patient, histology, and gene) that are known to affect the accurate identification of non-synonymous cancer-associated genes. Our goal is to adopt the current standard for non-synonymous mutations in an investigation of synonymous mutations. RESULTS: Here, we create an algorithm, MutSigCVsyn, an adaptation of MutSigCV, to identify cancer-associated genes that are enriched for synonymous mutations based on a non-coding background model that takes into account the mutational heterogeneity across these levels. Using MutSigCVsyn, we first analyzed 2572 cancer whole-genome samples from the Pan-cancer Analysis of Whole Genomes (PCAWG) to identify non-synonymous cancer drivers as a quality control. Indicative of the algorithm accuracy we find that 58.6% of these candidate genes were also found in Cancer Census Gene (CGC) list, and 66.2% were found within the PCAWG cancer driver list. We then applied it to identify 30 putative cancer-associated genes that are enriched for synonymous mutations within the same samples. One of the promising gene candidates is the B cell lymphoma 2 (BCL-2) gene. BCL-2 regulates apoptosis by antagonizing the action of proapoptotic BCL-2 family member proteins. The synonymous mutations in BCL2 are enriched in its anti-apoptotic domain and likely play a role in cancer cell proliferation. CONCLUSION: Our study introduces MutSigCVsyn, an algorithm that accounts for mutational heterogeneity at patient, histology, and gene levels, to identify cancer-associated genes that are enriched for synonymous mutations using whole genome sequencing data. We identified 30 putative candidate genes that will benefit from future experimental studies on the role of synonymous mutations in cancer biology.

Excessive concentrations of kinase inhibitors in translational studies impede effective drug repurposing.

Drug repositioning seeks to leverage existing clinical knowledge to identify alternative clinical settings for approved drugs. However, repositioning efforts fail to demonstrate improved success rates in late-stage clinical trials. Focusing on 11 approved kinase inhibitors that have been evaluated in 139 repositioning hypotheses, we use data mining to characterize the state of clinical repurposing. Then, using a simple experimental correction with human serum proteins in in vitro pharmacodynamic assays, we develop a measurement of a drug's effective exposure. We show that this metric is remarkably predictive of clinical activity for a panel of five kinase inhibitors across 23 drug variant targets in leukemia. We then validate our model's performance in six other kinase inhibitors for two types of solid tumors: non-small cell lung cancer (NSCLC) and gastrointestinal stromal tumors (GISTs). Our approach presents a straightforward strategy to use existing clinical information and experimental systems to decrease the clinical failure rate in drug repurposing studies.

How soluble misfolded proteins bypass chaperones at the molecular level.

Subpopulations of soluble, misfolded proteins can bypass chaperones within cells. The extent of this phenomenon and how it happens at the molecular level are unknown. Through a meta-analysis of the experimental literature we find that in all quantitative protein refolding studies there is always a subpopulation of soluble but misfolded protein that does not fold in the presence of one or more chaperones, and can take days or longer to do so. Thus, some misfolded subpopulations commonly bypass chaperones. Using multi-scale simulation models we observe that the misfolded structures that bypass various chaperones can do so because their structures are highly native like, leading to a situation where chaperones do not distinguish between the folded and near-native-misfolded states. More broadly, these results provide a mechanism by which long-time scale changes in protein structure and function can persist in cells because some misfolded states can bypass components of the proteostasis machinery.

Deciphering the Mechanistic Basis for Perfluoroalkyl-Protein Interactions.

Although rarely used in nature, fluorine has emerged as an important elemental ingredient in the design of proteins with altered folding, stability, oligomerization propensities, and bioactivity. Adding to the molecular modification toolbox, here we report the ability of privileged perfluorinated amphiphiles to noncovalently decorate proteins to alter their conformational plasticity and potentiate their dispersion into fluorous phases. Employing a complementary suite of biophysical, in-silico and in-vitro approaches, we establish structure-activity relationships defining these phenomena and investigate their impact on protein structural dynamics and intracellular trafficking. Notably, we show that the lead compound, perfluorononanoic acid, is 106 times more potent in inducing non-native protein secondary structure in select proteins than is the well-known helix inducer trifluoroethanol, and also significantly enhances the cellular uptake of complexed proteins. These findings could advance the rational design of fluorinated proteins, inform on potential modes of toxicity for perfluoroalkyl substances, and guide the development of fluorine-modified biologics with desirable functional properties for drug discovery and delivery applications.

Functional genomic analysis of adult and pediatric brain tumor isolates.

Background: Adult and pediatric tumors display stark differences in their mutation spectra and chromosome alterations. Here, we attempted to identify common and unique gene dependencies and their associated biomarkers among adult and pediatric tumor isolates using functional genetic lethal screens and computational modeling. Methods: We performed CRISRP-Cas9 lethality screens in two adult glioblastoma (GBM) tumor isolates and five pediatric brain tumor isolates representing atypical teratoid rhabdoid tumors (ATRT), diffuse intrinsic pontine glioma, GBM, and medulloblastoma. We then integrated the screen results with machine learning-based gene-dependency models generated from data from >900 cancer cell lines. Results: We found that >50% of candidate dependencies of 280 identified were shared between adult GBM tumors and individual pediatric tumor isolates. 68% of screen hits were found as nodes in our network models, along with shared and tumor-specific predictors of gene dependencies. We investigated network predictors associated with ADAR, EFR3A, FGFR1 (pediatric-specific), and SMARCC2 (ATRT-specific) gene dependency among our tumor isolates. Conclusions: The results suggest that, despite harboring disparate genomic signatures, adult and pediatric tumor isolates share a preponderance of genetic dependences. Further, combining data from primary brain tumor lethality screens with large cancer cell line datasets produced valuable insights into biomarkers of gene dependency, even for rare cancers. Importance of the Study: Our results demonstrate that large cancer cell lines data sets can be computationally mined to identify known and novel gene dependency relationships in adult and pediatric human brain tumor isolates. Gene dependency networks and lethality screen results represent a key resource for neuro-oncology and cancer research communities. We also highlight some of the challenges and limitations of this approach.

The role of migration in mutant dynamics in fragmented populations.

Mutant dynamics in fragmented populations have been studied extensively in evolutionary biology. Yet, open questions remain, both experimentally and theoretically. Some of the fundamental properties predicted by models still need to be addressed experimentally. We contribute to this by using a combination of experiments and theory to investigate the role of migration in mutant distribution. In the case of neutral mutants, while the mean frequency of mutants is not influenced by migration, the probability distribution is. To address this empirically, we performed in vitro experiments, where mixtures of GFP-labelled ("mutant") and non-labelled ("wid-type") murine cells were grown in wells (demes), and migration was mimicked via cell transfer from well to well. In the presence of migration, we observed a change in the skewedness of the distribution of the mutant frequencies in the wells, consistent with previous and our own model predictions. In the presence of de novo mutant production, we used modelling to investigate the level at which disadvantageous mutants are predicted to exist, which has implications for the adaptive potential of the population in case of an environmental change. In panmictic populations, disadvantageous mutants can persist around a steady state, determined by the rate of mutant production and the selective disadvantage (selection-mutation balance). In a fragmented system that consists of demes connected by migration, a steady-state persistence of disadvantageous mutants is also observed, which, however, is fundamentally different from the mutation-selection balance and characterized by higher mutant levels. The increase in mutant frequencies above the selection-mutation balance can be maintained in small ( N < N c ) demes as long as the migration rate is sufficiently small. The migration rate above which the mutants approach the selection-mutation balance decays exponentially with N / N c . The observed increase in the mutant numbers is not explained by the change in the effective population size. Implications for evolutionary processes in diseases are discussed, where the pre-existence of disadvantageous drug-resistant mutant cells or pathogens drives the response of the disease to treatments.

Agent-Based Models Help Interpret Patterns of Clinical Drug Resistance by Contextualizing Competition Between Distinct Drug Failure Modes.

Introduction: Modern targeted cancer therapies are carefully crafted small molecules. These exquisite technologies exhibit an astonishing diversity of observed failure modes (drug resistance mechanisms) in the clinic. This diversity is surprising because back of the envelope calculations and classic modeling results in evolutionary dynamics suggest that the diversity in the modes of clinical drug resistance should be considerably smaller than what is observed. These same calculations suggest that the outgrowth of strong pre-existing genetic resistance mutations within a tumor should be ubiquitous. Yet, clinically relevant drug resistance occurs in the absence of obvious resistance conferring genetic alterations. Quantitatively, understanding the underlying biological mechanisms of failure mode diversity may improve the next generation of targeted anticancer therapies. It also provides insights into how intratumoral heterogeneity might shape interpatient diversity during clinical relapse. Materials and Methods: We employed spatial agent-based models to explore regimes where spatial constraints enable wild type cells (that encounter beneficial microenvironments) to compete against genetically resistant subclones in the presence of therapy. In order to parameterize a model of microenvironmental resistance, BT20 cells were cultured in the presence and absence of fibroblasts from 16 different tissues. The degree of resistance conferred by cancer associated fibroblasts in the tumor microenvironment was quantified by treating mono- and co-cultures with letrozole and then measuring the death rates. Results and Discussion: Our simulations indicate that, even when a mutation is more drug resistant, its outgrowth can be delayed by abundant, low magnitude microenvironmental resistance across large regions of a tumor that lack genetic resistance. These observations hold for different modes of microenvironmental resistance, including juxtacrine signaling, soluble secreted factors, and remodeled ECM. This result helps to explain the remarkable diversity of resistance mechanisms observed in solid tumors, which subverts the presumption that the failure mode that causes the quantitatively fastest growth in the presence of drug should occur most often in the clinic. Conclusion: Our model results demonstrate that spatial effects can interact with low magnitude of resistance microenvironmental effects to successfully compete against genetic resistance that is orders of magnitude larger. Clinical outcomes of solid tumors are intrinsically connected to their spatial structure, and the tractability of spatial agent-based models like the ones presented here enable us to understand this relationship more completely.

Self-assembled ruthenium and osmium nanosystems display a potent anticancer profile by interfering with metabolic activity.

We disclose novel amphiphilic ruthenium and osmium complexes that auto-assemble into nanomedicines with potent antiproliferative activity by inhibition of mitochondrial respiration. The self-assembling units were rationally designed from the [M(p-cymene)(1,10-phenanthroline)Cl]PF6 motif (where M is either RuII or OsII) with an appended C16 fatty chain to achieve high cellular activity, nano-assembling and mitochondrial targeting. These amphiphilic complexes block cell proliferation at the sub-micromolar range and are particularly potent towards glioblastoma neurospheres made from patient-derived cancer stem cells. A subcutaneous mouse model using these glioblastoma stem cells highlights one of our C16 OsII nanomedicines as highly successful in vivo. Mechanistically, we show that they act as metabolic poisons, strongly impairing mitochondrial respiration, corroborated by morphological changes and damage to the mitochondria. A genetic strategy based on RNAi gave further insight on the potential involvement of microtubules as part of the induced cell death. In parallel, we examined the structural properties of these new amphiphilic metal-based constructs, their reactivity and mechanism.

Vascularized pancreas-on-a-chip device produced using a printable simulated extracellular matrix.

The extracellular matrix (ECM) influences cellular behavior, function, and fate. The ECM surrounding Langerhans islets has not been investigated in detail to explain its role in the development and maturation of pancreaticß-cells. Herein, a complex combination of the simulated ECM (sECM) has been examined with a comprehensive analysis of cell response and a variety of controls. The most promising results were obtained from group containing fibrin, collagen type I, Matrigel®, hyaluronic acid, methylcellulose, and two compounds of functionalized, ionically crosslinking bacterial cellulose (sECMbc). Even though the cell viability was not significantly impacted, the performance of group of sECMbc showed 2 to 4× higher sprouting number and length, 2 to 4× higher insulin secretion in static conditions, and 2 to 10× higher gene expression of VEGF-A, Endothelin-1, and NOS3 than the control group of fibrin matrix (sECMf). Each material was tested in a hydrogel-based, perfusable, pancreas-on-a-chip device and the best group-sECMbc has been tested with the drug Sunitinib to show the extended possibilities of the device for both diabetes-like screening as well as PDAC chemotherapeutics screening for potential personal medicine approach. It proved its functionality in seven days dynamic culture and is suitable as a physiological tissue model. Moreover, the device with the pancreatic-like spheroids was 3D bioprintable and perfusable.

SARS-CoV-2 Attack Rate and Population Immunity in Southern New England, March 2020 to May 2021.

Importance: In emergency epidemic and pandemic settings, public health agencies need to be able to measure the population-level attack rate, defined as the total percentage of the population infected thus far. During vaccination campaigns in such settings, public health agencies need to be able to assess how much the vaccination campaign is contributing to population immunity; specifically, the proportion of vaccines being administered to individuals who are already seropositive must be estimated. Objective: To estimate population-level immunity to SARS-CoV-2 through May 31, 2021, in Rhode Island, Massachusetts, and Connecticut. Design, Setting, and Participants: This observational case series assessed cases, hospitalizations, intensive care unit occupancy, ventilator occupancy, and deaths from March 1, 2020, to May 31, 2021, in Rhode Island, Massachusetts, and Connecticut. Data were analyzed from July 2021 to November 2021. Exposures: COVID-19-positive test result reported to state department of health. Main Outcomes and Measures: The main outcomes were statistical estimates, from a bayesian inference framework, of the percentage of individuals as of May 31, 2021, who were (1) previously infected and vaccinated, (2) previously uninfected and vaccinated, and (3) previously infected but not vaccinated. Results: At the state level, there were a total of 1 160 435 confirmed COVID-19 cases in Rhode Island, Massachusetts, and Connecticut. The median age among individuals with confirmed COVID-19 was 38 years. In autumn 2020, SARS-CoV-2 population immunity (equal to the attack rate at that point) in these states was less than 15%, setting the stage for a large epidemic wave during winter 2020 to 2021. Population immunity estimates for May 31, 2021, were 73.4% (95% credible interval [CrI], 72.9%-74.1%) for Rhode Island, 64.1% (95% CrI, 64.0%-64.4%) for Connecticut, and 66.3% (95% CrI, 65.9%-66.9%) for Massachusetts, indicating that more than 33% of residents in these states were fully susceptible to infection when the Delta variant began spreading in July 2021. Despite high vaccine coverage in these states, population immunity in summer 2021 was lower than planned owing to an estimated 34.1% (95% CrI, 32.9%-35.2%) of vaccines in Rhode Island, 24.6% (95% CrI, 24.3%-25.1%) of vaccines in Connecticut, and 27.6% (95% CrI, 26.8%-28.6%) of vaccines in Massachusetts being distributed to individuals who were already seropositive. Conclusions and Relevance: These findings suggest that future emergency-setting vaccination planning may have to prioritize high vaccine coverage over optimized vaccine distribution to ensure that sufficient levels of population immunity are reached during the course of an ongoing epidemic or pandemic.

Materials-driven approaches to understand extrinsic drug resistance in cancer.

Metastatic cancer has a poor prognosis, because it is broadly disseminated and associated with both intrinsic and acquired drug resistance. Critical unmet needs in effectively killing drug resistant cancer cells include overcoming the drug desensitization characteristics of some metastatic cancers/lesions, and tailoring therapeutic regimens to both the tumor microenvironment and the genetic profiles of the resident cancer cells. Bioengineers and materials scientists are developing technologies to determine how metastatic sites exclude therapies, and how extracellular factors (including cells, proteins, metabolites, extracellular matrix, and abiotic factors) at metastatic sites significantly affect drug pharmacodynamics. Two looming challenges are determining which feature, or combination of features, from the tumor microenvironment drive drug resistance, and what the relative impact is of extracellular signals vs. intrinsic cell genetics in determining drug response. Sophisticated systems biology tools that can de-convolve a crowded network of signals and responses, as well as controllable microenvironments capable of providing discrete and tunable extracellular cues can help us begin to interrogate the high dimensional interactions governing drug resistance in patients.

A pan-CRISPR analysis of mammalian cell specificity identifies ultra-compact sgRNA subsets for genome-scale experiments.

A genetic knockout can be lethal to one human cell type while increasing growth rate in another. This context specificity confounds genetic analysis and prevents reproducible genome engineering. Genome-wide CRISPR compendia across most common human cell lines offer the largest opportunity to understand the biology of cell specificity. The prevailing viewpoint, synthetic lethality, occurs when a genetic alteration creates a unique CRISPR dependency. Here, we use machine learning for an unbiased investigation of cell type specificity. Quantifying model accuracy, we find that most cell type specific phenotypes are predicted by the function of related genes of wild-type sequence, not synthetic lethal relationships. These models then identify unexpected sets of 100-300 genes where reduced CRISPR measurements can produce genome-scale loss-of-function predictions across >18,000 genes. Thus, it is possible to reduce in vitro CRISPR libraries by orders of magnitude-with some information loss-when we remove redundant genes and not redundant sgRNAs.

Phase II Study of Ponatinib in Advanced Gastrointestinal Stromal Tumors: Efficacy, Safety, and Impact of Liquid Biopsy and Other Biomarkers.

PURPOSE: The purpose of this study is to evaluate ponatinib for advanced gastrointestinal stromal tumors (GIST). PATIENTS AND METHODS: This single-arm phase II trial enrolled patients with metastatic and/or unresectable GIST with failure of prior tyrosine kinase inhibitor (TKI) treatment into two cohorts based on presence or absence of KIT exon 11 (ex11) primary mutations. Patients initially received ponatinib 45 mg once daily. Following a temporary clinical hold in October 2013, dose reductions were implemented to reduce risk of arterial occlusive events (AOE). Primary endpoint was 16-week clinical benefit rate (CBR) in KIT ex11-positive cohort. KIT mutations in circulating tumor DNA (ctDNA) were assessed. RESULTS: Forty-five patients enrolled (30 KIT ex11-positive and 15 KIT ex11-negative); median follow-up was 14.7 and 13.6 months, respectively, as of August 1, 2016. Sixteen-week CBR was 36% (KIT ex11-positive; primary endpoint) and 20% (KIT ex11-negative). ctDNA analyses (n = 37) demonstrated strong concordance of primary KIT mutations between plasma and tumor. At least two secondary mutations were detected in 35% of patients overall and 54% of KIT ex11-positive patients. Changes from baseline in mutated ctDNA levels were consistent with clinical activity. Ponatinib was ineffective in patients with KIT exon 9 primary mutations. Resistance was associated with emergence of V654A. AOEs and venous thromboembolic events occurred in three and two patients, respectively. Six patients died; two deaths (pneumonia and pulmonary embolism) were considered possibly ponatinib-related. CONCLUSIONS: Ponatinib demonstrated activity in advanced GIST, particularly in KIT ex11-positive disease. ctDNA analysis confirmed heterogeneous resistance mutations in TKI-pretreated advanced GIST. Safety was consistent with previous studies.

SARS-CoV-2 epidemic after social and economic reopening in three U.S. states reveals shifts in age structure and clinical characteristics.

State-level reopenings in late spring 2020 facilitated the resurgence of severe acute respiratory syndrome coronavirus 2 transmission. Here, we analyze age-structured case, hospitalization, and death time series from three states-Rhode Island, Massachusetts, and Pennsylvania-that had successful reopenings in May 2020 without summer waves of infection. Using 11 daily data streams, we show that from spring to summer, the epidemic shifted from an older to a younger age profile and that elderly individuals were less able to reduce contacts during the lockdown period when compared to younger individuals. Clinical case management improved from spring to summer, resulting in fewer critical care admissions and lower infection fatality rate. Attack rate estimates through 31 August 2020 are 6.2% [95% credible interval (CI), 5.7 to 6.8%] of the total population infected for Rhode Island, 6.7% (95% CI, 5.4 to 7.6%) in Massachusetts, and 2.7% (95% CI, 2.5 to 3.1%) in Pennsylvania.

SARS-CoV-2 attack rate and population immunity in southern New England, March 2020 - May 2021.

Estimating an infectious disease attack rate requires inference on the number of reported symptomatic cases of a disease, the number of unreported symptomatic cases, and the number of asymptomatic infections. Population-level immunity can then be estimated as the attack rate plus the number of vaccine recipients who had not been previously infected; this requires an estimate of the fraction of vaccines that were distributed to seropositive individuals. To estimate attack rates and population immunity in southern New England, we fit a validated dynamic epidemiological model to case, clinical, and death data streams reported by Rhode Island, Massachusetts, and Connecticut for the first 15 months of the COVID-19 pandemic, from March 1 2020 to May 31 2021. This period includes the initial spring 2020 wave, the major winter wave of 2020-2021, and the lagging wave of lineage B.1.1.7(Alpha) infections during March-April 2021. In autumn 2020, SARS-CoV-2 population immunity (equal to the attack rate at that point) in southern New England was still below 15%, setting the stage for a large winter wave. After the roll-out of vaccines in early 2021, population immunity in many states was expected to approach 70% by spring 2021, with more than half of this immune population coming from vaccinations. Our population immunity estimates for May 31 2021 are 73.4% (95% CrI: 72.9% - 74.1%) for Rhode Island, 64.1% (95% CrI: 64.0% - 64.4%) for Connecticut, and 66.3% (95% CrI: 65.9% - 66.9%) for Massachusetts, indicating that >33% of southern Englanders were still susceptible to infection when the Delta variant began spreading in July 2021. Despite high vaccine coverage in these states, population immunity in summer 2021 was lower than planned due to 34% (Rhode Island), 25% (Connecticut), and 28% (Massachusetts) of vaccine distribution going to seropositive individuals. Future emergency-setting vaccination planning will likely have to consider over-vaccination as a strategy to ensure that high levels of population immunity are reached during the course of an ongoing epidemic.

Genomic and experimental evidence that ALKATI does not predict single agent sensitivity to ALK inhibitors.

Genomic data can facilitate personalized treatment decisions by enabling therapeutic hypotheses in individual patients. Mutual exclusivity has been an empirically useful signal for identifying activating mutations that respond to single agent targeted therapies. However, a low mutation frequency can underpower this signal for rare variants. We develop a resampling based method for the direct pairwise comparison of conditional selection between sets of gene pairs. We apply this method to a transcript variant of anaplastic lymphoma kinase (ALK) in melanoma, termed ALKATI that was suggested to predict sensitivity to ALK inhibitors and we find that it is not mutually exclusive with key melanoma oncogenes. Furthermore, we find that ALKATI is not likely to be sufficient for cellular transformation or growth, and it does not predict single agent therapeutic dependency. Our work strongly disfavors the role of ALKATI as a targetable oncogenic driver that might be sensitive to single agent ALK treatment.

A global cancer data integrator reveals principles of synthetic lethality, sex disparity and immunotherapy.

BACKGROUND: Advances in cancer biology are increasingly dependent on integration of heterogeneous datasets. Large-scale efforts have systematically mapped many aspects of cancer cell biology; however, it remains challenging for individual scientists to effectively integrate and understand this data. RESULTS: We have developed a new data retrieval and indexing framework that allows us to integrate publicly available data from different sources and to combine publicly available data with new or bespoke datasets. Our approach, which we have named the cancer data integrator (CanDI), is straightforward to implement, is well documented, and is continuously updated which should enable individual users to take full advantage of efforts to map cancer cell biology. We show that CanDI empowered testable hypotheses of new synthetic lethal gene pairs, genes associated with sex disparity, and immunotherapy targets in cancer. CONCLUSIONS: CanDI provides a flexible approach for large-scale data integration in cancer research enabling rapid generation of hypotheses. The CanDI data integrator is available at https://github.com/GilbertLabUCSF/CanDI .

Refined Quantification of Infection Bottlenecks and Pathogen Dissemination with STAMPR.

Pathogen population dynamics during infection are critical determinants of infection susceptibility and define patterns of dissemination. However, deciphering these dynamics, particularly founding population sizes in host organs and patterns of dissemination between organs, is difficult because measuring bacterial burden alone is insufficient to observe these patterns. Introduction of allelic diversity into otherwise identical bacteria using DNA barcodes enables sequencing-based measurements of these parameters, in a method known as STAMP (Sequence Tag-based Analysis of Microbial Populations). However, bacteria often undergo unequal expansion within host organs, resulting in marked differences in the frequencies of barcodes in input and output libraries. Here, we show that these differences confound STAMP-based analyses of founding population sizes and dissemination patterns. We present STAMPR, a successor to STAMP, which accounts for such population expansions. Using data from systemic infection of barcoded extraintestinal pathogenic E. coli, we show that this new framework, along with the metrics it yields, enhances the fidelity of measurements of bottlenecks and dissemination patterns. STAMPR was also validated on an independent barcoded Pseudomonas aeruginosa data set, uncovering new patterns of dissemination within the data. This framework (available at https://github.com/hullahalli/stampr_rtisan), when coupled with barcoded data sets, enables a more complete assessment of within-host bacterial population dynamics. IMPORTANCE Barcoded bacteria are often employed to monitor pathogen population dynamics during infection. The accuracy of these measurements is diminished by unequal bacterial expansion rates. Here, we develop computational tools to circumvent this limitation and establish additional metrics that collectively enhance the fidelity of measuring within-host pathogen founding population sizes and dissemination patterns. These new tools will benefit future studies of the dynamics of pathogens and symbionts within their respective hosts and may have additional barcode-based applications beyond host-microbe interactions.

SARS-CoV-2 epidemic after social and economic reopening in three US states reveals shifts in age structure and clinical characteristics.

In the United States, state-level re-openings in spring 2020 presented an opportunity for the resurgence of SARS-CoV-2 transmission. One important question during this time was whether human contact and mixing patterns could increase gradually without increasing viral transmission, the rationale being that new mixing patterns would likely be associated with improved distancing, masking, and hygiene practices. A second key question to follow during this time was whether clinical characteristics of the epidemic would improve after the initial surge of cases. Here, we analyze age-structured case, hospitalization, and death time series from three states - Rhode Island, Massachusetts, and Pennsylvania - that had successful re-openings in May 2020 without summer waves of infection. Using a Bayesian inference framework on eleven daily data streams and flexible daily population contact parameters, we show that population-average mixing rates dropped by >50% during the lockdown period in March/April, and that the correlation between overall population mobility and transmission-capable mobility was broken in May as these states partially re-opened. We estimate the reporting rates (fraction of symptomatic cases reporting to health system) at 96.0% (RI), 72.1% (MA), and 75.5% (PA); in Rhode Island, when accounting for cases caught through general-population screening programs, the reporting rate estimate is 94.5%. We show that elderly individuals were less able to reduce contacts during the lockdown period when compared to younger individuals. Attack rate estimates through August 31 2020 are 6.4% (95% CI: 5.8% ‒ 7.3%) of the total population infected for Rhode Island, 5.7% (95% CI: 5.0% ‒ 6.8%) in Massachusetts, and 3.7% (95% CI: 3.1% ‒ 4.5%) in Pennsylvania, with some validation available through published seroprevalence studies. Infection fatality rates (IFR) estimates for the spring epidemic are higher in our analysis (>2%) than previously reported values, likely resulting from the epidemics in these three states affecting the most vulnerable sub-populations, especially the most vulnerable of the ≥80 age group.

Optimal SARS-CoV-2 vaccine allocation using real-time attack-rate estimates in Rhode Island and Massachusetts.

BACKGROUND: When three SARS-CoV-2 vaccines came to market in Europe and North America in the winter of 2020-2021, distribution networks were in a race against a major epidemiological wave of SARS-CoV-2 that began in autumn 2020. Rapid and optimized vaccine allocation was critical during this time. With 95% efficacy reported for two of the vaccines, near-term public health needs likely require that distribution is prioritized to the elderly, health care workers, teachers, essential workers, and individuals with comorbidities putting them at risk of severe clinical progression. METHODS: We evaluate various age-based vaccine distributions using a validated mathematical model based on current epidemic trends in Rhode Island and Massachusetts. We allow for varying waning efficacy of vaccine-induced immunity, as this has not yet been measured. We account for the fact that known COVID-positive cases may not have been included in the first round of vaccination. And, we account for age-specific immune patterns in both states at the time of the start of the vaccination program. Our analysis assumes that health systems during winter 2020-2021 had equal staffing and capacity to previous phases of the SARS-CoV-2 epidemic; we do not consider the effects of understaffed hospitals or unvaccinated medical staff. RESULTS: We find that allocating a substantial proportion (>75%) of vaccine supply to individuals over the age of 70 is optimal in terms of reducing total cumulative deaths through mid-2021. This result is robust to different profiles of waning vaccine efficacy and several different assumptions on age mixing during and after lockdown periods. As we do not explicitly model other high-mortality groups, our results on vaccine allocation apply to all groups at high risk of mortality if infected. A median of 327 to 340 deaths can be avoided in Rhode Island (3444 to 3647 in Massachusetts) by optimizing vaccine allocation and vaccinating the elderly first. The vaccination campaigns are expected to save a median of 639 to 664 lives in Rhode Island and 6278 to 6618 lives in Massachusetts in the first half of 2021 when compared to a scenario with no vaccine. A policy of vaccinating only seronegative individuals avoids redundancy in vaccine use on individuals that may already be immune, and would result in 0.5% to 1% reductions in cumulative hospitalizations and deaths by mid-2021. CONCLUSIONS: Assuming high vaccination coverage (>28%) and no major changes in distancing, masking, gathering size, hygiene guidelines, and virus transmissibility between 1 January 2021 and 1 July 2021 a combination of vaccination and population immunity may lead to low or near-zero transmission levels by the second quarter of 2021.