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Background The Janus kinase (JAK) pathway mediates the activity of many asthma-relevant cytokines, including IL-4 and IL-13. GDC-0214 is a potent, inhaled, small-molecule JAK inhibitor being developed for the treatment of asthma. Objective We sought to determine whether GDC-0214 reduces fractional exhaled nitric oxide (F eno ), a JAK1-dependent biomarker of airway inflammation, in patients with mild asthma. Methods We conducted a double-blind, randomized, placebo-controlled, phase 1 proof-of-activity study in adults with mild asthma and F eno higher than 40 parts per billion (ppb). Subjects were randomized 2:1 (GDC-0214:placebo) into 4 sequential ascending-dose cohorts (1 mg once daily [QD], 4 mg QD, 15 mg QD, or 15 mg twice daily). All subjects received 4 days of blinded placebo, then 10 days of either active drug or placebo. The primary outcome was placebo-corrected percent reduction in F eno from baseline to day 14. Baseline was defined as the average F eno during the blinded placebo period. Pharmacokinetics, safety, and tolerability were also assessed. Results Thirty-six subjects (mean age, 28 years; 54% females) were enrolled. Mean F eno at baseline across all subjects was 93 ± 43 ppb. At day 14, placebo-corrected difference in F eno was −23% (95% CI, −37.3 to −9) for 15 mg QD and −42% (95% CI, −57 to −27.4) for 15 mg twice daily. Higher plasma exposure was associated with greater F eno reduction. No dose-limiting adverse events, serious adverse events, or treatment discontinuations occurred. There were no major imbalances in adverse events or laboratory findings, or evidence of systemic JAK inhibition. Conclusions GDC-0214, an inhaled JAK inhibitor, caused dose-dependent reductions in F eno in mild asthma and was well tolerated without evidence of systemic toxicity.

BACKGROUND: Cell-free circulating tumor DNA (ctDNA) assays have been adopted to monitor therapeutic response in both early- and late-stage cancer. However, tests currently available require deep-targeted sequencing to detect cancer-specific mutations at low mutant allele frequency (MAF) levels in the circulation. Recently, we developed a tumor-agnostic approach called DELFI Tumor Fraction (DELFI-TF), a Bayesian probabilistic model designed to predict plasma tumor fractions based on genome-wide fragmentation-related features. METHODS: Overall, 692 longitudinal plasma samples collected from 153 patients with initially unresectable colorectal cancer (CRC) liver-only metastases participating in the phase III study CAIRO5 (NCT02162563) were split across a training (RAS/BRAF mutant) and a validation cohort (RAS/BRAF wildtype). For all cell-free DNA (cfDNA) samples of patients within the training cohort (n=312), the tumor burden was quantified as the MAF of the tumor-tissue-proven RAS/BRAF variant measured by digital droplet PCR (ddPCR). Using cfDNA fragment-sequencing statistics from low-coverage WGS data, a Bayesian regression model was trained against the MAF of the tumor-specific driver RAS/BRAF variant in all longitudinal timepoints in the training cohort. Changes in DELFI-TF scores during first-line FOLFOX/FOLFIRI and bevacizumab therapy were assessed to predict disease response. A tissue-informed approach was applied to detect minimal residual disease in the subgroup of patients treated with curative intent (complete resection of the primary tumor and liver metastases). RESULTS: In the training cohort, DELFI-TF scores strongly correlated with RAS/BRAF MAF measured by ddPCR (r=0.85, p CONCLUSIONS: DELFI-TF demonstrates the ability to use cfDNA fragmentomes to estimate cfDNA tumor burden with a performance comparable to standard approaches for treatment response monitoring and clinical outcome prediction. Citation Format: Bahar Alipanahi, Iris van 't Erve, Keith Lumbard, Laurel K. Millberg, Zach Skidmore, Lorenzo Rinaldi, Jacob Carey, Jennifer Tom, Cornelis J. A. Punt, Nicholas C. Dracopoli, Gerrit A. Meijer, Robert B. Scharpf, Victor E. Velculescu, Remond Fijneman, Alessandro Leal. Cell-free DNA fragmentation profiling for monitoring therapeutic response in metastatic colorectal cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5714.

OBJECTIVES: To explore candidate prognostic and predictive biomarkers identified in retrospective observational studies (interleukin-6, C-reactive protein, lactate dehydrogenase, ferritin, lymphocytes, monocytes, neutrophils, d-dimer, and platelets) in patients with coronavirus disease 2019 pneumonia after treatment with tocilizumab, an anti-interleukin-6 receptor antibody, using data from the COVACTA trial in patients hospitalized with severe coronavirus disease 2019 pneumonia. DESIGN: Exploratory analysis from a multicenter, randomized, double-blind, placebo-controlled, phase 3 trial. SETTING: Hospitals in North America and Europe. PATIENTS: Adults hospitalized with severe coronavirus disease 2019 pneumonia receiving standard care. INTERVENTION: Randomly assigned 2:1 to IV tocilizumab 8 mg/kg or placebo. MEASUREMENTS AND MAIN RESULTS: Candidate biomarkers were measured in 295 patients in the tocilizumab arm and 142 patients in the placebo arm. Efficacy outcomes assessed were clinical status on a seven-category ordinal scale (1, discharge; 7, death), mortality, time to hospital discharge, and mechanical ventilation (if not receiving it at randomization) through day 28. Prognostic and predictive biomarkers were evaluated continuously with proportional odds, binomial or Fine-Gray models, and additional sensitivity analyses. Modeling in the placebo arm showed all candidate biomarkers except lactate dehydrogenase and d-dimer were strongly prognostic for day 28 clinical outcomes of mortality, mechanical ventilation, clinical status, and time to hospital discharge. Modeling in the tocilizumab arm showed a predictive value of ferritin for day 28 clinical outcomes of mortality (predictive interaction, p = 0.03), mechanical ventilation (predictive interaction, p = 0.01), and clinical status (predictive interaction, p = 0.02) compared with placebo. CONCLUSIONS: Multiple biomarkers prognostic for clinical outcomes were confirmed in COVACTA. Ferritin was identified as a predictive biomarker for the effects of tocilizumab in the COVACTA patient population; high ferritin levels were associated with better clinical outcomes for tocilizumab compared with placebo at day 28.

The underrepresentation of non-Europeans in human genetic studies so far has limited the diversity of individuals in genomic datasets and led to reduced medical relevance for a large proportion of the world’s population. Population-specific reference genome datasets as well as genome-wide association studies in diverse populations are needed to address this issue. Here we describe the pilot phase of the GenomeAsia 100K Project. This includes a whole-genome sequencing reference dataset from 1,739 individuals of 219 population groups and 64 countries across Asia. We catalogue genetic variation, population structure, disease associations and founder effects. We also explore the use of this dataset in imputation, to facilitate genetic studies in populations across Asia and worldwide., Using whole-genome sequencing data from 1,739 individuals, the GenomeAsia 100K Project catalogues genetic variation, population structure and disease associations to facilitate genetic studies in Asian populations and increase representation in genetics studies worldwide.

SummaryBackgroundRetrospective observational studies suggest that interleukin-6 (IL-6), C-reactive protein (CRP), lactate dehydrogenase (LDH), ferritin, lymphocytes, monocytes, neutrophils, D-dimer, and platelets are associated with disease progression, treatment outcomes, or both, in patients with COVID-19 pneumonia. We explored these candidate prognostic and predictive biomarkers with efficacy outcomes after treatment with tocilizumab, an anti–IL-6 receptor antibody using data from the COVACTA trial for patients hospitalised with severe COVID-19 pneumonia.MethodsCandidate biomarkers were measured in 295 patients in the tocilizumab arm and 142 patients in the placebo arm. Efficacy outcomes assessed were clinical status on a seven-category ordinal scale (1, discharge; 7, death), mortality, time to hospital discharge, and mechanical ventilation (if not receiving it at randomisation) through day 28. Prognostic and predictive biomarkers were evaluated continuously with proportional odds, binomial or Fine-Gray models, and additional sensitivity analyses.FindingsModelling in the placebo arm showed all candidate biomarkers except LDH and D-dimer were strongly prognostic for day 28 clinical outcomes of mortality, mechanical ventilation, clinical status, and time to hospital discharge. Modelling in the tocilizumab arm showed a predictive value of ferritin for day 28 clinical outcomes of mortality (predictive interaction p=0.03), mechanical ventilation (predictive interaction p=0.01), and clinical status (predictive interaction p=0.02) compared with placebo.InterpretationMultiple biomarkers prognostic for clinical outcomes were confirmed in COVACTA. Ferritin was identified as a predictive biomarker for the effects of tocilizumab in the COVACTA patient population; high ferritin levels were associated with better clinical outcomes for tocilizumab compared with placebo at day 28.RESEARCH IN CONTEXTEvidence before this studyThe efficacy and safety of the anti–interleukin-6 receptor antibody tocilizumab in the treatment of patients hospitalised with COVID-19 pneumonia was investigated in COVACTA, a double-blind, randomised, placebo-controlled trial. The primary endpoint of improved clinical status on a seven-category ordinal scale (1, discharged/ready for discharge; 7, death) at day 28 was not met in this trial. Among the secondary endpoints, no difference in mortality at day 28 was observed, but time to hospital discharge was shorter in the tocilizumab group. Subgroup analysis suggested there might be a treatment benefit in patients grouped according to their ordinal scale category at baseline.We searched PubMed on September 14, 2020, using the terms “tocilizumab AND (COVID-19 OR coronavirus) AND biomarker” with no language or date restrictions. The search retrieved 18 articles, four of which identified laboratory measures as potential biomarkers in patients who received tocilizumab for the treatment of COVID-19 pneumonia. The biomarkers reported in these studies include interleukin-6, C-reactive protein, ferritin, fibrinogen, liver transaminases, lymphocytes, platelets, and D-dimer. However, these previous studies were single-centre, retrospective, observational studies. Larger, prospective, controlled trials are needed to investigate potential prognostic and predictive biomarkers to assess the outcomes and response to treatments for COVID-19.Added value of this studyThis exploratory analysis of data from COVACTA demonstrated interleukin-6, C-reactive protein, ferritin, neutrophils (percentage and absolute count), neutrophil-to-lymphocyte ratio, lymphocytes (percentage and absolute count), monocytes (percentage), and platelets as strong prognostic biomarkers in patients hospitalised with severe COVID-19 pneumonia. More important, ferritin showed predictive value for tocilizumab treatment effects on day 28 clinical outcomes of mortality, mechanical ventilation (among the subgroup of patients not receiving mechanical ventilation at randomisation), and clinical status compared with placebo.Implications of all the available evidenceIn patients with elevated levels of ferritin at baseline, tocilizumab decreased the probability of death, mechanical ventilation, and worsening clinical status at day 28 compared with placebo, suggesting that ferritin might be useful as a predictive biomarker of efficacy outcomes for tocilizumab in patients with severe COVID-19 pneumonia.

Population-scale genetic studies can identify drug targets and allow disease risk to be predicted with resulting benefit for management of individual health risks and system-wide allocation of health care delivery resources. Although population-scale projects are underway in many parts of the world, genetic variation between population groups means that additional projects are warranted. South Asia has a population whose genetics is the least characterized of any of the world’s major populations. Here we describe GenomeAsia studies that characterize population structure in South Asia and that create tools for economical and accurate genotyping at population-scale. Prior work on population structure characterized isolated population groups, the relevance of which to large-scale studies of disease genetics is unclear. For our studies we used whole genome sequence information from 4,807 individuals recruited in the health care delivery systems of Pakistan, India and Bangladesh to ensure relevance to population-scale studies of disease genetics. We combined this with WGS data from 927 individuals from isolated South Asian population groups, and developed a custom SNP array (called SARGAM) that is optimized for future human genetic studies in South Asia. We find evidence for high rates of reproductive isolation, endogamy and consanguinity that vary across the subcontinent and that lead to levels of homozygosity that approach 100 times that seen in outbred populations. We describe founder effects that increase the power to associate functional variants with disease processes and that make South Asia a uniquely powerful place for population-scale genetic studies.

In clinical trials, a placebo response refers to improvement in disease symptoms arising from the psychological effect of receiving a treatment rather than the actual treatment under investigation. Previous research has reported genomic variation associated with the likelihood of observing a placebo response, but these studies have been limited in scope and have not been validated. Here, we analyzed whole-genome sequencing data from 784 patients undergoing placebo treatment in Phase III Asthma or Rheumatoid Arthritis trials to assess the impact of previously reported variation on patient outcomes in the placebo arms and to identify novel variants associated with the placebo response. Contrary to expectations based on previous reports, we did not observe any statistically significant associations between genomic variants and placebo treatment outcome. Our findings suggest that the biological origin of the placebo response is complex and likely to be variable between disease areas.

Recent work has demonstrated that two archaic human groups (Neanderthals and Denisovans) interbred with modern humans and contributed to the contemporary human gene pool. These findings relied on the availability of high-coverage genomes from both Neanderthals and Denisovans. Here we search for evidence of archaic admixture from a worldwide panel of 1,667 individuals using an approach that does not require the presence of an archaic human reference genome. We find no evidence for archaic admixture in the Andaman Islands, as previously claimed, or on the island of Flores, where Homo floresiensis fossils have been found. However, we do find evidence for at least one archaic admixture event in sub-Saharan Africa, with the strongest signal in Khoesan and Pygmy individuals from Southern and Central Africa. The locations of these putative archaic admixture tracts are weighted against functional regions of the genome, consistent with the long-term effects of purifying selection against introgressed genetic material.

8549 Background: Current guidelines recommend that patients who have lung nodules with high risk of malignancy (ROM) ( > 65%) should undergo surgical and other ablative therapies. However, prior studies have shown that clinicians may opt for more conservative management in these high-risk patients. Percepta Genomic Sequencing Classifier (GSC), a RNA-seq based classifier derived from bronchial epithelial cells to assess risk of lung cancer, was designed to risk stratify lung nodules by both down classifying ROM as a “rule -out“ test with high sensitivity as well as up-classifying ROM as a “rule- in” test with high specificity for malignancy. This study assesses the impact of up-classification of high ROM to very high- risk (ROM > 90%) by Percepta GSC in increasing the number of ablative therapies recommended for high-risk lung nodules. Methods: This prospective randomized decision impact survey included 37 patients from the AEGIS I/ II cohorts and the Percepta Registry who were undergoing work up of a lung nodule and had a high ROM that was up-classified to very high ROM by Percepta GSC. 97 physicians assessed 10 randomly assigned patient cases. They then responded to a survey designed to test the hypothesis that including a Percepta GSC result will increase the recommendation for surgical or other ablative therapy in very high- risk patients as well as their level of confidence of this recommendation. Physicians were first presented with the patient’s clinical information without Percepta GSC and then with Percepta GSC. Results: 97 physicians provided a total of 682 evaluations of 37 patients. In this study, the recommendation for surgical or other ablative therapy increased from 19/341 (5.6%) prior to the Percepta GSC result to 157/341 (46%) after the Percepta GSC result (odds ratio of 4.76, p-value < 0.001). The number of extremely confident recommendations increased from 72/341 (21%) without Percepta GSC to 106/341 (31%) with Percepta GSC. Significantly more physicians had increased confidence in their recommended next step post-Percepta GSC when collapsing the confidence level responses into increased confidence (n = 93) and decreased confidence (n = 44) (p-value = 0.002). Conclusions: Percepta GSC had a quantifiable impact on clinical decision making. It increased the number of surgical and other ablative therapies recommended when patients were re-classified from high to very high- risk of lung cancer with a higher confidence in the recommended next step. By up-classifying nodules from high to very high ROM, Percepta GSC will improve the likelihood and timeliness of appropriate therapies and assist clinicians more effectively manage patients to improve patient outcomes.

An increasing emphasis on understanding the dynamics of microbial communities in various settings has led to the proliferation of longitudinal metagenomic sampling studies. Data from whole metagenomic shotgun sequencing and marker-gene survey studies have characteristics that drive novel statistical methodological development for estimating time intervals of differential abundance. In designing a study and the frequency of collection prior to a study, one may wish to model the ability to detect an effect, e.g., there may be issues with respect to cost, ease of access, etc. Additionally, while every study is unique, it is possible that in certain scenarios one statistical framework may be more appropriate than another. Here, we present a simulation paradigm implemented in the R Bioconductor software package microbiomeDASim available at http://bioconductor.org/packages/microbiomeDASim microbiomeDASim. microbiomeDASim allows investigators to simulate longitudinal differential abundant microbiome features with a variety of known functional forms with flexible parameters to control desired signal-to-noise ratio. We present metrics of success results on one particular method called metaSplines.

Both common and rare genetic variants of laccase domain-containing 1 (LACC1, previously C13orf31) are associated with inflammatory bowel disease, leprosy, Behcet disease, and systemic juvenile idiopathic arthritis. However, the functional relevance of these variants is unclear. In this study, we use LACC1-deficient mice to gain insight into the role of LACC1 in regulating inflammation. Following oral administration of Citrobacter rodentium, LACC1 knockout (KO) mice had more severe colon lesions compared with wildtype (WT) controls. Immunization with collagen II, a collagen-induced arthritis (CIA) model, resulted in an accelerated onset of arthritis and significantly worse arthritis and inflammation in LACC1 KO mice. Similar results were obtained in a mannan-induced arthritis model. Serum and local TNF in CIA paws and C. rodentium colons were significantly increased in LACC1 KO mice compared with WT controls. The percentage of IL-17A–producing CD4+ T cells was elevated in LACC1 KO mice undergoing CIA as well as aged mice compared with WT controls. Neutralization of IL-17, but not TNF, prevented enhanced mannan-induced arthritis in LACC1 KO mice. These data provide new mechanistic insight into the function of LACC1 in regulating TNF and IL-17 during inflammatory responses. We hypothesize that these effects contribute to immune-driven pathologies observed in individuals carrying LACC1 variants.

Introduction: Genome-wide association studies (GWAS) have discovered >30 loci associated with asthma risk. Despite the characterization of asthma as a heterogeneous disorder, most GWAS evaluate asthmatics as a single population. To explore whether genetics are associated with this phenotypic heterogeneity, we whole-genome sequenced (WGS) >5,000 severe, uncontrolled asthmatics who were clinically well characterized. Methods: After quality-control we had WGS data for 3,223 asthmatics of European ancestry. We performed common (minor allele frequency, MAF, ≥1%) variant and rare (MAF Results: Our risk analysis replicated ten previously reported asthma risk loci and identified a novel SNP in THSD4 that was significantly associated with asthma risk (P=4.43x10-8). This locus has been previously associated with both COPD risk and FEV1. In the rare variant burden GWAS, loss-of-function variants in IL33 were associated with decreased risk. We observed no overlap among the top genetic association signals for asthma risk and the other phenotypes. However, we found significant enrichment of genes implicated in Primary Ciliary Dyskinesia, PCD, as well as motile cilia genes, associated with asthma risk in the low-eosinophil asthmatics (P Conclusions: We found a new genome-wide significant association with asthma risk at a SNP in THSD4. We also identified enrichment of PCD genes with asthma risk among eosinophil-low patients. Replication of these findings in an independent cohort is needed to confirm sharing of asthma risk factors with COPD and PCD.

Summary Background Idiopathic pulmonary fibrosis (IPF) risk has a strong genetic component. Studies have implicated variations at several loci, including TERT , surfactant genes, and a single nucleotide polymorphism at chr11p15 (rs35705950) in the intergenic region between TOLLIP and MUC5B . Patients with IPF who have risk alleles at rs35705950 have longer survival from the time of IPF diagnosis than do patients homozygous for the non-risk allele, whereas patients with shorter telomeres have shorter survival times. We aimed to assess whether rare protein-altering variants in genes regulating telomere length are enriched in patients with IPF homozygous for the non-risk alleles at rs35705950. Methods Between Nov 1, 2014, and Nov 1, 2016, we assessed blood samples from patients aged 40 years or older and of European ancestry with sporadic IPF from three international phase 3 clinical trials (INSPIRE, CAPACITY, ASCEND), one phase 2 study (RIFF), and US-based observational studies (Vanderbilt Clinical Interstitial Lung Disease Registry and the UCSF Interstitial Lung Disease Clinic registry cohorts) at the Broad Institute (Cambridge, MA, USA) and Human Longevity (San Diego, CA, USA). We also assessed blood samples from non-IPF controls in several clinical trials. We did whole-genome sequencing to assess telomere length and identify rare protein-altering variants, stratified by rs35705950 genotype. We also assessed rare functional variation in TERT exons and compared telomere length and disease progression across genotypes. Findings We assessed samples from 1510 patients with IPF and 1874 non-IPF controls. 30 (3%) of 1046 patients with an rs35705950 risk allele had a rare protein-altering variant in TERT compared with 34 (7%) of 464 non-risk allele carriers (odds ratio 0·40 [95% CI 0·24–0·66], p=0·00039). Subsequent analyses identified enrichment of rare protein-altering variants in PARN and RTEL1 , and rare variation in TERC in patients with IPF compared with controls. We expanded our study population to provide a more accurate estimation of rare variant frequency in these four loci, and to calculate telomere length. The proportion of patients with at least one rare variant in TERT, PARN, TERC , or RTEL1 was higher in patients with IPF than in controls (149 [9%] of 1739 patients vs 205 [2%] of 8645 controls, p=2·44 × 10 −8 ). Patients with IPF who had a variant in any of the four identified telomerase component genes had telomeres that were 3·69–16·10% shorter than patients without a variant in any of the four genes and had an earlier mean age of disease onset than patients without one or more variants (65·1 years [SD 7·8] vs 67·1 years [7·9], p=0·004). In the placebo arms of clinical trials, shorter telomeres were significantly associated with faster disease progression (1·7% predicted forced vital capacity per kb per year, p=0·002). Pirfenidone had treatment benefit regardless of telomere length (p=4·24 × 10 −8 for telomere length lower than the median, p=0·0044 for telomere length greater than the median). Interpretation Rare protein-altering variants in TERT, PARN, TERC , and RTEL1 are enriched in patients with IPF compared with controls, and, in the case of TERT , particularly in individuals without a risk allele at the rs35705950 locus. This suggests that multiple genetic factors contribute to sporadic IPF, which might implicate distinct mechanisms of pathogenesis and disease progression. Funding Genentech, National Institutes of Health, Francis Family Foundation, Pulmonary Fibrosis Foundation, Nina Ireland Program for Lung Health, US Department of Veterans Affairs.

Motivation While large-scale whole genome sequencing is feasible the high costs compel investigators to focus on disease subjects. As a result large sequencing datasets of samples with different diseases are often readily available, but not healthy controls to contrast them with. While it is possible to perform an association study using only diseases, the associations could be driven by a disease acting as a control and not the focal disease. Methods We developed a genotype-on-phenotype reverse regression with a Bayesian spike and slab prior to enable association testing in datasets with multiple diseases. This method, referred to as revreg, flagged associations (both common and rare) that were driven by diseases that were not of primary interest. Results Based on simulations, revreg had 80% power to detect an odds ratio of 1.74 for common variants (3500 samples total) and 3.73 for rare variants (14,000 samples total), with minimal type I error. For common variants, we tested this method on 3657 whole genome sequenced samples aimed at discovering variants associated with disease risk of Chronic Obstructive Pulmonary Disease using three other diseases as controls. We demonstrated detection of six highly significant associations likely due to Age-Related Macular Degeneration. In an exome dataset of 8836 samples aimed at characterizing rare variants associated with disease risk of Asthma, using five other diseases as controls, we detected and removed genic regions due to AMD (C3, CFH, CFHR5, CFI, and DNMT3A) and RA (KRTAP13-4).

Complex biopharmaceuticals, such as recombinant blood coagulation factors, are addressing critical medical needs and represent a growing multibillion-dollar market. For commercial manufacturing of such, sometimes inherently unstable, molecules it is important to minimize product residence time in non-ideal milieu in order to obtain acceptable yields and consistently high product quality. Continuous perfusion cell culture allows minimization of residence time in the bioreactor, but also brings unique challenges in product recovery, which requires innovative solutions. In order to maximize yield, process efficiency, facility and equipment utilization, we have developed, scaled-up and successfully implemented a new integrated manufacturing platform in commercial scale. This platform consists of a (semi-)continuous cell separation process based on a disposable flow path and integrated with the upstream perfusion operation, followed by membrane chromatography on large-scale adsorber capsules in rapid cycling mode. Implementation of the platform at commercial scale for a new product candidate led to a yield improvement of 40% compared to the conventional process technology, while product quality has been shown to be more consistently high. Over 1,000,000 L of cell culture harvest have been processed with 100% success rate to date, demonstrating the robustness of the new platform process in GMP manufacturing. While membrane chromatography is well established for polishing in flow-through mode, this is its first commercial-scale application for bind/elute chromatography in the biopharmaceutical industry and demonstrates its potential in particular for manufacturing of potent, low-dose biopharmaceuticals. Biotechnol. Bioeng. 2012; 109: 3049–3058. © 2012 Wiley Periodicals, Inc.

Unprecedented global surveillance of viruses will result in massive sequence data sets that require new statistical methods. These data sets press the limits of Bayesian phylogenetics as the high-dimensional parameters that comprise a phylogenetic tree increase the already sizable computational burden of these techniques. This burden often results in partitioning the data set, for example, by gene, and inferring the evolutionary dynamics of each partition independently, a compromise that results in stratified analyses that depend only on data within a given partition. However, parameter estimates inferred from these stratified models are likely strongly correlated, considering they rely on data from a single data set. To overcome this shortfall, we exploit the existing Monte Carlo realizations from stratified Bayesian analyses to efficiently estimate a nonparametric hierarchical wavelet-based model and learn about the time-varying parameters of effective population size that reflect levels of genetic diversity across all partitions simultaneously. Our methods are applied to complete genome influenza A sequences that span 13 years. We find that broad peaks and trends, as opposed to seasonal spikes, in the effective population size history distinguish individual segments from the complete genome. We also address hypotheses regarding intersegment dynamics within a formal statistical framework that accounts for correlation between segment-specific parameters.

The interplay between C-C chemokine receptor type 5 (CCR5) host genetic background, disease progression, and intrahost HIV-1 evolutionary dynamics remains unclear because differences in viral evolution between hosts limit the ability to draw conclusions across hosts stratified into clinically relevant populations. Similar inference problems are proliferating across many measurably evolving pathogens for which intrahost sequence samples are readily available. To this end, we propose novel hierarchical phylogenetic models (HPMs) that incorporate fixed effects to test for differences in dynamics across host populations in a formal statistical framework employing stochastic search variable selection and model averaging. To clarify the role of CCR5 host genetic background and disease progression on viral evolutionary patterns, we obtain gp120 envelope sequences from clonal HIV-1 variants isolated at multiple time points in the course of infection from populations of HIV-1-infected individuals who only harbored CCR5-using HIV-1 variants at all time points. Presence or absence of a CCR5 wt/delta 32 genotype and progressive or long-term nonprogressive course of infection stratify the clinical populations in a two-way design. As compared with the standard approach of analyzing sequences from each patient independently, the HPM provides more efficient estimation of evolutionary parameters such as nucleotide substitution rates and d(N)/d(S) rate ratios, as shown by significant shrinkage of the estimator variance. The fixed effects also correct for nonindependence of data between populations and results in even further shrinkage of individual patient estimates. Model selection suggests an association between nucleotide substitution rate and disease progression, but a role for CCR5 genotype remains elusive. Given the absence of clear d(N)/d(S) differences between patient groups, delayed onset of AIDS symptoms appears to be solely associated with lower viral replication rates rather than with differences in selection on amino acid fixation

Age is the main risk factor for sporadic Alzheimer’s disease. Yet, cognitive decline in aged rodents has been less well studied, possibly due to concomitant changes in sensory or locomotor function that can complicate cognitive tests. We tested mice that were 3, 11, and 23 months old in cognitive, sensory, and motor measures, and postmortem measures of gliosis and neural activity (c-Fos). Hippocampal synaptic function was also examined. While age-related impairments were detectable in tests of spatial memory, greater age-dependent effects were observed in tests of associative learning [active avoidance (AA)]. Gross visual function was largely normal, but startle responses to acoustic stimuli decreased with increased age, possibly due to hearing impairments. Therefore, a novel AA variant in which light alone served as the conditioning stimuli was used. Age-related deficits were again observed. Mild changes in vision, as measured by optokinetic responses, were detected in 19- versus 4-month-old mice, but these were not correlated to AA performance. Thus, deficits in hearing or vision are unlikely to account for the observed deficits in cognitive measures. Increased gliosis was observed in the hippocampal formation at older ages. Age-related changes in neural function and plasticity were observed with decreased c-Fos in the dentate gyrus, and decreased synaptic strength and paired-pulse facilitation in CA1 slices. This work, which carefully outlines age-dependent impairments in cognitive and synaptic function, c-Fos activity, and gliosis during normal aging in the mouse, suggests robust translational measures that will facilitate further study of the biology of aging.

Background: Genetic analysis of microsatellite DNA is a powerful tool used in linkage analysis, gene mapping, and clinical diagnosis. To address the expanding needs of studies of short tandem repeats (STRs), we demonstrated high-performance STR analysis on a high-throughput microchannel plate-based platform. Methods: Energy-transfer-cassette-labeled STR amplicons were separated and typed on a microfabricated 96-channel radial capillary array electrophoresis (CAE) microchannel plate system. Four-color detection was accomplished with a laser-excited confocal fluorescence rotary scanner. Results: Multiplex STR analysis with single base-pair resolution was demonstrated on denaturing polyacrylamide gel media. The high-throughput multiplex capabilities of this genetic analysis platform were demonstrated by the simultaneous separation of STR amplicons representing 122 samples in ninety-six 5.5-cm-long channels in Conclusions: Energy-transfer-cassette labeling and microfabricated CAE microchannel plates allow high-performance multiplex STR analyses.

Energy-transfer (ET) dye-labeled primers significantly improve fluorescent DNA detection because they permit excitation at a single common wavelength and they produce well separated and intense acceptor dye emission. Recently, a new ET cassette technology was developed [Berti, L. et al. (2001) Anal. Biochem. 292, 188-197] that can be used to label any PCR, sequencing, or other primer of interest. In this report we examine the utility of this ET cassette technology by labeling seven different short tandem repeat (STR) specific primers with each of the four ET cassettes and analyzing the PCR products generated on a MegaBACE-1000 capillary array electrophoresis system. More than 60 amplicons were generated and successfully analyzed with the ET cassette-labeled primers. Both forward and reverse primers were labeled for multiplex PCR amplification and analysis. Single base pair resolution was achieved with all four ET cassettes. This ET cassette-primer labeling procedure is ideally suited for creating four-color fluorescent ET primers for STR and other DNA assays where large numbers of different loci are analyzed including sequencing, genetic identification, gene mapping, loss of heterozygosity testing, and linkage analysis.

An assay is described for high-throughput single nucleotide polymorphism (SNP) genotyping on a microfabricated capillary array electrophoresis (CAE) microchip. The assay targets the three common variants at the HFE locus associated with the genetic disease hereditary hemochromatosis (HHC). The assay employs allele-specific PCR (ASPCR) for the C282Y (845g->a), H63D (187c->g), and S65C (193a->t) variants using fluorescently-labeled energy-transfer (ET) allele-specific primers. Using a 96-channel radial CAE microplate, the labeled ASPCR products generated from 96 samples in a reference Caucasian population are simultaneously separated with single-base-pair resolution and genotyped in under 10 min. Detection is accomplished with a laser-excited rotary four-color fluorescence scanner. The allele-specific amplicons are differentiated on the basis of both their size and the color of the label emission. This study is the first demonstration of the combined use of ASPCR with ET primers and microfabricated radial CAE microplates to perform multiplex SNP analyses in a clinically relevant population.

Massive datasets in the gigabyte and terabyte range combined with the availability of increasingly sophisticated statistical tools yield analyses at the boundary of what is computationally feasible. Compromising in the face of this computational burden by partitioning the dataset into more tractable sizes results in stratified analyses, removed from the context that justified the initial data collection. In a Bayesian framework, these stratified analyses generate intermediate realizations, often compared using point estimates that fail to account for the variability within and correlation between the distributions these realizations approximate. However, although the initial concession to stratify generally precludes the more sensible analysis using a single joint hierarchical model, we can circumvent this outcome and capitalize on the intermediate realizations by extending the dynamic iterative reweighting MCMC algorithm. In doing so, we reuse the available realizations by reweighting them with importance weights, recycling them into a now tractable joint hierarchical model. We apply this technique to intermediate realizations generated from stratified analyses of 687 influenza A genomes spanning 13 years allowing us to revisit hypotheses regarding the evolutionary history of influenza within a hierarchical statistical framework., Published in at http://dx.doi.org/10.1214/10-AOAS349 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

The diversity of autoimmune responses poses a formidable challenge to the development of antigen-specific tolerizing therapy. We developed 'myelin proteome' microarrays to profile the evolution of autoantibody responses in experimental autoimmune encephalomyelitis (EAE), a model for multiple sclerosis (MS). Increased diversity of autoantibody responses in acute EAE predicted a more severe clinical course. Chronic EAE was associated with previously undescribed extensive intra- and intermolecular epitope spreading of autoreactive B-cell responses. Array analysis of autoantigens targeted in acute EAE was used to guide the choice of autoantigen cDNAs to be incorporated into expression plasmids so as to generate tolerizing vaccines. Tolerizing DNA vaccines encoding a greater number of array-determined myelin targets proved superior in treating established EAE and reduced epitope spreading of autoreactive B-cell responses. Proteomic monitoring of autoantibody responses provides a useful approach to monitor autoimmune disease and to develop and tailor disease- and patient-specific tolerizing DNA vaccines.