Ramy Arnaout

Quantitative deep sequencing reveals dynamic HIV-1 escape and large population shifts during CCR5 antagonist therapy in vivo.

High-throughput sequencing platforms provide an approach for detecting rare HIV-1 variants and documenting more fully quasispecies diversity. We applied this technology to the V3 loop-coding region of env in samples collected from 4 chronically HIV-infected subjects in whom CCR5 antagonist (vicriviroc [VVC]) therapy failed. Between 25,000–140,000 amplified sequences were obtained per sample. Profound baseline V3 loop sequence heterogeneity existed; predicted CXCR4-using populations were identified in a largely CCR5-using population. The V3 loop forms associated with subsequent virologic failure, either through CXCR4 use or the emergence of high-level VVC resistance, were present as minor variants at 0.8–2.8% of baseline samples. Extreme, rapid shifts in population frequencies toward these forms occurred, and deep sequencing provided a detailed view of the rapid evolutionary impact of VVC selection. Greater V3 diversity was observed post-selection. This previously unreported degree of V3 loop sequence diversity has implications for viral pathogenesis, vaccine design, and the optimal use of HIV-1 CCR5 antagonists.

Containment of Simian Immunodeficiency Virus Infection: Cellular Immune Responses and Protection from Rechallenge following Transient Postinoculation Antiretroviral Treatment

ABSTRACT To better understand the viral and host factors involved in the establishment of persistent productive infection by primate lentiviruses, we varied the time of initiation and duration of postinoculation antiretroviral treatment with tenofovir {9-[2-(R)-(phosphonomethoxy)propyl]adenine}while performing intensive virologic and immunologic monitoring in rhesus macaques, inoculated intravenously with simian immunodeficiency virus SIVsmE660. Postinoculation treatment did not block the initial infection, but we identified treatment regimens that prevented the establishment of persistent productive infection, as judged by the absence of measurable plasma viremia following drug discontinuation. While immune responses were heterogeneous, animals in which treatment resulted in prevention of persistent productive infection showed a higher frequency and higher levels of SIV-specific lymphocyte proliferative responses during the treatment period compared to control animals, despite the absence of either detectable plasma viremia or seroconversion. Animals protected from the initial establishment of persistent productive infection were also relatively or completely protected from subsequent homologous rechallenge. Even postinoculation treatment regimens that did not prevent establishment of persistent infection resulted in downmodulation of the level of plasma viremia following treatment cessation, compared to the viremia seen in untreated control animals, animals treated with regimens known to be ineffective, or the cumulative experience with the natural history of plasma viremia following infection with SIVsmE660. The results suggest that the host may be able to effectively control SIV infection if the initial exposure occurs under favorable conditions of low viral burden and in the absence of ongoing high level cytopathic infection of responding cells. These findings may be particularly important in relation to prospects for control of primate lentiviruses in the settings of both prophylactic and therapeutic vaccination for prevention of AIDS.

The Landscape of Inappropriate Laboratory Testing: A 15-Year Meta-Analysis

Background Laboratory testing is the single highest-volume medical activity and drives clinical decision-making across medicine. However, the overall landscape of inappropriate testing, which is thought to be dominated by repeat testing, is unclear. Systematic differences in initial vs. repeat testing, measurement criteria, and other factors would suggest new priorities for improving laboratory testing. Methods A multi-database systematic review was performed on published studies from 1997–2012 using strict inclusion and exclusion criteria. Over- vs. underutilization, initial vs. repeat testing, low- vs. high-volume testing, subjective vs. objective appropriateness criteria, and restrictive vs. permissive appropriateness criteria, among other factors, were assessed. Results Overall mean rates of over- and underutilization were 20.6% (95% CI 16.2–24.9%) and 44.8% (95% CI 33.8–55.8%). Overutilization during initial testing (43.9%; 95% CI 35.4–52.5%) was six times higher than during repeat testing (7.4%; 95% CI 2.5–12.3%; P for stratum difference <0.001). Overutilization of low-volume tests (32.2%; 95% CI 25.0–39.4%) was three times that of high-volume tests (10.2%; 95% CI 2.6–17.7%; P<0.001). Overutilization measured according to restrictive criteria (44.2%; 95% CI 36.8–51.6%) was three times higher than for permissive criteria (12.0%; 95% CI 8.0–16.0%; P<0.001). Overutilization measured using subjective criteria (29.0%; 95% CI 21.9–36.1%) was nearly twice as high as for objective criteria (16.1%; 95% CI 11.0–21.2%; P = 0.004). Together, these factors explained over half (54%) of the overall variability in overutilization. There were no statistically significant differences between studies from the United States vs. elsewhere (P = 0.38) or among chemistry, hematology, microbiology, and molecular tests (P = 0.05–0.65) and no robust statistically significant trends over time. Conclusions The landscape of overutilization varies systematically by clinical setting (initial vs. repeat), test volume, and measurement criteria. Underutilization is also widespread, but understudied. Expanding the current focus on reducing repeat testing to include ordering the right test during initial evaluation may lead to fewer errors and better care.

Prize-based contests can provide solutions to computational biology problems

Advances in biotechnology have fuelled the generation of unprecedented quantities of data across the life sciences. However, finding individuals who can address such “big data” problems effectively has become a significant research bottleneck. Historically, prize-based contests have had striking success in attracting unconventional individuals who can solve difficult challenges. To determine whether this approach could solve a real “big data” biologic algorithm problem, we used a complex immunogenomics problem as the basis for a two-week online contest broadcast to participants outside academia and biomedical disciplines. Participants in our contest generated over 600 submissions containing 89 novel computational approaches to the problem. Thirty submissions exceeded the benchmark performance of NIH’s MegaBLAST. The best achieved both greater accuracy and speed (x1000). Here we show the potential of using online prize-based contests to access individuals without domain-specific backgrounds to address big data challenges in life sciences.

HIV-1 dynamics revisited: biphasic decay by cytotoxic T lymphocyte killing?

The biphasic decay of blood viraemia in patients being treated for human immunodeficiency virus type 1 (HIV–1) infection has been explained as the decay of two distinct populations of cells: the rapid death of productively infected cells followed by the much slower elimination of a second population the identity of which remains unknown. Here we advance an alternative explanation based on the immune response against a single population of infected cells. We show that the biphasic decay can be explained simply, without invoking multiple compartments: viral load falls quickly while cytotoxic T lymphocytes (CTL) are still abundant, and more slowly as CTL disappear. We propose a method to test this idea, and develop a framework that is readily applicable to treatment of other infections.

Autism gene Ube3a and seizures impair sociability by repressing VTA Cbln1

Maternally inherited 15q11-13 chromosomal triplications cause a frequent and highly penetrant type of autism linked to increased gene dosages of UBE3A, which encodes a ubiquitin ligase with transcriptional co-regulatory functions. Here, using in vivo mouse genetics, we show that increasing UBE3A in the nucleus downregulates the glutamatergic synapse organizer Cbln1, which is needed for sociability in mice. Epileptic seizures also repress Cbln1 and are found to expose sociability impairments in mice with asymptomatic increases in UBE3A. This Ube3a–seizure synergy maps to glutamate neurons of the midbrain ventral tegmental area (VTA), where Cbln1 deletions impair sociability and weaken glutamatergic transmission. We provide preclinical evidence that viral-vector-based chemogenetic activation of, or restoration of Cbln1 in, VTA glutamatergic neurons reverses the sociability deficits induced by Ube3a and/or seizures. Our results suggest that gene and seizure interactions in VTA glutamatergic neurons impair sociability by downregulating Cbln1, a key node in the expanding protein interaction network of autism genes.

Customized care 2020: how medical sequencing and network biology will enable personalized medicine

Applications of next-generation nucleic acid sequencing technologies will lead to the development of precision diagnostics that will, in turn, be a major technology enabler of precision medicine. Terabyte-scale, multidimensional data sets derived using these technologies will be used to reverse engineer the specific disease networks that underlie individual patients’ conditions. Modeling and simulation of these networks in the presence of virtual drugs, and combinations of drugs, will identify the most efficacious therapy for precision medicine and customized care. In coming years the practice of medicine will routinely employ network biology analytics supported by high-performance supercomputing.

Antibody repertoire deep sequencing reveals antigen-independent selection in maturing B cells

Significance Antibodies play essential roles in vaccination, infection, autoimmunity, aging, and cancer. A key question is how the antibody repertoire achieves its remarkable diversity. Part of the answer is that B cells, which express antibodies on their surface, are selected for survival based on the specific antigens that their antibodies bind, with antigen specificity determined by the protein sequence of antibodies’ antigen-binding regions. Unexpectedly, we find that B cells are also selected based on whether their antibodies have a loose or tight “elbow joint,” independent of the sequence of their antigen-binding regions. This discovery, enabled by sequencing technology and mathematics, adds a surprising new dimension to our understanding of antibody repertoires, and might one day help us shape them ourselves. Antibody repertoires are known to be shaped by selection for antigen binding. Unexpectedly, we now show that selection also acts on a non–antigen-binding antibody region: the heavy-chain variable (VH)–encoded “elbow” between variable and constant domains. By sequencing 2.8 million recombined heavy-chain genes from immature and mature B-cell subsets in mice, we demonstrate a striking gradient in VH gene use as pre-B cells mature into follicular and then into marginal zone B cells. Cells whose antibodies use VH genes that encode a more flexible elbow are more likely to mature. This effect is distinct from, and exceeds in magnitude, previously described maturation-associated changes in heavy-chain complementarity determining region 3, a key antigen-binding region, which arise from junctional diversity rather than differential VH gene use. Thus, deep sequencing reveals a previously unidentified mode of B-cell selection.

Elementary, My Dear Doctor Watson

> Mr. Watson – Come here – I want to see you. > > —Alexander Graham Bell, March 1876 Many readers will recognize these as the first words ever transmitted by telephone. The Watson on the other end of the line was Thomas A. Watson, Bells assistant. Over a century later, we may soon be hearing these words again, this time at the hospital. But this time the Watson on the other end may not be a person but a machine: IBMs Watson, a state-of-the-art computer system capable of answering questions posed to it in natural human language. No one knows exactly what the arrival of the new Watson will mean for medicine, or for laboratory medicine in particular, but it is clear that change is coming, and coming soon enough to warrant speculation on what to expect. For most people, Watson entered popular consciousness on Valentines Day 2011 with its now-legendary victory over human competitors on the TV game show Jeopardy! (1, 2). In a 3-day, nationally televised performance, Watson defeated the shows 2 greatest champions, Ken Jennings and Brad Rutter, prompting Jennings to quip, “I for one welcome our new computer overlords” (3). Watsons victory did not come out of the blue, however. It was the culmination of a 4-year, multimillion-dollar effort by a 25-member team as part of an IBM project called DeepQA (“question answering”) (3, 4). The projects goal: to build a computer system that can sort through massive amounts of data—terabytes, or hundreds of millions of Web pages, in the case of the Jeopardy! performance (3)—to answer virtually any question a human might ask, however he or she might ask it, instantly. Because the questions on Jeopardy! can be about anything, because they can involve puns, slang, and other wordplay, and because contestants have only seconds to answer them, winning on …

Robust estimates of overall immune-repertoire diversity from high-throughput measurements on samples.

The diversity of an organisms B- and T-cell repertoires is both clinically important and a key measure of immunological complexity. However, diversity is hard to estimate by current methods, because of inherent uncertainty in the number of B- and T-cell clones that will be missing from a blood or tissue sample by chance (the missing-species problem), inevitable sampling bias, and experimental noise. To solve this problem, we developed Recon, a modified maximum-likelihood method that outputs the overall diversity of a repertoire from measurements on a sample. Recon outputs accurate, robust estimates by any of a vast set of complementary diversity measures, including species richness and entropy, at fractional repertoire coverage. It also outputs error bars and power tables, allowing robust comparisons of diversity between individuals and over time. We apply Recon to in silico and experimental immune-repertoire sequencing data sets as proof of principle for measuring diversity in large, complex systems.