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The final phase of the reionization process can be probed by rest-frame UV absorption spectra of quasars at z>6, shedding light on the properties of the diffuse intergalactic medium within the first Gyr of the Universe. The ESO Large Programme "XQR-30: the ultimate XSHOOTER legacy survey of quasars at z~5.8-6.6" dedicated ~250 hours of observations at the VLT to create a homogeneous and high-quality sample of spectra of 30 luminous quasars at z~6, covering the rest wavelength range from the Lyman limit to beyond the MgII emission. Twelve quasar spectra of similar quality from the XSHOOTER archive were added to form the enlarged XQR-30 sample, corresponding to a total of ~350 hours of on-source exposure time. The median effective resolving power of the 42 spectra is R~11400 and 9800 in the VIS and NIR arm, respectively. The signal-to-noise ratio per 10 km/s pixel ranges from ~11 to 114 at $\lambda \simeq 1285$ \AA rest frame, with a median value of ~29. We describe the observations, data reduction and analysis of the spectra, together with some first results based on the E-XQR-30 sample. New photometry in the H and K bands are provided for the XQR-30 quasars, together with composite spectra whose characteristics reflect the large absolute magnitudes of the sample. The composite and the reduced spectra are released to the community through a public repository, and will enable a range of studies addressing outstanding questions regarding the first Gyr of the Universe., Comment: 21 pages, 10 figures. Final version accepted by MNRAS

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Rationale & Objective In the general population, cognitive impairment is associated with increased mortality, and higher levels of education are associated with lower risks for cognitive impairment and mortality. These associations are not well studied in patients receiving long-term hemodialysis and were the focus of the current investigation. Study Design Prospective cohort study. Setting & Participants Adult hemodialysis patients treated in 20 Italian dialysis clinics. Exposures Patients’ cognitive function across 5 domains (memory, attention, executive function, language, and perceptual-motor function), measured using a neuropsychological assessment comprising 10 tests; and patients’ self-reported years of education. Outcome All-cause mortality. Analytical Approach Nested multivariable Cox regression models were used to examine associations of cognition (any domain impaired, number of domains impaired, and global function score from principal components analysis of unadjusted test scores) and education with mortality and whether there were interactions between them. Results 676 (70.6%) patients participated, with a median age of 70.9 years and including 38.8% women. Cognitive impairment was present in 79.4% (527/664; 95% CI, 76.3%-82.5%). During a median follow-up of 3.3 years (1,874 person-years), 206 deaths occurred. Compared to no cognitive impairment, adjusted HRs for mortality were 1.77 (95% CI, 1.07-2.93) for any impairment, 1.48 (95% CI, 0.82-2.68) for 1 domain impaired, 1.88 (95% CI, 1.01-3.53) for 2 domains, and 2.01 (95% CI, 1.14-3.55) for 3 to 5 domains. The adjusted HR was 0.68 (95% CI, 0.51-0.92) per standard deviation increase in global cognitive function score. Compared with primary or lower education, adjusted HRs were 0.79 (95% CI, 0.53-1.20) for lower secondary and 1.13 (95% CI, 0.80-1.59) for upper secondary or higher. The cognition-by-education interaction was not significant (P = 0.7). Limitations Potential selection bias from nonparticipation and missing data; no data for cognitive decline; associations with education were not adjusted for other socioeconomic factors. Conclusions Cognitive impairment is associated with premature mortality in hemodialysis patients. Education does not appear to be associated with mortality.

Background Investigation of outbreaks to identify the primary case is crucial for the interruption and prevention of transmission of infectious diseases. These individuals may have a higher risk of participating in near future transmission events when compared to the other patients in the outbreak, so directing more transmission prevention resources towards these individuals is a priority. Although the genetic characterization of intra-host viral populations can aid the identification of transmission clusters, it is not trivial to determine the directionality of transmissions during outbreaks, owing to complexity of viral evolution. Here, we present a new computational framework, PYCIVO: primary case inference in viral outbreaks. This framework expands upon our earlier work in development of QUENTIN, which builds a probabilistic disease transmission tree based on simulation of evolution of intra-host hepatitis C virus (HCV) variants between cases involved in direct transmission during an outbreak. PYCIVO improves upon QUENTIN by also adding a custom heterogeneity index and identifying the scenario when the primary case may have not been sampled. Results These approaches were validated using a set of 105 sequence samples from 11 distinct HCV transmission clusters identified during outbreak investigations, in which the primary case was epidemiologically verified. Both models can detect the correct primary case in 9 out of 11 transmission clusters (81.8%). However, while QUENTIN issues erroneous predictions on the remaining 2 transmission clusters, PYCIVO issues a null output for these clusters, giving it an effective prediction accuracy of 100%. To further evaluate accuracy of the inference, we created 10 modified transmission clusters in which the primary case had been removed. In this scenario, PYCIVO was able to correctly identify that there was no primary case in 8/10 (80%) of these modified clusters. This model was validated with HCV; however, this approach may be applicable to other microbial pathogens. Conclusions PYCIVO improves upon QUENTIN by also implementing a custom heterogeneity index which empowers PYCIVO to make the important ‘No primary case’ prediction. One or more samples, possibly including the primary case, may have not been sampled, and this designation is meant to account for these scenarios.

Persons who inject drugs (PWID) are at increased risk for overdose death (ODD), infections with HIV, hepatitis B (HBV) and hepatitis C virus (HCV), and noninfectious health conditions. Spatiotemporal identification of PWID communities is essential for developing efficient and cost-effective public health interventions for reducing morbidity and mortality associated with injection-drug use (IDU). Reported ODDs are a strong indicator of the extent of IDU in different geographic regions. However, ODD quantification can take time, with delays in ODD reporting occurring due to a range of factors including death investigation and drug testing. This delayed ODD reporting may affect efficient early interventions for infectious diseases. We present a novel model, Dynamic Overdose Vulnerability Estimator (DOVE), for assessment and spatiotemporal mapping of ODDs in different U.S. jurisdictions. Using Google® Web-search volumes (i.e., the fraction of all searches that include certain words), we identified a strong association between the reported ODD rates and drug-related search terms for 2004–2017. A machine learning model (Extremely Random Forest) was developed to produce yearly ODD estimates at state and county levels, as well as monthly estimates at state level. Regarding the total number of ODDs per year, DOVE’s error was only 3.52% (Median Absolute Error, MAE) in the United States for 2005–2017. DOVE estimated 66,463 ODDs out of the reported 70,237 (94.48%) during 2017. For that year, the MAE of the individual ODD rates was 4.43%, 7.34%, and 12.75% among yearly estimates for states, yearly estimates for counties, and monthly estimates for states, respectively. These results indicate suitability of the DOVE ODD estimates for dynamic IDU assessment in most states, which may alert for possible increased morbidity and mortality associated with IDU. ODD estimates produced by DOVE offer an opportunity for a spatiotemporal ODD mapping. Timely identification of potential mortality trends among PWID might assist in developing efficient ODD prevention and HBV, HCV, and HIV infection elimination programs by targeting public health interventions to the most vulnerable PWID communities.

Hepatitis C virus (HCV) infection is a global public health problem. The implementation of public health interventions (PHI) to control HCV infection could effectively interrupt HCV transmission. PHI targeting high-risk populations, e.g., people who inject drugs (PWID), are the most efficient but there is a lack of tools for prioritizing individuals within a high-risk community. Here, we present Intelligent Network DisRuption Analysis (INDRA), a targeted strategy for efficient interruption of hepatitis C transmissions. Using a large HCV transmission network among PWID in Indiana as an example, we compare effectiveness of random and targeted strategies in reducing the rate of HCV transmission in two settings: (1) long-established and (2) rapidly spreading infections (outbreak). Identification of high centrality for the network nodes co-infected with HIV or >1 HCV subtype indicates that the network structure properly represents the underlying contacts among PWID relevant to the transmission of these infections. Changes in the network’s global efficiency (GE) were used as a measure of the PHI effects. In setting 1, simulation experiments showed that a 50% GE reduction can be achieved by removing 11.2 times less nodes using targeted vs random strategies. A greater effect of targeted strategies on GE was consistently observed when networks were simulated: (1) with a varying degree of errors in node sampling and link assignment, and (2) at different levels of transmission reduction at affected nodes. In simulations considering a 10% removal of infected nodes, targeted strategies were ~2.8 times more effective than random in reducing incidence. Peer-education intervention (PEI) was modeled as a probabilistic distribution of actionable knowledge of safe injection practices from the affected node to adjacent nodes in the network. Addition of PEI to the models resulted in a 2–3 times greater reduction in incidence than from direct PHI alone. In setting 2, however, random direct PHI were ~3.2 times more effective in reducing incidence at the simulated conditions. Nevertheless, addition of PEI resulted in a ~1.7-fold greater efficiency of targeted PHI. In conclusion, targeted PHI facilitated by INDRA outperforms random strategies in decreasing circulation of long-established infections. Network-based PEI may amplify effects of PHI on incidence reduction in both settings.

La malattia policistica renale autosomica dominante dell'adulto (ADPKD) è una delle malattie genetiche più comuni, con un'incidenza di 1 su 1000, ed è la principale causa genetica di insufficienza renale dell'adulto. Se ne distinguono due tipi; il tipo I, causato da mutazioni del gene PKD1, e il tipo II, causato da mutazioni del gene PKD2. La malattia ha un esordio tra i 40 e i 50 anni, ma può manifestarsi anche prima, e ha un'impronta sistemica. La diagnosi è spesso casuale, nel corso di indagini motivate da altri quesiti clinici, oppure promossa dalla presenza di segni clinici correlati. Nelle procedure diagnostiche, l'ADPKD deve essere distinta dalle cisti renali semplici, uniche o multiple, che interessano parzialmente i reni e che generalmente non presentano un'evoluzione critica. È possibile che nell'attività clinica possano essere diagnosticate erroneamente le condizioni che comportano la presenza di cisti renali, rischiando di sottovalutare il rilievo clinico dell'ADPKD. Per verificare tale ipotesi, è stato condotto uno studio epidemiologico nel setting della Medicina Generale italiana e da cui è emersa una prevalenza (1.9‰) quasi quattro volte superiore rispetto a quella registrata (0.5‰) prima della informazione fornita al medico di medicina generale sulle caratteristiche cliniche e diagnostiche dell'ADPKD. È auspicabile l'impegno della Medicina Generale, assieme alle Associazioni dei Pazienti a alle Società Scientifiche nefrologiche, al fine di contribuire a una ottimale gestione clinica della condizione, finalizzata al benessere ottimale del paziente.

Background Hepatitis C is a major public health problem in the United States and worldwide. Outbreaks of hepatitis C virus (HCV) infections associated with unsafe injection practices, drug diversion, and other exposures to blood are difficult to detect and investigate. Effective HCV outbreak investigation requires comprehensive surveillance and robust case investigation. We previously developed and validated a methodology for the rapid and cost-effective identification of HCV transmission clusters. Global Hepatitis Outbreak and Surveillance Technology (GHOST) is a cloud-based system enabling users, regardless of computational expertise, to analyze and visualize transmission clusters in an independent, accurate and reproducible way. Results We present and explore performance of several GHOST implemented algorithms using next-generation sequencing data experimentally obtained from hypervariable region 1 of genetically related and unrelated HCV strains. GHOST processes data from an entire MiSeq run in approximately 3 h. A panel of seven specimens was used for preparation of six repeats of MiSeq libraries. Testing sequence data from these libraries by GHOST showed a consistent transmission linkage detection, testifying to high reproducibility of the system. Lack of linkage among genetically unrelated HCV strains and constant detection of genetic linkage between HCV strains from known transmission pairs and from follow-up specimens at different levels of MiSeq-read sampling indicate high specificity and sensitivity of GHOST in accurate detection of HCV transmission. Conclusions GHOST enables automatic extraction of timely and relevant public health information suitable for guiding effective intervention measures. It is designed as a virtual diagnostic system intended for use in molecular surveillance and outbreak investigations rather than in research. The system produces accurate and reproducible information on HCV transmission clusters for all users, irrespective of their level of bioinformatics expertise. Improvement in molecular detection capacity will contribute to increasing the rate of transmission detection, thus providing opportunity for rapid, accurate and effective response to outbreaks of hepatitis C. Although GHOST was originally developed for hepatitis C surveillance, its modular structure is readily applicable to other infectious diseases. Worldwide availability of GHOST for the detection of HCV transmissions will foster deeper involvement of public health researchers and practitioners in hepatitis C outbreak investigation.

Despite the significant public health problems associated with hepatitis B virus (HBV) in sub-Saharan Africa, many countries in this region do not have systematic HBV surveillance or genetic information on HBV circulating locally. Here, we report on the genetic characterization of 772 HBV strains from Tanzania. Phylogenetic analysis of the S-gene sequences showed prevalence of HBV genotype A (HBV/A, n=671, 86.9 %), followed by genotypes D (HBV/D, n=95, 12.3 %) and E (HBV/E, n=6, 0.8 %). All HBV/A sequences were further classified into subtype A1, while the HBV/D sequences were assigned to a new cluster. Among the Tanzanian sequences, 84 % of HBV/A1 and 94 % of HBV/D were unique. The Tanzanian and global HBV/A1 sequences were compared and were completely intermixed in the phylogenetic tree, with the Tanzanian sequences frequently generating long terminal branches, indicating a long history of HBV/A1 infections in the country. The time to the most recent common ancestor was estimated to be 188 years ago [95 % highest posterior density (HPD): 132 to 265 years] for HBV/A1 and 127 years ago (95 % HPD: 79 to 192 years) for HBV/D. The Bayesian skyline plot showed that the number of transmissions ‘exploded’ exponentially between 1960–1970 for HBV/A1 and 1970–1990 for HBV/D, with the effective population of HBV/A1 having expanded twice as much as that of HBV/D. The data suggest that Tanzania is at least a part of the geographic origin of the HBV/A1 subtype. A recent increase in the transmission rate and significant HBV genetic diversity should be taken into consideration when devising public health interventions to control HBV infections in Tanzania.

Background Ultra-Deep Sequencing (UDS) enabled identification of specific changes in human genome occurring in malignant tumors, with current approaches calling for the detection of specific mutations associated with certain cancers. However, such associations are frequently idiosyncratic and cannot be generalized for diagnostics. Mitochondrial DNA (mtDNA) has been shown to be functionally associated with several cancer types. Here, we study the association of intra-host mtDNA diversity with Hepatocellular Carcinoma (HCC). Results UDS mtDNA exome data from blood of patients with HCC (n = 293) and non-cancer controls (NC, n = 391) were used to: (i) measure the genetic heterogeneity of nucleotide sites from the entire population of intra-host mtDNA variants rather than to detect specific mutations, and (ii) apply machine learning algorithms to develop a classifier for HCC detection. Average total entropy of HCC mtDNA is 1.24-times lower than of NC mtDNA (p = 2.84E-47). Among all polymorphic sites, 2.09% had a significantly different mean entropy between HCC and NC, with 0.32% of the HCC mtDNA sites having greater (p p n = 61) and NC (n = 159) patients in a validation dataset that was not used for the RF parameter optimization. Conclusions Polymorphic sites contributing most to the mtDNA association with HCC are scattered along the mitochondrial genome, affecting all mitochondrial genes. The findings suggest that application of heterogeneity profiles of intra-host mtDNA variants from blood may help overcome barriers associated with the complex association of specific mutations with cancer, enabling the development of accurate, rapid, inexpensive and minimally invasive diagnostic detection of cancer.

Background & Aims: The host genetic environment contributes significantly to the outcomes of hepatitis C virus (HCV) infection and therapy response, but little is known about any effects of HCV infection on the host beyond any changes related to adaptive immune responses. HCV persistence is associated strongly with mitochondrial dysfunction, with liver mitochondrial DNA (mtDNA) genetic diversity linked to disease progression. Methods: We evaluated the genetic diversity of 2 mtDNA genomic regions (hypervariable segments 1 and 2) obtained from sera of 116 persons using next-generation sequencing. Results: Results were as follows: (1) the average diversity among cases with seronegative acute HCV infection was 4.2 times higher than among uninfected controls; (2) the diversity level among cases with chronic HCV infection was 96.1 times higher than among uninfected controls; and (3) the diversity was 23.1 times higher among chronic than acute cases. In 2 patients who were followed up during combined interferon and ribavirin therapy, mtDNA nucleotide diversity decreased dramatically after the completion of therapy in both patients: by 100% in patient A after 54 days and by 70.51% in patient B after 76 days. Conclusions: HCV infection strongly affects mtDNA genetic diversity. A rapid decrease in mtDNA genetic diversity observed after therapy-induced HCV clearance suggests that the effect is reversible, emphasizing dynamic genetic relationships between HCV and mitochondria. The level of mtDNA nucleotide diversity can be used to discriminate recent from past infections, which should facilitate the detection of recent transmission events and thus help identify modes of transmission. Keywords: Disease Biomarkers, mtDNA, Noninvasive

Rationale & Objective Clinical practice guidelines for dietary intake in hemodialysis focus on individual nutrients. Little is known about associations of dietary patterns with survival. We evaluated the associations of dietary patterns with cardiovascular and all-cause mortality among adults treated by hemodialysis. Study Design Prospective cohort study. Setting & Participants 8,110 of 9,757 consecutive adults on hemodialysis (January 2014 to June 2017) treated in a multinational private dialysis network and with analyzable dietary data. Exposures Data-driven dietary patterns based on the GA2LEN food frequency questionnaire. Participants received a score for each identified pattern, with higher scores indicating closer resemblance of their diet to the identified pattern. Quartiles of standardized pattern scores were used as primary exposures. Outcomes Cardiovascular and all-cause mortality. Analytical Approach Principal components analysis with varimax rotation to identify common dietary patterns. Adjusted proportional hazards regression analyses with country as a random effect to estimate the associations between dietary pattern scores and mortality. Associations were expressed as adjusted HRs with 95% CIs, using the lowest quartile score as reference. Results During a median follow-up of 2.7 years (18,666 person-years), there were 2,087 deaths (958 cardiovascular). 2 dietary patterns, “fruit and vegetable” and “Western,” were identified. For the fruit and vegetable dietary pattern score, adjusted HRs, in ascending quartiles, were 0.94 (95% CI, 0.76-1.15), 0.83 (95% CI, 0.66-1.06), and 0.91 (95% CI, 0.69-1.21) for cardiovascular mortality and 0.95 (95% CI, 0.83-1.09), 0.84 (95% CI, 0.71-0.99), and 0.87 (95% CI, 0.72-1.05) for all-cause mortality. For the Western dietary pattern score, the corresponding estimates were 1.10 (95% CI, 0.90-1.35), 1.11 (95% CI, 0.87-1.41), and 1.09 (95% CI, 0.80-1.49) for cardiovascular mortality and 1.01 (95% CI, 0.88-1.16), 1.00 (95% CI, 0.85-1.18), and 1.14 (95% CI, 0.93-1.41) for all-cause mortality. Limitations Self-reported food frequency questionnaire, data-driven approach. Conclusions These findings did not confirm an association between mortality among patients receiving long-term hemodialysis and the extent to which dietary patterns were either high in fruit and vegetables or consistent with a Western diet.

Background Molecular surveillance and outbreak investigation are important for elimination of hepatitis C virus (HCV) infection in the United States. A web-based system, Global Hepatitis Outbreak and Surveillance Technology (GHOST), has been developed using Illumina MiSeq-based amplicon sequence data derived from the HCV E1/E2-junction genomic region to enable public health institutions to conduct cost-effective and accurate molecular surveillance, outbreak detection and strain characterization. However, as there are many factors that could impact input data quality to which the GHOST system is not completely immune, accuracy of epidemiological inferences generated by GHOST may be affected. Here, we analyze the data submitted to the GHOST system during its pilot phase to assess the nature of the data and to identify common quality concerns that can be detected and corrected automatically. Results The GHOST quality control filters were individually examined, and quality failure rates were measured for all samples, including negative controls. New filters were developed and introduced to detect primer dimers, loss of specimen-specific product, or short products. The genotyping tool was adjusted to improve the accuracy of subtype calls. The identification of “chordless” cycles in a transmission network from data generated with known laboratory-based quality concerns allowed for further improvement of transmission detection by GHOST in surveillance settings. Parameters derived to detect actionable common quality control anomalies were incorporated into the automatic quality control module that rejects data depending on the magnitude of a quality problem, and warns and guides users in performing correctional actions. The guiding responses generated by the system are tailored to the GHOST laboratory protocol. Conclusions Several new quality control problems were identified in MiSeq data submitted to GHOST and used to improve protection of the system from erroneous data and users from erroneous inferences. The GHOST system was upgraded to include identification of causes of erroneous data and recommendation of corrective actions to laboratory users.

Background Intra-host hepatitis C virus (HCV) populations are genetically heterogeneous and organized in subpopulations. With the exception of blood transfusions, transmission of HCV occurs via a small number of genetic variants, the effect of which is frequently described as a bottleneck. Stochasticity of transmission associated with the bottleneck is usually used to explain genetic differences among HCV populations identified in the source and recipient cases, which may be further exacerbated by intra-host HCV evolution and differential biological capacity of HCV variants to successfully establish a population in a new host. Results Transmissibility was formulated as a property that can be measured from experimental Ultra-Deep Sequencing (UDS) data. The UDS data were obtained from one large hepatitis C outbreak involving an epidemiologically defined source and 18 recipient cases. k-Step networks of HCV variants were constructed and used to identify a potential association between transmissibility and network centrality of individual HCV variants from the source. An additional dataset obtained from nine other HCV outbreaks with known directionality of transmission was used for validation. Transmissibility was not found to be dependent on high frequency of variants in the source, supporting the earlier observations of transmission of minority variants. Among all tested measures of centrality, the highest correlation of transmissibility was found with Hamming centrality (r = 0.720; p = 1.57 E-71). Correlation between genetic distances and differences in transmissibility among HCV variants from the source was found to be 0.3276 (Mantel Test, p = 9.99 E-5), indicating association between genetic proximity and transmissibility. A strong correlation ranging from 0.565–0.947 was observed between Hamming centrality and transmissibility in 7 of the 9 additional transmission clusters (p

Many biological analysis techniques require measurement of similarity between sequences from large genomic datasets, which often involves extraction of all pairs of close DNA or RNA sequences. We present a k-mer-based tool to efficiently perform such sequence similarity queries for large viral datasets produced by next-generation sequencing.

Motivation Genomic analysis has become one of the major tools for disease outbreak investigations. However, existing computational frameworks for inference of transmission history from viral genomic data often do not consider intra-host diversity of pathogens and heavily rely on additional epidemiological data, such as sampling times and exposure intervals. This impedes genomic analysis of outbreaks of highly mutable viruses associated with chronic infections, such as human immunodeficiency virus and hepatitis C virus, whose transmissions are often carried out through minor intra-host variants, while the additional epidemiological information often is either unavailable or has a limited use. Results The proposed framework QUasispecies Evolution, Network-based Transmission INference (QUENTIN) addresses the above challenges by evolutionary analysis of intra-host viral populations sampled by deep sequencing and Bayesian inference using general properties of social networks relevant to infection dissemination. This method allows inference of transmission direction even without the supporting case-specific epidemiological information, identify transmission clusters and reconstruct transmission history. QUENTIN was validated on experimental and simulated data, and applied to investigate HCV transmission within a community of hosts with high-risk behavior. It is available at https://github.com/skumsp/QUENTIN. Supplementary information Supplementary data are available at Bioinformatics online.

The recent epidemic history of hepatitis B virus (HBV) infections in the United States is complex, as indicated by current disparity in HBV genotype distribution between acute and chronic hepatitis B cases and the rapid decline in hepatitis B incidence since the 1990s. We report temporal changes in the genetic composition of the HBV population using whole-genome sequences ( n = 179) from acute hepatitis B cases ( n = 1,206) identified through the Sentinel County Surveillance for Acute Hepatitis (1998 to 2006). HBV belonged mainly to subtypes A2 (75%) and D3 (18%), with times of their most recent common ancestors being 1979 and 1987, respectively. A2 underwent rapid population expansions in ca. 1995 and ca. 2002, coinciding with transient rises in acute hepatitis B notification rates among adults; D3 underwent expansion in ca. 1998. A2 strains from cases identified after 2002, compared to those before 2002, tended to cluster phylogenetically, indicating selective expansion of specific strains, and were significantly reduced in genetic diversity ( P = 0.001) and frequency of drug resistance mutations ( P = 0.001). The expansion of genetically close HBV A2 strains was associated with risk of infection among male homosexuals ( P = 0.03). Incident HBV strains circulating in the United States were recent in origin and restricted in genetic diversity. Disparate transmission dynamics among phylogenetic lineages affected the genetic composition of HBV populations and their capacity to maintain drug resistance mutations. The tendency of selectively expanding HBV strains to be transmitted among male homosexuals highlights the need to improve hepatitis B vaccination coverage among at-risk adults. IMPORTANCE Hepatitis B virus (HBV) remains an important cause of acute and chronic liver disease globally and in the United States. Genetic analysis of HBV whole genomes from cases of acute hepatitis B identified from 1998 to 2006 in the United States showed dominance of genotype A2 (75%), followed by D3 (18%). Strains of both subtypes were recent in origin and underwent rapid population expansions from 1995 to 2000, indicating increase in transmission rate for certain HBV strains during a period of decline in the reported incidence of acute hepatitis B in the United States. HBV A2 strains from a particular cluster that experienced the most recent population expansion were more commonly detected among men who have sex with men. Vaccination needs to be stepped up to protect persons who remain at risk of HBV infection.

Hepatitis C virus (HCV) infection presents an important, but underappreciated public health problem in Africa. In Cote d'Ivoire, very little is known about the molecular dynamics of HCV infection. Plasma samples (n ¼608) from preg- nant women collected in 1995 from Cote d'Ivoire were analyzed in this study. Only 18 specimens (� 3%) were found to be HCV PCR-positive. Phylogenetic analysis of the HCV NS5b sequen- ces showed that the HCV variants belong to genotype 1 (HCV1) (n ¼12, 67%) and genotype 2 (HCV2) (n ¼6, 33%), with a maximum genetic diversity among HCV variants in each genotype being 20.7% and 24.0%, respectively. Although all HCV2 variants were genetically distant from each other, six HCV1 variants formed two tight sub-clusters belonging to HCV1a and HCV1b. Analysis of molecular variance (AMOVA) showed that the genetic structure of HCV iso- lates from West Africa with Cote d'Ivoire includ- ed were significantly different from Central African strains (P ¼0.0001). Examination of intra- host viral populations using next-generation sequencing of the HCV HVR1 showed a signifi- cant variation in intra-host genetic diversity among infected individuals, with some strains composed of sub-populations as distant from each other as viral populations from different hosts. Collectively, the results indicate a com- plex HCV evolution in Cote d'Ivoire, similar to the rest of West Africa, and suggest a unique HCV epidemic history in the country. J. Med. Virol. 2014. Published 2014. This article is a U. S. Government work and is in the public domain in the USA.

There has been a great progress in the development of antiviral agents licensed for treatment of Human immunodeficiency virus (HIV), Herpesviruses, Hepatitis viruses and respiratory viruses1. The emergence of HIV as a major human pathogen and the intensive use of antiretroviral compounds have also provided a better understanding of the genesis of antiviral resistance2. However, there is not a simple method for measuring directly the effect a drug has over a viral population and its individual intra-host variants. Such measure could help screen promising drugs that affect the viral population and also detect the individual variants that are naturally more resistant. In this paper, we use the effects of Interferon (IFN) on HCV as a proof of concept, finding that our drug-resistance estimates, calculated during the first 48 hour after IFN injection, are strongly associated with outcome of therapy at week 12. Hepatitis C virus (HCV) infects nearly 3% of the world's population and is a major cause of liver disease worldwide3. There is no vaccine against HCV and up to recently the standard-of-care therapy involved the combined use of pegylated interferon (peg-IFN) and ribavirin (RBV). This combination therapy is expensive, effective in only 50%-60% of patients, and can be associated with frequent and serious adverse side effects in more than 75% of patients4, 5 In order to improve cost-effectiveness and ameliorate patient hardship, it would be desirable to predict the response at early onset of therapy. IFNs are crucial components of the innate immune system. IFN-α acts by inducing production of interferon stimulating genes (ISGs) to establish a non-specific antiviral state within the cell with direct inhibition of viral replication. It is also known to exert immunomodulatory effects that enhance immune response and accelerate clearance of infected cells6. When exogenously administered as a single injection, IFN-α induces a decline of HCV RNA in two phases: a rapid phase lasting for 24-48 hours, followed by a slower phase of decline over the ensuing weeks. The initial rapid decline is defined by the rate of viral clearance and the effectiveness of IFN in blocking viral production. Successful treatment results in sustained undetectable HCV RNA after completion of therapy. Treatment failure results either from nonresponse (minimal declines in viral titer during therapy) or relapse (robust initial responses followed by rebounds of viral titers after therapy)6. Several independent predictors of a sustained virologic response (SVR) to IFN/RBV therapy have been identified. These include HCV genotypes 2 and 3, low pretreatment viral load, Asian or Caucasian ethnicity, younger age, absence of advanced fibrosis or cirrhosis, and absence of steatosis7. More recently, Genome Wide Association Studies have identified single-nucleotide polymorphisms near the IL28B gene (encoding IFN-λ3) as being particularly associated with spontaneous and treatment-induced clearance of HCV infection8, 9. However, IL28B variations may account for only ∼15% of inter-individual variability of SVR10. The interaction of these host factors determines the therapy effect on the virus, which is directly evidenced by a decline in viral titer, the reason why the rate and magnitude of decline in the first weeks of treatment can predict the outcome of therapy6. HCV exists in infected patients as a large viral population of intra-host variants, which may be differentially resistant to IFN treatment and, therefore, likely to display variable temporal patterns during the first phase of decline following IFN injection. Assessing the spectrum of HCV variants and measuring the IFN resistance of individual variants could be critical for understanding the variability in therapy outcomes. Next-generation sequencing (NGS) technologies in conjunction with computational analysis allow for quantitative assessment of viral intra-host variants, providing data on the intra-host dynamics of individual HCV variants and an opportunity for measuring their resistance to IFN. The HVR1 region of HCV is used here as a tag or marker of individual intra-host viral strains for estimating their relative frequencies over a short period of time. Since the model is based solely on changes in viral titer or the relative frequency of intra-host viral variants persisting during antiviral therapy, without consideration of other viral factors, host factors or type of drug administered, it may be applicable to measure and predict HCV treatment response with other drug regimens, including the newly available direct acting antiviral (DAA) agents. In addition, the presented analytical framework should be applicable to drug resistance of other viral infections.

Introduction: Y chromosome STR haplotypes have been widely used in population studies to establish the origin of diverse populations. Objective: We analyzed Y chromosome STR haplotypes (8 loci) in 134 Caucasian-mestizo and 137 African-descent Colombian unrelated individuals to correlate the geographical origin with historical data as well as the genetic relationships and possible admixture patterns. Materials and methods: One hundred samples of African descent and 137 Caucasian-mestizo samples analyzed for Y chromosome STR haplotypes by PCR followed by acrylamide electrophoresis. Results: No evidence of population substructure was found for the African descent. Two point fifty nine per cent of the haplotypes were shared between the two groups with the possible existence of Caucasian gene flow towards Afro-descendants. Conclusion: The Caucasian-Mestizo Colombian population is grouped with other populations of the Iberian Peninsula and Europe, while the Afro-Colombian population is grouped with other African populations reported. Introducción. Los haplotipos STR de cromosoma Y han sido ampliamente utilizados en estudios de poblaciones para establecer el origen de diversas poblaciones. Objetivo. Se analizaron haplotipos STR del cromosoma Y (8 loci ) en 134 afrodescendientes y caucásico-mestizos no relacionados de Colombia, para correlacionar el origen geográfico con los datos históricos, así como las relaciones genéticas y posibles patrones de mezcla. Materiales y métodos. Se analizaron los haplotipos STR del cromosoma Y mediante PCR seguidas de electroforesis en acrilamida, de 134 muestras de afrodescendientes y 137 muestras de caucásicos mestizos. Resultados. No se encontró evidencia de subestructuración de la población afrodescendiente. El 2,59 % de los haplotipos eran compartidos en los dos grupos analizados, con la posible existencia de flujo génico de caucásico-mestizos hacia los afrodescendientes. Conclusión. La población caucásico-mestiza colombiana se agrupa con otras poblaciones de la península Ibérica y Europa, mientras que la población afrodescendiente colombiana se agrupa con otras poblaciones africanas reportadas.

The molecular detection of transmission of rapidly mutating pathogens such as hepatitis C virus (HCV) is commonly achieved by assessing the genetic relatedness of strains among infected patients. We describe the development of a novel mass spectrometry (MS)-based approach to identify HCV transmission. MS was used to detect products of base-specific cleavage of RNA molecules obtained from HCV polymerase chain reaction fragments. The MS-peak profiles were found to reflect variation in the HCV genomic sequence and the intrahost composition of the HCV population. Serum specimens originating from 60 case patients from 14 epidemiologically confirmed outbreaks and 25 unrelated controls were tested. Neighbor-joining trees constructed using MS-peak profile-based Hamming distances showed 100% accuracy, and linkage networks constructed using a threshold established from the Hamming distances between epidemiologically unrelated cases showed 100% sensitivity and 99.93% specificity in transmission detection. This MS-based approach is rapid, robust, reproducible, cost-effective, and applicable to investigating transmissions of other pathogens.