Search

Abstract Aberrant DNA methylation patterns have been used for cancer detection. However, DNA hemi-methylation, present at about 10% CpG dinucleotides, has been less well studied. Here we show that a majority of differentially hemi-methylated regions (DHMRs) in liver tumor DNA or plasma cells free (cf) DNA do not overlap with differentially methylated regions (DMRs) of the same samples, indicating that DHMRs could serve as independent biomarkers. Furthermore, we analyzed the cfDNA methylomes of 215 samples from individuals with liver or brain cancer and individuals without cancer (controls), and trained machine learning models using DMRs, DHMRs or both. The models incorporated with both DMRs and DHMRs show a superior performance compared to models trained with DMRs or DHMRs, with AUROC being 0.978, 0.990, and 0.983 in distinguishing control, liver and brain cancer, respectively, in a validation cohort. This study supports the potential of utilizing both DMRs and DHMRs for multi-cancer detection.

Abstract Spatial omics technologies can help identify spatially organized biological processes, but existing computational approaches often overlook structural dependencies in the data. Here, we introduce Smoother, a unified framework that integrates positional information into non-spatial models via modular priors and losses. In simulated and real datasets, Smoother enables accurate data imputation, cell-type deconvolution, and dimensionality reduction with remarkable efficiency. In colorectal cancer, Smoother-guided deconvolution reveals plasma cell and fibroblast subtype localizations linked to tumor microenvironment restructuring. Additionally, joint modeling of spatial and single-cell human prostate data with Smoother allows for spatial mapping of reference populations with significantly reduced ambiguity.

Abstract The ATR kinase, which coordinates cellular responses to DNA replication stress, is also essential for the proliferation of normal unstressed cells. Although its role in the replication stress response is well defined, the mechanisms by which ATR supports normal cell proliferation remain elusive. Here, we show that ATR is dispensable for the viability of G0-arrested naïve B cells. However, upon cytokine-induced proliferation, Atr-deficient B cells initiate DNA replication efficiently, but by mid-S phase they display dNTP depletion, fork stalling, and replication failure. Nonetheless, productive DNA replication and dNTP levels can be restored in Atr-deficient cells by suppressing origin firing, such as partial inhibition of CDC7 and CDK1 kinase activities. Together, these findings indicate that ATR supports the proliferation of normal unstressed cells by tempering the pace of origin firing during the early S phase to avoid exhaustion of dNTPs and importantly also other replication factors.

Immunotherapies have shown limited efficacy in patients with glioma. Here, based on an in vivo kinome knockout CRISPR screen, the authors show that checkpoint kinase 2 promotes CD8 T cell immune evasion and that its depletion or inhibition improve survival and response to PD1/PDL1 blockade in preclinical glioma models.

Abstract Background Efficient presentation of mutant peptide fragments by the human leukocyte antigen class I (HLA-I) genes is necessary for immune-mediated killing of cancer cells. According to recent reports, patient HLA-I genotypes can impact the efficacy of cancer immunotherapy, and the somatic loss of HLA-I heterozygosity has been established as a factor in immune evasion. While global deregulated expression of HLA-I has also been reported in different tumor types, the role of HLA-I allele-specific expression loss — that is, the preferential RNA expression loss of specific HLA-I alleles — has not been fully characterized in cancer. Methods Here, we use RNA and whole-exome sequencing data to quantify HLA-I allele-specific expression (ASE) in cancer using our novel method arcasHLA-quant. Results We show that HLA-I ASE loss in at least one of the three HLA-I genes is a pervasive phenomenon across TCGA tumor types. In pancreatic adenocarcinoma, tumor-specific HLA-I ASE loss is associated with decreased overall survival specifically in the basal-like subtype, a finding that we validated in an independent cohort through laser-capture microdissection. Additionally, we show that HLA-I ASE loss is associated with poor immunotherapy outcomes in metastatic melanoma through retrospective analyses. Conclusions Together, our results highlight the prevalence of HLA-I ASE loss and provide initial evidence of its clinical significance in cancer prognosis and immunotherapy treatment.

Abstract Background The emergence of SARS-CoV-2 underscores the need to better understand the evolutionary processes that drive the emergence and adaptation of zoonotic viruses in humans. In the betacoronavirus genus, which also includes SARS-CoV and MERS-CoV, recombination frequently encompasses the receptor binding domain (RBD) of the Spike protein, which is responsible for viral binding to host cell receptors. In this work, we reconstruct the evolutionary events that have accompanied the emergence of SARS-CoV-2, with a special emphasis on the RBD and its adaptation for binding to its receptor, human ACE2. Methods By means of phylogenetic and recombination analyses, we found evidence of a recombination event in the RBD involving ancestral linages to both SARS-CoV and SARS-CoV-2. We then assessed the effect of this recombination at protein level by reconstructing the RBD of the closest ancestors to SARS-CoV-2, SARS-CoV, and other Sarbecoviruses, including the most recent common ancestor of the recombining clade. The resulting information was used to measure and compare, in silico, their ACE2-binding affinities using the physics-based trRosetta algorithm. Results We show that, through an ancestral recombination event, SARS-CoV and SARS-CoV-2 share an RBD sequence that includes two insertions (positions 432-436 and 460-472), as well as the variants 427N and 436Y. Both 427N and 436Y belong to a helix that interacts directly with the human ACE2 (hACE2) receptor. Reconstruction of ancestral states, combined with protein-binding affinity analyses, suggests that the recombination event involving ancestral strains of SARS-CoV and SARS-CoV-2 led to an increased affinity for hACE2 binding and that alleles 427N and 436Y significantly enhanced affinity as well. Conclusions We report an ancestral recombination event affecting the RBD of both SARS-CoV and SARS-CoV-2 that was associated with an increased binding affinity to hACE2. Structural modeling indicates that ancestors of SARS-CoV-2 may have acquired the ability to infect humans decades ago. The binding affinity with the human receptor would have been subsequently boosted in SARS-CoV and SARS-CoV-2 through further mutations in RBD.

p53 is able to induce ferroptosis in response to reactive oxygen species (ROS)-induced stress and suppresses tumour growth. Here, the authors show that iPLA2β suppresses p53-medated ferroptosis by cleaving and detoxifying peroxidized lipids and that this is independent of canonical ferroptosis regulator GPX4.

Abstract Background Macrophages are the most common infiltrating immune cells in gliomas and play a wide variety of pro-tumor and anti-tumor roles. However, the different subpopulations of macrophages and their effects on the tumor microenvironment remain poorly understood. Methods We combined new and previously published single-cell RNA-seq data from 98,015 single cells from a total of 66 gliomas to profile 19,331 individual macrophages. Results Unsupervised clustering revealed a pro-tumor subpopulation of bone marrow-derived macrophages characterized by the scavenger receptor MARCO, which is almost exclusively found in IDH1-wild-type glioblastomas. Previous studies have implicated MARCO as an unfavorable marker in melanoma and non-small cell lung cancer; here, we find that bulk MARCO expression is associated with worse prognosis and mesenchymal subtype. Furthermore, MARCO expression is significantly altered over the course of treatment with anti-PD1 checkpoint inhibitors in a response-dependent manner, which we validate with immunofluorescence imaging. Conclusions These findings illustrate a novel macrophage subpopulation that drives tumor progression in glioblastomas and suggest potential therapeutic targets to prevent their recruitment.

Chemotherapy resistance in recurrent gliomas is a large hurdle for successful therapy. Here, the authors show that some recurrent gliomas harbour O-6-methylguanine-DNA methyltransferase (MGMT) genomic rearrangements, and in vitro and in vivo these contribute to temozolomide resistance.

Abstract Background Gynecologic malignancy is one of the leading causes of mortality in female adults worldwide. Comprehensive genomic analysis has revealed a list of molecular aberrations that are essential to tumorigenesis, progression, and metastasis of gynecologic tumors. However, targeting such alterations has frequently led to treatment failures due to underlying genomic complexity and simultaneous activation of various tumor cell survival pathway molecules. A compilation of molecular characterization of tumors with pharmacological drug response is the next step toward clinical application of patient-tailored treatment regimens. Results Toward this goal, we establish a library of 139 gynecologic tumors including epithelial ovarian cancers (EOCs), cervical, endometrial tumors, and uterine sarcomas that are genomically and/or pharmacologically annotated and explore dynamic pharmacogenomic associations against 37 molecularly targeted drugs. We discover lineage-specific drug sensitivities based on subcategorization of gynecologic tumors and identify TP53 mutation as a molecular determinant that elicits therapeutic response to poly (ADP-Ribose) polymerase (PARP) inhibitor. We further identify transcriptome expression of inhibitor of DNA biding 2 (ID2) as a potential predictive biomarker for treatment response to olaparib. Conclusions Together, our results demonstrate the potential utility of rapid drug screening combined with genomic profiling for precision treatment of gynecologic cancers.

Abstract Background Recent large-scale cancer sequencing studies have discovered many novel cancer driver genes (CDGs) in human cancers. Some studies also suggest that CDG mutations contribute to cancer-associated epigenomic and transcriptomic alterations across many cancer types. Here we aim to improve our understanding of the connections between CDG mutations and altered cancer cell epigenomes and transcriptomes on pan-cancer level and how these connections contribute to the known association between epigenome and transcriptome. Method Using multi-omics data including somatic mutation, DNA methylation, and gene expression data of 20 cancer types from The Cancer Genome Atlas (TCGA) project, we conducted a pan-cancer analysis to identify CDGs, when mutated, have strong associations with genome-wide methylation or expression changes across cancer types, which we refer as methylation driver genes (MDGs) or expression driver genes (EDGs), respectively. Results We identified 32 MDGs, among which, eight are known chromatin modification or remodeling genes. Many of the remaining 24 MDGs are connected to chromatin regulators through either regulating their transcription or physically interacting with them as potential co-factors. We identified 29 EDGs, 26 of which are also MDGs. Further investigation on target genes’ promoters methylation and expression alteration patterns of these 26 overlapping driver genes shows that hyper-methylation of target genes’ promoters are significantly associated with down-regulation of the same target genes and hypo-methylation of target genes’ promoters are significantly associated with up-regulation of the same target genes. Conclusion This finding suggests a pivotal role for genetically driven changes in chromatin remodeling in shaping DNA methylation and gene expression patterns during tumor development.

Understanding the cellular constituents of the prostate is essential for identifying the cell of origin for prostate adenocarcinoma. Here, we describe a comprehensive single-cell atlas of the adult mouse prostate epithelium, which displays extensive heterogeneity. We observe distal lobe-specific luminal epithelial populations (LumA, LumD, LumL, and LumV), a proximally enriched luminal population (LumP) that is not lobe-specific, and a periurethral population (PrU) that shares both basal and luminal features. Functional analyses suggest that LumP and PrU cells have multipotent progenitor activity in organoid formation and tissue reconstitution assays. Furthermore, we show that mouse distal and proximal luminal cells are most similar to human acinar and ductal populations, that a PrU-like population is conserved between species, and that the mouse lateral prostate is most similar to the human peripheral zone. Our findings elucidate new prostate epithelial progenitors, and help resolve long-standing questions about anatomical relationships between the mouse and human prostate.

Summary: Single-cell technologies provide the opportunity to identify new cellular states. However, a major obstacle to the identification of biological signals is noise in single-cell data. In addition, single-cell data are very sparse. We propose a new method based on random matrix theory to analyze and denoise single-cell sequencing data. The method uses the universal distributions predicted by random matrix theory for the eigenvalues and eigenvectors of random covariance/Wishart matrices to distinguish noise from signal. In addition, we explain how sparsity can cause spurious eigenvector localization, falsely identifying meaningful directions in the data. We show that roughly 95% of the information in single-cell data is compatible with the predictions of random matrix theory, about 3% is spurious signal induced by sparsity, and only the last 2% reflects true biological signal. We demonstrate the effectiveness of our approach by comparing with alternative techniques in a variety of examples with marked cell populations. The Bigger Picture: Single-cell technologies are able to capture information of a biological system cell by cell. Such a level of precision is changing the way we understand complex systems such as cancer or the immune system. However, a major challenge in studying single-cell systems and their underlying biological phenomena is their inherently noisy nature due to their complexity. Random matrix theory is a field with many applications in different branches of mathematics and physics. In the words of one of its developers, the theoretical physicist Freeman Dyson, it describes a “black box in which a large number of particles are interacting according to unknown laws.” A complex system with a large number of components (such as genes, biomolecules, or cells) interacting according to unknown laws is the epitome of systems biology. Therefore, random matrix theory looks like a suitable framework to mathematically describe the noise and complexity of gene-cell expression data coming from single-cell biology.

Summary: Non-recirculating tissue-resident memory T cells (TRMs) are the predominant T cell subset in diverse tissue sites, where they mediate protective immune responses in situ. Here, we reveal a role for TRM in maintaining immune homeostasis in the human pancreas through interactions with resident macrophages and the PD-1/PD-L1 inhibitory pathway. Using tissues obtained from organ donors, we identify that pancreas T cells comprise CD8+PD-1hi TRMs, which are phenotypically, functionally, and transcriptionally distinct compared to TRMs in neighboring jejunum and lymph node sites. Pancreas TRMs cluster with resident macrophages throughout the exocrine areas; TRM effector functions are enhanced by macrophage-derived co-stimulation and attenuated by the PD-1/PD-L1 pathways. Conversely, in samples from chronic pancreatitis, TRMs exhibit reduced PD-1 expression and reduced interactions with macrophages. These findings suggest important roles for PD-1 and TRM-macrophage interactions in controlling tissue homeostasis and immune dysfunctions underlying inflammatory disease, with important implications for PD-1-based immunotherapies. : Non-recirculating tissue-resident memory T cells (TRMs) mediate immune responses in non-lymphoid tissues. Using a human organ donor tissue resource, Weisberg et al. reveal that PD-1hi pancreas TRMs are regulated by PD-L1+ macrophages during homeostasis. Comparison with chronic pancreatitis patient samples shows how pancreas TRM regulation is altered during inflammation. Keywords: memory T cells, pancreas, chronic pancreatitis, tissue immunity, mucosal immunity, PD-1, macrophage

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare and aggressive hematologic malignancy for which there is still no effective therapy. In order to identify genetic alterations useful for a new treatment design, we used whole-exome sequencing to analyze 14 BPDCN patients and the patient-derived CAL-1 cell line. The functional enrichment analysis of mutational data reported the epigenetic regulatory program to be the most significantly undermined (P

Regulatory B cells (Breg) have immune suppressive functions in various autoimmune/inflammation models and diseases and are found to be enriched in diverse B cell subsets. The lack of a unique marker or set of markers efficiently identifying Breg cells impedes detailed investigation into their origin, development, and immunological roles. Here, we perform transcriptome analysis of IL-10-expressing B cells to identify key regulators for Breg biogenesis and function and identify CD9, a tetraspanin-family transmembrane protein, as a key surface marker for most mouse IL-10+ B cells and their progenitors. CD9 plays a role in the suppressive function of IL-10+ B cells in ex vivo T cell proliferation assays through a mechanism that is dependent upon B/T cell interactions. CD9+ B cells also demonstrate inhibition of Th1-mediated contact hypersensitivity in an in vivo model system. Taken together, our findings implicate CD9 in the immunosuppressive activity of regulatory B cells.

Breast implant-associated lymphoma (BIA-ALCL) has recently been recognized as an independent peripheral T-cell lymphoma (PTCL) entity. In this study, we generated the first BIA-ALCL patient-derived tumor xenograft (PDTX) model (IL89) and a matching continuous cell line (IL89_CL#3488) to discover potential vulnerabilities and druggable targets. We characterized IL89 and IL89_CL#3488, both phenotypically and genotypically, and demonstrated that they closely resemble the matching human primary lymphoma. The tumor content underwent significant enrichment along passages, as confirmed by the increased variant allele frequency (VAF) of mutations. Known aberrations (JAK1 and KMT2C) were identified, together with novel hits, including PDGFB, PDGFRA, and SETBP1. A deep sequencing approach allowed the detection of mutations below the Whole Exome Sequencing (WES) sensitivity threshold, including JAK1G1097D, in the primary sample. RNA sequencing confirmed the expression of a signature of differentially expressed genes in BIA-ALCL. Next, we tested IL89’s sensitivity to the JAK inhibitor ruxolitinib and observed a potent anti-tumor effect, both in vitro and in vivo. We also implemented a high-throughput drug screening approach to identify compounds associated with increased responses in the presence of ruxolitinib. In conclusion, these new IL89 BIA-ALCL models closely recapitulate the primary correspondent lymphoma and represent an informative platform for dissecting the molecular features of BIA-ALCL and performing pre-clinical drug discovery studies, fostering the development of new precision medicine approaches.

The relationship between anti-tumoral immunity and cancer progression is complex. Recently, immune editing has emerged as a model to explain the interplay between the immune system and the selection of genetic alterations in cancer. In this model, the immune system selects cancer cells that grow as these are fit to escape immune surveillance during tumor development. Gliomas and glioblastoma, the most aggressive and most common of all primary malignant brain tumors are genetically heterogeneous, are relatively less antigenic, and are less responsive to immunotherapy than other cancers. In this review, we provide an overview of the relationship between glioma´s immune suppressive features, anti-tumoral immunity and cancer genomics. In this context, we provide a critical discussion of evidence suggestive of immune editing in this disease and discuss possible alternative explanations for these findings.

ABSTRACT Methicillin-susceptible Staphylococcus aureus (MSSA) accounts for the majority of S. aureus infections globally, and yet surprisingly little is known about its clonal evolution. We applied comparative whole-genome sequencing (WGS) analyses to epidemiologically and geographically diverse ST398-MSSA, a pandemic lineage affecting both humans and livestock. Bayesian phylogenetic analysis predicted divergence of human-associated ST398-MSSA ~40 years ago. Isolates from Midwestern pigs and veterinarians differed substantially from those in New York City (NYC). Pig ST398 strains contained a large region of recombination representing imports from multiple sequence types (STs). Phylogeographic analyses supported the spread of ST398-MSSA along local cultural and migratory links between parts of the Caribbean, North America, and France, respectively. Applying pairwise single-nucleotide polymorphism (SNP) distances as a measure of genetic relatedness between isolates, we observed that ST398 not only clustered in households but also frequently extended across local social networks. Isolates collected from environmental surfaces reflected the full diversity of colonizing individuals, highlighting their potentially critical role as reservoirs for transmission and diversification. Strikingly, we observed high within-host SNP variability compared to our previous studies on the dominant methicillin-resistant Staphylococcus aureus (MRSA) clone USA300. Our data indicate that the dynamics of colonization, persistence, and transmission differ substantially between USA300-MRSA and ST398-MSSA. Taken together, our study reveals local and international routes of transmission for a major MSSA clone, indicating key impacts of recombination and mutation on genetic diversification and highlighting important ecological differences from epidemic USA300. Our study demonstrates extensive local and international routes of transmission for a major MSSA clone despite the lack of substantial antibiotic resistance. IMPORTANCE Unlike methicillin-resistant Staphylococcus aureus (MRSA), surprisingly little is known about the clonal evolution of methicillin-susceptible S. aureus (MSSA), although these strains account for the majority of S. aureus infections. To better understand how MSSA spreads and becomes established in communities, we applied comparative bacterial whole-genome sequencing to pandemic ST398-MSSA, a clone of clinical importance affecting humans and livestock in different geographic regions. Phylogeographic analyses identified that ST398-MSSA spread along local cultural and migratory links between parts of the Caribbean, North America, and France, respectively. We observed high within-host SNP variability compared to our previous studies on the dominant MRSA clone USA300. Our data indicate that the dynamics of colonization, persistence, and transmission differ substantially between USA300 MRSA and ST398 MSSA.

Follicular lymphoma (FL) is an indolent disease, but 30%–40% of cases undergo histologic transformation to an aggressive malignancy, typically represented by diffuse large B cell lymphoma (DLBCL). The pathogenesis of this process remains largely unknown. Using whole-exome sequencing and copy-number analysis, we show here that the dominant clone of FL and transformed FL (tFL) arise by divergent evolution from a common mutated precursor through the acquisition of distinct genetic events. Mutations in epigenetic modifiers and antiapoptotic genes are introduced early in the common precursor, whereas tFL is specifically associated with alterations deregulating cell-cycle progression and DNA damage responses (CDKN2A/B, MYC, and TP53) as well as aberrant somatic hypermutation. The genomic profile of tFL shares similarities with that of germinal center B cell-type de novo DLBCL but also displays unique combinations of altered genes with diagnostic and therapeutic implications.

Missense mutations in ATM kinase, a master regulator of DNA damage responses, are found in many cancers, but their impact on ATM function and implications for cancer therapy are largely unknown. Here we report that 72% of cancer-associated ATM mutations are missense mutations that are enriched around the kinase domain. Expression of kinase-dead ATM (AtmKD/-) is more oncogenic than loss of ATM (Atm-/-) in mouse models, leading to earlier and more frequent lymphomas with Pten deletions. Kinase-dead ATM protein (Atm-KD), but not loss of ATM (Atm-null), prevents replication-dependent removal of Topo-isomerase I-DNA adducts at the step of strand cleavage, leading to severe genomic instability and hypersensitivity to Topo-isomerase I inhibitors. Correspondingly, Topo-isomerase I inhibitors effectively and preferentially eliminate AtmKD/-, but not Atm-proficientor Atm-/- leukemia in animal models. These findings identify ATM kinase-domain missense mutations as a potent oncogenic event and a biomarker for Topo-isomerase I inhibitor based therapy.

Endemic Burkitt lymphoma (eBL) is primarily found in children in equatorial regions and represents the first historical example of a virus-associated human malignancy. Although Epstein-Barr virus (EBV) infection and MYC translocations are hallmarks of the disease, it is unclear whether other factors may contribute to its development. We performed RNA-Seq on 20 eBL cases from Uganda and showed that the mutational and viral landscape of eBL is more complex than previously reported. First, we found the presence of other herpesviridae family members in 8 cases (40%), in particular human herpesvirus 5 and human herpesvirus 8 and confirmed their presence by immunohistochemistry in the adjacent non-neoplastic tissue. Second, we identified a distinct latency program in EBV involving lytic genes in association with TCF3 activity. Third, by comparing the eBL mutational landscape with published data on sporadic Burkitt lymphoma (sBL), we detected lower frequencies of mutations in MYC, ID3, TCF3 and TP53, and a higher frequency of mutation in ARID1A in eBL samples. Recurrent mutations in two genes not previously associated with eBL were identified in 20% of tumors: RHOA and cyclin F (CCNF). We also observed that polyviral samples showed lower numbers of somatic mutations in common altered genes in comparison to sBL specimens, suggesting dual mechanisms of transformation, mutation versus virus driven in sBL and eBL respectively.

Influenza is an RNA virus that causes mild to severe respiratory symptoms in humans and other hosts. Every year approximately half a million people around the world die from seasonal Influenza. But this number is substantially larger in the case of pandemics, with the most dramatic instance being the 1918 “Spanish flu” that killed more than 50 million people worldwide. In the last few years, thousands of Influenza genomic sequences have become publicly available, including the 1918 pandemic strain and many isolates from non-human hosts. Using these data and developing adequate bioinformatic and statistical tools, some of the major questions surrounding Influenza evolution are becoming tractable. Are the mutations and reassortments random? What are the patterns behind the virus’s evolution? What are the necessary and sufficient conditions for a virus adapted to one host to infect a different host? Why is Influenza seasonal? In this review, we summarize some of the recent progress in understanding the evolution of the virus.

Cancer is a clonal evolutionary process, caused by successive accumulation of genetic alterations providing milestones of tumor initiation, progression, dissemination, and/or resistance to certain therapeutic regimes. To unravel these milestones we propose a framework, tumor evolutionary directed graphs (TEDG), which is able to characterize the history of genetic alterations by integrating longitudinal and cross-sectional genomic data. We applied TEDG to a chronic lymphocytic leukemia (CLL) cohort of 70 patients spanning 12 years and show that: (a) the evolution of CLL follows a time-ordered process represented as a global flow in TEDG that proceeds from initiating events to late events; (b) there are two distinct and mutually exclusive evolutionary paths of CLL evolution; (c) higher fitness clones are present in later stages of the disease, indicating a progressive clonal replacement with more aggressive clones. Our results suggest that TEDG may constitute an effective framework to recapitulate the evolutionary history of tumors.

ABSTRACT With the advent of deep sequencing, genomic surveillance has become a popular method for detection of infectious disease, supplementing information gathered by classic clinical or serological techniques to identify host-determinant markers and trace the origin of transmission. However, two main factors complicate genomic surveillance. First, pathogens exhibiting high genetic diversity demand higher levels of scrutiny to obtain an accurate representation of the entire population. Second, current systems of detection are nonuniform, with significant gaps in certain geographic locations and animal reservoirs. Despite past unforeseen pandemics like the 2009 swine-origin H1N1 influenza virus, there is no standardized way of evaluating surveillance. A more complete surveillance system should capture a greater proportion of pathogen diversity. Here we present a novel quantitative method of assessing the completeness of genomic surveillance that incorporates the time of sequence collection, as well as the pathogen’s evolutionary rate. We propose the q2 coefficient, which measures the proportion of sequenced isolates whose closest neighbor in the past is within a genetic distance equivalent to 2 years of evolution, roughly the median time of changing strain selection for influenza A vaccines. Easily interpretable and significantly faster than other methods, the q2 coefficient requires no full phylogenetic characterization or use of arbitrary clade definitions. Application of the q2 coefficient to influenza A virus confirmed poor sampling of swine and avian populations and identified regions with deficient surveillance. We demonstrate that the q2 coefficient can not only be applied to other pathogens, including dengue and West Nile viruses, but also used to describe surveillance dynamics, particularly the effects of different public health policies. IMPORTANCE Surveillance programs have become key assets in determining the emergence or prevalence of pathogens circulating in human and animal populations. Genomic surveillance, in particular, provides comprehensive information on the history of isolates and potential molecular markers for infectivity and pathogenicity. Current techniques for evaluating genomic surveillance are inaccurate, ignoring the pathogen’s evolutionary rate and biodiversity, as well as the timing of sequence collection. Using sequence data, we propose the q2 coefficient as a quantitative measure of surveillance completeness that combines elements of time and evolution without defining arbitrary criteria for clades or species. Through several case studies of influenza A, dengue, and West Nile viruses, we employed the q2 coefficient to identify sampling deficiencies in different host species and locations, as well as examine the effects of different public health policies through historical records of the q2 coefficient. These results can guide public health agencies to focus resource allocation and virus collection to bolster specific problems in surveillance.

Electronic health record (EHR) systems offer an exceptional opportunity for studying many diseases and their associated medical conditions within a population. The increasing number of clinical record entries that have become available electronically provides access to rich, large sets of patients' longitudinal medical information. By integrating and comparing relations found in the EHRs with those already reported in the literature, we are able to verify existing and to identify rare or novel associations. Of particular interest is the identification of rare disease co-morbidities, where the small numbers of diagnosed patients make robust statistical analysis difficult. Here, we introduce ADAMS, an Application for Discovering Disease Associations using Multiple Sources, which contains various statistical and language processing operations. We apply ADAMS to the New York-Presbyterian Hospital's EHR to combine the information from the relational diagnosis tables and textual discharge summaries with those from PubMed and Wikipedia in order to investigate the co-morbidities of the rare diseases Kaposi sarcoma, toxoplasmosis, and Kawasaki disease. In addition to finding well-known characteristics of diseases, ADAMS can identify rare or previously unreported associations. In particular, we report a statistically significant association between Kawasaki disease and diagnosis of autistic disorder.

Identifying emerging viral pathogens and characterizing their transmission is essential to developing effective public health measures in response to an epidemic. Phylogenetics, though currently the most popular tool used to characterize the likely host of a virus, can be ambiguous when studying species very distant to known species and when there is very little reliable sequence information available in the early stages of the outbreak of disease. Motivated by an existing framework for representing biological sequence information, we learn sparse, tree-structured models, built from decision rules based on subsequences, to predict viral hosts from protein sequence data using popular discriminative machine learning tools. Furthermore, the predictive motifs robustly selected by the learning algorithm are found to show strong host-specificity and occur in highly conserved regions of the viral proteome.

Although vaccines pose the best means of preventing influenza infection, strain selection and optimal implementation remain difficult due to antigenic drift and a lack of understanding global spread. Detecting viral movement by sequence analysis is complicated by skewed geographic and seasonal distributions in viral isolates. We propose a probabilistic method that accounts for sampling bias through spatiotemporal clustering and modeling regional and seasonal transmission as a binomial process. Analysis of H3N2 not only confirmed East-Southeast Asia as a source of new seasonal variants, but also increased the resolution of observed transmission to a country level. H1N1 data revealed similar viral spread from the tropics. Network analysis suggested China and Hong Kong as the origins of new seasonal H3N2 strains and the United States as a region where increased vaccination would maximally disrupt global spread of the virus. These techniques provide a promising methodology for the analysis of any seasonal virus, as well as for the continued surveillance of influenza.

In June of 2009, the World Health Organization declared the first influenza pandemic of the 21(st) century, and by July, New York City's New York-Presbyterian Hospital (NYPH) experienced a heavy burden of cases, attributable to a novel strain of the virus (H1N1pdm).We present the signs in the NYPH electronic health records (EHR) that distinguished the 2009 pandemic from previous seasonal influenza outbreaks via various statistical analyses. These signs include (1) an increase in the number of patients diagnosed with influenza, (2) a preponderance of influenza diagnoses outside of the normal flu season, and (3) marked vaccine failure. The NYPH EHR also reveals distinct age distributions of patients affected by seasonal influenza and the pandemic strain, and via available longitudinal data, suggests that the two may be associated with distinct sets of comorbid conditions as well. In particular, we find significantly more pandemic flu patients with diagnoses associated with asthma and underlying lung disease. We further observe that the NYPH EHR is capable of tracking diseases at a resolution as high as particular zip codes in New York City.The NYPH EHR permits early detection of pandemic influenza and hypothesis generation via identification of those significantly associated illnesses. As data standards develop and databases expand, EHRs will contribute more and more to disease detection and the discovery of novel disease associations.

ABSTRACT Environmental metagenomic samples and samples obtained as an attempt to identify a pathogen associated with the emergence of a novel infectious disease are important sources of novel microorganisms. The low costs and high throughput of sequencing technologies are expected to allow for the genetic material in those samples to be sequenced and the genomes of the novel microorganisms to be identified by alignment to those in a database of known genomes. Yet, for various biological and technical reasons, such alignment might not always be possible. We investigate a frequency analysis technique which on one hand allows for the identification of genetic material without relying on alignment and on the other hand makes possible the discovery of nonoverlapping contigs from the same organism. The technique is based on obtaining signatures of the genetic data and defining a distance/similarity measure between signatures. More precisely, the signatures of the genetic data are the frequencies of k-mers occurring in them, with k being a natural number. We considered an entropy-based distance between signatures, similar to the Kullback-Leibler distance in information theory, and investigated its ability to categorize negative-sense single-stranded RNA (ssRNA) viral genetic data. Our conclusion is that in this viral context, the technique provides a viable way of discovering genetic relationships without relying on alignment. We envision that our approach will be applicable to other microbial genetic contexts, e.g., other types of viruses, and will be an important tool in the discovery of novel microorganisms. IMPORTANCE Multiple factors contribute to the emergence of novel infectious diseases. Implementation of effective measures against such diseases relies on the rapid identification of novel pathogens. Another important source of novel microorganisms is environmental metagenomic samples. The low costs and high throughput of sequencing technologies provide a method for the identification of novel microorganisms by sequence alignment. There are several obstacles to this method, as follows: our knowledge of biology is biased by an anthropomorphic view, microbial genomic material could be a minuscule fraction of the sample, the sequencing and enrichment technologies can be a source of errors and biases, and finally, microbes have high diversity and high evolutionary rates. As a result, novel microorganisms could have very low genetic similarity to already known genomes, and the identification by alignment could be computationally prohibitive. We investigate a frequency analysis technique which allows for the identification of novel genetic material without relying on alignment.

Atlantic salmon (Salmo salar L.) mariculture has been associated with epidemics of infectious diseases that threaten not only local production, but also wild fish coming into close proximity to marine pens and fish escaping from them. Heart and skeletal muscle inflammation (HSMI) is a frequently fatal disease of farmed Atlantic salmon. First recognized in one farm in Norway in 1999, HSMI was subsequently implicated in outbreaks in other farms in Norway and the United Kingdom. Although pathology and disease transmission studies indicated an infectious basis, efforts to identify an agent were unsuccessful. Here we provide evidence that HSMI is associated with infection with piscine reovirus (PRV). PRV is a novel reovirus identified by unbiased high throughput DNA sequencing and a bioinformatics program focused on nucleotide frequency as well as sequence alignment and motif analyses. Formal implication of PRV in HSMI will require isolation in cell culture and fulfillment of Koch's postulates, or prevention or modification of disease through use of specific drugs or vaccines. Nonetheless, as our data indicate that a causal relationship is plausible, measures must be taken to control PRV not only because it threatens domestic salmon production but also due to the potential for transmission to wild salmon populations.

Three human influenza pandemics occurred in the twentieth century, in 1918, 1957, and 1968. Influenza pandemic strains are the results of emerging viruses from non-human reservoirs to which humans have little or no immunity. At least two of these pandemic strains, in 1957 and in 1968, were the results of reassortments between human and avian viruses. Also, many cases of swine influenza viruses have reportedly infected humans, in particular, the recent H1N1 influenza virus of swine origin, isolated in Mexico and the United States. Pigs are documented to allow productive replication of human, avian, and swine influenza viruses. Thus it has been conjectured that pigs are the "mixing vessel" that create the avian-human reassortant strains, causing the human pandemics. Hence, studying the process and patterns of viral reassortment, especially in pigs, is a key to better understanding of human influenza pandemics. In the last few years, databases containing sequences of influenza A viruses, including swine viruses, collected since 1918 from diverse geographical locations, have been developed and made publicly available. In this paper, we study an ensemble of swine influenza viruses to analyze the reassortment phenomena through several statistical techniques. The reassortment patterns in swine viruses prove to be similar to the previous results found in human viruses, both in vitro and in vivo, that the surface glycoprotein coding segments reassort most often. Moreover, we find that one of the polymerase segments (PB1), reassorted in the strains responsible for the last two human pandemics, also reassorts frequently.

Since the spring of 1977, two subtypes of influenza A virus (H3N2 and H1N1) have been seasonally infecting the human population. In this work we study the distribution of patient ages within the populations that exhibit the symptomatic disease caused by each of the different subtypes of seasonal influenza viruses. When the publicly available extensive information is pooled across multiple geographical locations and seasons, striking differences emerge between these subtypes. We report that the symptomatic flu due to H1N1 is distributed mainly in a younger population relative to H3N2. (The median age of the H3N2 patients is 23 years while H1N1 patients are 9 years old.) These distinct characteristic spectra of age groups, possibly carried over from previous pandemics, are consistent with previous reports from various regional population studies and also findings on the evolutionary dynamics of each subtype. Moreover, they are relevant to age-related risk assessments, modeling of epidemiological networks for specific age groups, and age-specific vaccine design. Recently, a novel H1N1 virus has spread around the world. Preliminary reports suggest that this new strain causes symptomatic disease in the younger population in a similar fashion to the seasonal H1N1 strains.

The innate immune response provides a first line of defense against pathogens by targeting generic differential features that are present in foreign organisms but not in the host. These innate responses generate selection forces acting both in pathogens and hosts that further determine their co-evolution. Here we analyze the nucleic acid sequence fingerprints of these selection forces acting in parallel on both host innate immune genes and ssRNA viral genomes. We do this by identifying dinucleotide biases in the coding regions of innate immune response genes in plasmacytoid dendritic cells, and then use this signal to identify other significant host innate immune genes. The persistence of these biases in the orthologous groups of genes in humans and chickens is also examined. We then compare the significant motifs in highly expressed genes of the innate immune system to those in ssRNA viruses and study the evolution of these motifs in the H1N1 influenza genome. We argue that the significant under-represented motif pattern of CpG in an AU context--which is found in both the ssRNA viruses and innate genes, and has decreased throughout the history of H1N1 influenza replication in humans--is immunostimulatory and has been selected against during the co-evolution of viruses and host innate immune genes. This shows how differences in host immune biology can drive the evolution of viruses that jump into species with different immune priorities than the original host.

We report results from the analysis of complete mitochondrial DNA (mtDNA) sequences from 112 Japanese semi-supercentenarians (aged above 105 years) combined with previously published data from 96 patients in each of three non-disease phenotypes: centenarians (99-105 years of age), healthy non-obese males, obese young males and four disease phenotypes, diabetics with and without angiopathy, and Alzheimer's and Parkinson's disease patients. We analyze the correlation between mitochondrial polymorphisms and the longevity phenotype using two different methods. We first use an exhaustive algorithm to identify all maximal patterns of polymorphisms shared by at least five individuals and define a significance score for enrichment of the patterns in each phenotype relative to healthy normals. Our study confirms the correlations observed in a previous study showing enrichment of a hierarchy of haplogroups in the D clade for longevity. For the extreme longevity phenotype we see a single statistically significant signal: a progressive enrichment of certain "beneficial" patterns in centenarians and semi-supercentenarians in the D4a haplogroup. We then use Principal Component Spectral Analysis of the SNP-SNP Covariance Matrix to compare the measured eigenvalues to a Null distribution of eigenvalues on Gaussian datasets to determine whether the correlations in the data (due to longevity) arises from some property of the mutations themselves or whether they are due to population structure. The conclusion is that the correlations are entirely due to population structure (phylogenetic tree). We find no signal for a functional mtDNA SNP correlated with longevity. The fact that the correlations are from the population structure suggests that hitch-hiking on autosomal events is a possible explanation for the observed correlations.

It is well known that the dinucleotide CpG is under-represented in the genomic DNA of many vertebrates. This is commonly thought to be due to the methylation of cytosine residues in this dinucleotide and the corresponding high rate of deamination of 5-methycytosine, which lowers the frequency of this dinucleotide in DNA. Surprisingly, many single-stranded RNA viruses that replicate in these vertebrate hosts also have a very low presence of CpG dinucleotides in their genomes. Viruses are obligate intracellular parasites and the evolution of a virus is inexorably linked to the nature and fate of its host. One therefore expects that virus and host genomes should have common features. In this work, we compare evolutionary patterns in the genomes of ssRNA viruses and their hosts. In particular, we have analyzed dinucleotide patterns and found that the same patterns are pervasively over- or under-represented in many RNA viruses and their hosts suggesting that many RNA viruses evolve by mimicking some of the features of their host's genes (DNA) and likely also their corresponding mRNAs. When a virus crosses a species barrier into a different host, the pressure to replicate, survive and adapt, leaves a footprint in dinucleotide frequencies. For instance, since human genes seem to be under higher pressure to eliminate CpG dinucleotide motifs than avian genes, this pressure might be reflected in the genomes of human viruses (DNA and RNA viruses) when compared to those of the same viruses replicating in avian hosts. To test this idea we have analyzed the evolution of the influenza virus since 1918. We find that the influenza A virus, which originated from an avian reservoir and has been replicating in humans over many generations, evolves in a direction strongly selected to reduce the frequency of CpG dinucleotides in its genome. Consistent with this observation, we find that the influenza B virus, which has spent much more time in the human population, has adapted to its human host and exhibits an extremely low CpG dinucleotide content. We believe that these observations directly show that the evolution of RNA viral genomes can be shaped by pressures observed in the host genome. As a possible explanation, we suggest that the strong selection pressures acting on these RNA viruses are most likely related to the innate immune response and to nucleotide motifs in the host DNA and RNAs.

This manuscript describes a novel strategy to improve HIV DNA vaccine design. Employing a new information theory based bioinformatic algorithm, we identify a set of nucleotide motifs which are common in the coding region of HIV, but are under-represented in genes that are highly expressed in the human genome. We hypothesize that these motifs contribute to the poor protein expression of gag, pol, and env genes from the c-DNAs of HIV clinical isolates. Using this approach and beginning with a codon optimized consensus gag gene, we recode the nucleotide sequence so as to remove these motifs without modifying the amino acid sequence. Transfecting the recoded DNA sequence into a human kidney cell line results in doubling the gag protein expression level compared to the codon optimized version. We then turn both sequences into DNA vaccines and compare induced antibody response in a murine model. Our sequence, which has the motifs removed, induces a five-fold increase in gag antibody response compared to the codon optimized vaccine.