CELL-FREE NUCLEIC ACIDS FOR THE ANALYSIS OF THE HUMAN MICROBIOME AND ITS COMPONENTS

Abstract

Methods, devices, compositions, and equipment are provided for the analysis of the microbiome or individual components thereof in an individual. These methods are used in the determination of infection, the analysis of microbiome structure, the determination of an individual's immunocompetence, and similar applications. In some embodiments of the invention, the individual is treated with a therapeutic regimen, such as drugs, diet, radiation therapy, and the like.

Description

CELL-FREE NUCLEIC ACIDS FOR THE ANALYSIS OF THE HUMAN MICROBIOME AND ITS COMPONENTS BACKGROUND OF THE INVENTION The human microbiome is now recognized as an important component of human health. Analyses at the community level have shed light on factors that shape the structure of the microbiome's bacterial and viral components, such as age, diet, geographic location, antibiotic treatment, and disease. For example, an individual's microbiome can be altered by an infection with a pathogenic organism, resulting in an increased prevalence of that organism systemically or in an undesirable tissue. The microbiome can also be altered by changes in an individual's immunocompetence. For each variety of purposes, it would be desirable to have a method for the rapid identification of specific microbiome components, such as the presence and prevalence of commensal, mutualistic, parasitic, opportunistic, and pathogenic organisms in an individual's microbiome, as well as an analysis of the overall microbiome structure. The present invention provides sensitive, rapid, and non-invasive methods for verifying microbiome composition in clinical samples. BRIEF DESCRIPTION OF THE INVENTION The invention provides methods, devices, compositions, and equipment for analyzing the microbiome or individual components thereof in an individual. The methods are used in determining infection, analyzing microbiome structure, determining an individual's immunocompetence, and similar applications. In some embodiments, the invention provides methods for determining the presence and prevalence of microorganisms in an individual, comprising the steps of: (i) providing a sample of cell-free nucleic acids, i.e., DNA and / or RNA from an individual; (ii) performing high-throughput sequencing, for example, of approximately 10⁵ and up to approximately 10⁹ or more reads per sample; and (iii) performing bioinformatics analysis to extract host sequences, i.e., from humans, cats, dogs, etc.from the analysis; and (iv) determine the presence and prevalence of microbial sequences, for example by comparing the convergence of the sequence map to a microbial reference sequence to cover the host reference sequence. The subtraction of host sequences may involve identifying a reference host sequence and masking or mimicking microbial sequences present in the reference host genome. Similarly, determining the presence of a microbial sequence by comparison with a microbial reference sequence may involve identifying a microbial reference sequence and masking or mimicking host sequences present in the reference microbial genome. A feature of the invention is the non-biased analysis of cell-free nucleic acids from an individual. The methods of the invention generally include a non-biased amplification step, for example, by performing PCR with universal primers, or by ligating adapters to the nucleic acid and amplifying with adapter-specific / ROQnn / Lznz / E / Yi primers. The methods of the invention are typically performed without sequence-specific amplification of the microbial sequences. A benefit of this approach is that the analysis includes all available microbiome sequences; however, it requires bioinformatics analysis to identify sequences of interest within the complex dataset predetermined by the host sequences. A further benefit of the methods of the invention is the ability to produce a rapid assessment of an individual's microbiome, for example the analysis can be completed in less than approximately 3 days, less than approximately 2 days, less than one day, for example less than approximately 24 hours, less than approximately 20 hours, less than approximately 18 hours, less than approximately 14 hours, less than approximately 12 hours, less than approximately 6 hours, less than approximately 2 hours, less than approximately 30 minutes, less than approximately 15 minutes, less than approximately 1 minute. In some modalities, cell-free nucleic acid analysis is used to calculate a pathogenicity score, where the pathogenicity score is a numerical or alphanumeric value that summarizes the overall pathogenicity of the organism to facilitate interpretation, for example, by a healthcare practitioner. Different microbes present in the microbiome can be assigned different scores. Analysis of the presence and prevalence of microbial sequences can be used to determine the presence of an infection, assess the response to therapy (including antimicrobial treatment such as antibiotics, antiviral agents, immunization, passive immunotherapy, and similar therapies), evaluate dietary responses, assess immunosuppression, and evaluate responses in clinical trials, and more. The information obtained from these analyses can be used to diagnose a condition, verify treatment effectiveness, select or modify therapeutic regimens, and optimize therapy. With this approach, therapeutic and / or diagnostic regimens can be individualized and designed based on specificity data obtained at different time points during the course of treatment, thus providing a regimen that is appropriate for each individual.Furthermore, patient samples can be obtained at any point during the treatment process, after exposure to a pathogen, during the course of the infection, etc., for analysis. The analysis of the presence and prevalence of microbial sequences can be provided as a report. This report can be provided to the individual, a healthcare professional, etc. In some methods, cell-free nucleic acid is obtained from a selected biological sample consisting of blood, serum, cerebrospinal fluid, synovial fluid, urine, and feces. The nucleic acid is extracted from the cell-free portion of the sample; for example, the serum or plasma portion of blood may be used. In some methods, the nucleic acid is selected from the group consisting of double-stranded DNA, single-stranded DNA, single-stranded DNA hairpins, DNA / RNA hybrids, single-stranded RNA, double-stranded RNA, and RNA hairpins. In some methods, the nucleic acid is selected from the group consisting of double-stranded DNA, single-stranded DNA, and cDNA. In some methods, the nucleic acid is mRNA. In some methods, the nucleic acid is circulating cell-free DNA. In some methods, the quantification of one or more nucleic acids is used to determine the prevalence of a microorganism in the sample. In some methods, the quantity of one or more nucleic acids above a predetermined threshold value is indicative of infection or altered prevalence. In some methods, different predetermined threshold values ​​exist for different microbial organisms. In some methods, temporal differences in the quantity of one or more nucleic acids are indicative of changes in infection, altered prevalence, response to therapy, etc. In some embodiments, the invention provides computer-readable means comprising: a set of instructions recorded thereon for causing a computer to perform the steps of: (i) receiving high-throughput sequencing data of one or more nucleic acids detected in a cell-free nucleic acid sample from a subject; (ii) performing bioinformatics analyses to subtract host sequences, i.e., human, cat, dog, etc., from the analysis; and (ii) determining the presence and prevalence of microbial sequences, for example by comparing the coverage of the sequence maps with a microbial reference sequence to cover the host reference sequence. In some embodiments, the invention provides reagents and equipment for practicing one or more of the methods described herein. In some embodiments, the compositions and methods are provided for evaluating the immunocompetence of an individual, particularly a human individual, by microbiome analysis, for example, by virome analysis. In some embodiments of the invention, the individual is treated with an immunosuppressive regimen, for example, drugs, radiation therapy, and the like. In some embodiments, the individual is a graft recipient treated with an immunosuppressive regimen. In some embodiments, the individual has an autoimmune disease treated with an immunosuppressive regimen. In other embodiments, the individual is evaluated for immunocompetence in the absence of an immunosuppressive regimen. In some modalities, an individual's viral load is measured at two points in time, where a change in viral load is indicative of a change in immunocompetence. The individual can then be treated according to the immunocompetence assessment; for example, an indication of undesirable increased immunocompetence in a transplant patient is treated with increased levels of immunosuppressive agents, or an undesirable decrease in immunocompetence is treated with therapeutic agents, such as antivirals. Nucleic acid analysis is used to identify and quantify cell-free nucleic acids from non-human cells in a sample collected from a patient. The composition of the microbiome components is determined as previously described. The structure of the viral component of the microbiome (the virome) allows for prediction of immunocompetence. In some modalities, the methods also include establishing a virome profile before, at the start of, or during an immunosuppressive regimen, which is used as a reference for changes in the individual virome. In some modalities, circulating cell-free DNA is anellovirus DNA. In particular, the viral load of the anelloviridae family is a predictor of immune resistance, which correlates with the likelihood of organ transplant rejection. Although other viruses can also be predictive, it is common for patients to be treated with antivirals that affect the viral load of those viruses. In some embodiments, the invention provides methods for diagnosing or predicting transplant status or outcome, comprising the steps of: (i) providing a sample from a subject who has received a donor transplant; (ii) determining the presence or absence of one or more virome nucleic acids; and (iii) diagnosing or predicting transplant status or outcome based on the virome load. In some embodiments, transplant status or outcome comprises rejection, tolerance, absence of rejection based on allograft damage, transplant function, transplant survival, chronic transplant injury, or pharmacological immunosuppression titer. In some embodiments, the quantity of one or more nucleic acids above a predetermined threshold value is indicative of viral load and immunocompetence.In some modalities, the threshold is a normative value for clinically stable post-transplant patients with no evidence of transplant rejection or other pathologies. In some modalities, different predetermined threshold values ​​exist for different transplant outcomes or states. In some modalities, temporal differences in the amount of one or more nucleic acids are indicative of immunocompetence. In any of the modalities described herein, the transplant graft may be any solid organ, bone marrow, or skin graft. In some modalities, the transplant is selected from the group consisting of kidney, heart, liver, pancreas, lung, intestine, and skin grafts. In some embodiments, the invention provides reagents and equipment for practicing one or more of the methods described herein. All publications and patent applications mentioned in this specification are incorporated herein by reference to the same degree as if each individual publication or patent application were specifically or individually indicated as incorporated by reference. BRIEF DESCRIPTION OF THE FIGURES The novel features of the invention are set forth in detail in the appended claims. A better understanding of the features and advantages of the present invention will be gained with reference to the following detailed description, which sets forth illustrative embodiments in which the principles of the invention are used, and the accompanying Figures in which: Figures 1A-1F. Study design, read statistics, and phylogenetic distribution. Fig. 1A. Immunosuppression reduces the risk of rejection in transplantation but increases the risk of infection. Fig. 1B. Study design. 656 plasma samples were collected, and cell-free DNA was purified and sequenced to an average depth of 1.2 Gbp per sample. Fig. 1C. Number of samples collected as a function of time for different patient groups in the study. Fig. 1D. Treatment protocol for patients in the study cohort. All patients are treated with maintenance immunosuppression (based on tacrolimus (TAC) for adult heart and lung transplant recipients and cyclosporine (CYC) for pediatric patients). CMV-positive transplant recipients (donor or recipient, CMV+) are treated with anti-CMV prophylaxis, valganciclovir (VAL).Mean level of tacrolimus measured in blood of transplant recipients treated with a CT-based protocol (actual dotted line, solid line, window average filter) Fig.1E. Fraction of readings remaining after filtering of low-quality and duplicate readings (mean, 86%, left) and after removal of human and low-complexity readings (mean, 2%, right). Fig.1F. Relative genomic abundance at different levels of taxonomic classification after removal of human readings (average over all samples from all organ transplant recipients (n = 656)). Figure 2A-2C. Relative viral genomic abundance as a function of drug dose and comparison with a healthy reference. Fig. 2A. Mean virome composition for patients treated with the immunosuppressant tacrolimus (47 patients, 380 samples) as a function of antiviral drug dose (valganciclovir) and tacrolimus concentration measured in blood. To account for the delayed effect of virome composition on drug dose, drug dose data were filtered by an average window filter (window size of 45 days, see Figure 1C). Herpesvirales and caudovirales dominate the virome when patients receive low doses of immunosuppressant and antiviral drugs. Conversely, anelloviridae dominate the virome when patients receive high doses of these drugs. Fig. 2B.Comparison of the virome composition corresponding to healthy references (n = 9), day 1 post-transplant samples with low drug exposure (n = 13), and samples corresponding to high drug exposure (tacrolimus > 9 ng / mL, valganciclovir > 600 mg, n = 68). The virome structure for day (1) samples and the virome structure measured for a group of healthy individuals (H) are distinct from the anellovirus-dominated distribution measured for samples corresponding to high drug doses (D). Pie charts show mean fractions, p-values ​​in box plots or square plots for the Mann-Whitney test. Fig. 2C. Bray-Curtis beta diversity of all samples, among patients with the same type of transplant (heart or lungs), within subjects for patients treated with a similar drug dose (tacrolimus level ± 0).5 ng / mL, valganciclovir ± 50 mg), and for samples collected from the same subjects within a one-month time interval. Figure 3A-3D. Temporal dynamics of microbiome composition after transplantation. Fig. 3A. Relative abundance of dsDNA and ssDNA viruses for different time periods (average for all samples). The relative abundance of ssDNA viruses increases rapidly after the onset of drug therapy post-transplant. After 6 months, the opposite trend is observed. Fig. 3B. Viral genome abundance at the family and order level of the taxonomic classification for different time periods. The Anelloviridae fraction expands rapidly in the first few months after transplantation. The Herpesvirales, Caudovirales, and Adenoviridae fractions decrease during the same period. After 6 months, opposite trends are observed. Fig. 3C. Variation over time in the relative abundance of bacterial lineages.Compared to viral abundance, the representation of different bacterial lineages does not change relatively during the observed post-transplant period. Fig. 3D. Shannon entropy as a measure of alpha diversity within the sample for bacterial and viral genera as a function of time (data pooled over a one-month period). Figure 4A-4C. Virome composition and total viral load in the absence and presence of antiviral prophylaxis. Fig. 4A. Absolute viral load as a function of time, measured as copies of the viral genome per copies of the human genome / ROQnn / Lznz / E / Yi detected by sequencing. Box plots are shown for different time periods with the centers of the time periods marked on the x-axis. For all patient classes, the total viral load increases in the first few weeks after transplantation (black line is the sigmoid fit, change in load of 7.4 ± 3). Fig. 4B. Viral load and composition for CMV+ cases treated with immunosuppressants and antivirals (78 patients, 543 samples). Fig. 4C. Viral load and composition for CMV- / - cases treated only with immunosuppressants (12 patients, 75 samples). Figures 5A-5C. Lower anellovirus load in patients with graft rejection. Fig. 5A. Time dependence of anellovirus load in the subgroup of patients with a severe rejection episode (grade 2R / 3A biopsy, red data, 20 patients, 177 time points) and in the group of patients without a post-transplant course without severe rejection (blue data, 40 patients, 285 time points). Box plots are shown for different time periods with the centers of the time periods marked on the x-axis. Solid lines are cubic slots (smoothing parameter 0.75). The inset shows a box representing the expected inverse association of rejection and infection incidence with immunocompetence. Fig. 5B. Anellovirus load relative to the average load measured for all samples at the same time point.The time-normalized viral load for patients without rejection (N = 208) is compared with the viral load measured for patients experiencing a moderate rejection event (grade 1R biopsy, N = 102) and patients experiencing a severe rejection event (grade > 2R / 3A biopsy, N = 22). The p-values ​​reflect the probability that the mean viral load is higher for subgroups at greater risk of rejection. The p-values ​​are calculated by random sampling from the population with the largest number of measurement points. N-times random sampling, p = ∑(median(AreRejection) > median(AsInRejection)) / N), where N = 104 and AreRejection and AsInRejection are the relative viral loads of the populations at higher and lower risk of rejection and non-rejection, respectively. Fig. 5C. Test of the performance of relative anellovirus load in classifying patients with rejection versus severe rejection, receiver operating characteristic curve, area under the curve = 0.72. Figures 6A-6F. Genome Sizes and Hit Statistics, qPCR Assays, and Influence of Read Length on the Measured Relative Abundance of Species at Different Levels of Taxonomic Classification. (Fig. 6A) Distribution of genome sizes in the reference database with 1401 viral genomes, 32 fungal genomes, and 1980 bacterial genomes. (Fig. 6B) Distribution of unique hits per million unique molecules sequenced (average number of hits specified in the x-axis label). (Fig. 6C) Distribution of genome equivalents (infectious agents / diploid human) for viruses, bacteria, and fungi (average number of genome equivalents specified in the x-axis label). (Fig. 6D) Comparison of sequencing hits found per million total reads sequenced with the number of viral copies detected using qPCR. For qPCR assays, DNA was purified from 1 mL of plasma and eluted into a volume of 100 pL. (Fig.6E) CMV and parvovirus load measurements for selected cases. The highest CMV loads (genome equivalents, viral / diploid human, GE) measured for all samples corresponded to two cases of clinically diagnosed disseminated CMV infection (a and b, the shaded area denotes the clinical diagnosis time window, * denotes the time of death), (o) shows the follow-up over time of a particular patient suffering from CMV viremia. Parvovirus was detected in a pediatric heart transplant patient immediately after transplantation (d).* (Fig. 6F) Influence of read length on the measured relative abundance of species at different levels of taxonomic classification (n = 52). Spearman's sample-to-sample correlation, r, and p-value, p, (two-sample MannWhitney U test) for the abundance of the most abundant node extracted from the 50 and 100 bp datasets: r = 0.80, p = 0.8 (a), r = 0.86, p = 0.4 (b), r = 0.92, p = 0.6 (c), r = 0.84, p = 0.5 (d), r = 0.7, p = 0.28 (e), r = 0.99, p = 1 (f). Figures 7A-7E. Average Drug Dose and Measured Levels for Adult Heart and Lung Transplant Patients Post-Transplant and Influence of Drug Dose on Virome Composition. (Fig. 7A-7C) Average dose of valganciclovir and prednisone (Fig. 7A and 7C) administered and measured blood level of tacrolimus (Fig. 7B) for a subset of adult heart and lung transplant patients in this study. (Fig. 7D) Compared to the viral component, the bacterial component of the microbiome is relatively insensitive to antivirals and immunosuppressants. (Fig. 7E) Virome composition as a function of anti-CMV (valganciclovir) and immunosuppressant (prednisone) drug doses. Figure 8A-8B. Temporal Dynamics of the Bacterial Component of the Microbiome after Transplantation, (A) Relative abundance of bacterial genera as a function of time. (Fig. 8B) Relative abundance of bacterial lineages as a function of time. Figure 9A-9C. Virome Composition and Total Viral Load for Different Classes of Patients, (Fig.9A and 9B) viral load and composition for adult heart transplant recipients (Fig.9A), adult lung transplant recipients (Fig.9B), and pediatric heart transplant recipients (Fig.9C) positive for CMV, treated with immunosuppressants and antivirals. Figure 10A-10C. CMV-induced allograft damage or injury. Fig. 10A. Correlation between clinical report of CMV (human herpesvirus 5, HHV-5) infection from specific body fluids (BAL and serum) with donor organ cfd DNA signal concurrent with clinical test date (P values; Mann-Whitney U test). Fig. 10B. P values ​​for the correlation between clinical diagnosis of infection and cell-free DNA level (dotted line indicates Bonferoni-corrected significance threshold) for infections with more than one positive clinical test result. Fig. 10C. A ROC curve testing the performance of CMV-derived cell-free DNA level in CMV-positive and CMV-negative patients (AUC=0.91). Figure 11A-11B. Infectome verification. Fig. 11A. Clinical test frequency compared with the incidence of viral infections detected by sequencing. Fig. 11B. Time-series data for patients who tested positive (red arrows) for specific infections relative to those who were not tested. (1) Adenovirus signal in L78 with clinical positives highlighted relative to untested patients (L34). (2) Polyomavirus signal in L69 with a positive test relative to the sustained signal in the untested patient (L57). (3) Three herpesvirus infections (HHV-4, 5, and 8) in L58 with both positive (red) and negative (black) tests for CMV (HHV-5) highlighted. (4) Microsporidiosis signal in I6, with four positive tests shown, relative to the signal observed in L78, who had symptoms of microsporidiosis but was not tested.The data are genome equivalents / «oann / i 7n7 / E / YL logged relative to human where zero values ​​were replaced with the assay detection limit (the number of genome equivalents consistent with a single sequence read assigned to the target genome). DETAILED DESCRIPTION OF THE INVENTION Particularly preferred embodiments of the invention will now be discussed in detail. Examples of preferred embodiments are illustrated in the following examples section. Unless otherwise defined, all technical and scientific terms used herein have the same meaning commonly understood by a person skilled in the art to which this invention pertains. All patents and publications referred to herein are incorporated by reference in their entirety. Where a range of values ​​is provided, it is understood that every intervening value, down to one-tenth of the lower limit unless the context clearly dictates otherwise, between the upper and lower limits of that range and any other value stated or intervening within that stated range, is encompassed within the invention. The upper and lower limits of those smaller ranges may be independently included within the smaller ranges and are also encompassed within the invention, subject to any limits specifically excluded within the stated range. Where the stated range includes one or both of the limits, the ranges excluded by either or both of those included limits are also included within the invention. Certain intervals are presented here with numerical values ​​preceded by the term "approximately." The term "approximately" is used here to provide literal support for the exact number it precedes, just as a number that is close to or approximately the number preceding the term is. In determining whether a number is close to or approximately a specifically stated number, the close or approximate number not stated may be a number which, in the context in which it is presented, provides the substantial equivalent of the specifically stated number. The practice of the present invention employs, unless otherwise indicated, conventional techniques of immunology, biochemistry, chemistry, molecular biology, microbiology, cell biology, genomics, and recombinant DNA, which are within the realm of experience in the art. See Sambrook, Fritsch, and Maniatis, MOLECULAR CLONING: A LABORATORY MANUAL, 2nd edition (1989); CURRENT PROTOCOLS IN MOLECULAR BIOLOGY (FM Ausubel, et al., eds., (1987)); the METHODS IN ENZYMOLOGY series (Academic Press, Inc.): PCR 2: A PRACTICAL APPROACH (MJ MacPherson, BD Hames, and GR Taylor, eds., (1995)); Harlow and Lañe, eds. (1988) ANTIBODIES, A LABORATORY MANUAL, and ANIMAL CELL CULTURE (Rl Freshney, ed. (1987)). The invention provides methods, devices, compositions, and equipment for analyzing the microbiome or individual components thereof in an individual. These methods are used in determining infection, analyzing microbiome structure, determining an individual's immunocompetence, and similar applications. In some embodiments, the invention provides methods for determining whether a patient or subject is immunocompetent. The term "individual," "patient," or "subject" as used herein includes humans as well as other animals. / βοαηη / ίζηζ / Β / γι Definitions. As used herein, the term “diagnose” or “diagnosis” of a condition or outcome includes the prediction or diagnosis of the condition or outcome, determination of predisposition to a condition or outcome, verification of treatment of the patient, diagnosis of a therapeutic response of a patient, and prognosis of the condition or outcome, progress, and response to the particular treatment. Microbiota. As used herein, the term microbiota refers to the collection of microorganisms present within an individual, usually a mammal and most commonly a human. The microbiota may include pathogenic species; species that constitute the normal flora of a tissue, e.g., skin, oral cavity, etc., but are undesirable in other tissues, e.g., blood, lungs, etc.; commensal organisms found in the absence of disease; and so on. A subset of the microbiome is the virome, which comprises the viral components of the microbiome. The term “microbiome component” as used herein refers to individual strains or species. The component may be a viral component, a bacterial component, a fungal component, etc. In a healthy animal, although internal tissues, such as the brain and muscles, are presumed to be relatively free of bacterial species, surface tissues, such as the skin and mucous membranes, are constantly in contact with environmental organisms and are readily colonized by various microbial species. The mixture of organisms known or presumed to be present in humans at any anatomical site is referred to as the "indigenous microbiota," encompassing various components of the indigenous microbiota. In addition to indigenous microorganisms, there are several transient components, such as pathogenic and opportunistic infections. The reference sequences of the organisms described below are publicly available and are sourced, for example, from the GenBank database. The human gut microbiota is dominated by species found within two bacterial genera: members of the Bacteroidetes and Firmicutes constitute >90% of the bacterial population. Actinobacteria (e.g., members of the genus Bifidobacterium) and Proteobacteria, among several other groups, are less prominently represented. Common species of interest include prominent or less abundant members of this community and may comprise, but are not limited to, Bacteroides thetaiotaomicron; Bacteroides caccae; Bacteroides fragilis; Bacteroides melaninogenicus; Bacteroides oralis; Bacteroides uniformis; Lactobacillus; Clostridium perfringens; Clostridium septicum; Clostridium tetani; Bifidobacterium bifidum; Staphylococcus aureus; Enterococcus faecalis; Escherichia coli; and Salmonella enteritidis. Klebsiella sp.; Enterobacter sp.; Proteus mirabilis; Pseudomonas aeruginosa; Peptosireptococcus sp.; Peptococcus sp., Faecalibacterium sp,; Roseburia sp.; Ruminococcus sp.; Dorea sp.; Alistipes sp.; etc. In the skin microbiome, most bacteria fall into four different groups: Actinobacteria, Firmicutes, Bacteroidetes, and Proteobacteria. Microorganisms generally considered to colonize the skin include coryneforms from the Actinobacteria group (the genera Corynebacterium, Propionibacterium, such as Propionibacterium acnes, and Brevibacterium), the genus Micrococcus, and Staphylococcus spp. The most commonly isolated fungal species are Malassezia spp., which are especially prevalent in sebaceous areas. Demodex mites (such as Demodex folliculorum and Demodex brevis) may also be present. Other types of fungi thought to grow on the skin include Debaryomyces and Cryptococcus spp. As non-commensal organisms, burn wounds are commonly infected with S. pyogenes, Enterococcus spp., or Pseudomonas aeruginosa, and can also be infected with fungi and / or viruses.Epidermidis is a very common skin commensal, but it is also the most frequent cause of hospital-acquired infections associated with medical devices such as catheters or heart valves. For a review, see Nat Rev Microbiol. (2011) Apr; 9(4):244-53. Pathogenic species can be bacteria, viruses, protozoan parasites, fungal species, etc. Bacteria include Brucella sp., Treponema sp., Mycobacterium sp., Listeria sp., Legionella sp., Helicobacter sp., Streptococcus sp., Neisseria sp., Clostridium sp., Staphylococcus sp., or Bacillus sp.; including, but not limited to, Treponema pallidum, Mycobacterium tuberculosis, Mycobacterium leprae, Listeria monocytogenes, Legionella pneumophila, Helicobacter pylori, Streptococcus pneumoniae, Neisseria meningitidis, Clostridium novyi, Clostridium botulinum, Staphylococcus aureus, Bacillus anthracis, etc. Parasitic pathogens include Trichomonas, Toxoplasma, Giardia, Cryptosporidium, Plasmodium, Leishmania, Trypanosoma, Entamoeba, Schistosoma, Filariae, Asearia, Fasciola; including without limitation Trichomonas vaginalis, Toxoplasma gondii, Giardia intestinalis, Cryptosporidium parva, Plasmodium falciparum, Trypanosoma cruzi, Entamoeba histolytica, Giardia lamblia, Fasciola hepatica, etc. Viruses that infect humans include, for example, Adeno-associated viruses; Aichi virus; Australian bat lyssavirus; BK polyomavirus; Banna virus; Barmah forest virus; Bunyamwera virus; American jackrabbit bunyavirus; white-footed jackrabbit bunyavirus; Cercopithecine herpesvirus; Chandipura virus; Chikungunya virus; Cosavirus A; cowpox virus; Coxsackievirus; Crimean-Congo hemorrhagic fever virus; Dengue virus; Dhori virus; Dugbe virus; Duvenhage virus; Eastern equine encephalitis virus; Ebola virus; Echovirus; encephalomyocarditis virus; Epstein-Barr virus; European bat lysavirus; GB C virus / Hepatitis G virus; Hantaan virus; Hendra virus; Hepatitis A virus; Hepatitis B virus; Hepatitis C virus; Hepatitis E virus; Hepatitis delta virus; equine pox virus; human adenovirus; human astrovirus; human coronavirus; human cytomegalovirus; human enterovirus 68, 70;Human herpesvirus 1; human herpesvirus 2; human herpesvirus 6; human herpesvirus 7; human herpesvirus 8; Human Immunodeficiency Virus; Human papillomavirus 1; human papillomavirus 2; human papillomavirus 16, 18; human parainfluenza; human parvovirus B19; human respiratory syncytial virus; human rhinovirus; human SARS coronavirus; human spumaretrovirus; human T-lymphotropic virus; human torovirus; Influenza A virus; Influenza B virus; Influenza C virus; Isfahan virus; JC polyomavirus; Japanese encephalitis virus; Junin arenavirus; K1 polyomavirus; Kunjin virus; Lagos bat virus; Lake Victoria marburg virus; Langat virus; Lassa virus; Lordsdale virus; Louping II virus; lymphocytic choriomeningitis virus; Machupo virus; Mayaro virus; MERS coronavirus; Measles viruses; Mengo encephalomyocarditis virus; Merkel cell polyomavirus; Mokola virus; Molluscum contagiosum virus;Monkeypox virus; Mumps virus; Murray Valley encephalitis virus; New York virus; / Roann / Lznz / E / Yii virus; Nipah; norovirus; O'nyong-nyong virus; Orf virus; Oropouche virus; Pichinde virus; Poliovirus; Punta toro phlebovirus; Puumala virus; Rabies virus; Rift Valley fever virus; Rosavirus A; Ross River virus; Rotavirus A; Rotavirus B; Rotavirus C; Rubella virus; Sagiyama virus; Salivirus A; Sicilian phlebotomus fever virus; Sapporo virus; SemLiki forest virus; Seoul virus; Simios foam virus; Simios 5 virus; Sindbis virus; Southampton virus; St. Louis encephalitis virus; Powassan garrapatas virus; Torque teño virus; Toscana virus; Uukuniemi virus; Vaccinia virus; Varicela-zoster virus; Varióla virus; Venezuelan equine encephalitis virus; vesicular estomatitis virus: western equine encephalitis virus; polyomavirus WU; West Nile virus; Yaba mono tumor virus; Yaba-like disease virus; Amarilla fiber virus; Zika virus; Anelloviridae. The Anelloviridae family consists of non-enveloped, circular, single-stranded DNA viruses. Three genera of anelloviruses are known to infect humans: TTV, TTMDV, and TTMV. The Torque Teño Virus (TTV) is a single-stranded, non-enveloped DNA virus with a circular, negative-sense genome. A smaller virus, later named Teño Torque-like Mini Virus (TTMV), has also been characterized, and a third virus with a genome size between that of TTV and TTMV was discovered and subsequently named Teño Teño-like Midi Virus (TTMDV). Recent changes in nomenclature have classified the three anelloviruses capable of infecting humans into the genera Alphatorquevirus (TTV), Betatorquevirus (TTMV), and Gammatorquevirus (TTMDV) of the Anelloviridae family. To date, anelloviruses are still considered "orphan" viruses awaiting identification and linkage to human diseases. Human anelloviruses differ in genome size, ranging from 3.8 kb to 3.9 kb for TTV, 3.2 kb for TTMDV, and 2.8 kb to 2.9 kb for TTMV. A defining characteristic of anelloviruses is the extreme diversity found both within and between anellovirus species; they can exhibit nucleotide-level divergence of up to 33%–50%. Despite this nucleotide sequence diversity, anelloviruses share conserved transcriptional genomic organization profiles, a non-coding GC-rich region, and sequence motifs that result in shared virion structure and genetic functions. Anellovirus infections are highly prevalent in the general population. A study in Japan found that 75%–100% of tested patients were infected with at least one of three human anelloviruses, and may have been infected with multiple species. Anelloviruses can infect infants and young children, with the earliest documented infections occurring within the first few months of life. These viruses have been found in nearly every body site, fluid, and tissue tested, including blood plasma, serum, peripheral blood mononuclear cells (PBMCs), nasopharyngeal aspirates, bone marrow, saliva, breast milk, feces, as well as various tissues, including the thyroid gland, lymph nodes, lung, liver, spleen, pancreas, and kidney. The replication dynamics of anelloviruses are virtually unknown due to their ability to propagate in culture.Positive-strand TTV DNA, indicative of local viral replication, has been described in circulating hepatocytes, bone marrow cells, and PBMCs. Anelloviruses are primarily spread through fecal-oral transmission, although mother-to-child and respiratory tract transmission have also been reported. There are conflicting reports regarding the presence of TTV in umbilical cord blood specimens. Reference sequences for Anellovirus can be accessed in GenBank, for example, Torque teño 1 minivirus, Accession: NC_014097.1; Torque teño 6 minivirus, Accession: NC_014095.1; Torque teño 2 midivirus, Accession: NC_014093.1; Torque teño 1 midivirus, Accession: NC_009225.1; Torque teño 3 virus, Accession: NC_014081.1; Torque teño 19 virus, Accession: NC_014078.1; Torque teño 8 minivirus, Accession: NC_014068.1. The term "antibiotic", as used herein, includes all commonly used bacteriostatic and bactericidal antibiotics, usually those administered orally.Antibiotics include aminoglycosides, such as amikacin, gentamicin, kanamycin, neomycin, streptomycin, and tobramycin; cephalosporins, such as cefamandole, cefazolin, cephalexin, cephaloglycin, cephaloridine, cephalothin, cefapirin, and cephradine; macrolides, such as erythromycin and troleandomycin; penicillins, such as penicillin G, amoxicillin, ampicillin, carbenicillin, cloxacillin, dicloxacillin, methicillin, nafcillin, oxacillin, phenethylamine, and ticarcillin; peptide antibiotics, such as bacitracin, colistimethate, colistin, and polymyxin B; tetracyclines, such as chlortetracycline, demeclocycline, doxycycline, methacycline, minocycline, tetracycline, and oxytetracycline; and miscellaneous antibiotics such as chloramphenicol, clindamycin, cycloserine, lincomycin, rifampin, spectinomycin, vancomycin, and viomycin. Additional antibiotics are described in “Remington’s Pharmaceutical Sciences”, 16th Ed., (Mack Pub. Co., 1980), pp. 1121-1178. Antiviral agents. Individuals may receive antiviral therapy, which will alter the viral load for those viruses affected by the therapy. Examples of viral infections treated in this way include HIV, Bowenoid Papulosis, Chickenpox, Childhood HIV Disease, Smallpox, Hepatitis C, Dengue, Enteroviral Epidermodysplasia Verruciformis, Erythema Infectiosum (Fifth Disease), Buschke-Löwenstein Giant Condylomata Acuminata, Hand, Foot, and Mouth Disease, Herpes Simplex, Human Herpesvirus 6, Herpes Zoster, Kaposi's Varicelliform Eruption, Measles, Rubella, Milker's Nodules, Molluscum Contagiosum, Monkeypox, Orf, Roseola Infantum, Rubella, Smallpox, Viral Hemorrhagic Fever, Genital Warts, and Non-Genital Warts. Antiviral agents include azldouridine, anasmicin, amantadine, bromovinyldeoxusidine, chlorovinyldeoxusidine, cytabine, didanosine, deoxynojirimycin, dideoxycytidine, dideoxyinosine, dideoxynucleoside, desciclovir, deoxyciclovir, edoxuridine, enviroxime, fiacitabine, foscamet, fialuridine, fluorothymidine, floxuridine, hypericin, interferon, interleukin, isethionate, nevirapine, pentamidine, ribavirin, rimantadine, stavirdine, sargramostine, suramin, tricosanthin, tribromothymidine, trichlorothymidine, vidarabine, zldoviridine, zalcitabine, and 3-azido-3-deoxythymidine, and analogues, derivatives, salts, esters, prodrugs, codrugs pharmaceutically acceptable and protected forms thereof. The immunosuppression regimen, or immunosuppressant, as used herein, refers to the treatment of an individual, for example, a graft recipient, with agents to decrease the host immune system's immune responses against autoantigens or the graft. Exemplary immunosuppression regimens are described in greater detail herein. Primary immunosuppressive agents include calcineurin inhibitors, which bind to proteins to inhibit calcineurin activity, and include, for example, tacrolimus, cyclosporine A, etc. Cyclosporine and tacrolimus levels should be carefully monitored. Initially, levels may be maintained in the range of 10 ng / mL to 20 ng / mL, but after 3 months, levels may be lowered (5 ng / mL to 10 ng / mL) to reduce the risk of nephrotoxicity. Adjuvant agents are usually combined with a calcineurin inhibitor and include steroids, azathioprine, mycophenolate mofetil, and sirolimus. Protocols of interest include a calcineurin inhibitor with mycophenolate mofetil. The use of adjuvant agents allows clinicians to achieve adequate immunosuppression while reducing the dose and toxicity of individual agents. Mycophenolate mofetil has assumed an important role in immunosuppression in kidney transplant recipients after several clinical trials demonstrated a markedly reduced prevalence of acute cellular rejection compared to azathioprine and a reduction in treatment failures at 1 year. Antibody-based therapy can use monoclonal antibodies (e.g., Muromonab-CD3) or polyclonal antibodies or anti-CD25 antibodies (e.g., basiliximab, daclizumab) and is administered in the initial post-transplant period (up to 8 weeks). Antibody-based therapy allows for avoiding or reducing the dose of calcineurin inhibitors, potentially reducing the risk of nephrotoxicity. The adverse effect profile of polyclonal and monoclonal antibodies limits their use in some patients. The term “nucleic acid” as used herein refers to a polynucleotide comprising two or more nucleotides. This may be either DNA or RNA. A “variant nucleic acid” is a polynucleotide that has the same nucleotide sequence as the original nucleic acid, except that it has at least one modified nucleotide, for example, deleted, inserted, or replaced, respectively. The variant may have a nucleotide sequence that is at least approximately 80%, 90%, 95%, or 99% identical to the nucleotide sequence of the original nucleic acid. Circulating, or cell-free, DNA was first detected in human blood plasma in 1948 (Mandel, P., Metals, P., CR Acad. Sel. Paris, 142, 241-243 (1948)). Since then, its relationship with diseases in several areas has been established (Tong, YK, Lo, YM, Clin Chim Acta, 363, 187-196 (2006)). Studies reveal that most circulating nucleic acids in blood originate from necrotic or apoptotic cells (Glacona, MB, et al., Pancreas, 17, 89-97 (1998)), and elevated levels of apoptotic nucleic acids are generally observed in diseases such as cancer. (Glacona, MB, et al., Pancreas, 17, 89-97 (1998); Fournie, GJ, et al., Cancer Lett, 91, 221-227 (1995)).Particularly in cancer, where circulating DNA carries prominent disease signals, including oncogene mutations, microsatellite alterations, and, for certain cancers, viral genomic sequences, plasma DNA or RNA has been increasingly studied as a potential biomarker of disease. For example, Diehl et al. recently demonstrated that a quantitative assay for low levels of circulating tumor DNA in total circulating DNA could serve as a better marker for detecting colorectal cancer relapse compared to carcinoembryonic antigen, the clinically standard biomarker. (Diehl, F., et al., Proc Nati Acad Sel, 102, 16368-16373 (2005); Diehl, F., et al., Nat Med, 14, 985-990 (2008)).Maheswaran et al. reported the use of circulating plasma cell genotyping to detect activating mutations in epidermal growth factor receptors in lung cancer patients that could affect drug treatment. (Maheswaran, S., et al., N Engl J Med, 359, 366-377 (2008)). These results collectively establish plasma-free circulating DNA as a useful species for cancer treatment screening. Circulating DNA has also been useful in healthy individuals for fetal diagnosis, with fetal DNA circulating in maternal blood serving as a marker for sex, rhesus D status, fetal aneuploidy, and sex-linked disorders.Fan et al. recently demonstrated a strategy for detecting fetal aneuploidy by cell-free DNA bombardment sequencing of a maternal blood sample, a methodology that can replace more invasive and risky techniques such as amniocentesis or chorionic villus sampling. (Fan, HC, Blumenfeld, YJ, Chitkara, U., Hudgins, L., Quake, SR, Proc Nati Acad Sel, 105, 16266-16271 (2008)). The term “derived from” as used herein refers to an origin or source and may include naturally occurring, recombinant, unpurified, or purified molecules. A nucleic acid derived from a parent nucleic acid may comprise the parent nucleic acid in part or in whole, and may be a fragment or variant of the parent nucleic acid. A nucleic acid derived from a biological sample may be purified from that sample. A “target nucleic acid” in the method according to the present invention is a nucleic acid, either DNA or RNA, to be detected. An organism-derived target nucleic acid is a polynucleotide having a sequence derived from and specific to that organism. A pathogen-derived target nucleic acid refers to a polynucleotide having a polynucleotide sequence derived from that specific pathogen. In some modalities, less than 1 pg, 5 pg, 10 pg, 20 pg, 30 pg, 40 pg, 50 pg, 100 pg, 200 pg, 500 pg, 1 ng, 5 ng, 10 ng, 20 ng, 30 ng, 40 ng, 50 ng, 100 ng, 200 ng, 500 ng, 1 pg, 5 pg, 10 pg, 20 pg, 30 pg, 40 pg, 50 pg, 100 pg, 200 pg, 500 pg or 1 mg of nucleic acids are obtained from the sample for analysis. In some cases, approximately 1 pg - 5 pg, 5 pg - 10 pg, 10 pg - 100 pg, 100 pg - 1 ng, 1 ng - 5 ng, 5 ng - 10 ng, 10 ng - 100 ng, 100 ng - 1 pg of nucleic acids are obtained from the sample for analysis. In some applications, the methods described herein are used to detect and / or quantify nucleic acid sequences corresponding to a microbe of interest, or a microbiome of organisms. The methods described herein can analyze at least 1, 2, 3, 4, 5, 10, 20, 50, 100, 200, 500, 1,000, 2,000, 5,000, 10,000, 20,000, 50,000, 100,000, 200,000, 300,000, 400,000, 500,000, 600,000, 700,000, 800,000, and 900,000 sequences. 106, 5x106, 107, 5x107,108, 5x108,109 or more sequence reads. In some applications, the methods described herein are used to detect and / or quantify gene expression, for example, by determining the presence of a microorganism's mRNA relative to its DNA. In some applications, the methods described herein provide highly discriminative and quantitative analyses of multiple genes. The methods described herein can discriminate and quantify the expression of at least 1, 2, 3, 4, 5, 10, 20, 50, 100, 200, 500, 1,000, 2,000, 5,000, 10,000, 20,000, 50,000, 100,000, or more different target nucleic acids. A sample containing cell-free nucleic acids is obtained from a subject. That subject may be a human, a domestic animal such as a cow, chicken, pig, horse, rabbit, dog, cat, goat, etc. In some embodiments, the cells used in the present invention are taken from a patient. Samples include, for example, the cellular fraction of whole blood, sweat, tears, saliva, oophoresis, sputum, lymph, bone marrow suspension, urine, semen, vaginal fluid, cerebrospinal fluid, cerebrospinal fluid, ascites, milk, secretions from the respiratory, intestinal, or genitourinary tracts, a washing from a tissue or organ (e.g., lung), or tissue that has been removed from organs, such as the breast, lung, intestine, skin, cervix, prostate, pancreas, heart, liver, and stomach.These samples can be separated by centrifugation, elutriation, density gradient separation, apheresis, affinity sorting, decantation, FACS, Hypaque centrifugation, etc. Once a sample is obtained, it can be used directly, frozen, or maintained in an appropriate culture medium for short periods of time. To obtain a blood sample, any known technique may be used, such as a syringe or other vacuum suction device. A blood sample may optionally be pretreated or processed before use. A sample, such as a blood sample, can be analyzed using any of the methods and systems described herein within 4 weeks, 2 weeks, 1 week, 6 days, 5 days, 4 days, 3 days, 2 days, 1 day, 12 hours, 6 hours, 3 hours, 2 hours, or 1 hour from the time the sample is obtained, or longer if frozen. When a sample is obtained from a subject (e.g., a blood sample), the quantity may vary depending on the size of the subject and the condition being tested. In some modalities, at least 10 mL, 5 mL, 1 mL, 0.5 mL, 250 pL, 200 pL, 150 pL, 100 pL, 50 pL, 40 pL, 30 pL, 20 pL, 10 pL, 9 pL, 8 pL, 7 pL, 6 pL, 5 pL, 4 pL, 3 pL, 2 pL, or 1 pL of a sample are obtained.In some modalities, 1 pL - 50 pL, 2 pL - 40 pL, 3 pL - 30 pL, or 4 pL - 20 pL of sample are obtained. In some modalities, more than 5 pL, 10 pL, 15 pL, 20 pL, 25 pL, 30 pL, 35 pL, 40 pL, 45 pL, 50 pL, 55 pL, 60 pL, 65 pL, 70 pL, 75 pL, 80 pL, 85 pL, 90 pL, 95 pL, or 100 pL of a sample are obtained. The cell-free fraction is preferably blood serum or blood plasma. The term “cell-free fraction” of a biological sample as used herein refers to a fraction of the biological sample that is substantially cell-free. The term “substantially cell-free” as used herein refers to a preparation of the biological sample comprising less than approximately 20,000 cells per mL, preferably less than approximately 2,000 cells per mL, more preferably less than approximately 200 cells per mL, and more preferably less than approximately 20 cells per mL. In contrast to certain methods of the prior art, genomic DNA is not excluded from the cell-free sample and typically comprises approximately 50% to approximately 90% of the nucleic acids present in the sample. The method of the present invention may further comprise preparing a cell-free fraction from a biological sample. The cell-free fraction may be prepared using conventional techniques known in the art. For example, a cell-free fraction from a blood sample may be obtained by centrifuging the blood sample for approximately 3–30 min, preferably approximately 3–15 min, more preferably approximately 3–10 min, or more preferably approximately 3–5 min, at a low speed of approximately 200–20,000 g, preferably approximately 200–10,000 g, more preferably approximately 200–5,000 g, or more preferably approximately 350–4,500 g. The biological sample may be obtained by ultrafiltration to separate the cells and their fragments from a cell-free fraction comprising soluble DNA or RNA.Conventionally, ultrafiltration is carried out using a 0.22 pm membrane filter. / Aoοαηη / ίζηζ / Ε / γι The method of the present invention may further comprise concentrating (or enriching) the target nucleic acid in the cell-free fraction of the biological sample. The target nucleic acid may be concentrated using conventional techniques known in the art, such as solid-phase absorption in the presence of a high salt concentration, organic extraction by phenol-chloroform followed by precipitation with ethanol or isopropyl alcohol, or direct precipitation in the presence of a high salt concentration or 70%–80% ethanol or isopropyl alcohol. The concentrated target nucleic acid may be at least approximately 2, 5, 10, 20, or 100 times more concentrated than the cell-free fraction. The target nucleic acid, whether concentrated or not, may be used for amplification according to the method of the present invention. In some embodiments, the invention provides methods for diagnosing or predicting transplant rejection. The term “transplant rejection” encompasses both acute and chronic transplant rejection. “Acute rejection” or “AR” is the rejection by the immune system of a tissue transplant recipient when the transplanted tissue is immunologically foreign. Acute rejection is characterized by infiltration of the transplanted tissue by the recipient’s immune cells, which carry out their effector function and destroy the transplanted tissue. The onset of acute rejection is rapid and generally occurs in humans within a few weeks after transplant surgery. Generally, acute rejection can be inhibited or suppressed with immunosuppressive drugs such as rapamycin, cyclosporine A, anti-CD40L monoclonal antibody, and similar agents. Chronic transplant rejection (CTR) typically occurs in humans within several months to years after grafting, even in the presence of successful immunosuppression of acute rejection. Fibrosis is a common factor in chronic rejection of all types of organ transplants. Chronic rejection can typically be described by a range of specific disorders that are characteristic of the particular organ. For example, in lung transplants, these disorders include fibroproliferative destruction of the airway (bronchiolitis obliterans); in heart transplants or cardiac tissue transplants, such as valve replacements, these disorders include fibrotic atherosclerosis; in kidney transplants, these disorders include obstructive nephropathy, nephrosclerosis, and tubulointerstitial nephropathy; and in liver transplants, these disorders include vanishing bile duct syndrome.Chronic rejection can also be characterized by ischemic damage, denervation of the transplanted tissue, hyperlipidemia, and hypertension associated with immunosuppressive drugs. In some embodiments, the invention also includes methods for determining the effectiveness of an immunosuppressive regimen for a subject who has received a transplant, for example, an allograft. Certain embodiments of the invention provide methods for predicting transplant survival in a transplant recipient. The invention provides methods for diagnosing or predicting whether a transplant in a patient or transplant recipient will survive or be lost. In certain embodiments, the invention provides methods for diagnosing or predicting the presence of long-term graft survival. “Long-term” graft survival means graft survival for at least approximately 5 years beyond the current sampling, despite the occurrence of one or more prior episodes of acute rejection. In certain embodiments, transplant survival is determined for patients in whom at least one episode of acute rejection has occurred. Therefore, these embodiments provide methods for determining or predicting transplant survival after acute rejection.Transplant survival is determined or estimated in certain modalities within the context of transplant therapy, for example, immunosuppressive therapy, where immunosuppressive therapies are well-established. In other modalities, methods are provided for determining the type and / or severity of acute rejection (and not just its presence). As is well known in the field of transplantation, the transplanted organ, tissue, or cells can be allogeneic or xenogeneic, so grafts can be allografts or xenografts. A characteristic of the graft-tolerant phenotype detected or identified by objective methods is that it occurs without immunosuppressive therapy; that is, it is present in a host not undergoing immunosuppressive therapy, so no immunosuppressive agents are administered to the host. The transplanted graft can be a solid organ or skin graft. Examples of organ transplants that can be analyzed by the methods described herein include, but are not limited to, kidney transplantation, pancreas transplantation, liver transplantation, heart transplantation, lung transplantation, bowel transplantation, pancreas transplantation following kidney transplantation, and simultaneous pancreas and kidney transplantation. Microbiome Detection and Analysis The methods of the invention involve high-throughput sequencing of a cell-free nucleic acid sample from an individual, followed by bioinformatics analysis to determine the presence and prevalence of microbial sequences. These sequences may be from indigenous organisms, such as the normal gut microbiome, skin microbiome, etc., or from non-indigenous pathogens, such as opportunistic or pathogenic infections. The analysis may be performed on the entire microbiome or on components thereof, such as the virome, bacterial microbiome, fungal microbiome, protozoan microbiome, etc. Examples of nucleic acids include, but are not limited to, double-stranded DNA, single-stranded DNA, single-stranded DNA hairpins, DNA / RNA hybrids, RNA (e.g., mRNA or miRNA), and RNA hairpins. In some embodiments, the nucleic acid is DNA. In some embodiments, the nucleic acid is RNA.For example, cell-free RNA and DNA are present in human plasma. Microbiome nucleic acid genotyping, and / or the detection, identification, and / or quantification of microbiome-specific nucleic acids, generally includes an initial sample amplification step, although there may be cases where sufficient cell-free nucleic acids are available for direct sequencing. When the nucleic acid is RNA, the amplification step may be preceded by a reverse transcriptase reaction to convert the RNA to DNA. Preferably, the amplification is non-biased; that is, the amplification primers are either universal primers or adaptor primers specific to the nucleic acids being analyzed.Examples of PCR techniques include, but are not limited to, hot-initiated PCR, nested PCR, polononi in situ PCR, circular in situ amplification (RCA), bridging PCR, peak titration PCR, and emulsion PCR. Other suitable amplification methods include ligase chain reaction (LCR), transcription amplification, replication of the self-sustaining sequence / «oann / i 7n7 / E / YL, selective amplification of target polynucleotide sequences, consensus sequence-primed polymerase chain reaction (CP-PCR), arbitrarily primed polymerase chain reaction (AP-PCR), degenerate oligonucleotide-primed PCR (DOP-PCR), and nucleic acid-based sequence amplification (NABSA). Other amplification methods can be used to amplify specific polymorphic loci positions, including those described in U.S. Patents Nos. 5,242,794, 5,494,810, 4,988,617, and 6,582,938. After amplification, the amplified nucleic acid is sequenced. Sequencing can be performed using high-throughput systems, some of which allow the detection of a sequenced nucleotide immediately after or upon its incorporation into a growing strand—that is, real-time or substantially real-time sequence detection. In some cases, high-throughput sequencing generates at least 1,000, at least 5,000, at least 10,000, at least 20,000, at least 30,000, at least 40,000, at least 50,000, at least 100,000, or at least 500,000 sequence reads per hour; with each read being at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 120, or at least 150 bases per read. Sequencing can be performed using the nucleic acids described herein as genomic DNA, cDNA derived from RNA transcripts, or RNA as a template. In some modalities, high-throughput sequencing involves the use of technology available from Helicos BioSciences Corporation (Cambridge, Massachusetts), such as Single Molecule Sequencing by Synthesis (SMSS). SMSS is unique because it allows for the sequencing of an entire genome without the need for a pre-amplification step. This reduces distortion and non-linearity in nucleic acid measurements. SMSS is described in part in U.S. Publication Requests Nos. 2006002471 I; 20060024678; 20060012793; 20060012784; and 20050100932. In some modalities, high-throughput sequencing involves the use of technology available from 454 Lifesciences, Inc. (Branford, Connecticut), such as the Pico Titer Piate device. This device includes a fiber-optic plate that transmits a chemiluminescent signal generated by the sequencing reaction to be recorded by a CCD camera in the instrument. This use of fiber-optic devices allows the detection of a minimum of 20 million base pairs in 4.5 hours. The methods of using bead amplification followed by fiber optic detection are described in Marguiles, M., et al. Genome sequencing in microfabricated high-density pricolitre reactors, Nature, doi: 10.1038 / nature03959; and as well as in US Publication Requests Nos. 20020012930; 20030058629; 20030100102; 20030148344; 20040248161; 20050079510, 20050124022; and 20060078909. In some modalities, high-throughput sequencing is performed using a clonal single-molecule array (Solexa, Inc.) or sequencing by synthesis (SBS) using reversible terminator chemistry. These technologies are described in part in U.S. Patent Nos. 6,969,488; 6,897,023; 6,833,246; 6,787,308; and U.S. Patent Publication Applications Nos. 20040106130; 20030064398; 20030022207; and Constans, A., The Scientist 2003, 17(13):36. / Roann / Lznz / E / Yii In some aspects of this field, high-throughput RNA or DNA sequencing can be performed using AnyDot.chips (Genovoxx, Germany), which allows for the verification of biological processes (e.g., miRNA expression or allelic variability (SNP detection)). In particular, AnyDot microcircuits enable a 10x–50x improvement in the detection of the fluorescent nucleotide signal. AnyDot.chips microcircuits and methods of their use are described in part in International Publication Applications Nos. WO 02088382, WO 03020968, WO 0303 1947, WO 2005044836, PCTEP 05105657, and PCMEP 05105655. and German Patent Applications Nos. DE 101 49 786, DE 102 14 395, DE 103 56 837, DE 10 2004 009 704, DE 10 2004 025 696, DE 10 2004 025 746, DE 10 2004 025 694, DE 10 2004 025 695, DE 10 2004 025 744, DE 10 2004 025 745, and DE 10 2005 012 301. Other high-throughput sequencing systems include those described in Venter, J., et al. Science February 16, 2001; Adams, M. et al., Science March 24, 2000; and M. J. Levene, et al. Science 299:682686, January 2003; as well as U.S. Publication Request No. 20030044781 and 2006 / 0078937. All such systems involve sequencing a target nucleic acid molecule having a plurality of bases by the temporary addition of bases via a polymerization reaction that is measured on a nucleic acid molecule; that is, the activity of a nucleic acid polymerizing enzyme on the template nucleic acid molecule to be sequenced is monitored in real time.The sequence can then be deduced by identifying which bases are being incorporated into the growing complementary strand of the target nucleic acid by the catalytic activity of the nucleic acid polymerizing enzyme at each step in the base addition sequence. The polymerase on the target nucleic acid molecule complex is positioned to move along the target nucleic acid molecule and extend the oligonucleotide primer to an active site. A plurality of labeled nucleotide analogs are provided near the active site, with each distinguishable type of nucleotide analog being complementary to a different nucleotide in the target nucleic acid sequence.The growing nucleic acid strand is extended using polymerase to add a nucleotide analog to the nucleic acid strand at the active site, where the added nucleotide analog is complementary to the nucleotide of the target nucleic acid at the active site. The nucleotide analog added to the oligonucleotide primer as a result of the polymerization step is identified. The steps of providing labeled nucleotide analogs, polymerizing the growing nucleic acid strand, and identifying the added nucleotide analog are repeated so that the nucleic acid strand is further extended and the target nucleic acid sequence is determined. In some methods, sequencing is performed by bombardment. In bombardment sequencing, DNA is randomly cut into numerous small segments, which are then sequenced using the chain-termination method to obtain reads. Multiple overlapping reads of the target DNA are obtained by performing several rounds of this fragmentation and sequencing. Computer programs then use the overlapping ends of the different reads to assemble them into a continuous sequence. In some embodiments, the invention provides methods for the detection and quantification of microbial sequences using sequencing. In this case, the sensitivity of the detection can be estimated. There are two components to sensitivity: (i) the number of molecules analyzed (sequencing depth) and (ii) the percentage error of the sequencing process. Regarding sequencing depth, a common estimate for inter-individual variation is approximately one base per thousand differences. Currently, sequencers such as the Illumina Genome Analyzer have read lengths exceeding 36 base pairs. Although the fraction of host DNA in blood can vary depending on the individual's condition, 90% can be taken as an initial estimate. In this donor DNA fraction, approximately one in 10 molecules analyzed will be microbial.The Genome Analyzer can process approximately 10 million molecules per analysis channel, and there are 8 analysis channels per instrument assay. Therefore, if one sample is loaded per channel, approximately 10⁶ molecules should be detectable, which can be identified as microbial and provide information about the microbiome's status. Increased sensitivity can be achieved simply by sequencing more molecules, i.e., by using more channels. The sequencing error rate also affects the sensitivity of this technique. Typical sequencing error rates for base substitutions vary between platforms but are between 0.5% and 1.5%. This places a potential limit on sensitivity of 0.16% to 0.50%. However, it is possible to systematically decrease the sequencing error rate by resequencing the sample template multiple times, as demonstrated by Helicos BioSciences (Harris, TD, et al., Science, 320, 106-109 (2008)). A single resequencing application would reduce the expected error rate. After sequencing, the sequence dataset is loaded into a data processor for bioinformatics analysis to subtract host sequences (human, cat, dog, etc.) from the analysis and to determine the presence and prevalence of microbial sequences, for example, by comparing the coverage of the sequence map to a microbial reference sequence. Subtracting host sequences may involve identifying a reference host sequence and masking microbial sequences or sequences that mimic microbial sequences present in the reference host genome.Similarly, determining the presence of a microbial sequence by comparison with a microbial reference sequence may include the step of identifying a microbial reference sequence, and masking host sequences or sequences that mimic the microbial ones present in the reference microbial genome. The dataset is optionally cleaned to verify sequence quality, remove sequencer-specific nucleotide remnants (adapter sequences), and merge overlapping paired end reads to create a higher-quality consensus sequence with fewer reading errors. Representative sequences are identified as those with identical start sites and lengths, and duplicates can be removed from the analysis. An important feature of the invention is the subtraction of human sequences from the analysis. Since the identification / sequencing steps are not diverted, the predominance of sequences in all samples will be that of the host sequences. The subtraction process can be optimized in several ways to improve the speed and accuracy of the process, for example, by performing multiple subtractions where the initial alignment is established on a coarse filter, i.e., with a fast aligner, and performing additional alignments with a fine filter, i.e., a sensitive aligner. The read databases are initially aligned against a human reference genome, including but not limited to the hg19 reference sequences from GenBank, to bioinformatically subtract the host DNA. Each sequence is aligned with the sequence that best fits the human reference sequence. Sequences positively identified as human are bioinformatically removed from the analysis. The reference human sequence can also be optimized by adding contigs with a high match rate, including, without limitation, highly repetitive sequences present in the genome that are not well represented in reference databases. It has been observed that a significant number of reads that do not align with hg19 are eventually identified as human at a later stage of the line when using a database that includes a large set of human sequences, such as the complete NCBI NT database. Removing these reads at the beginning of the analysis can be accomplished by constructing an expanded human reference. This reference is created by identifying human contigs in a different reference human sequence database, such as the NCBI NT database, that has high convergence after subtracting the initial human read.These contigs are added to the human reference to create a more complete reference set. Additionally, newly mounted human contigs from section studies can be used as an extra mask for human-derived readings. Regions of the human genome reference sequence containing non-human sequences can be masked, for example, viral and bacterial sequences integrated into the reference sample genome. For example, the Epstein-Barr virus (EBV) has approximately 80% of its genome incorporated into hg19. Sequence reads identified as non-human are then aligned to a nucleotide database of microbial reference sequences. The database can be selected from those microbial sequences known to be associated with the host, for example, the set of human commensal and pathogenic microorganisms. The microbial database can be optimized to mask or remove contaminating sequences. For example, it has been observed that many entries in public databases include artificial sequences not derived from microorganisms, such as primer sequences, host sequences, and other contaminants. It is desirable to perform one or more initial alignments on a database. Regions that show irregularities in read coverage when multiple samples are aligned can be masked or removed as an artifact. The detection of such irregular coverage can be performed using various metrics, such as the ratio between the coverage of a specific nucleotide and the average coverage of the entire sequence containing that nucleotide.In general, a sequence that is represented as having more than approximately 5X, 10X, 25X, 50X, or 100X coverage of the average coverage of that reference sequence is artificial. Alternatively, a binomial test can be applied to provide a probability of coverage per base given the total coverage of the reference sequence. Removal of the contaminating sequence / βοαηη / ίζηζ / Β / γι from the reference databases allows for the accurate identification of microbes. One benefit of the methods of the invention is that the databases are improved by aligning the samples; for example, a database can be aligned with 1, 10, 20, 50, 100, or more samples to improve the database before commercial or clinical use. Each high-confidence reading can be aligned with multiple organisms in the given microbial database. To accurately assign an organism's abundance based on this potential map redundancy, a selected algorithm is used to calculate the most probable organism (e.g., see Lindner et al. Nucí. Acids Res. (2013) 41 (1): e10). For example, the GRAMMy or GASIC algorithms can be used to calculate the most probable organism that could come from a given reading. This data provides information regarding the presence of a microbe in the cell-free nucleic acid sample. Alignment and assignment to a host or microbial sequence can be performed according to recognized methods in the field. For example, a 50-nt read can be assigned as similar to that of a given genome if there are no more than 1 similarity, no more than 2 similarities, no more than 3 similarities, no more than 4 similarities, no more than 5 similarities, etc., throughout the read. Commercial algorithms are generally used for alignment and identification. A non-limiting example of such an alignment algorithm is the Bowtie2 program (Johns Hopkins University). For example, the preset options in end-to-end mode can be selected based on the desired alignment speed. --very-fast The same as: -D 5 -R 1 -NO -L 22 -i S,0,2.50 --fast The same as: -D 10 -R 2 -NO -L 22 -i S,0,2.50 --sensitive Same as: -D 15 -R 2 -L 22 -i S,1,1.15 --highly-sensitive The same as: -D 20 -R 3 -NO -L 20 -i S, 1,0.503Π' are then totaled and used to calculate the estimated number of reads assigned to each organism in a given sample, in a determination of the organism's prevalence in the cell-free nucleic acid sample. The analysis normalizes the counts for the size of the microbial genome to provide a coverage estimate for the microbe. The normalized coverage for each microbe is compared to the host sequence coverage in the same sample to account for differences in sequencing depth between samples. The final determination provides a dataset of microbial organisms represented by sequences in the sample, and the prevalence of those microorganisms. This data is aggregated and optionally presented for easy visualization, for example, in the form of a report provided to the individual or healthcare provider, or in a searchable format with hyperlinked data. The coverage estimate can be aggregated with sample metadata and stored in tables and figures for each sample or sample group. Optionally, the filtered host sequences can be used for other purposes, such as in personalized medicine. For example, certain SNPs in the human genome can allow physicians to identify drug sensitivities for a given number of patients. Human-derived sequences can reveal the integration of viruses into the host genome (e.g., EBV, HPV, polyomavirus) or be used for synergistic clinical applications (e.g., cell-free tumor DNA can be used to monitor cancer progression in parallel with monitoring for infection in patients who are highly susceptible to infection due to chemotherapy). In some approaches, cell-free nucleic acid analysis is used to calculate a pathogenicity score, where the pathogenicity score is a numerical or alphanumeric value that summarizes the overall pathogenicity of the organism to facilitate interpretation, for example, by a healthcare practitioner. Different microbes present in the microbiome can be assigned different scores. The final “pathogenicity score” is a combination of many different factors and is typically presented as an arbitrary unit, for example, ranging from 0–1, 0–10, or 0–100; as a percentile of all observed pathogenicity scores for a microbe of interest; and so on. The parameters and their specific weights can be determined experimentally, for example, by adjusting the function to the severity of the observed disease, or manually by adjusting the weight of different parameters and criteria. Factors relevant to calculating a pathogenicity score may include, but are not limited to, the abundance of the microbe, for example, as calculated by the number of reads relative to human reads, relative to the abundance of the microbe in a reference subject or group of subjects, for example, a test population, a known infection, a known uninfected individual, etc. Specific mutations found in the microbe's genome may be determined by referencing a database of toxicity, pathogenicity, antibiotic resistance, etc., associated with the microbe, and may include, but are not limited to, SNPs, indels (insertions and deletions), plasmids, etc. The matching of specific microbes includes, but is not limited to, relationships and groups of specific organisms.The expression of certain sequences, for example, the detection of mRNA, may not be relevant to the pathogenicity score, for example, as informative of whether a microbe is actively replicating or is latent; etc. Geographic characteristics may also be included, where geography is indicative of exposure to microbes of interest, for example, host travel history; interactions with infected individuals, and the like. Reagents and related equipment are also provided for performing one or more of the methods described above. The specific reagents and equipment used may vary considerably. Reagents of interest include reagents specifically designed for use in the following: (i) profiling of a microbiome and an individual; (ii) identification of microbiome profiles; and (iii) detection and / or quantification of one or more nucleic acids from a microbiome in a sample obtained from an individual. Equipment may include the reagents necessary to perform nucleic acid extraction and / or nucleic acid detection using the methods described herein, such as POR and sequencing.The kit may also include a data analysis software package, which may include reference profiles for comparison with the test profile, and in particular may include optimized reference databases as described above. The / RQann / i 7n7 / E / YL kits may include reagents such as buffers and H2O. These kits may also include information such as references to scientific literature, package insert materials, clinical trial results and / or summaries thereof, which indicate or establish the activities and / or benefits of the composition, and / or describe the dosage, administration, side effects, drug interactions, or other information useful to the healthcare provider. These kits may also include instructions for accessing a database. This information may be based on the results of various studies, for example, studies using experimental animals involving in vivo models and studies based on human clinical trials. The kits described herein may be provided, marketed, and / or promoted to healthcare providers, including physicians, nurses, pharmacists, compounding officers, and similar professionals.The equipment can also, in some forms, be sold directly to the consumer. Any of the above methods may be performed by a computer program comprising computer-executable logic recorded on a computer-readable medium. For example, the computer program may perform one or all of the following functions: (i) control the isolation of nucleic acids from a sample, (ii) pre-amplify nucleic acids from the sample, (iii) amplify, sequence, or arrange specific sections in the sample, (iv) identify and quantify a microbial sequence in the sample, (v) compare data on the presence or prevalence of a microbe detected in the sample with a predetermined threshold, (vi) determine an infection, microbiome health, immunocompetence status or outcome, and (vii) declare the sample's status with respect to infection, microbiome health, immunocompetence, etc. Computer-executable logic can run on any computer, which can be any of a variety of general-purpose computers, such as a personal computer, network server, workstation, or other current or future computer platform. In some embodiments, a computer program product is described as comprising a computer-useful medium that has the computer-executable logic (computer software program, including program code) stored therein. The computer-executable logic can be executed by a processor, causing the processor to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware using, for example, a hardware state machine.The implementation of the hardware state machine to perform the functions described herein will be evident to those skilled in the relevant techniques. The program can provide a method for assessing the microbial status in an individual by having access to data that reflect the microbiome profile and the individual, and / or the quantification of one or more nucleic acids of the microbiome in the individual's circulation. In one embodiment, the computer executing the computer logic of the invention may also include a digital input device, such as a scanning device. The digital input device may provide information about a nucleic acid, for example, its presence or prevalence. In some embodiments, the invention provides a computer-readable means comprising a set of instructions recorded thereon for causing a computer to perform the steps of (i) receiving data from one or more nucleic acids selected in a sample; and (ii) diagnosing or predicting a condition based on microbiome quantification. Microbial reference sequence databases and human reference sequence databases are also provided. These databases will typically comprise optimized datasets as described above. In some embodiments, the methods of the invention provide an individual's status with respect to infection. In some of these embodiments, the microbial infection is a pathogen, where any presence of the pathogen sequence indicates a clinically relevant infection. In other embodiments, the prevalence is indicative of the microbial load, where a predetermined level is indicative of clinical relevance. In some embodiments, the individual is treated or considered for treatment with antimicrobial therapy, for example, antibiotics, passive or active immunotherapy, antivirals, etc. An individual may be tested before, during, and after therapy. A microbial infection can also be indicated by the load of a commensal organism, where the level of a commensal in a blood sample is indicative of intestinal health, e.g., degradation of the intestinal lumen. A comparison of microbial RNA, alone or in relation to microbial DNA, can be made, where an excess of RNA for a microbial sequence, for example, over 5X, 10X, 15X, 20X, or 25X magnification of the microbial DNA, is indicative of an active infection. In some cases, the microbe thus analyzed is one capable of causing a latent infection, for example, herpesvirus, hepatitis virus, etc. In other approaches, the overall microbiome assessment is of interest, where the presence or relative prevalence of microorganism classes is relevant. It is known that diet and drug treatments, such as statins, antibiotics, and immunosuppressants, can affect the overall health of the microbiome, and therefore determining its composition is important. In some approaches, temporal differences in the quantity of one or more nucleic acids in the microbiome can be used to verify the effectiveness of antimicrobial treatment or to select a treatment. For example, the quantity of one or more nucleic acids in the microbiome can be determined before and after treatment. A decrease in one or more nucleic acids after treatment may indicate that the treatment was successful. Additionally, the quantity of one or more nucleic acids in the microbiome can be used to choose between treatments, for example, treatments with different strengths. In one aspect, the invention provides methods for diagnosing or predicting immunocompetence status or transplant outcome in a subject receiving an immunosuppressive regimen. After immunosuppression, samples can be obtained from the patient, as described above, and analyzed for the presence or absence of one or more nucleic acids from the microbiome, including the virome. In some embodiments, the sample is blood, plasma, serum, or urine. The proportion and / or quantity of microbial nucleic acids can be monitored over time, and the increase in this proportion can be used to determine immunocompetence. Quantification of the viral load can be determined by any suitable method known in the art, including those described herein, such as sequencing, nucleic acid arrays, or PCR. In some embodiments, the quantity of one or more nucleic acids from the microbiome in a sample from the immunosuppressed recipient is used to determine the status or outcome of the transplant. Thus, in some embodiments, the methods of the invention further comprise quantifying one or more nucleic acids from the microbiome. In some embodiments, the quantity of one or more nucleic acids from the donor sample is determined as a percentage of the total nucleic acids in the sample. In some embodiments, the quantity of one or more nucleic acids from the donor sample is determined as a ratio of the total nucleic acids in the sample. In some embodiments, the quantity of one or more nucleic acids from the donor sample is determined as a ratio or percentage compared to one or more reference nucleic acids in the sample.For example, the amount of one or more microbiome nucleic acids can be determined as 10% of the total nucleic acids in the sample. Alternatively, the amount of one or more microbiome nucleic acids can be a 1:10 ratio compared to the total nucleic acids in the sample. Furthermore, the amount of one or more microbiome nucleic acids can be determined as 10% or a 1:10 ratio of a reference gene such as β-globin. In some modalities, the amount of one or more microbiome nucleic acids can be determined as a concentration. For example, the amount of one or more nucleic acids in the donor sample can be determined as 1 pg / mL. In some modalities, the quantity of one or more microbiome nucleic acids above a predetermined level is indicative of immunocompetence. For example, normative values ​​for chemically stable patients with no evidence of graft rejection or other pathologies can be determined. An increase in the quantity of one or more microbiome nucleic acids below the normative values ​​for chemically stable patients after transplantation could indicate a stable outcome. On the other hand, a quantity of one or more microbiome nucleic acids above the normative values ​​for chemically stable patients after transplantation could indicate increased immunocompetence and a risk of graft rejection. In some modalities, different predetermined threshold values ​​are indicative of different transplant outcomes or states. For example, as discussed earlier, an increase in the amount of one or more microbiome nucleic acids above normative values ​​for clinically stable post-transplant patients could indicate a change in transplant status or outcome, such as transplant rejection or transplant damage. However, an increase in the amount of one or more microbiome nucleic acids above normative values ​​for clinically stable post-transplant patients but below a predetermined threshold level could indicate a less serious condition, such as a viral infection, rather than transplant rejection. An increase in the amount of one or more microbiome nucleic acids above a higher threshold could indicate transplant rejection. In some cases, temporal differences in the quantity of one or more nucleic acids of the microbiome are indicative of immunocompetence. For example, a transplant recipient can be monitored over time to determine the quantity of one or more nucleic acids of the microbiome. A temporary decrease in the quantity of one or more nucleic acids of the microbiome, which subsequently returns to normal values, may indicate a less serious condition than transplant rejection. On the other hand, a sustained decrease in the quantity of one or more nucleic acids of the microbiome may indicate a serious condition such as the absence of effective immunosuppression and graft rejection. In some modalities, temporal differences in the quantity of one or more nucleic acids in the microbiome can be used to verify the effectiveness of an immunosuppressive treatment and to select the appropriate treatment. For example, the quantity of one or more nucleic acids in the microbiome can be determined before and after immunosuppressive treatment. A decrease in one or more nucleic acids in the microbiome after treatment may indicate that the treatment was successful in preventing transplant rejection. Additionally, the quantity of one or more nucleic acids in the microbiome can be used to choose between immunosuppressive treatments, for example, treatments of varying strengths.For example, a lower amount of one or more nucleic acids in the microbiome may indicate a need for a more potent immunosuppressant, while a higher amount of one or more nucleic acids in the microbiome may indicate that a less potent immunosuppressant can be used. The invention provides methods that are sensitive and specific. In some modalities, the methods described herein for diagnosing or predicting transplant status or outcome have at least 50%, 60%, 70%, 80%, 90%, 95%, or 100% sensitivity. In some modalities, the methods described herein have at least 50% sensitivity. In some modalities, the methods described herein have at least 78% sensitivity. In some modalities, the methods described herein have a specificity of approximately 70% to approximately 100%. In some modalities, the methods described herein have a specificity of approximately 80% to approximately 100%. In some modalities, the methods described herein have a specificity of approximately 90% to approximately 100%. In some modalities, the methods described herein have a specificity of approximately 100%. The invention provides a non-invasive diagnostic method for individuals, including those undergoing immunosuppressive regimens, antimicrobial treatment, etc., by verifying cell-free DNA or RNA sequences from non-human sources. For example, it can be used to test individuals carrying a number of viruses, where the viral load shown here varies with the individual's immunocompetence. The preferred viruses for verifying immunocompetence are anelloviruses, in which the viral load shown here correlates with the individual's immunocompetence. In some embodiments, the invention provides methods, devices, compositions, and equipment for the detection and / or quantification of circulating nucleic acids, usually free in plasma, or viral particles, for the diagnosis, prognosis, detection, and / or treatment of an infection, immunocompetence, transplant status, or outcome. / Roann / Lznz / E / Yii In some specific embodiments, the invention provides an approach for the non-invasive detection of immunocompetence in transplant recipients by virome analysis, which avoids the potential problems of DNA microchimerism from other external sources and is generalizable to all organ recipients regardless of gender. In some embodiments, a genetic fingerprint is generated for the individual's virome. This approach allows for the reliable identification of sequences that can be produced in such a way that they are independent of the donor and recipient genders. Following an immunosuppressive regimen, for example, in conjunction with transplantation, treatment for an autoimmune disease, etc., body fluids such as blood can be collected from the patient and analyzed for markers. Examples of body fluids include, but are not limited to, swabs, sputum, biopsies, secretions, cerebrospinal fluid, bile, blood, lymphatic fluid, saliva, and urine. The detection, identification, and / or quantification of virome sequences can be performed using real-time PCR, integrated microcircuits, high-throughput pump sequencing of circulating nucleic acids (e.g., cell-free DNA), as well as other known methods, including those described herein. Viral load can be monitored over time, and an increase in this proportion can be used to determine immunocompetence status. In any of the modalities described herein, the transplant graft may be either a solid organ or a skin graft. Examples of organ transplants that can be analyzed by the methods described herein include, but are not limited to, kidney transplants, pancreas transplants, liver transplants, heart transplants, lung transplants, intestinal transplants, pancreas transplants following kidney transplantation, and simultaneous pancreas-kidney transplants. In some embodiments, the methods of the invention are used to determine the efficacy of a therapy for the treatment of a disease, including infection, either at the individual level or in the analysis of a group of patients, for example, in a clinical trial format. These embodiments typically involve comparing two points in time for a patient or group of patients. The patient's condition is expected to differ between the two points in time as a result of a therapeutic agent, therapeutic regimen, or disease challenge to a patient under treatment. Examples of formats for these modalities may include, without limitation, analyzing the microbiome at two or more points in time, where a first point in time is a diagnosed but untreated patient; and a second or more additional points in time is a patient treated with a candidate therapeutic agent or regimen. In another format, a first point in time is a patient diagnosed as being in remission from a disease, for example, according to current clinical criteria, as a result of a candidate therapeutic agent or regimen. A second or subsequent point in time is a patient treated with a candidate therapeutic agent or regimen and challenged with a disease-inducing agent, for example, in a vaccination setting. In these clinical trial formats, each set of time points can correspond to a single patient, a group of patients, for example, a cohort group, or a mixture of individual and group data. / «οαηη / ι zoz / e / yl Additional control data may also be included in these clinical trial formats, such as a placebo group, a control group, and similar data, as is standard practice. Formats of interest include crossover, randomized, double-blind, placebo-controlled, parallel-group trials, and studies capable of testing drug efficacy, among others. See, for example, Clinical Trials: A Methodological Perspective, Second Edition, S. Plantados, Wiley-Interscience; 2005, ISBN-13: 978-0471727811; and Design and Analysis of Clinical Trials: Concepts and Methodologies, S. Chow and J. Liu, Wiley-Interscience; 2003; ISBN-13: 978-0471249856, each specifically referenced herein. EXAMPLES Temporary response of the human virome to immunosuppression and antiviral therapy The viral component of the microbiome, the human virome, remains relatively unstudied (Wylie et al. (2012) Transí Res 160, 283-290), and little is known about the effects of immune modulation and antiviral therapies on virome composition. Previously, the virome of the healthy gut was shown to remain remarkably stable over time (Reyes et al. (2010), Nature 466, 334-338), and the predominant source of variation is inter-subject differences, although an association between diet and virome composition was found (Minot et al. (2011). Genome Research 21, 1616-1625). Immunosuppressive therapies significantly reduce the risk of graft rejection in organ transplantation, but increase recipients' susceptibility to infections. Infections with viral pathogens, particularly cytomegalovirus (CMV), occur frequently and increase the recipient's risk of graft failure. Organ transplant recipients are therefore frequently given prophylactic or preventive antiviral therapies targeting CMV. The inverse relationship between the level of immunosuppression and the risks of infection and rejection leaves only a narrow therapeutic window available for patient treatment. Post-transplant care is further complicated by numerous limitations of currently available methods for diagnosing infection and rejection. Diagnosing rejection relies primarily on invasive biopsies, which suffer from interobserver variability, high cost, and patient discomfort. Diagnosing infections is challenging given that infection symptoms lessen after immunosuppression, and commonly used diagnostic methods such as antigen detection and PCR-based molecular tests depend on a specific target and therefore a prior hypothesis regarding the source of infection. As a final complication, patient-to-patient variability in sensitivity to immunosuppressive drugs can lead to over- or under-immunosuppression, increasing the risk of infection or rejection respectively. There are few substantive methods for measuring immune system health, and the relationship between immunocompetence and the viral component of the microbiome is poorly understood. Organ transplant recipients are treated with post-transplant therapy that combines immunosuppressive and antiviral drugs, offering a window into the effects of immune modulation on the human virome. We used cell-free DNA sequencing in plasma to investigate drug-virome interactions in a cohort of organ transplant recipients (656 samples, 96 patients) and found that antivirals and immunosuppressants strongly affect the plasma virome structure. We observed marked virome composition dynamics at the start of therapy and found that the total viral load increases with immunosuppression, while the bacterial component of the microbiome remains largely unaffected.The data provide clues about the relationship between the human virome, the state of the immune system, and the effects of drug treatment, and offer a potential application of virome status to predict immunocompetence. In this study, we sequenced cell-free DNA from circulating plasma to investigate drug-microbiome interactions following organ transplantation. We examined infection patterns in heart and lung transplant recipients undergoing combined immunosuppressant and antiviral prophylaxis. We found that immunosuppressants and antivirals strongly influence the structure of the viral component of the microbiome but not the bacterial component. Strong compositional dynamics were observed at the start of drug therapy, when the virome composition of different individuals converged to a similar, drug-induced state. The total viral load increased markedly in response to therapy, as viruses, particularly anelloviruses, took advantage of reduced immunocompetence.Finally, we show that measuring the anellovirus load allows for the stratification of receptors with and without rejection. A total of 656 plasma samples were collected longitudinally from 96 solid organ transplant recipients (41 adult hearts, 24 pediatric hearts, and 31 adult lungs). Cell-free DNA was purified from the plasma and sequenced. In total, we obtained 820 gigabases (Gbp) of sequencing data, with an average of 1.25 Gbp per sample (Illumina HISeq, 1 x 50 bp reads, Figure 1B). Organ transplant recipients were continuously enrolled in the study over a period of more than two years, and samples were collected from recipients at regular time points post-transplant, with the highest collection frequency occurring in the first few months after transplantation. Figure 1C shows the number of samples analyzed as a function of time post-transplant for different patient classes. Patients in the cohort were treated with antiviral prophylaxis and immunosuppression as part of standardized post-transplant therapy (Figure 1D). Maintenance immunosuppression was tacrolimus-based for adult heart and lung transplant recipients and was supplemented with mycophenolate mofetil and prednisone. Pediatric patients were treated with cyclosporine-based antirejection therapy. CMV-positive transplant recipients (pre-CMV infection in the recipient and / or donor), but not CMV-negative recipients, were treated with antiviral prophylaxis. The protocol design involves high doses of immunosuppressive and antiviral drugs for the first few months after transplantation, after which drug doses are gradually reduced as the risks of rejection and infection decrease.Given the narrow therapeutic window available for immunosuppression and the high patient-to-patient variability in tacrolimus pharmacokinetics, tacrolimus concentration is measured directly in the blood and the dose is adjusted to maintain the target drug level. Figure 1D shows the mean tacrolimus blood level measured for patients treated with tacrolimus and illustrates the design of the drug treatment protocol. DNA sequence analysis. Microbiome-derived sequences were identified after computational subtraction of human-derived sequences. Up to this point, duplicate and low-quality reads were removed, and the remaining reads were mapped to the human reference genome, as construct hg19 (BWA (Lí and Durbin, 2009), see Methods). Unassigned reads were then harvested, and low-complexity reads were removed. Figure 1E shows the distribution of the fraction of remaining reads after applying duplicate and quality filters (average of 86%) and the distribution of the fraction remaining after subtraction of human reads (average of 2%). To identify infectious agents, the remaining high-quality, unique, non-human reads were mapped using BLAST to a reference database of viral (n=1401), bacterial (n=1980), and fungal (n=32) genomes (downloaded from NCBI, Figure 6A). 0.12% of the sequential reads were uniquely aligned to at least one of the target genomes (Figure 6B, 6C). We used a quantitative PCR (qPCR) assay for a subset of the sequencing-identified targets (herpesvirus 4, 5, and 6, and parvovirus) to validate the positive hits identified by the sequencing-based approach. We found quantitative agreement between the viral counts measured by sequencing and qPCR (Figure 6D). Furthermore, we found that the sensitivity of the sequencing assay for herpesvirus detection is on par with qPCR measurements. The largest capture cross-section available for the sequencing assay—the entire target genome versus the target region of the PCR amplicon—is thus sufficient to overcome the signal loss in sequencing caused by the finite efficiency of sequencing library preparation and sampling. The highest CMV burdens measured using sequencing across all samples in the study corresponded to adult heart transplant recipients with clinically diagnosed disseminated CMV infection (see Figure 6E). To test for the presence of potential contaminants in the reagents used for DNA extraction and sequencing library preparation, we performed control experiments. In the first experiment, we prepared two samples using a known template (Lambda gDNA, Pacbio Item No. 001-119-535) and purified DNA for sequencing using the workflow described above (Illumina MiSeq, 3.4 and 3.5 million reads). Lambda-derived sequences were removed, and the remaining sequences (0.4%) were aligned to the BLAST reference database described above. No evidence of the various infectious agents discussed in this work was found, but we did detect sequences related to the Enterobacteriaceae family (genus Proteobacteria), primarily E. coli (>97%), and bacterial phages (<1%), which are likely remnants of the lambda DNA culture.In a second control, we prepared a sample for nuclease-free water sequencing. This sample was included in a sequencing assay along with a sample unrelated to this work, and only a limited number of sequences were recruited—15 in total—which were assigned to the genomes of two bacterial species. Again, no evidence of the infectious agents discussed later was found. We studied the composition of the plasma microbiome at different levels of taxonomic classification using GRAMMy, a tool that utilizes sequence similarity data obtained with BLAST to perform a maximum probability estimate of relative species abundance. GRAMMy takes into account differences in target genome size and read assignment ambiguity. Note that this approach only allows for estimating the abundance of species for which genomic data are available in the reference database. Figure 1F shows the relative abundance of species at different levels of taxonomic classification (averaged across all samples). We found that viruses (73%) are most abundantly represented in bacteria (25%) and fungi (2%) (panel a of Figure 1F). Among viruses, we found that ssDNA viruses occupy a larger fraction (72%) than dsDNA viruses (28%).Seven distinct viral families were found (abundance > 0.75%), with one dominant family, Anelloviridae, contributing 68% of the total population (Figure 1F, panel b). The Anelloviridae fraction is composed primarily (97%) of viruses from the genus Alphatorquevirus (Figure 1F, panel c). The genus Alphatorque is the genus of Torque Teño Viruses (TTVs), and sequences related to 14 different Torque Teño virotypes were identified (Figure 1F, panel d). Polyomavirus infections are widespread in the human population, and polyomavirus DNAemia is uncommon in the first year after solid organ transplantation. Polyomavirus-derived sequences were found in 75 samples (11%) from 36 patients in this cohort. Evidence was found of the presence of BK (41%), JO (27%), TS (4%), polyomavirus WU (6%), SV40 (6%) and the recently discovered HPyV6 (13%) (Schowalter et al., 2010) (Figure 1F, panel e).Among the bacteria, Proteobacteria (36%), Firmicutes (50%), Actinobacteria (10%), Bacteroidetes (4%) are the genera most abundantly represented in the sample (Figure 1F, panel f). To investigate potential misassignments of the relatively short reads available for this study (50 bp), we examined the dependence of abundance estimates on read length, based on longer, paired final reads (2 x 100 bp) collected from a subset of samples (n = 55). We found that abundance estimates on 50 bp and 100 bp subreads are similar for all taxonomic classification levels reported here (Figure 6F). Sensitivity of virome composition to drug dose. Available clinical data on drug dose were used to analyze drug-microbiome interactions. Here, we examined data for adult heart and lung transplant patients treated with a tacrolimus-based antirejection protocol (47 patients and 380 observations), thus excluding pediatric patients treated with cyclosporine and patients who switched from tacrolimus to cyclosporine immunosuppression due to drug intolerance. Data on antiviral drug (valganciclovir) dosage and measured blood levels of tacrolimus were collected from individual patient records, and the mean composition was extracted for samples corresponding to different drug levels.To account for a delayed effect of microbiome composition on dose changes, drug level and dose data were filtered from the average of the sliding window (see Figure 1C and Figures 7A-7C; window size of 45 days). We found that the structure of the viral component of the microbiome is a sensitive function of drug dose (47 patients, 380 samples, Figure 2A). However, the structure of the bacterial component of the microbiome was not significantly altered by drug therapy, as discussed later (Figure 7D). Herpesvirales and caudovirales dominated the virome when patients received low doses of valganciclovir and tacrolimus. In contrast, high doses of immunosuppressants and antivirals resulted in a virome structure dominated by anelloviridae (occupancy up to 94% at high drug levels). Antiviral prophylaxis is intended to prevent CMV disease, but other herpesviruses are susceptible to the drug, so it is not surprising that a high dose of valganciclovir results in a smaller fraction of herpesvirales.The observation that anelloviridae takes advantage of the suppression of the host's immune system is consistent with several observations in the literature: it has previously been shown that the incidence of anelloviridae increases with progression to AIDS in HIV-positive patients, and that the total TTV anellovirus burden increases after liver transplantation. Furthermore, an increased prevalence of anelloviridae has recently been reported in pediatric patients with fever. We then compared the virome composition measured in organ transplant recipients with the composition observed in healthy individuals not receiving immunosuppressants or antivirals (n = 9, sequencing data available from a previous study). Here, we compared the healthy composition with the composition measured in organ transplant recipients at the start of drug therapy (day one post-surgery, n = 13), corresponding to minimal drug exposure, and with the composition measured in transplant recipients exposed to high drug levels (also post-transplantation: tacrolimus > 9 ng / mL, valgancicolvir > 600 mg, n = 68). We found a similar virome composition for both healthy reference samples and reference samples corresponding to minimal drug exposure (Figure 2B).However, the composition of healthy reference samples and minimal drug exposure are different from the anelloviridae-dominated composition measured for samples with high drug exposure. Tacrolimus-based immunosuppressive therapy is supplemented with induction therapy within the first 3 days post-transplant (using antithymocyte globulin, daclizumab, or basiliximab), and patients also receive the corticosteroid prednisone as part of post-transplant therapy. The time-dose profile for prednisone and tacrolimus is similar: high doses at the start of therapy followed by a gradual dose reduction (Figure 7A-7C). The data in Figure 2A thus reflect the combined effect of prednisone and tacrolimus. An analysis of the differential effect of prednisone and valganciclovir on virome composition (Figure 7E) shows the same trend observed in Figure 2A: high doses of prednisone lead to a greater representation of anellovirus. Finally, we note that a subset of patients were not treated with antiviral drugs.The data from this subset of patients allowed us to further unravel the differential effects of antiviral drugs and immunosuppressants on virome composition, as described below. The distribution of microbiome diversity. We studied the diversity of bacterial and viral components of the microbiome. Within-subject diversity was lower than between-subject diversity for both bacteria and viruses (Bray-Curtis beta diversity, bacterial composition at the genus level, viral composition at the family and order level, Figure 2C). Distributing data for patients according to transplant type (heart or lung) or age did not reduce diversity. Within subjects, diversity was lower for samples collected within a one-month period, again for both bacteria and viruses. For viruses, but not for bacteria, we found that diversity was lower when comparing samples collected at similar drug doses (tacrolimus level ± 0.5 ng / mL, valganciclovir ± 50 mg).Taken together with the sensitivity of population averages to drug dose in Figure 2A, we thus find that the virome composition for patients who are the object of the same pharmaceutical therapy converges to a similar state. Dynamic response of the virome to changes in drug dose. A strong temporal response of the virome to changes in drug dose was observed, consistent with the sensitivity of the virome composition to drug dose. Figure 3A shows the time dependence of the relative genomic abundance of ssDNA and dsDNA viruses (data from all groups and patient samples, n = 656). The fraction of ssDNA viruses expands rapidly during the first few months after transplantation, followed by the opposite trend after 6 months. Figure 3B shows the time-dependent relative composition of the most abundant viruses grouped at the family level, and their order and proportion provide further details on the dynamics of the virome composition (data from all groups and patient samples, n = 656).The dsDNA fraction consists of caudovirales, adenoviridae, polyomaviridae, and herpesvirales, which together comprise 95% of the virome in the first few weeks after transplantation. ssDNA viruses comprise only 5% of the initial virome and consist mainly of members of the anelloviridae family. The fraction occupied by adenoviridae, caudovirales, and herpesvirales decreases sharply in the first few months because these virotypes are effectively targeted by antiviral prophylaxis. In contrast, the relative abundance of anelloviridae increases rapidly as these virotypes largely escape the target of antiviral drugs and take advantage of the patients' reduced immunocompetence (peaking at 84% during months 4.5–6). Six months after the organ transplant procedure, opposite trends were observed, consistent with the reduction in antiviral and immunosuppressive drugs prescribed by the treatment protocol. Compared to the viral component, the bacterial component of the microbiome remains relatively stable over time, an observation made at the taxonomic genus level (Figure 3C, n = 656, and Figures 8A–8B). Figure 3D shows the within-sample alpha diversity for bacterial and viral genera as a function of time (Shannon entropy, one-month time periods, 590 bacterial genera, 168 viral genera examined). The diversity of viral genera observed decreases at the start of therapy (1.05 ± 0.5 at month 1 to 0.31 ± 0.33 at months 4-5, p <106, Mann-Whitney U test), while the alpha diversity of bacteria remains relatively unchanged during the course of post-transplant therapy (2.2 ± 1.14 at month 1 to 2.6 ± 0.85 at months 4-5, p = 0.1, Mann-Whitney U test). Increase in total viral load at baseline post-transplant therapy. To gain insight into the effect of therapeutic drugs on total viral load, we extracted the absolute genomic abundance of all viruses relative to the human genome copy number, normalizing the viral target genome coverage to the human genome coverage. For all patient groups in this study, an increase in total viral load at baseline therapy was observed (Figure 4A), regardless of transplant type (heart or lung) or age (adult or pediatric) (change in load, 7.4 ± 3, adjustments and shaping, black line).Combined with the relative abundance data of / ROQnn / Lznz / E / Yi, the data after total viral load reveal a net reduction in herpesviral load and a net increase in anelloviridae load in the first 3 months after transplantation for patients who are simultaneously treated with antivirals and immunosuppressants. Thus, the data show a differential effect of the combination of antivirals and immunosuppressants on different virotypes. The data also show a reduction in the total adenoviral load, indicating that adenoviral replication is suppressed by valganciclovir, consistent with previous studies. Figure 4B summarizes the data for all transplant types, but the same trends are observed when stratified according to different transplant types: adult heart transplant recipients (n = 268, Figure 9A), adult lung transplant recipients (n = 166, Figure 8B), and pediatric patients are treated with cyclosporine as opposed to tacrolimus (n = 99, Figure 9C). Not all patients in the study cohort received antiviral and immunosuppressive drugs. In transplant cases, both the donor and recipient showed no evidence of CMV infection prior to a CMV antibody assay. The risks of complications from antiviral prophylaxis were judged to outweigh the potential risk of newly acquired CMV infection, and these patients were therefore not treated with antiviral prophylaxis. These patients were thus treated with immunosuppressants alone. Figure 9C shows the time-dependent viral load and composition of the CMV-negative cases (n = 75). The net effect of immunosuppressant-only therapy is an expansion of all virotypes, including herpesviruses and adenoviridae. Reducing immunosuppression leads to a decrease in the total viral load. Lower anellovirus load in patients experiencing graft rejection. Given the correlation between anellovirus load and the degree of immunosuppression (see Figure 2A and Figure 4A-4C), and given the association between immunocompetence and rejection risk, we questioned whether anellovirus load could be used to classify graft recipients as having or not rejecting the graft. Figure 5A shows the measured anellovirus load for patients with and without rejection as a function of time post-transplant. Here, patients are classified as rejectors if they experience a moderate or severe rejection episode as determined by biopsy, biopsy grade > 2R / 3A (in red; 20 patients, 177 data points).Patients without rejection correspond to patients who are not diagnosed with moderate or severe graft damage throughout their post-transplant course (in blue; biopsy grades <2R / 3A, 40 patients, 285 data points). Figure 5A shows that the anellovirus load is significantly lower for rejecting individuals at almost all time points. We then directly compared the anellovirus load for rejecting patients with the load measured for patients without rejection. To account for the time dependence of the anellovirus load described above (Figure 5A), we extracted the anellovirus load relative to the mean load measured for all samples at the same time point. Figure 5B shows the time-normalized load for patients without rejection (N = 208) compared with the load measured for patients experiencing a moderate rejection event (biopsy grade 1R, N = 102) and patients experiencing a severe rejection event (biopsy grade 2R / 3A, N = 22). The figure shows that the time-normalized loads are significantly lower for patients at higher risk of rejection.The P values ​​were calculated by random sampling of the population with a larger number of measurement points, p = sum (median(RejectionQ)>median(Nonrejection)) / N, where N = 104 and Rejection and Nonrejection are the relative viral loads for populations at greater or lesser risk of rejection and without rejection, respectively (p = 0.011, p = 0.0002 and p = 0.036). These observations are consistent with the finding that the risk of rejection and the incidence of infection have an inverse association with patient immunocompetence (see the inset in Figure 5A). The lower viral load observed in patients with rejection is thus indicative of a higher level of immunocompetence in this subgroup of patients, even though these patients are treated with the same immunosuppressive protocol. Patient-to-patient variability in sensitivity to immune suppression is known to occur, and the lack of predictive power of immunosuppression is a significant risk factor in transplantation. A commercially available assay currently used to measure immunocompetence has not been found to be predictive of acute rejection or significant infections. The development of methods for the direct measurement of immunocompetence, which could replace or complement existing assays, would therefore be important.The total anellovirus load recorded in organ transplant recipients could serve as an alternative marker. Figure 5C shows a receiver operating characteristic and tests the performance of the relative anellovirus load in classifying patients with and without rejection (area under the curve = 0.72). We have studied drug-microbiome interactions after solid organ transplantation by sequencing cell-free DNA in recipient plasma. The data reveal approximately the fundamental structure of the human plasma virome and how it responds to pharmacological perturbation; they also show the relative insensitivity to immunosuppression of the bacterial component of the microbiome. These data are useful in the design and optimization of post-transplant therapy protocols. For example, they show that tapering high-dose antiviral prophylaxis leads to a resurgence of the herpesvirus fraction. CMV DNA loading has previously been shown to prevent CMV disease relapse and rejection, raising the question of whether patients would benefit from long-term prophylactic therapy. The marked increase in anelloviridae abundance following immunosuppression also warrants further consideration. Anelloviruses are ubiquitous in the human population, and although their pathogenicity has not been established, they are currently under investigation as potential cofactors in carcinogenesis. The sensitivity of anelloviridae to immunosuppression makes organ transplantation an ideal setting for studying anelloviridae properties, particularly in light of the increased incidence of cancer observed in transplant recipients. The observation of a lower-than-average anellovirus load in patients experiencing a rejection episode is indicative of insufficient immunosuppression in this subgroup of patients, even though these patients received the immunosuppressant levels prescribed by the protocol.This suggests that there would be value in designing assays that allow for the direct measurement of a patient's level of immunocompetence, in addition to measurements of circulating drug levels. The total anellovirus load identified in the blood of a transplant recipient could serve as a marker of the individual patient's overall immunosuppression status. ζκοαηη / ι 7n7 / E / YL High-throughput DNA sequencing is finding use in the diagnosis of infections without a hypothesis. This approach is particularly relevant in the context of transplantation, given that infections frequently occur in transplant recipients and are difficult to diagnose in immunocompromised individuals, and because sequence analysis can additionally provide information on graft health through the quantification of circulating donor-derived human DNA in plasma. In other areas of infectious diseases, it may be valuable to develop subtraction methods to remove human DNA and enrich viral and microbial DNA. Experimental Procedures Clinical sample collection: Patients were enrolled at Stanford University Hospital (SUH) or Lucile Packard Children's Hospital (LPCH) and were excluded if they were multi-organ transplant recipients. This study was approved by the Stanford University Institutional Review Board (protocol #17666), and enrollment began in March 2010. For details on patient recruitment and post-transplant treatment, see the extended Experimental Procedures section. Plasma processing and DNA extraction: Plasma was extracted from whole blood samples within three hours of sample collection, as described above (Fan et al., 2008), and stored at -80°C. When required for analysis, plasma samples were thawed and circulating DNA was immediately extracted from 0.5 mL–1 mL of plasma using the QIA Circulating Nucleic Acid Kit (Qiagen). Sequencing Library Preparation and Sequencing: Sequencing libraries were prepared from purified patient plasma DNA using the NEBNext DNA Library Prep Master Mix Set for Illumina with standard Illumina indexed adapters (purchased from IDT), or using an automated microfluidic-based library preparation platform (Mondrian ST, Ovation SP Ultralow library system). The libraries were characterized using the Agilent Bioanalyzer 2100 (High Sensitivity DNA Kit) and quantified by qPCR. Samples were part of 26 different sequencing assays and were sequenced over a 22-month period. On average, 6 samples were sequenced per lane. Post-Transplant Verification and Clinical Sample Collection. This analysis represents a substudy of a prospective cohort study funded by the National Institutes of Health (RC4 AI092673) to study the clinical utility of a donor-derived cell-free DNA assay for the diagnosis of acute and chronic rejection and allograft failure following thoracic organ transplantation. Patients were enrolled if they received a heart or lung transplant at Stanford University Hospital (SUH) or Lucile Packard Children’s Hospital (LPCH), and were excluded if they were recipients of multiple organ transplants or if they were followed at centers other than SUH or LPCH after transplantation. This study was approved by the Stanford University Institutional Review Board (protocol # 17666), and enrollment began in March 2010. Details of the Post-Transplant Therapeutic Protocol for Adult Heart Transplant Recipients. Post-transplant immunosuppression consisted of 500 mg of methylprednisolone administered immediately post-operatively, followed by 125 mg every 8 hours for three doses. Antimyocyte globulin (rATG) 1 mg / kg was administered on days 1, 2, and 3 post-operatively. Maintenance immunosuppression consisted of 20 mg of prednisone twice daily, beginning 1 day post-operatively, and was changed to <0.1 mg / kg / day at 6 months post-operatively and further changed if endomyocardial biopsies showed no evidence of cellular rejection. Tacrolimus was started 1 day after surgery and the dosage was further adjusted to maintain a level of 10 ng / mL - 15 ng / mL during months 0-6, 7 ng / mL - 10 ng / mL during months 6-12, and 5 ng / mL - 10 ng / mL thereafter.Mycophenolatemofetil was started at 1,000 mg twice daily 1 day after surgery and dose adjustments were made, if required, in response to leukopenia. All patients received standard CMV prophylaxis (antiviral) consisting of 5 mg / kg of IV ganciclovir, adjusted for renal function, every 12 hours starting on postoperative day 1 unless both the donor and recipient were CMV-negative. When able to tolerate oral medications, recipients started with valganciclovir 900 mg twice daily for 2 weeks, then 900 mg daily until 6 months post-transplant, followed by 450 mg daily until 12 months post-transplant, at which point antiviral prophylaxis was discontinued. Valganciclovir dose reduction was performed in the event of leukopenia. CMV recipients of a CMV allograft also received hyperimmune globulin, 150 mg / kg IV, within 72 hr of transplantation, 100 mg / kg at weeks 2, 4, 6, and 8 post-transplant, and 50 mg / kg at weeks 12 and 16 post-transplant. CMV-allograft recipients were not treated with antiviral prophylaxis until May 2012; subsequently, these recipients were treated with 400 mg of acyclovir twice daily for one year. Antifungal prophylaxis consisted of 300 mg of itraconazole daily for the first 3 years post-transplant, and prophylaxis against Pneumocystis jirovecii infection consisted of trimethoprim / sulfamethoxazole, 80 mg of the TMP component daily. Pneumocystis infection prophylaxis continued indefinitely, and patients intolerant to TMP-SMX were treated with atovaquone, dapsone, or inhaled pentamidine. All heart transplant recipients were checked for acute cellular rejection by surveillance endomyocardial biopsies performed at scheduled intervals after transplantation: weekly during the first month, twice weekly until the third month, weekly until the sixth month, and then at months 9, 12, 16, 20, and 24. Biopsies were graded according to the revised ISHLT 2004 grading scale (0, 1R, 2R, 3R) (29). Blood samples were collected from heart transplant recipients at the following time points after transplantation: weeks 2, 4, and 6; months 2, 2.5, 3, 4, 5, 6, 8, 10, 12, 16, 20, and 24. A subset of heart transplant recipients also had blood samples collected 1 day after transplantation. If blood sampling and endomyocardial biopsies were performed on the same day, care was taken to ensure that blood was collected before the biopsy procedure. Pediatric Heart Transplant Recipients. Induction immunosuppression initially consisted of 1 mg / kg of daclizumab IV every 2 weeks for a total of 5 doses, and was changed to 10 mg–20 mg of basiliximab IV on days 0 and 4 postoperatively, beginning in August 2011. Recipients were also immediately treated with pulses of 10 mg / kg of methylprednisolone IV every 8 hours for 3 doses, followed by 0.5 mg / kg of prednisone twice daily for the first 14 days after transplantation; corticosteroids were subsequently discontinued during the first year after transplantation, in the absence of acute rejection. Calcineurin inhibition consisted primarily of cyclosporine, with target levels of 300–350 ng / mL for months 0–3, 275–325 ng / mL for months 4–6, 250–300 ng / mL for months 7–12, and 200–250 ng / mL after month 12 post-transplant. Cyclosporine-tolerant patients were treated with tacrolimus. Protocols for prophylaxis against opportunistic infections and surveillance endomyocardial biopsies were similar to those for adult heart transplant recipients. Lung Transplant Recipients. Immunosuppression after transplantation consisted of 500-1000 mg of methylprednisolone administered immediately postoperatively, followed by 0.5 mg / kg IV twice daily. Basiliximab, 20 mg IV, was administered on days 0 and 4 for induction immunosuppression. Maintenance immunosuppression consisted of 0.5 mg / kg of methylprednisolone IV twice daily on days 0-3 postoperatively, followed by 0.5 mg / kg of prednisone daily until day 30, and subsequently changed every 2-3 months to 0.1 mg / kg daily for months 6-12 postoperatively. Tacrolimus was started on day 0 after surgery and the dosage was adjusted to maintain a level of 12 ng / mL - 15 ng / mL during months 0-6, 10 ng / mL - 15 ng / mL during months 6-12, and 5 ng / mL - 10 ng / mL thereafter.Mycophenolate mofetil was initiated at 500 mg twice daily on day 0 postoperatively, and dose adjustments were made, if required, in response to leukopenia. Antiviral, antifungal, and PCP prophylaxis were similar to that used in the adult heart transplant cohort. All lung transplant recipients were screened for acute cellular rejection by protocol transbronchial biopsies performed at 1, 5, 3, 6, 12, 18, and 24 months post-transplant. Biopsies were also performed if clinically indicated, based on symptoms or pulmonary function test results. Blood samples were collected from lung transplant recipients for study purposes at the following intervals: three times on day 1, twice on day 2, and once on day 3 post-transplant, followed by weeks 1 and 2, and months 1.5, 2, 3, 4.5, 6, 9, 12, 18, and 24. Blood samples were drawn before protocol biopsies and clinically indicated biopsies. Workflow for Identifying Pathogen-Derived Sequences. Exact duplicates were removed using the C-based tool fastq.cpp. Low-quality reads were removed using the quality filter included in the fastx package (fastq_quality_filter -Q33 -q21 - p50). The remaining reads were subsequently aligned using BWA with the human reference genome construct hg 19 (bwaaln - q25). Unassigned or untraced reads were collected using samtools (samtools view-F4), and low-complexity reads were removed using Seqclean (seqclean -I 40 -c 1). The reads were then aligned against a selection of viral, bacterial, and fungal reference genomes, and all references were downloaded to ncbi_fung. Figure 6A shows the distribution of genome sizes. The following parameters were used for BLAST alignment: backslash = 1, penalty = -3, word_size = 12, gapopen = 5, gapextend = 2, evaluate = 10, relativeity = 90, culling_limit = 2. BLAST hits with alignment lengths shorter than 45 bp were removed. Longer reads (2, 3, 100 bp, n = 55) were available for a subset of samples. To test the robustness of the genomic abundance estimates, the length dependence of the composition measurement was examined. Here, reads were trimmed to lengths of 40, 50, 65, 80, and 100 bp (fastx_trimmer) and analyzed using the workflow described above. Here, the alignment length hits of less than 37, 45, 59, 72 and 80 bp were removed for readings of 40, 50, 65, 80 and 100 bp, respectively.Genome abundance relative to the estimated genome abundance was calculated using GRAMMy. This tool uses nucleic acid sequence similarity data derived from BLAST to perform a maximum probability estimate of the relative abundance of species in the sample. GRAMMy filters hits by BLAST alignment metrics (E-score, alignment length, and percent identity) and takes into account the size of the target genome and the ambiguity of the read assignments when assessing the relative abundance of the candidate reference genome. The GRAMMy was calculated using the following parameters: python grammy_rdt.py; python grammy_pre.py -q “40,40,1” input set; python grammy_em.py -b 5 -t 0.0001 -n 100 input.mtx; grammy_post.py input.est setinput.btp. Adapted order sequences were used to combine strain-level abundance estimates to obtain abundances at higher taxonomic levels. Here, a minimal taxonomy was constructed for the reference database using Taxtastic. Quantification of Absolute Viral Load. To quantify the viral load of infectious agents in the samples, blast hit results were collected, and the best hits from each read were selected using an adapted sequence of commands (Bioperl). Figure 6B shows the distribution of the number of unique viral, bacterial, and fungal blast hits per million unique sequenced molecules. Figure 6C shows the number of viral, bacterial, and fungal genome copies relative to the number of human genome copies present in the sample. The infectious agent genome coverage was normalized to the human genome coverage. Validation by qPCR of Sequencing Results for Selected Viral Targets. Standard qPCR instruments for the quantification of human herpesvirus 4, 5, and 6 and parvovirus (PrimerDesign, genesig) were used to validate sequencing results for a subset of cell-free DNA samples. qPCR assays were performed on cell-free DNA extracted from approximately 1 mL of plasma and eluted in 100 mL of Tris buffer (50 mM [pH 8.1–8.2]). Plasma extraction and PCR experiments were performed at different facilities. No template controls were tested to verify that PCR reagents were included in each experiment. Figure 6D compares the relative number of blast hits per million reads acquired for viral genome copy number as determined by qPCR. Template-less Control. A template-less control experiment was performed. A sequencing library was generated from nuclease-free water (S01001, Nugen). The library was prepared along with 7 additional sample libraries (cell-free human DNA) to test for any potential sample-to-sample crosstalk during library preparation. To ensure sufficiently dense clustering on the Illumina flow cell, the sample was sequenced alongside an unrelated sample. While the unrelated sample recruited 16 million samples, the template-less control library generated only 15 reads, which were mapped to two species in the reference database: the genomes of Methanocalcodoccus janaschii (9 matches) and Bacillus subtilis (5 matches).No evidence of human-related sequences was found, indicating that sample-to-sample contamination was low. Example 2 Clinical Microbiome Verification Using the methods described in Example 1, CMV genome reads were quantified for each sample. Increased CMV abundance was observed in samples that were clinically positive for infection (p=7.109, Mann-Whitney U test, Figure 10C); the level of CMV-derived DNA in our samples was consistent with clinical reports of CMV, with an AUC of 0.91 (Figure 10C). These data indicate that CMV surveillance can be performed in parallel with rejection verification using the same sequence data, and allow us to examine whether other viral infections could be similarly verified. We identified pathogenic viruses and characterized oncoviruses (Figure 11A) as well as commensal torqueviruses (TTVs, genus Alphatorquevirus), which is consistent with previous observations of a link between immunosuppression and TTV abundance. The frequency of clinical testing for these viruses varied considerably, with frequent surveillance of CMV (human herpesvirus 5, HHV-5, n=1082 tests in our cohort) compared to other pathogens (Figure 11A). We assessed the incidence of infection (number of samples in which a given virus was detected via sequencing) in relation to the frequency of clinical screening.Although CMV was most frequently selected (335 samples), its incidence as determined by sequencing (detected in 22 samples) was similar to that of the other pathogens that were not routinely selected, including adenovirus and polyomavirus (clinically tested four times and once, respectively, Figure 11 A). Adenoviruses are a community-acquired respiratory infection that can cause graft loss in lung transplant recipients and pose a particularly high risk to pediatric patients. Samples were collected from a pediatric patient (L78, panel 1 in Figure 11B) who tested positive for adenovirus. This patient also had the highest adenovirus-derived DNA load across the entire cohort. Sustained adenovirus loads were also observed in several other adult transplant patients who were not clinically selected (e.g., L34, panel 1 in Figure 11B), as testing was typically restricted to pediatric lung transplant cases. Polyomavirus is a known cause of allograft rejection after kidney transplantation but is not routinely included in surveillance after lung transplantation. We detected polyomavirus in two patients who were not tested for this pathogen (L57 and L15, panel 2 of Figure 11B). In both cases, clinical records indicated persistent renal failure, which may have resulted from polyomavirus infection. In a final example of the benefit of broad, non-hypothetical screening for infections, we examined a patient exhibiting a high load of human herpesvirus (HHV) 8 (panel 3 in Figure 11B), an oncovirus that can cause complications following solid organ transplantation. This patient (L58) tested positive for two other herpesviruses (HHV-4a and HHV-5), both of which have the potential to stimulate HHV-8 reactivation. Although post-transplant screening for HHV-8 is recommended only in specific clinical circumstances, the use of sequencing allows for the identification of the virus in non-suspected cases that would otherwise go undetected. Clinical Microbiome Verification. In addition to viruses measured in serum, we also observed the correlation between cell-free measurements and fungal and bacterial infections detected in other body fluids, including Klebsiella pneumoniae infections detected via urine culture (ROC = 0.98) and fungal infection detected in bronchoalveolar lavage (BAL). Performance on bacterial and fungal correlations was sensitive to both the type of infection and the body fluid investigated. We observed better performance for body fluids that coupled more strongly with blood and also observed sensitivity to background signal. For example, the most commonly cultured bacterial infection (Pseudomonas) was detected in cell-free measurements for more than 80% of our patients, which contrasted sharply with the most commonly detected viral pathogens (CMV), detected in only 6% of our patient samples. This highlights an important distinction between commensal infections (including Pseudomonas), which are part of the normal flora, and non-commensal infections, which are exclusively pathogenic and have a lower background signal. This distinction may explain the differences in sensitivity and specificity measured for infections caused by commonly cultured commensals (e.g., AUC = 0.66 and 0.62 for P. aeruginosa and E. coli, respectively) compared to non-commensals (AUC = 0.91 for CMV). In the case of commensal bacteria, the clinical issue is not their presence or absence, but rather their presence or absence in inappropriate body sites. In our cohort, we also detected cell-free DNA derived from microsporidia, a non-commensal fungus that can cause intestinal infections in immunocompromised patients. We measured a sustained microsporidia load in L78 (panel 4 of Figure 11B), a patient who exhibited canonical symptoms of microsporidiosis. Adenovirus infection (L78, panel 1 of Figure 11B) was suspected, although endoscopy and sigmoidoscopy results were inconclusive, and stool samples tested negative for C. diff as well as adenovirus. Based on our sequencing data, microsporidiosis is the most likely explanation for the patient's symptoms, since the microsporidia signal measured in this patient is similar to that of I6, a patient from an unrelated cohort who tested positive for microsporidia (panel 4 of Figure 11B). With over 10 trillion fragments per mL of plasma, cell-free DNA is a rich window into human physiology, with applications rapidly expanding in cancer diagnosis and treatment verification, prenatal genetic diagnosis, and heart transplant rejection verification via genome transplant dynamics (GTD). In this work, we applied GTD principles to lung transplantation—a particularly challenging type of solid organ transplantation that is limited by poor survival rates, as well as an inaccurate and invasive test for allograft rejection. Because lung transplant recipients with allograft infection and acute rejection can present clinically with similar symptoms, we extended the scope of the GTD to the screening for infectious diseases. We first demonstrated a strong correlation between clinical test results and cDNA derived from / βοαηη / ίζηζ / Β / γι CMV – a leading cause of graft injury after transplantation. We further show that hypothesis-free infection screening revealed numerous previously untested pathogens, including undiagnosed cases of adenovirus, polyomavirus, HHV-8, and microsporidia in patients who had similar levels of microbial cell-DNA compared to patients with positive clinical test results and associated symptoms. These examples illustrate the benefit of broad, sequencing-based infection screening as opposed to pathogen-specific testing. This approach can be immediately useful as a tool to help determine the occurrence and source of an infection. This may be particularly relevant in the context of transplantation, where the incidence of infections is high, where rejection and infection can occur, and where the symptoms of infection and rejection are difficult to distinguish. Although preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that these embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of the invention. The following claims are intended to define the scope of the invention, and the methods and structures within the scope of these claims and their equivalents are therefore covered.

Claims

1. A method for detecting microbial nucleic acids comprising: (a) providing a cell-free sample comprising cell-free nucleic acids from an immunocompromised human subject, wherein the immunocompromised human subject has or is suspected of having a microbial infection; (b) performing high-throughput sequencing of the cell-free nucleic acids from the immunocompromised human subject to generate sequence reads comprising sequence reads from at least one microbe; (c) performing bioinformatic analysis on the sequence reads comprising sequence reads from at least one microbe to detect the sequence reads of the at least one microbe; and (d) identifying the at least one microbe to a genus level based on the sequence reads of the at least one microbe.

2. The method of claim 1, wherein the immunocompromised human subject is a human transplant recipient.

3. The method of claim 1, wherein the cell-free nucleic acids comprise cell-free DNA.

4. The method of claim 1, further comprising selectively extracting cell-free DNA from cell-free nucleic acids to obtain extracted cell-free DNA, wherein the high-throughput sequencing in (b) is performed on the extracted cell-free DNA.

5. The method of claim 1, wherein the cell-free nucleic acids comprise cell-free RNA.

6. The method of claim 1, further comprising selectively extracting cell-free RNA from cell-free nucleic acids to obtain extracted cell-free RNA, wherein the high-throughput sequencing in (b) is performed on the extracted cell-free RNA.

7. The method of claim 1, wherein the sample is a sample of body fluid.

8. The method of claim 1, wherein the sample is selected from the group consisting of plasma, cerebrospinal fluid, and synovial fluid.

9. The method of claim 1, wherein the sample is a plasma sample.

10. The method of claim 1, wherein the at least one microbe comprises at least one fungus.

11. The method of claim 1, wherein the at least one microbe comprises a microbe selected from the group consisting of at least one bacterium, at least one virus, at least one parasite, and a combination thereof.

12. The method of claim 1, wherein the at least one microbe comprises: / Roann / Lznz / E / Yii (i) at least one virus selected from the group consisting of: adenovirus, adeno-associated virus, anelovirus, human cytomegalovirus, human respiratory syncytial virus, human rhinovirus, human influenza virus, human coronavirus, human SARS coronavirus, hepatitis virus, human herpesvirus 1, human herpesvirus 2, human herpesvirus 6, BK polyomavirus, JC polyomavirus, cytomegalovirus, parainfluenza virus, human papillomavirus, Epstein-Barr virus, and human T-lymphotropic virus; (i) at least one bacteria selected from the group consisting of: Brucella, Treponema, Mycobacterium, Listeria, Helicobacter, Legionella, Streptococcus, Neisseria, Clostridium, Staphylococcus, Pseudomonas, Micrococcus, Multi-drug resistant Staphylococcus Aureus (MRSA), Klebsiella pneumonia, gram-positive bacteria, gram-negative bacteria, and Bacillus;(iii) at least one parasite selected from the group consisting of: Trichomonas, Toxoplasma, Glardia, Cryptosporidium, Plasmodium, Leishmania, Trypanosoma, Entamoeba, Schistosoma, Filariae, Asearla, and Fasciola; (iv) at least one fungus selected from the group consisting of: Candida, Aspergillus, Cryptococcus, Pneumocystis, and yeast; or (v) any combination thereof.

13. The method of claim 2, wherein the transplant recipient is experiencing transplant rejection at the time the sample comprising cell-free nucleic acids from the transplant recipient is obtained from the transplant recipient.

14. The method of claim 13, wherein transplant rejection is acute rejection.

15. The method of claim 13, wherein transplant rejection is chronic rejection.

16. The method of claim 2, wherein the transplant recipient is a recipient of a solid organ transplant or graft and the at least one microbe is associated with an infection of the solid organ transplant or graft.

17. The method of claim 2, wherein the human transplant recipient is a bone marrow transplant recipient.

18. The method of claim 2, wherein the transplant recipient is a recipient of at least one transplant or graft of an organ selected from the group consisting of kidney, heart, liver, pancreas, lung, intestine, skin, and any combination thereof.

19. The method of claim 2, further comprising determining a standardized quantity of at least one microbe in the sample based on sequence readings of at least one microbe.

20. The method of claim 2, wherein the sample is a body fluid and the at least one microbe is associated with an infection in a solid organ.

21. The method of claim 20, further comprising detecting infection in the solid organ based on the quantity of at least one microbe in the sample, wherein the quantity of at least one microbe in the sample is determined based on a number of sequence reads of the at least one microbe. / Aoain / Ize / E / Gi 22. The method of claim 21, further comprising administering an antimicrobial treatment to the human transplant recipient to treat the infection in the solid organ.

23. The method of claim 1, further comprising detecting antimicrobial resistance in at least one microbe based on sequence reads of at least one microbe.

24. The method of claim 2, further comprising obtaining a second sample from the transplant recipient at a second time point and performing (b) and (c) on the second sample.

25. The method of claim 24, wherein the second time point is a time point after the administration of an antimicrobial treatment to the transplant recipient, and further comprising detecting a decrease in the amount of at least one microbe in response to the antimicrobial treatment.

26. The method of claim 1, further comprising attaching adapters to cell-free nucleic acids before (b) producing cell-free nucleic acids attached to the adapters and sequencing the cell-free nucleic acids attached to the adapters.

27. The method of claim 1, further comprising, prior to (b), performing a non-biased amplification of cell-free nucleic acids.

28. The method of claim 1, wherein (b) generates at least 10,000,000 sequence reads.

29. The method of claim 1, wherein (b) generates at least 100,000,000 sequence reads.

30. The method of claim 1, wherein at least one microbe is detected with an AUC of more than 0.9.