Sex-specific associations between AD genotype and the microbiome of human amyloid beta knock-in (hAß-KI) mice.

INTRODUCTION: Emerging evidence links changes in the gut microbiome to late-onset Alzheimer's disease (LOAD), necessitating examination of AD mouse models with consideration of the microbiome. METHODS: We used shotgun metagenomics and untargeted metabolomics to study the human amyloid beta knock-in (hAß-KI) murine model for LOAD compared to both wild-type (WT) mice and a model for early-onset AD (3xTg-AD). RESULTS: Eighteen-month female (but not male) hAß-KI microbiomes were distinct from WT microbiomes, with AD genotype accounting for 18% of the variance by permutational multivariate analysis of variance (PERMANOVA). Metabolomic diversity differences were observed in females, however no individual metabolites were differentially abundant. hAß-KI mice microbiomes were distinguishable from 3xTg-AD animals (81% accuracy by random forest modeling), with separation primarily driven by Romboutsia ilealis and Turicibacter species. Microbiomes were highly cage specific, with cage assignment accounting for more than 40% of the PERMANOVA variance between the groups. DISCUSSION: These findings highlight a sex-dependent variation in the microbiomes of hAß-KI mice and underscore the importance of considering the microbiome when designing studies that use murine models for AD. HIGHLIGHTS: Microbial diversity and the abundance of several species differed in human amyloid beta knock-in (hAß-KI) females but not males. Correlations to Alzheimer's disease (AD) genotype were stronger for the microbiome than the metabolome. Microbiomes from hAß-KI mice were distinct from 3xTg-AD mice. Cage effects accounted for most of the variance in the microbiome and metabolome.

Utilizing cost-effective portable equipment to enhance COVID-19 variant tracking both on-site and at a large scale.

Despite optimistic predictions on the eventual end of COVID-19 (Coronavirus Disease 2019), caution is necessary regarding the emergence of new variants to sustain a positive outlook and effectively address any potential future outbreaks. However, ongoing efforts to track COVID-19 variants are concentrated in developed countries and unique social practices and remote habitats of indigenous peoples present additional challenges. By combining small-sized equipment that is easily accessible and inexpensive, we performed SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) whole genome sequencing and measured the sample-to-answer time and accuracy of this portable variant tracking tool. Our portable design determined the variant of SARS-CoV-2 in an infected individual within 9 hours and 15 minutes without external power or internet connection, surpassing the speed of previous portable tools. It took only 16 minutes to complete sequencing run, whole genome assembly, and lineage determination using a single standalone laptop. We then demonstrated the capability to produce 289 SARS-CoV-2 whole genome sequences in a single portable sequencing run, representing a significant improvement over an existing throughput of 96 sequences per run. We verified the accuracy of portable sequencing by comparison with two other independent sequencing methods. We showed that our high-throughput data consistently represented the circulating variants in Los Angeles, United States, when compared with publicly available sequences. Our scheme is designed to be flexible, rapid, and accurate, making it a valuable tool for large-scale surveillance operations in low- and middle-income countries as well as targeted surveys for vulnerable populations in remote locations.IMPORTANCEThere have been significant efforts to track COVID-19 (Coronavirus Disease 2019) variants, accumulating over 15 million SARS-CoV-2 sequences as of 2023. However, the distribution of global survey data is highly skewed, with nearly half of all countries having inadequate or low levels of genomic surveillance. In addition, indigenous peoples face more severe threats from COVID-19, due to their generally remote residence and unique social practices. Cost-effective portable sequencing tools have been used to investigate Ebola and Zika outbreaks. However, these tools have a sample-to-answer time of around 24 hours and require an internet connection for data transfer to an off-site cloud server. In our study, we rapidly determined COVID-19 variants using only small and inexpensive equipment, with a completion time of 9 hours and 15 minutes. Furthermore, we produced 289 near-full-length SARS-CoV-2 genome sequences from a single portable Nanopore sequencing run, representing a threefold increase in throughput compared with existing Nanopore sequencing methods.

Portable Nanopore sequencing solution for next-generation HIV drug resistance testing.

BACKGROUND: Tackling HIV drug resistance is one of major challenges for ending AIDS epidemic, but the elevated expense of cutting-edge genomics hampers the advancement of HIV genotype testing for clinical care. METHODS: We developed a HIV genotype testing pipeline that centers on a cost-efficient portable Nanopore sequencer. Accuracy verification was conducted through comparison with parallel data obtained via fixed-site Pacbio sequencing. Our complete pol-gene sequencing strategy coupled with portable high-throughput sequencing was applied to identify drug resistance mutations across 58 samples sourced from the ART-treated Los Angeles General Medical Center Rand Schrader Clinic (LARSC) cohort (7 samples from 7 individuals) and the ART-naïve Center for HIV/AIDS Vaccine Immunology (CHAVI) cohort (51 samples from 38 individuals). RESULTS: A total of 472 HIV consensus sequences, each tagged with a unique molecular identifier, were produced from over 1.4 million bases acquired through portable Nanopore sequencing, which matched those obtained independently via Pacbio sequencing. With this desirable accuracy, we first documented the linkage of multidrug cross-resistance mutations across Integrase Strand Transfer inhibitors (INSTIs) and Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs) from an individual failing a second-generation INSTI regimen. By producing more than 500 full-length HIV pol gene sequences in a single portable sequencing run, we detected Protease Inhibitor (PI), Nucleoside Reverse Transcriptase Inhibitor (NRTI), NNRTI and INSTI resistance mutations. All drug resistance mutations identified through portable sequencing were cross-validated using fixed-site Pacbio sequencing. CONCLUSIONS: Our accurate and affordable HIV drug resistance testing solution is adaptable for both individual patient care and large-scale surveillance initiatives.

Precision detection of recent HIV infections using high-throughput genomic incidence assay.

HIV incidence is a key measure for tracking disease spread and identifying populations and geographic regions where new infections are most concentrated. The HIV sequence population provides a robust signal for the stage of infection. Large-scale and high-precision HIV sequencing is crucial for effective genomic incidence surveillance. We produced 1,034 full-length envelope gene sequences from a seroconversion cohort by conducting HIV microdrop sequencing and measuring the genomic incidence assay's genome similarity index (GSI) dynamics. The measured dynamics of 9 of 12 individuals aligned with the GSI distribution estimated independently using 417 publicly available incident samples. We enhanced the capacity to identify individuals with recent infections, achieving predicted detection accuracies of 92% (89%-94%) for cases within 6 months and 81% (74%-87%) for cases within 9 months. These accuracy levels agreed with the observed detection accuracy intervals of an independent validation data set. Additionally, we produced 131 full-length envelope gene sequences from eight individuals with chronic HIV infection. This analysis confirmed a false recency rate (FRR) of 0%, which was consistent with 162 publicly available chronic samples. The mean duration of recent infection (MDRI) was 238 (209-267) days, indicating an 83% improvement in performance compared to current recent infection testing algorithms. The shifted Poisson mixture model was then used to estimate the time since infection, and the model estimates showed an 88% consistency with the days post infection derived from HIV RNA test dates and/or seroconversion dates. HIV microdrop sequencing provides unique prospects for large-scale incidence surveillance using high-throughput sequencing. IMPORTANCE Accurate identification of recently infected individuals is vital for prioritizing specific populations for interventions, reducing onward transmission risks, and optimizing public health services. However, current HIV-specific antibody-based methods have not been satisfactory in accurately identifying incident cases, hindering the use of HIV recency testing for prevention efforts and partner protection. Genomic incidence assays offer a promising alternative for identifying recent infections. In our study, we used microdroplet technologies to produce a large number of complete HIV envelope gene sequences, enabling the accurate detection of early infection signs. We assessed the dynamics of the incidence assay's metrics and compared them with statistical models. Our approach demonstrated high accuracy in identifying individuals with recent infections, achieving predicted detection rates exceeding 90% within 6 months and over 80% within 9 months of infection. This high-resolution method holds significant potential for enhancing the effectiveness of HIV incidence screening for case-based surveillance in public health initiatives.

Immune cell identifier and classifier (ImmunIC) for single cell transcriptomic readouts.

Single cell RNA sequencing has a central role in immune profiling, identifying specific immune cells as disease markers and suggesting therapeutic target genes of immune cells. Immune cell-type annotation from single cell transcriptomics is in high demand for dissecting complex immune signatures from multicellular blood and organ samples. However, accurate cell type assignment from single-cell RNA sequencing data alone is complicated by a high level of gene expression heterogeneity. Many computational methods have been developed to respond to this challenge, but immune cell annotation accuracy is not highly desirable. We present ImmunIC, a simple and robust tool for immune cell identification and classification by combining marker genes with a machine learning method. With over two million immune cells and half-million non-immune cells from 66 single cell RNA sequencing studies, ImmunIC shows 98% accuracy in the identification of immune cells. ImmunIC outperforms existing immune cell classifiers, categorizing into ten immune cell types with 92% accuracy. We determine peripheral blood mononuclear cell compositions of severe COVID-19 cases and healthy controls using previously published single cell transcriptomic data, permitting the identification of immune cell-type specific differential pathways. Our publicly available tool can maximize the utility of single cell RNA profiling by functioning as a stand-alone bioinformatic cell sorter, advancing cell-type specific immune profiling for the discovery of disease-specific immune signatures and therapeutic targets.

Full-spectrum HIV drug resistance mutation detection by high-resolution complete pol gene sequencing.

BACKGROUND: Drug resistance mutation testing is a key element for HIV clinical management, informing effective treatment regimens. However, resistance screening in current clinical practice is limited in reporting linked cross-class resistance mutations and minority variants, both of which may increase the risk of virological failure. METHODS: To address these limitations, we obtained 358 full-length pol gene sequences from 52 specimens of 20 HIV infected individuals by combining microdroplet amplification, unique molecular identifier (UMI) labeling, and long-read high-throughput sequencing. RESULTS: We conducted a rigorous assessment of the accuracy of our pipeline for precision drug resistance mutation detection, verifying that a sequencing depth of 35 high-throughput reads achieved complete, error-free pol gene sequencing. We detected 26 distinct drug resistance mutations to Protease Inhibitors (PIs), Nucleoside Reverse Transcriptase Inhibitors (NRTIs), Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs), and Integrase Strand Transfer Inhibitors (INSTIs). We detected linked cross-class drug resistance mutations (PI+NRTI, PI+NNRTI, and NRTI+NNRTI) that confer cross-resistance to multiple drugs in different classes. Fourteen different types of minority mutations were also detected with frequencies ranging from 3.2% to 19%, and the presence of these mutations was verified by Sanger reference sequencing. We detected a putative transmitted drug resistance mutation (TDRM) in one individual that persisted for over seven months from the first sample collected at the acute stage of infection prior to seroconversion. CONCLUSIONS: Our comprehensive drug resistance mutation profiling can advance clinical practice by reporting mutation linkage and minority variants to better guide antiretroviral therapy options.

Gut microbiome in people living with HIV is associated with impaired thiamine and folate syntheses.

People living with HIV have a high incidence of cardiovascular and neurological diseases as comorbid disorders that are commonly linked to inflammation. While microbial translocation can augment inflammation during HIV infection, functional microbiome shifts that may increase pro-inflammatory responses have not been fully characterized. In addition, defining HIV-induced microbiome changes has been complicated by high variability among individuals. Here we conducted functional annotation of previously-published 16S ribosomal RNA gene sequences of 305 HIV positive and 249 negative individuals, with adjustment for geographic region, sex, sexual behavior, and age. Metagenome profiles were inferred from these individuals' 16S data. HIV infection was associated with impaired microbial vitamin B synthesis; around half of the gene families in thiamine and folate biosynthesis pathways were significantly less abundant in the HIV positive group than the negative control. These results are consistent with the high prevalence of thiamine and folate deficiencies in HIV infections. These HIV-induced microbiota shifts have the potential to influence cardiovascular and neurocognitive diseases, given the documented associations between B-vitamin deficiencies, inflammation, and these diseases. We also observed that most essential amino acid biosynthesis pathways were downregulated in the microbiome of HIV-infected individuals. Microbial vitamin B and amino acid synthesis pathways were not significantly recovered by antiretroviral treatment when we compared 262 ART positive and 184 ART negative individuals. Our meta-analysis provides a new outlook for understanding vitamin B and amino acid deficiencies in HIV patients, suggesting that interventions for reversing HIV-induced microbiome shifts may aid in lessening the burdens of HIV comorbidities.

High-precision and cost-efficient sequencing for real-time COVID-19 surveillance.

COVID-19 global cases have climbed to more than 33 million, with over a million total deaths, as of September, 2020. Real-time massive SARS-CoV-2 whole genome sequencing is key to tracking chains of transmission and estimating the origin of disease outbreaks. Yet no methods have simultaneously achieved high precision, simple workflow, and low cost. We developed a high-precision, cost-efficient SARS-CoV-2 whole genome sequencing platform for COVID-19 genomic surveillance, CorvGenSurv (Coronavirus Genomic Surveillance). CorvGenSurv directly amplified viral RNA from COVID-19 patients' Nasopharyngeal/Oropharyngeal (NP/OP) swab specimens and sequenced the SARS-CoV-2 whole genome in three segments by long-read, high-throughput sequencing. Sequencing of the whole genome in three segments significantly reduced sequencing data waste, thereby preventing dropouts in genome coverage. We validated the precision of our pipeline by both control genomic RNA sequencing and Sanger sequencing. We produced near full-length whole genome sequences from individuals who were COVID-19 test positive during April to June 2020 in Los Angeles County, California, USA. These sequences were highly diverse in the G clade with nine novel amino acid mutations including NSP12-M755I and ORF8-V117F. With its readily adaptable design, CorvGenSurv grants wide access to genomic surveillance, permitting immediate public health response to sudden threats.

Microdrop Human Immunodeficiency Virus Sequencing for Incidence and Drug Resistance Surveillance.

BACKGROUND: Precise and cost-efficient human immunodeficiency virus (HIV) incidence and drug resistance surveillances are in high demand for the advancement of the 90-90-90 "treatment for all" target. METHODS: We developed microdrop HIV sequencing for the HIV incidence and drug resistance assay (HIDA), a single-blood-draw surveillance tool for incidence and drug resistance mutation (DRM) detection. We amplified full-length HIV envelope and pol gene sequences within microdroplets, and this compartmental amplification with long-read high-throughput sequencing enabled us to recover multiple unique sequences. RESULTS: We achieved greater precision in determining the stage of infection than current incidence assays, with a 1.2% false recency rate (proportion of misclassified chronic infections) and a 262-day mean duration of recent infection (average time span of recent infection classification) from 83 recently infected and 81 chronically infected individuals. Microdrop HIV sequencing demonstrated an increased capacity to detect minority variants and linked DRMs. By screening all 93 World Health Organization surveillance DRMs, we detected 6 pretreatment drug resistance mutations with 2.6%-13.2% prevalence and cross-linked mutations. CONCLUSIONS: HIDA with microdrop HIV sequencing may promote global HIV real-time surveillance by serving as a precise and high-throughput cross-sectional survey tool that can be generalized for surveillance of other pathogens.

CCMP: software-as-a-service approach for fully-automated microbiome profiling.

Microbiome profiling holds great promise for the development of novel disease biomarkers and therapeutics. Next-generation sequencing is currently the preferred method for microbiome data collection and multiple standardized tools, packages, and pipelines have been developed for the purpose of raw data processing and microbial annotation. However, these currently available pipelines come with entry-level barriers such as high-performance hardware, software installation, and sequential command-line scripting that often deter end-users. We thus created Cloud Computing for Microbiome Profiling (CCMP, https://ccmp.usc.edu), a public cloud-based web tool which combines the analytical power of current microbiome analysis platforms with a user-friendly interface. CCMP is a free-of-charge software-as-a-service (SaaS) that simplifies user experience by enabling users to complete their analysis in a single step, uploading raw sequencing data files. Once users upload 16S ribosomal RNA gene sequence data, our pipeline performs taxonomic annotation, abundance profiling, and statistical tests to report microbiota signatures altered by diseases or experimental conditions. CCMP took a 125 gigabyte (GB) input of 16S ribosomal RNA gene sequence data from 1052 specimens in FASTQ format and reported figures and tables of taxonomic annotations, statistical tests, α and ß diversity calculations, and principal coordinate analyses within 21 hours. CCMP is the first fully-automated web interface that integrates three key solutions for large-scale data analysis: cloud computing, fast file transfer technology, and microbiome analysis tools. As a reliable platform that supplies consistent microbiome analysis, CCMP will advance microbiome research by making effortful bioinformatics easily accessible to public.

CCMP: Software-as-a-service approach for fully-automated microbiome profiling.

Microbiome profiling holds great promise for the development of novel disease biomarkers and therapeutics. Next-generation sequencing is currently the preferred method for microbiome data collection and multiple standardized tools, packages, and pipelines have been developed for the purpose of raw data processing and microbial annotation. However, these currently available pipelines come with entry-level barriers such as high-performance hardware, software installation, and sequential command-line scripting that often deter end-users. We thus created Cloud Computing for Microbiome Profiling (CCMP, https://ccmp.usc.edu), a public cloud-based web tool which combines the analytical power of current microbiome analysis platforms with a user-friendly interface. CCMP is a free-of-charge software-as-a-service (SaaS) that simplifies user experience by enabling users to complete their analysis in a single step, uploading raw sequencing data files. Once users upload 16S ribosomal RNA gene sequence data, our pipeline performs taxonomic annotation, abundance profiling, and statistical tests to report microbiota signatures altered by diseases or experimental conditions. CCMP took a 125 gigabyte (GB) input of 16S ribosomal RNA gene sequence data from 1052 specimens in FASTQ format and reported figures and tables of taxonomic annotations, statistical tests, α and ß diversity calculations, and principal coordinate analyses within 21 h. CCMP is the first fully-automated web interface that integrates three key solutions for large-scale data analysis: cloud computing, fast file transfer technology, and microbiome analysis tools. As a reliable platform that supplies consistent microbiome analysis, CCMP will advance microbiome research by making effortful bioinformatics easily accessible to public.