Sequencing Methods Research Articles

Machine Learning Approaches for Microorganism Identification, Virulence Assessment, and Antimicrobial Susceptibility Evaluation Using DNA Sequencing Methods: A Systematic Review

Machine Learning Approaches for Microorganism Identification, Virulence Assessment, and Antimicrobial Susceptibility Evaluation Using DNA Sequencing Methods: A Systematic Review

Ring Chromosome 17 Syndrome—A Case Report and Discussion of Diagnostic Methods

ABSTRACTRing chromosome 17 and 17p13.3 deletion syndrome are phenotypically heterogeneous diseases with similar clinical features. The ring chromosome 17 phenotypic features range from the Miller–Dieker syndrome characterized by deletion of the PAFAH1B1 gene, lissencephaly, hypotonia, dysphagia, café au lait spots, and severe intellectual disability, to a milder phenotype characterized by microcephaly, seizures, delayed development, minor facial dysmorphic features, clinodactyly, short stature, café au lait spots, retinal flecking, and deletion of the YWHAE and CRK genes. Similarly, the phenotypic features of the 17p13.3 deletion syndrome range from the Miller–Dieker syndrome caused by loss of function of the PAFAH1B1 gene and characterized by lissencephaly, microcephaly, seizures, hypotonia, and severe intellectual disability to a milder phenotype characterized by nonspecific white matter changes, microcephaly, seizures, delayed development, short stature, and deletion of the YWHAE and CRK genes. Café au lait spots and retinal or axillary freckling have been noted in the ring chromosome 17 syndrome but not in 17p13.3 deletion syndrome. We report a 5‐year‐old girl with a history of intrauterine growth retardation, short stature, intractable epilepsy, expressive language disorder, clinodactyly, multiple café au lait spots, and retinal freckling who was initially diagnosed with 17p13.3 deletion syndrome involving YWHAE and CRK but not PAFAH1B1 on CGH array. However, cytogenetic analysis of G‐banded chromosomes revealed mosaic ring chromosome 17. Optical genome mapping simultaneously identified the 17p13.3 deletion and the mosaic ring chromosome 17. This case report highlights the limitations of the arrays and sequencing methods for identifying structural variants, the need to investigate further deletions and duplications identified by arrays, mainly considering atypical phenotypic features, and suggests that OGM could be used as a first‐tier test with exome sequencing for the diagnosis of patients with dysmorphic features, intellectual disability, and seizure disorder.

Molecular and clinical profiles of pediatric monogenic diabetes subtypes: comprehensive genetic analysis of 138 patients.

Single gene variants that give rise to neonatal diabetes mellitus (NDM), maturity onset diabetes of the young (MODY) and syndromic forms of diabetes mellitus (SDM) are responsible for 3.1-4.2% of all diabetes cases. This single-center study with a relatively larger sample size aimed to evaluate the clinical and genetic characteristics of Chinese children with suspected monogenic diabetes (MD) using next generation sequencing (NGS) methods. Data were collected from 1550 consecutive children diagnosed with diabetes/hyperglycemia at the Endocrinology Department of Children's Hospital of Nanjing Medical University from 2012 to 2023. The genotype and phenotype of 138 children with suspected MD were retrospectively analyzed. Among 138 children, 16, 97, and 25 patients with NDM, suspected MODY and SDM were assessed by NGS, with a pick-up rate of 87.5%, 57.8%, and 56%, respectively. In total, there was a high pick-up rate of MD, with 58% (80 of 138) among antibody-negative pediatric patients. Pathogenic variants were found in GCK, HNF1A, INS, KCNJ11, INSR, HNF4A, ABCC8, WFS1, ALMS1, HNF1B, BLK and ZFP57 genes with 13 novel variants in addition to 4 patients with CNVs. In this cohort, GCK-MODY was the leading cause and the mildest type of MODY. GCK-MODY displayed favorable lipid profile when compared to non-GCK-MODY and MODYX, which might be cardioprotective. Following an accurate genetic diagnosis of diabetes, 19 patients switched from insulin therapy to oral agents or lifestyle interventions. NGS tests helped to identify the precise etiology of monogenic diabetic patients which has implications for better individualized management.

Structural and functional consequences of non-synonymous SNPs within the LAMA2 protein: a molecular dynamics perspective

Clinical phenotypic presentations associated with LAMA2 deficiency have shown a variety of manifestations. LAMA2 mutations are mainly linked to congenital muscular dystrophy, but there is also mounting evidence suggesting their presence in inflammatory breast cancer, laryngopharyngeal squamous cell carcinoma, and ventricular tachycardia related to coronary artery disease and cardiomyopathy. This study examined the structural and functional impacts of 144 non-synonymous single nucleotide polymorphisms (nsSNPs) within the LAMA2 gene. Through multi-tiered sequence and structure-based methods, 11 deleterious and destabilizing mutations were identified (A1362T, E1308Q, E1360G, I1276S, L1195P, M1359T, P1232H, P1238A, P1272L, Y1234H, Y1338C). Further, four mutations (L1195P, Y1234H, P1238A, A1362T), which aligned with conserved positions, were subjected to 500 ns molecular dynamics (MD) simulations. RMSD calculated from MD trajectories highlighted structural disparities between wild-type and mutant forms, with the latter showing greater flexibility. Radius of gyration analysis indicated reduced compactness, solvent accessibility changes suggested unfolding, and hydrogen bond (HB) analysis demonstrated disrupted integrity. The HB analysis revealed disruptions in structural integrity due to diminished hydrogen bonds in mutants. Secondary structure analysis revealed significant alterations in secondary structural content. Principal Component Analysis unveiled increased dynamic behavior in mutants. Gibbs free energy landscape analysis reflected distinct energy minima regions in mutants, indicating structural destabilization. Overall, this study revealed the functional and structural ramifications of nsSNPs in the LAMA2 gene, providing valuable insights into potential disease-causing mutations and warranting future research on understanding LAMA2 associated diseases and disorders.

Illumina SBS Sequencing and DNBSEQ Perform Similarly for Single-Cell Transcriptomics

Background/Objectives: High-throughput single-cell RNA sequencing (scRNA-seq) workflows produce libraries that demand extensive sequencing. However, standard next-generation sequencing (NGS) methods remain expensive, contributing to the high running costs of single-cell experiments and often negatively affecting the sample numbers and statistical strength of such projects. In recent years, a plethora of new sequencing technologies have become available to researchers through several manufacturers, often providing lower-cost alternatives to standard NGS. Methods: In this study, we compared data generated from mouse scRNA-seq libraries sequenced with both standard Illumina sequencing by synthesis (Illumina SBS) and MGI’s DNA nanoball sequencing (DNBSEQ). Results: Our findings reveal similar overall performance using both technologies. DNBSEQ exhibited mildly superior sequence quality compared to Illumina SBS, as evidenced by higher Phred scores, lower read duplication rates and a greater number of genes mapping to the reference genome. Yet these improvements did not translate into meaningful differences in single-cell analysis parameters in our experiments, including detection of additional genes within cells, gene expression saturation levels and numbers of identified cells, with both technologies demonstrating equally robust performance in these aspects. The data produced by both sequencing platforms also produced comparable analytical outcomes for single-cell analysis. No significant difference in the annotation of cells into different cell types was observed and the same top genes were differentially expressed between populations and experimental conditions. Conclusions: Overall, our data demonstrate that alternative technologies can be applied to sequence scRNA-seq libraries, generating virtually indistinguishable results compared to standard methods, and providing cost-effective alternatives.

Scale of risk factors associated with emotional exhaustion in innovative educational environments: psychometric study of teachers

Educational innovation is a defining feature within educational institutions, necessitating a heightened emphasis on its promotion. However, exposure to these processes and participation tend to be highly demanding and exhausting for the teachers. Consequently, it becomes imperative for educational authorities to proactively monitor teachers’ involvement in innovation, utilizing appropriate instruments to identify and assess the associated risk factors. This study proposes a rigorously validated and reliable model for measuring the risk factors associated with emotional exhaustion among teachers in innovative educational environments. Employing a cross-sectional design, the study scrutinized the psychometric properties of a sample comprising 535 university teachers from the same higher education institution actively engaged in educational innovation. The results from the investigation revealed that the measurement model demonstrated robust evidence of construct validity, as ascertained through both exploratory and confirmatory factor analysis. Predictive validity was evaluated utilizing Path Analysis, while convergence validity was assessed via Average Variance Extracted. Discriminant validity was established through the Homotrait-Heterotrait ratio, and gender invariance was validated through nested-model sequencing methods. Additionally, reliability assessments were conducted using both Cronbach’s alpha and McDonald’s omega coefficients. The resultant measurement model, characterized by its parsimony, offers educational institutions a valuable instrument for safeguarding faculty wellbeing amidst the demands of educational innovation.

Factors impacting target-enriched long-read sequencing of resistomes and mobilomes.

We investigated the efficiency of target-enriched long-read sequencing (TELSeq) for detecting antimicrobial resistance genes (ARGs) and mobile genetic elements (MGEs) within complex matrices. We aimed to overcome limitations associated with traditional antimicrobial resistance (AMR) detection methods, including short-read shotgun metagenomics, which can lack sensitivity, specificity, and the ability to provide detailed genomic context. By combining biotinylated probe-based enrichment with long-read sequencing, we facilitated the amplification and sequencing of ARGs, eliminating the need for bioinformatic reconstruction. Our experimental design included replicates of human fecal microbiota transplant material, bovine feces, pristine prairie soil, and a mock human gut microbial community, allowing us to examine variables including genomic DNA input and probe set composition. Our findings demonstrated that TELSeq markedly improves the detection rates of ARGs and MGEs compared to traditional sequencing methods, underlining its potential for accurate AMR monitoring. A key insight from our research is the importance of incorporating mobilome profiles to better predict the transferability of ARGs within microbial communities, prompting a recommendation for the use of combined ARG-MGE probe sets for future studies. We also reveal limitations for ARG detection from low-input workflows, and describe the next steps for ongoing protocol refinement to minimize technical variability and expand utility in clinical and public health settings. This effort is part of our broader commitment to advancing methodologies that address the global challenge of AMR.

Single Laboratory Evaluation of the Q20+ Nanopore Sequencing Kit for Bacterial Outbreak Investigations

Leafy greens are a significant source of produce-related Shiga toxin-producing Escherichia coli (STEC) outbreaks in the United States, with agricultural water often implicated as a potential source. Current FDA outbreak detection protocols are time-consuming and rely on sequencing methods performed in costly equipment. This study evaluated the potential of Oxford Nanopore Technologies (ONT) with Q20+ chemistry as a cost-effective, rapid, and accurate method for identifying and clustering foodborne pathogens. The study focuses on assessing whether ONT Q20+ technology could facilitate near real-time pathogen identification, including SNP differences, serotypes, and antimicrobial resistance genes. This pilot study evaluated different combinations of two DNA extraction methods (Maxwell RSC Cultured Cell DNA kit and Monarch high molecular weight extraction kits) and two ONT library preparation protocols (ligation and the rapid barcoding sequencing kit) using five well-characterized strains representing diverse foodborne pathogens. High-quality, closed bacterial genomes were obtained from all combinations of extraction and sequencing kits. However, variations in assembly length and genome completeness were observed, indicating the need for further optimization. In silico analyses demonstrated that Q20+ nanopore sequencing chemistry accurately identified species, genotype, and virulence factors, with comparable results to Illumina sequencing. Phylogenomic clustering showed that ONT assemblies clustered with reference genomes, though some indels and SNP differences were observed, likely due to sequencing and analysis methodologies rather than inherent genetic variation. Additionally, the study evaluated the impact of a change in the sampling rates from 4 kHz (260 bases pair second) to 5 kHz (400 bases pair second), finding no significant difference in sequencing accuracy. This evaluation workflow offers a framework for evaluating novel technologies for use in surveillance and foodborne outbreak investigations. Overall, the evaluation demonstrated the potential of ONT Q20+ nanopore sequencing chemistry to assist in identifying the correct strain during outbreak investigations. However, further research, validation studies, and optimization efforts are needed to address the observed limitations and fully realize the technology’s potential for improving public health outcomes and enabling more efficient responses to foodborne disease threats.

ITSxpress version 2: software to rapidly trim internal transcribed spacer sequences with quality scores for amplicon sequencing.

The nuclear ribosomal RNA (rRNA) internal transcribed spacer (ITS) regions are commonly used to identify fungi and other eukaryotic taxa in amplicon sequencing. The highly conserved rRNA regions flanking the ITS are often trimmed before being used for taxonomic assignment. The Python software package ITSxpress rapidly trims single-end or paired-end sequences in FASTQ format for use in amplicon sequence variant clustering methods like DADA2. This new major release of ITSxpress improves the paired-end merging method, simplifies installation of the QIIME 2 ITSxpress plugin, removes major dependencies, adds use cases, and is compatible with newer compression formats. This article discusses the modifications to ITSxpress that improve the output and user experience, leading to a major version increase.IMPORTANCEITSxpress is a sequence trimming method applied to internal transcribed spacer (ITS) amplicon sequences before calling amplicon sequence variants (ASVs). The ITS region is used to understand the composition of eukaryotic microbial communities. ITS sequences provide good taxonomic resolution due to their hypervariability, but are flanked by conserved regions that allow their primers to be more universal. Amplicons generated with such primers contain regions with different evolutionary rates, and trimming these conserved regions results in better taxonomic classification and a more valid set of ASVs. This package can be used for most amplicon sequencing methods including for newer long-read sequencing formats, such as PacBio. ITSxpress can be installed from Bioconda, used as a Docker image, or installed from source code. The package works well with high-performance computing clusters or laptops due to its low-resource requirements.

Decoding the Genetic Code: Scientific Exploration of the Telomere-totelomere (T2T) Genome

Abstract: With the development of third-generation sequencing technology, genome assembly has entered a new era. The combination of multiple sequencing methods can combine the strengths of each, resulting in higher-quality assembly results. Telomere-to-Telomere (T2T) genome refers to a zero gap genome that is assembled at the T2T level of one or more chromosomes through the combination of multiple sequencing technologies, such as Pacific Biosciences High-Fidelity (PacBio HiFi), Oxford Nanopore Technologies (ONT) Ultra-long, High-throughput Chromosome Conformation Capture (Hi-C), and others. High-quality reference genomes are the basis for genomics research, and the T2T genome has enabled the exploration of unknown areas of the genome, such as telomeres and centromeres, which has provided a more in-depth direction for research. This review provides a comprehensive overview of the T2T genome, reviewing the development of sequencing technologies and then outlining sequencing strategies, assembly methods, quality assessment processes, and analysis software for the T2T genome with practical examples. Representative T2T genomic species of plants, animals, and microorganisms (e.g., human, Arabidopsis, brewer's yeast, and so on) are presented separately. A summary of the potential and challenges of current T2T genomic applications is provided, along with an outlook for future developments.

Enhancing SARS-CoV-2 Lineage Surveillance through the Integration of a Simple and Direct qPCR-Based Protocol Adaptation with Established Machine Learning Algorithms.

Emerging and evolving Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) lineages, adapted to changing epidemiological conditions, present unprecedented challenges to global public health systems. Here, we introduce an adapted analytical approach that complements genomic sequencing, applying a cost-effective quantitative polymerase chain reaction (qPCR)-based assay. Viral RNA samples from SARS-CoV-2 positive cases detected by diagnostic laboratories or public health network units in Ceará, Brazil, were tracked for genomic surveillance and analyzed by using paired-end sequencing combined with integrative genomic analysis. Validation of a key structural variation was conducted with gel electrophoresis for the presence of a specific open reading frame 7a(ORF7a) gene deletion within the "BE.9" lineages tracked. The analytical innovation of our method is the optimization of a simple intercalating dye-based qPCR assay through repositioning primers from the ARTIC v4.1 amplicon panel to detect large molecular patterns. This assay distinguishes between "BE.9" and "non-BE.9" lineages, particularly BQ.1, without the need for expensive probes or sequencing. The protocol was validated against lineage predictions from next-generation sequencing (NGS) using 525 paired samples, achieving 93.3% sensitivity, 95.1% specificity, and 92.4% agreement, as measured by Cohen's Kappa coefficient. Machine learning (ML) models were trained using the melting curves from intercalating dye-based qPCR of 1724 samples, enabling highly accurate lineage assignment. Among them, the support vector machine (SVM) model had the best performance and after fine-tuning showed ∼96.52% (333/345) accuracy in comparison to the test data set. Our integrated approach provides an adapted analytical method that is both cost-effective and scalable, suitable for rapid assessment of emerging variants, especially in resource-limited settings. In this work, the protocol is applied to improve the monitoring of SARS-CoV-2 sublineages but can be extended to track any key molecular signature, including large insertions and deletions (indels) commonly observed in pathogenic agent subtypes. By offering a complement to traditional sequencing methods and utilizing easily trainable machine learning algorithms, our methodology contributes to enhanced molecular surveillance strategies and supports global efforts in pandemic control.

Relationships Between Polymorphisms in HLA-G 3'UTR Region and COVID-19 Disease Severity.

The objective of this study is to investigate the the relationships between HLA-G gene variants and sHLA-G with susceptibility to SARS-CoV-2 infection. In this case-control study, 65 Patients with COVID-19 were and 67 healthy controls were genotyped for their main functional polymorphisms namely, the 14-bp Ins/Del (rs371194629), +3003C/T (rs1707), +3010C/G (rs1710), +3027A/C (rs17179101), +3035C/T (rs17179108), +3142C/G (rs1063320), +3187A/G (rs9380142) and +3196C/G (rs1610696) in the exon 8 of the 3' untranslated regions (3' UTRs) using sanger sequencing method. Associations were assessed for five inheritance models (codominant, dominant, recessive, over-dominant and log-additive). Moreover, the levels of plasma soluble HLA-G (sHLA-G) were explored using ELISA method. Our results revealed that the 14-bp INS/DEL polymorphism was strongly associated with COVID-19 symptoms development for almost all tested inheritance models (p < 0.001). Inversely, the (+3196C/G) polymorphism exhibited a protective effect against COVID-19. In addition, three haplotypes; UTR-1, UTR-3, and UTR-5 were found associated with COVID-19 symptoms (p < 0.05), The level of HLA-G in the serum was significantly higher in COVID-19 individuals than in healthy individuals (p < 0.001).These findings suggest that HLA-G gene polymorphisms in the regulatory 3'UTR region of the HLA-G gene may influence the host immune response to SARS-CoV-2 infection. A deeper comprehension of the functional effect of these associated polymorphisms could be useful in identifying high-risk individuals and in developing adaptive treatments for patients.

Development of a flavor-oriented synthetic microbial community for pour-over rice wine: A comprehensive microbial community analysis

Huangjiu, a traditional Chinese alcoholic beverage, varies widely in quality and consistency when brewed using the traditional pour-over rice wine technique, largely due to the variability in its microbial community in an open fermentation environment. This study streamlined the microbial complexity using amplicon sequencing and culture-dependent methods, leading to the identification of a set of core microbial species instrumental in flavor development. A synthetic microbial community was crafted from these key species and employed in fermentation experiments. In this study, we demonstrated that the synthetic microbial community not only replicated the major flavor profiles of traditional pour-over rice wine but also it is further proved that the core species directly determines the main flavor of pour-over rice wine, these findings are supported by our quantitative analysis of volatile compounds and sensory evaluation data. This research underscores the potential of synthetic microbial communities in standardizing production processes and improving the sensory quality of traditional beverages like Huangjiu.

Exploring structural diversity across the protein universe with The Encyclopedia of Domains.

The AlphaFold Protein Structure Database (AFDB) contains more than 214 million predicted protein structures composed of domains, which are independently folding units found in multiple structural and functional contexts. Identifying domains can enable many functional and evolutionary analyses but has remained challenging because of the sheer scale of the data. Using deep learning methods, we have detected and classified every domain in the AFDB, producing The Encyclopedia of Domains. We detected nearly 365 million domains, over 100 million more than can be found by sequence methods, covering more than 1 million taxa. Reassuringly, 77% of the nonredundant domains are similar to known superfamilies, greatly expanding representation of their domain space. We uncovered more than 10,000 new structural interactions between superfamilies and thousands of new folds across the fold space continuum.

An Investigation into Diagnostic Strategies for Central Nervous System Infections Through the Integration of Metagenomic Next-Generation Sequencing and Conventional Diagnostic Methods

An Investigation into Diagnostic Strategies for Central Nervous System Infections Through the Integration of Metagenomic Next-Generation Sequencing and Conventional Diagnostic Methods

MalEXLNet:A semantic analysis and detection method of malware API sequence based on EXLNet model

MalEXLNet:A semantic analysis and detection method of malware API sequence based on EXLNet model

Sequencing and Analysis of Wolbachia Strains from A and B Supergroups Detected in Sylvatic Mosquitoes from Brazil

Wolbachia are endosymbiotic bacteria that infect a wide range of arthropods and filarial nematodes, often manipulating host reproduction. The efficacy of Wolbachia-based interventions for dengue and chikungunya control has been validated through numerous field studies in recent years. This study aimed to investigate the diversity and prevalence of Wolbachia infections in sylvatic mosquitoes from two locations in Recife, Brazil. Multiple mosquito species were screened for Wolbachia using both target marker gene amplification coupled with Sanger sequencing and whole-genome sequencing (WGS) approaches. Phylogenetic analyses were conducted to classify Wolbachia strains into supergroups and assess their evolutionary relationships. Results revealed the presence of Wolbachia in eleven mosquito species examined, with different infection rates. Both supergroups A and B of Wolbachia strains were identified, with Aedes albopictus showing co-infection by both supergroups through the WGS approach. We also detected indirect evidence of Wolbachia horizontal transmission among mosquitoes and other distant host orders. This study provides valuable insights into the distribution and diversity of Wolbachia in sylvatic mosquitoes from Brazil and adds new important data about Wolbachia detection through target marker gene amplicon coupled with Sanger sequencing and WGS methods, highlighting its complementarity to ascertain the presence of Wolbachia in mosquito samples.

A novel approach to detecting microduplication in split hand/foot malformation type 3 at the single-cell level: SHFM as a case study

BackgroundSplit hand/foot malformation (SHFM) is a congenital limb deficiency characterized by missing or shortened central digits. Several gene loci have been associated with SHFM. Identifying microduplications at the single-cell level is challenging in clinical practice, and traditional detection methods may lead to misdiagnoses in embryos and pregnant women.ResultsIn this research, we utilized a low cell count and whole-genome amplification products to employ single nucleotide polymorphism arrays, next-generation sequencing, and third-generation sequencing methods to detect copy number variants of microduplications in a SHFM3 case with limited DNA. Additionally, Karyomapping and combined linkage analysis were conducted to validate the results.ConclusionsThis study establishes a new strategy for identifying microduplications or microdeletions at the single-cell level in clinical preimplantation genetic testing, enhancing the efficiency and accuracy of diagnosing microduplication or microdeletion diseases during IVF-PGT and prenatal diagnosis.

Detecting significant expression patterns in single-cell and spatial transcriptomics with a flexible computational approach

Gene expression data holds the potential to shed light on multiple biological processes at once. However, data analysis methods for single cell sequencing mostly focus on finding cell clusters or the principal progression line of the data. Data analysis for spatial transcriptomics mostly addresses clustering and finding spatially variable genes. Existing data analysis methods are effective in finding the main data features, but they might miss less pronounced, albeit significant, processes, possibly involving a subset of the samples. In this work we present SPIRAL: Significant Process InfeRence ALgorithm. SPIRAL is based on Gaussian statistics to detect all statistically significant biological processes in single cell, bulk and spatial transcriptomics data. The algorithm outputs a list of structures, each defined by a set of genes working simultaneously in a specific population of cells. SPIRAL is unique in its flexibility: the structures are constructed by selecting subsets of genes and cells based on statistically significant and consistent differential expression. Every gene and every cell may be part of one structure, more or none. SPIRAL also provides several visual representations of structures and pathway enrichment information. We validated the statistical soundness of SPIRAL on synthetic datasets and applied it to single cell, spatial and bulk RNA-sequencing datasets. SPIRAL is available at https://spiral.technion.ac.il/.

Vaginal Microbiome and the Risk of Preterm Birth in Women Living With HIV: A Scoping Review.

There are sparse data on the role of thevaginal microbiome (VMB) in pregnancy among pregnant women living with HIV (PWLWH) and its association with spontaneous preterm birth (sPTB). We conducted a scoping review to assess associations between vaginal microbiota and sPTB among PWLWH. Three studies were included, representing a total of 180 PWLWH out of 652 total pregnancies. All studies used modern DNA sequencing methods (16S rRNA amplification, metagenomics, or metatranscriptomics). PWLWH had higher VMB richness and diversity compared to HIV-uninfected pregnant women and higher sPTB rates in two of three studies. A higher proportion of sPTB among PWLWH was observed in those with Lactobacillus-deficient, anaerobe-dominant vaginal microbiota. In two of three studies, higher concentrations of vaginal inflammation markers were associated with increased VMB richness and diversity. HIV status was independently associated with sPTB. It is unclear if increased vaginal microbial diversity among PWLWH or increased vaginal inflammation contributes more to PTB, but HIV does appear to alter the VMB in pregnant individuals and may also affect PTB rates in microbiome-independent pathways. Given the limited number of studies, heterogeneity in sample size, sample collection methods, and inconsistent results it is difficult to causally link HIV, VMB, inflammatory cytokines, and sPTB.