Clinical impact of next-generation sequencing multi-gene panel highlighting the landscape of germline alterations in ovarian cancer patients

BRCA1 and BRCA2 are the most frequently mutated genes in ovarian cancer (OC), crucial both for the identification of cancer predisposition and therapeutic choices. However, germline variants in other genes could be involved in OC susceptibility. We characterized OC patients to detect mutations in genes other than BRCA1/2 that could be associated with a high risk to develop OC, and that could permit patients to enter the most appropriate treatment and surveillance program. Next-Generation Sequencing analysis with a 94-gene panel was performed on germline DNA of 219 OC patients. We identified 34 pathogenic/likely-pathogenic variants in BRCA1/2 and 38 in other 21 genes. Patients with pathogenic/likely-pathogenic variants in non-BRCA1/2 genes developed mainly OC alone compared to the other groups that developed also breast cancer or other tumors (p=0.001). Clinical correlation analysis showed that low-risk patients were significantly associated with platinum sensitivity (p<0.001). Regarding PARP inhibitors (PARPi) response, patients with pathogenic mutations in non-BRCA1/2 genes had significantly worse PFS and OS. Moreover, a statistically significant worse PFS was found for every increase of one thousand platelets before PARPi treatment. To conclude, knowledge about molecular alterations in genes beyond BRCA1/2 in OC could allow for more personalized diagnostic, predictive, prognostic, and therapeutic strategies for OC patients.

Transposable elements: structure and dynamic of the autonomous retrotransposon R2 in Arthropoda with non-canonical reproduction

Eukaryotic ribosomal DNA constitutes a multi gene family organized in a cluster called nucleolar organizer region (NOR); this region is composed usually by hundreds to thousands of tandemly repeated units. Ribosomal genes, being repeated sequences, evolve following the typical pattern of concerted evolution. The autonomous retroelement R2 inserts in the ribosomal gene 28S, leading to defective 28S rDNA genes. R2 element, being a retrotransposon, performs its activity in the genome multiplying its copy number through a “copy and paste” mechanism called target primed reverse transcription. It consists in the retrotranscription of the element’s mRNA into DNA, then the DNA is integrated in the target site. Since the retrotranscription can be interrupted, but the integration will be carried out anyway, truncated copies of the element will also be present in the genome. The study of these truncated variants is a tool to examine the activity of the element. R2 phylogeny appears, in general, not consistent with that of its hosts, except some cases (e.g. Drosophila spp. and Reticulitermes spp.); moreover R2 is absent in some species (Fugu rubripes, human, mouse, etc.), while other species have more R2 lineages in their genome (the turtle Mauremys reevesii, the Japanese beetle Popilia japonica, etc). R2 elements here presented are isolated in 4 species of notostracan branchiopods and in two species of stick insects, whose reproductive strategies range from strict gonochorism to unisexuality. From sequencing data emerges that in Triops cancriformis (Spanish gonochoric population), in Lepidurus arcticus (two putatively unisexual populations from Iceland) and in Bacillus rossius (gonochoric population from Capalbio) the R2 elements are complete and encode functional proteins, reflecting the general features of this family of transposable elements. On the other hand, R2 from Italian and Austrian populations of T. cancriformis (respectively unisexual and hermaphroditic), Lepidurus lubbocki (two elements within the same Italian population, gonochoric but with unfunctional males) and Bacillus grandii grandii (gonochoric population from Ponte Manghisi) have sequences that encode incomplete or non-functional proteins in which it is possible to recognize only part of the characteristic domains. In Lepidurus couesii (Italian gonochoric populations) different elements were found as in L. lubbocki, and the sequencing is still in progress. Two hypothesis are given to explain the inconsistency of R2/host phylogeny: vertical inheritance of the element followed by extinction/diversification or horizontal transmission. My data support previous study that state the vertical transmission as the most likely explanation; nevertheless horizontal transfer events can’t be excluded. I also studied the element’s activity in Spanish populations of T. cancriformis, in L. lubbocki, in L. arcticus and in gonochoric and parthenogenetic populations of B. rossius. In gonochoric populations of T. cancriformis and B. rossius I found that each individual has its own private set of truncated variants. The situation is the opposite for the remaining hermaphroditic/parthenogenetic species and populations, all individuals sharing – in the so far analyzed samples - the majority of variants. This situation is very interesting, because it isn’t concordant with the Muller’s ratchet theory that hypothesizes the parthenogenetic populations being either devoided of transposable elements or TEs overloaded. My data suggest a possible epigenetic mechanism that can block the retrotransposon activity, and in this way deleterious mutations don’t accumulate.

Functional and molecular characterization of ethylene responsive factor genes involved in grape ripening

Grape berry is considered a non climacteric fruit, but there are some evidences that ethylene plays a role in the control of berry ripening. This PhD thesis aimed to give insights in the role of ethylene and ethylene-related genes in the regulation of grape berry ripening. During this study a small increase in ethylene concentration one week before véraison has been measured in Vitis vinifera L. ‘Pinot Noir’ grapes confirming previous findings in ‘Cabernet Sauvignon’. In addition, ethylene-related genes have been identified in the grapevine genome sequence. Similarly to other species, biosynthesis and ethylene receptor genes are present in grapevine as multi-gene families and their expression appeared tissue or developmental specific. All the other elements of the ethylene signal transduction cascade were also identified in the grape genome. Among them, there were ethylene response factors (ERF) which modulate the transcription of many effector genes in response to ethylene. In this study seven grapevine ERFs have been characterized and they showed tissue and berry development specific expression profiles. Two sequences, VvERF045 and VvERF063, seemed likely involved in berry ripening control due to their expression profiles and their sequence annotation. VvERF045 was induced before véraison and was specific of the ripe berry, by sequence similarity it was likely a transcription activator. VvERF063 displayed high sequence similarity to repressors of transcription and its expression, very high in green berries, was lowest at véraison and during ripening. To functionally characterize VvERF045 and VvERF063, a stable transformation strategy was chosen. Both sequences were cloned in vectors for over-expression and silencing and transferred in grape by Agrobacterium-mediated or biolistic-mediated gene transfer. In vitro, transgenic VvERF045 over-expressing plants displayed an epinastic phenotype whose extent was correlated to the transgene expression level. Four pathogen stress response genes were significantly induced in the transgenic plants, suggesting a putative function of VvERF045 in biotic stress defense during berry ripening. Further molecular analysis on the transgenic plants will help in identifying the actual VvERF045 target genes and together with the phenotypic characterization of the adult transgenic plants, will allow to extensively define the role of VvERF045 in berry ripening.

New approaches to open problems in gene expression microarray data

In the past decade, the advent of efficient genome sequencing tools and high-throughput experimental biotechnology has lead to enormous progress in the life science. Among the most important innovations is the microarray tecnology. It allows to quantify the expression for thousands of genes simultaneously by measurin the hybridization from a tissue of interest to probes on a small glass or plastic slide. The characteristics of these data include a fair amount of random noise, a predictor dimension in the thousand, and a sample noise in the dozens. One of the most exciting areas to which microarray technology has been applied is the challenge of deciphering complex disease such as cancer. In these studies, samples are taken from two or more groups of individuals with heterogeneous phenotypes, pathologies, or clinical outcomes. these samples are hybridized to microarrays in an effort to find a small number of genes which are strongly correlated with the group of individuals. Eventhough today methods to analyse the data are welle developed and close to reach a standard organization (through the effort of preposed International project like Microarray Gene Expression Data -MGED- Society [1]) it is not unfrequant to stumble in a clinician's question that do not have a compelling statistical method that could permit to answer it.The contribution of this dissertation in deciphering disease regards the development of new approaches aiming at handle open problems posed by clinicians in handle specific experimental designs. In Chapter 1 starting from a biological necessary introduction, we revise the microarray tecnologies and all the important steps that involve an experiment from the production of the array, to the quality controls ending with preprocessing steps that will be used into the data analysis in the rest of the dissertation. While in Chapter 2 a critical review of standard analysis methods are provided stressing most of problems that In Chapter 3 is introduced a method to adress the issue of unbalanced design of miacroarray experiments. In microarray experiments, experimental design is a crucial starting-point for obtaining reasonable results. In a two-class problem, an equal or similar number of samples it should be collected between the two classes. However in some cases, e.g. rare pathologies, the approach to be taken is less evident. We propose to address this issue by applying a modified version of SAM [2]. MultiSAM consists in a reiterated application of a SAM analysis, comparing the less populated class (LPC) with 1,000 random samplings of the same size from the more populated class (MPC) A list of the differentially expressed genes is generated for each SAM application. After 1,000 reiterations, each single probe given a "score" ranging from 0 to 1,000 based on its recurrence in the 1,000 lists as differentially expressed. The performance of MultiSAM was compared to the performance of SAM and LIMMA [3] over two simulated data sets via beta and exponential distribution. The results of all three algorithms over low- noise data sets seems acceptable However, on a real unbalanced two-channel data set reagardin Chronic Lymphocitic Leukemia, LIMMA finds no significant probe, SAM finds 23 significantly changed probes but cannot separate the two classes, while MultiSAM finds 122 probes with score >300 and separates the data into two clusters by hierarchical clustering. We also report extra-assay validation in terms of differentially expressed genes Although standard algorithms perform well over low-noise simulated data sets, multi-SAM seems to be the only one able to reveal subtle differences in gene expression profiles on real unbalanced data. In Chapter 4 a method to adress similarities evaluation in a three-class prblem by means of Relevance Vector Machine [4] is described. In fact, looking at microarray data in a prognostic and diagnostic clinical framework, not only differences could have a crucial role. In some cases similarities can give useful and, sometimes even more, important information. The goal, given three classes, could be to establish, with a certain level of confidence, if the third one is similar to the first or the second one. In this work we show that Relevance Vector Machine (RVM) [2] could be a possible solutions to the limitation of standard supervised classification. In fact, RVM offers many advantages compared, for example, with his well-known precursor (Support Vector Machine - SVM [3]). Among these advantages, the estimate of posterior probability of class membership represents a key feature to address the similarity issue. This is a highly important, but often overlooked, option of any practical pattern recognition system. We focused on Tumor-Grade-three-class problem, so we have 67 samples of grade I (G1), 54 samples of grade 3 (G3) and 100 samples of grade 2 (G2). The goal is to find a model able to separate G1 from G3, then evaluate the third class G2 as test-set to obtain the probability for samples of G2 to be member of class G1 or class G3. The analysis showed that breast cancer samples of grade II have a molecular profile more similar to breast cancer samples of grade I. Looking at the literature this result have been guessed, but no measure of significance was gived before.

Molecular diversity of Primula apennina and phylogeny of Primula Subsection Euauricula

Primula apennina Widmer is endemic to the North Apennines (Italy). ISSR were used to detect the genetic diversity within and among six populations representative of the species distribution range. High levels of genetic diversity were revealed both at population (PPB = 75.92%, HS = 0.204, Hpop = 0.319) and at species level (PPB = 96.95%, HT = 0.242, Hsp = 0.381). Nei gene diversity statistics (15.7%), Shannon diversity index (16.3%) and AMOVA (14%) detected a moderate level of interpopulation diversity. Principal coordinate and bayesian analyses clustered the populations in three major groups along a geographic gradient. The correlation between genetic and geographic distances was positive (Mantel test, r = 0.232). All together, these analyses revealed a weak but significant spatial genetic structure in P. apennina, with gene flow acting as a homogenizing force that prevents a stronger differentiation of populations. Conservation measures are suggested based on the observed pattern of genetic variability. P. apennina belongs to Primula subsect. Euauricula which includes 15 species distributed on the whole Alps and Apennines. A phylogenetic analysis was carried out using AFLP markers in order both to clarify the relationships among the species of subsection Euauricula that remained unresolved in previous works and to make some hypoteses on their evolutive dynamics. NJ, PCO and BAPS analyses strongly confirmed the monophyly of P. subsect. Euauricula and all the species form strongly supported clades. NJ tree topology suggested a simultaneous fragmentations of ancestral species in a large number of isolated populations that survived in refugia along the unglaciated margins of the Alps in response to the Pleistocene climatic oscillations.

Myc-mediated control of gene transcription in cancer cells

The Myc oncoproteins belong to a family of transcription factors composed by Myc, N-Myc and L-Myc. The most studied components of this family are Myc and N-Myc because their expressions are frequently deregulated in a wide range of cancers. These oncoproteins can act both as activators or repressors of gene transcription. As activators, they heterodimerize with Max (Myc associated X-factor) and the heterodimer recognizes and binds a specific sequence elements (E-Box) onto gene promoters recruiting histone acetylase and inducing transcriptional activation. Myc-mediated transcriptional repression is a quite debated issue. One of the first mechanisms defined for the Myc-mediated transcriptional repression consisted in the interaction of Myc-Max complex Sp1 and/or Miz1 transcription factors already bound to gene promoters. This interaction may interfere with their activation functions by recruiting co-repressors such as Dnmt3 or HDACs. Moreover, in the absence of , Myc may interfere with the Sp1 activation function by direct interaction and subsequent recruitment of HDACs. More recently the Myc/Max complex was also shown to mediate transcriptional repression by direct binding to peculiar E-box. In this study we analyzed the role of Myc overexpression in Osteosarcoma and Neuroblastoma oncogenesis and the mechanisms underling to Myc function. Myc overexpression is known to correlate with chemoresistance in Osteosarcoma cells. We extended this study by demonstrating that c-Myc induces transcription of a panel of ABC drug transporter genes. ABCs are a large family trans-membrane transporter deeply involved in multi drug resistance. Furthermore expression levels of Myc, ABCC1, ABCC4 and ABCF1 were proved to be important prognostic tool to predict conventional therapy failure. N-Myc amplification/overexpression is the most important prognostic factor for Neuroblastoma. Cyclin G2 and Clusterin are two genes often down regulated in neuroblastoma cells. Cyclin G2 is an atypical member of Cyclin family and its expression is associated with terminal differentiation and apoptosis. Moreover it blocks cell cycle progression and induces cell growth arrest. Instead, CLU is a multifunctional protein involved in many physiological and pathological processes. Several lines of evidences support the view that CLU may act as a tumour suppressor in Neuroblastoma. In this thesis I showed that N-Myc represses CCNG2 and CLU transcription by different mechanisms. • N-Myc represses CCNG2 transcription by directly interacting with Sp1 bound in CCNG2 promoter and recruiting HDAC2. Importantly, reactivation of CCNG2 expression through epigenetic drugs partially reduces N-Myc and HDAC2 mediated cell proliferation. • N-Myc/Max complex represses CLU expression by direct binding to a peculiar E-box element on CLU promoter and by recruitment of HDACs and Polycomb Complexes, to the CLU promoter. Overall our findings strongly support the model in which Myc overexpression/amplification may contribute to some aspects of oncogenesis by a dual action: i) transcription activation of genes that confer a multidrug resistant phenotype to cancer cells; ii), transcription repression of genes involved in cell cycle inhibition and cellular differentiation.

Multi-level models and infrastructures for simulating biological system development

The hierarchical organisation of biological systems plays a crucial role in the pattern formation of gene expression resulting from the morphogenetic processes, where autonomous internal dynamics of cells, as well as cell-to-cell interactions through membranes, are responsible for the emergent peculiar structures of the individual phenotype. Being able to reproduce the systems dynamics at different levels of such a hierarchy might be very useful for studying such a complex phenomenon of self-organisation. The idea is to model the phenomenon in terms of a large and dynamic network of compartments, where the interplay between inter-compartment and intra-compartment events determines the emergent behaviour resulting in the formation of spatial patterns. According to these premises the thesis proposes a review of the different approaches already developed in modelling developmental biology problems, as well as the main models and infrastructures available in literature for modelling biological systems, analysing their capabilities in tackling multi-compartment / multi-level models. The thesis then introduces a practical framework, MS-BioNET, for modelling and simulating these scenarios exploiting the potential of multi-level dynamics. This is based on (i) a computational model featuring networks of compartments and an enhanced model of chemical reaction addressing molecule transfer, (ii) a logic-oriented language to flexibly specify complex simulation scenarios, and (iii) a simulation engine based on the many-species/many-channels optimised version of Gillespie’s direct method. The thesis finally proposes the adoption of the agent-based model as an approach capable of capture multi-level dynamics. To overcome the problem of parameter tuning in the model, the simulators are supplied with a module for parameter optimisation. The task is defined as an optimisation problem over the parameter space in which the objective function to be minimised is the distance between the output of the simulator and a target one. The problem is tackled with a metaheuristic algorithm. As an example of application of the MS-BioNET framework and of the agent-based model, a model of the first stages of Drosophila Melanogaster development is realised. The model goal is to generate the early spatial pattern of gap gene expression. The correctness of the models is shown comparing the simulation results with real data of gene expression with spatial and temporal resolution, acquired in free on-line sources.

Role of SP-A gene polymorphism in lung transplantation

Lung transplantation is a widely accepted therapeutic option for end stage lung disease. Clinical outcome is yet challenged by primary graft failure responsible for the majority of the early mortality, by chronic allograft dysfunction and chronic rejection accounting for more than 30% of deaths after the third postoperative year. Pulmonary surfactant proteins (SP) A, B, C and D are one of the first host defense mechanisms the lung can mount. SP-A in particular, produced by the type II pneumocytes, is active in the innate and adaptive immune system being an opsonin, but also regulating the macrophage and lymphocyte response. The main hypothesis for this project is that pulmonary surfactant protein A polymorphism may determine the early and long term lung allograft survival. Of note SP-A biologic activity seems to be genetically determined and SP-A polymorphisms have been associated to various lung disease. The two SP-A genes SP-A1 and SP-A2 have several polymorphisms within the coding region, SP-A1 (6A, 6A2-20), and SP-A2(1A, 1A0-13). The SP-A gene expression is regulated by cAMP, TTF-1 and glucocorticoids. In vitro studies have indicated that SP-A1 and SP-A2 gene variants may have a variable response to glucocorticoids. We proposed to determine if SP-A gene polymorphism predicts primary graft dysfunction and/or chronic lung allograft dysfunction and if SP-A may serve as a biomarker of lung allograft dysfunction. We also proposed to study the interaction between immunosuppressive drugs and SP-A expression and determine whether this is dependent on SP-A polymorphisms. This study will generate novel information improving our understanding of lung allograft dysfunction. It is conceivable that the information will stimulate the interest for a multi centre study to investigate if SP-A polymorphism may be integrated in the donor lung selection criteria and/or to implement post transplant tailored immunosuppression.

A molecular phylogeny of bivalve mollusks: ancient radiations and divergences as revealed by mitochondrial genes

The main scope of my PhD is the reconstruction of the large-scale bivalve phylogeny on the basis of four mitochondrial genes, with samples taken from all major groups of the class. To my knowledge, it is the first attempt of such a breadth in Bivalvia. I decided to focus on both ribosomal and protein coding DNA sequences (two ribosomal encoding genes -12s and 16s -, and two protein coding ones - cytochrome c oxidase I and cytochrome b), since either bibliography and my preliminary results confirmed the importance of combined gene signals in improving evolutionary pathways of the group. Moreover, I wanted to propose a methodological pipeline that proved to be useful to obtain robust results in bivalves phylogeny. Actually, best-performing taxon sampling and alignment strategies were tested, and several data partitioning and molecular evolution models were analyzed, thus demonstrating the importance of molding and implementing non-trivial evolutionary models. In the line of a more rigorous approach to data analysis, I also proposed a new method to assess taxon sampling, by developing Clarke and Warwick statistics: taxon sampling is a major concern in phylogenetic studies, and incomplete, biased, or improper taxon assemblies can lead to misleading results in reconstructing evolutionary trees. Theoretical methods are already available to optimize taxon choice in phylogenetic analyses, but most involve some knowledge about genetic relationships of the group of interest, or even a well-established phylogeny itself; these data are not always available in general phylogenetic applications. The method I proposed measures the "phylogenetic representativeness" of a given sample or set of samples and it is based entirely on the pre-existing available taxonomy of the ingroup, which is commonly known to investigators. Moreover, it also accounts for instability and discordance in taxonomies. A Python-based script suite, called PhyRe, has been developed to implement all analyses.

Genetic Manipulation, Gene Silencing and Biomarker Development in Multiple Experimental Models.

The thesis is set in three different parts, according to the relative experimental models. First, the domestic pig (Sus scrofa) is part of the study on reproductive biotechnologies: the transgenesis technique of Sperm Mediated Gene Transfer is widely studied starting from the quality of the semen, through the study of multiple uptakes of exogenous DNA and lastly used in the production of multi-transgenic blastocysts. Finally we managed to couple the transgenesis pipeline with sperm sorting and therefore produced transgenic embryos of predetermined sex. In the second part of the thesis the attention is on the fruit fly (Drosophila melanogaster) and on its derived cell line: the S2 cells. The in vitro and in vivo models are used to develop and validate an efficient way to knock down the myc gene. First an efficient in vitro protocol is described, than we demonstrate how the decrease in myc transcript remarkably affects the ribosome biogenesis through the study of Polysome gradients, rRNA content and qPCR. In vivo we identified two optimal drivers for the conditional silencing of myc, once the flies are fed with RU486: the first one is throughout the whole body (Tubulin), while the second is a head fat body driver (S32). With these results we present a very efficient model to study the role of myc in multiple aspects of translation. In the third and last part, the focus is on human derived lung fibroblasts (hLF-1), mouse tail fibroblasts and mouse tissues. We developed an efficient assay to quantify the total protein content of the nucleus on a single cell level via fluorescence. We coupled the protocol with classical immunofluorescence so to have at the same time general and particular information, demonstrating that during senescence nuclear proteins increase by 1.8 fold either in human cells, mouse cells and mouse tissues.

Development of machine learning methods for multi-modal biomarkers detection and integration

In medicine, innovation depends on a better knowledge of the human body mechanism, which represents a complex system of multi-scale constituents. Unraveling the complexity underneath diseases proves to be challenging. A deep understanding of the inner workings comes with dealing with many heterogeneous information. Exploring the molecular status and the organization of genes, proteins, metabolites provides insights on what is driving a disease, from aggressiveness to curability. Molecular constituents, however, are only the building blocks of the human body and cannot currently tell the whole story of diseases. This is why nowadays attention is growing towards the contemporary exploitation of multi-scale information. Holistic methods are then drawing interest to address the problem of integrating heterogeneous data. The heterogeneity may derive from the diversity across data types and from the diversity within diseases. Here, four studies conducted data integration using customly designed workflows that implement novel methods and views to tackle the heterogeneous characterization of diseases. The first study devoted to determine shared gene regulatory signatures for onco-hematology and it showed partial co-regulation across blood-related diseases. The second study focused on Acute Myeloid Leukemia and refined the unsupervised integration of genomic alterations, which turned out to better resemble clinical practice. In the third study, network integration for artherosclerosis demonstrated, as a proof of concept, the impact of network intelligibility when it comes to model heterogeneous data, which showed to accelerate the identification of new potential pharmaceutical targets. Lastly, the fourth study introduced a new method to integrate multiple data types in a unique latent heterogeneous-representation that facilitated the selection of important data types to predict the tumour stage of invasive ductal carcinoma. The results of these four studies laid the groundwork to ease the detection of new biomarkers ultimately beneficial to medical practice and to the ever-growing field of Personalized Medicine.

New insights into vaginal environment during pregnancy: a multi-omics approach

The present thesis aims to provide a thorough comprehension of the vaginal ecosystem of pregnant women and enhance the knowledge of pregnancy pathophysiology. The first study emphasized the importance of limiting protein intake from animal sources, consuming carbohydrates, and avoiding starting pregnancy overweight to maintain a healthy vaginal environment characterized by lactobacilli and related metabolites. In the second paper, a reduction in bacterial diversity, an increase in Lactobacillus abundance, and a decrease in bacterial vaginosis-related genera were observed during pregnancy. Lactobacillus abundance correlated with higher levels of lactate, sarcosine, and amino acids, while bacterial vaginosis-related genera were associated with amines, formate, acetate, alcohols, and short-chain fatty acids. An association between intrapartum antibiotic prophylaxis for Group B Streptococcus and higher vaginal abundance of Prevotella was found. Moreover, women experiencing a first-trimester miscarriage displayed a higher abundance of Fusobacterium. The third study explored the presence of macrolides and tetracyclines resistance genes in the vaginal environment, highlighting that different vaginal microbiota types were associated with distinct resistance profiles. Lactobacilli-dominated ecosystems showed fewer or no resistance genes, while women with increased bacterial vaginosis-related genera were positive for resistance genes. The last two papers aimed to identify potential biomarkers of vaginal health or disease status. The fourth paper showed that positivity for Torquetenovirus decreased from the first to the third trimester, being more prevalent in women with higher vaginal leukocyte counts. Torquetenovirus-positive samples showed higher levels of cytokines, propionate, and cadaverine. Lactobacillus species decreased in Torquetenovirus-positive samples, while Sneathia and Shuttleworthia increased. The last work pointed out the association between clade 2 of Gardnerella vaginalis and bacterial vaginosis. Moreover, as the number of simultaneously detected G. vaginalis clades increased, bacterial vaginosis-associated bacteria also tended to increase. Additionally, sialidase gene levels negatively correlated with Lactobacillus and positively correlated with Gardnerella, Atopobium, Prevotella, Megasphaera, and Sneathia.