Dynamic Alteration in the Vaginal Secretory Proteome across the Early and Mid-Trimesters of Pregnancy.

Pregnancy is characterized by intense physiological and structural alterations in the vagina, cervix, and overlying fetal membranes. High vaginal fluid (HVF) is a proximal fluid that covers the lower part of the female reproductive system and the severity of vaginal pathology often adversely affects pregnancy outcomes. To identify the correlation of vaginal fluid proteome dynamics and physiological changes during the progression of pregnancy, a longitudinal study was performed on 20 pregnant women who delivered a baby in >37 weeks without any complications. SWATH-MS-based label-free quantitative proteomics was performed to profile the HVF proteome at three time points defined as V1 (7-12 weeks), V2 (18-20 weeks), and V3 (26-28 weeks). Linear mixed-effect models were used to estimate protein abundance as a function of the period of gestational age. In this study, we identified 1015 HVF proteins and 61 of them were significantly altered until late second trimester. Our result demonstrates that the HVF proteins reveal gestational age-specific expression patterns and the function of these proteins is associated with tissue remodeling, organ development, and microbial defense. Our study provides an opportunity to monitor the underlying physiology of pregnancy that may be further probed for the biomarker identification in pregnancy-related adverse outcomes. Data are available via ProteomeXchange with identifiers PXD014846 and PXD021811.

Comparison of first trimester dating methods for gestational age estimation and their implication on preterm birth classification in a North Indian cohort.

BACKGROUND: Different formulae have been developed globally to estimate gestational age (GA) by ultrasonography in the first trimester of pregnancy. In this study, we develop an Indian population-specific dating formula and compare its performance with published formulae. Finally, we evaluate the implications of the choice of dating method on preterm birth (PTB) rate. This study's data was from GARBH-Ini, an ongoing pregnancy cohort of North Indian women to study PTB. METHODS: Comparisons between ultrasonography-Hadlock and last menstrual period (LMP) based dating methods were made by studying the distribution of their differences by Bland-Altman analysis. Using data-driven approaches, we removed data outliers more efficiently than by applying clinical parameters. We applied advanced machine learning algorithms to identify relevant features for GA estimation and developed an Indian population-specific formula (Garbhini-GA1) for the first trimester. PTB rates of Garbhini-GA1 and other formulae were compared by estimating sensitivity and accuracy. RESULTS: Performance of Garbhini-GA1 formula, a non-linear function of crown-rump length (CRL), was equivalent to published formulae for estimation of first trimester GA (LoA, - 0.46,0.96 weeks). We found that CRL was the most crucial parameter in estimating GA and no other clinical or socioeconomic covariates contributed to GA estimation. The estimated PTB rate across all the formulae including LMP ranged 11.27-16.50% with Garbhini-GA1 estimating the least rate with highest sensitivity and accuracy. While the LMP-based method overestimated GA by 3 days compared to USG-Hadlock formula; at an individual level, these methods had less than 50% agreement in the classification of PTB. CONCLUSIONS: An accurate estimation of GA is crucial for the management of PTB. Garbhini-GA1, the first such formula developed in an Indian setting, estimates PTB rates with higher accuracy, especially when compared to commonly used Hadlock formula. Our results reinforce the need to develop population-specific gestational age formulae.

Statistical Characterisation of Fetal Anatomy in Simple Obstetric Ultrasound Video Sweeps.

OBJECTIVE: We present a statistical characterisation of fetal anatomies in obstetric ultrasound video sweeps where the transducer follows a fixed trajectory on the maternal abdomen. METHODS: Large-scale, frame-level manual annotations of fetal anatomies (head, spine, abdomen, pelvis, femur) were used to compute common frame-level anatomy detection patterns expected for breech, cephalic, and transverse fetal presentations, with respect to video sweep paths. The patterns, termed statistical heatmaps, quantify the expected anatomies seen in a simple obstetric ultrasound video sweep protocol. In this study, a total of 760 unique manual annotations from 365 unique pregnancies were used. RESULTS: We provide a qualitative interpretation of the heatmaps assessing the transducer sweep paths with respect to different fetal presentations and suggest ways in which the heatmaps can be applied in computational research (e.g., as a machine learning prior). CONCLUSION: The heatmap parameters are freely available to other researchers (https://github.com/agleed/calopus_statistical_heatmaps).

Development and external validation of Indian population-specific Garbhini-GA2 model for estimating gestational age in second and third trimesters.

Background: A large proportion of pregnant women in lower and middle-income countries (LMIC) seek their first antenatal care after 14 weeks of gestation. While the last menstrual period (LMP) is still the most prevalent method of determining gestational age (GA), ultrasound-based foetal biometry is considered more accurate in the second and third trimesters. In LMIC settings, the Hadlock formula, originally developed using data from a small Caucasian population, is widely used for estimating GA and foetal weight worldwide as the pre-programmed formula in ultrasound machines. This approach can lead to inaccuracies when estimating GA in a diverse population. Therefore, this study aimed to develop a population-specific model for estimating GA in the late trimesters that was as accurate as the GA estimation in the first trimester, using data from GARBH-Ini, a pregnancy cohort in a North Indian district hospital, and subsequently validate the model in an independent cohort in South India. Methods: Data obtained by longitudinal ultrasonography across all trimesters of pregnancy was used to develop and validate GA models for the second and third trimesters. The gold standard for GA estimation in the first trimester was determined using ultrasonography. The Garbhini-GA2, a polynomial regression model, was developed using the genetic algorithm-based method, showcasing the best performance among the models considered. This model incorporated three of the five routinely measured ultrasonographic parameters during the second and third trimesters. To assess its performance, the Garbhini-GA2 model was compared against the Hadlock and INTERGROWTH-21st models using both the TEST set (N = 1493) from the GARBH-Ini cohort and an independent VALIDATION dataset (N = 948) from the Christian Medical College (CMC), Vellore cohort. Evaluation metrics, including root-mean-squared error, bias, and preterm birth (PTB) rates, were utilised to comprehensively assess the model's accuracy and reliability. Findings: With first trimester GA dating as the baseline, Garbhini-GA2 reduced the GA estimation median error by more than three times compared to the Hadlock formula. Further, the PTB rate estimated using Garbhini-GA2 was more accurate when compared to the INTERGROWTH-21st and Hadlock formulae, which overestimated the rate by 22.47% and 58.91%, respectively. Interpretation: The Garbhini-GA2 is the first late-trimester GA estimation model to be developed and validated using Indian population data. Its higher accuracy in GA estimation, comparable to GA estimation in the first trimester and PTB classification, underscores the significance of deploying population-specific GA formulae to enhance antenatal care. Funding: The GARBH-Ini cohort study was funded by the Department of Biotechnology, Government of India (BT/PR9983/MED/97/194/2013). The ultrasound repository was partly supported by the Grand Challenges India-All Children Thriving Program, Biotechnology Industry Research Assistance Council, Department of Biotechnology, Government of India (BIRAC/GCI/0115/03/14-ACT). The research reported in this publication was made possible by a grant (BT/kiData0394/06/18) from the Grand Challenges India at Biotechnology Industry Research Assistance Council (BIRAC), an operating division jointly supported by DBT-BMGF-BIRAC. The external validation study at CMC Vellore was partly supported by a grant (BT/kiData0394/06/18) from the Grand Challenges India at Biotechnology Industry Research Assistance Council (BIRAC), an operating division jointly supported by DBT-BMGF-BIRAC and by Exploratory Research Grant (SB/20-21/0602/BT/RBCX/008481) from Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras. An alum endowment from Prakash Arunachalam (BIO/18-19/304/ALUM/KARH) partly funded this study at the Centre for Integrative Biology and Systems Medicine, IIT Madras.

Incidence of and risk factors for small vulnerable newborns in north India: a secondary analysis of a prospective pregnancy cohort.

BACKGROUND: Globally, recent estimates have shown there have been 3·6 million stillbirths and neonatal deaths in 2022, with nearly 60% occurring in low-income and middle-income countries. The Small Vulnerable Newborn Consortium has proposed a framework combining preterm birth (<37 weeks of gestation), small for gestational age (SGA) by INTERGROWTH-21st standard, and low birthweight (<2500 g) under the category small vulnerable newborns (SVN). Reliable data on SVN from sub-Saharan Africa, central Asia, and south Asia are sparse. We aimed to estimate the incidence of SVN and its types, and quantify risk factors, both overall and trimester-specific, from a pregnancy cohort in north India. METHODS: In the GARBH-Ini (Interdisciplinary Group for Advanced Research on Birth Outcomes-DBT India Initiative) pregnancy cohort, 8000 participants were enrolled with less than 20 weeks' gestation between May 11, 2015, and Aug 8, 2020, at a secondary-care hospital in north India. The cohort was followed up across the antenatal period for a detailed study on preterm birth. We conducted a secondary analysis of cohort data for the outcome of SVN, classified into its types: preterm-SGA, preterm-nonSGA, and term-SGA. We estimated the relative risk and population attributable fraction of candidate risk factors for SVN (modified Poisson regression) and its types (multinomial regression). FINDINGS: 7183 (89·9%) of 7990 participants completed the study. Among 6206 newborns included for analysis, the incidence of SVN was 48·4% (35·1% term-SGA newborns [n=2179], 9·7% preterm-nonSGA newborns [n=605], and 3·6% preterm-SGA newborns [n=222]). Compared with term-nonSGA newborns, proportions of stillbirths and neonatal deaths within 72 h of birth among SVN were three times and 2·5 times higher, respectively. Preterm-SGA newborns had the highest incidence of stillbirth (15 [6·8%] of 222) and neonatal deaths (six [4·2%] of 142). Low body-mass index (BMI <18·5 kg/m2) of participants at the start of pregnancy was associated with higher risk for preterm-SGA (adjusted relative risk [RR] 1·61 [95% CI 1·17-2·22]), preterm-nonSGA (1·35 [1·09-1·68]), and term-SGA (1·44 [1·27- 1·64]), with population attributable fraction ranging from 8·7% to 13·8%. Pre-eclampsia (adjusted RR 1·48 [95% CI 1·30-1·71]), short cervical length (1·15 [1·04-1·26]), and bacterial vaginosis (1·13 [0·88-1·45]) were other important antenatal risk factors. INTERPRETATION: In a comprehensive analysis of SVN and its types from north India, we identified risk factors to guide prioritisation of interventions. Complemented with risk-stratification tools, this focused approach will enhance antenatal care, and accelerate achievement of Sustainable Development Goals-namely, to end preventable deaths of newborns and children younger than 5 years by 2030 (target 3·2). FUNDING: Department of Biotechnology, Government of India and Grand Challenges India-Biotechnology Industry Research Assistance Council, Government of India. TRANSLATION: For the Hindi translation of the abstract see Supplementary Materials section.

Longitudinal changes in serum immune markers during normal pregnancy in a North-Indian population.

PROBLEM: The objective of this study was to examine levels of cytokines across normal term pregnancy in an Indian population. Additionally we have also explored for possible associations between inflammatory markers and fetal growth parameters. METHOD OF STUDY: A multiplex panel of 24 analytes was used to examine levels of inflammatory markers in maternal serum at three time points during pregnancy and in cord blood from women with no reported comorbidities who delivered a singleton live baby at term (N = 23), enrolled in the GARBH-Ini pregnancy cohort. Linear mixed models were applied to construct longitudinal cytokine trajectories with gestational age. Pearson correlation was used to calculate intra-visit correlation between cytokines. Principal component analysis (PCA) was performed to examine cytokine combinations prevalent across pregnancy, and their association with fetal growth parameters was determined by multivariable regression. RESULTS: Significant increase in sFLT-1, Flt3L, PLGF, IL-4, and IL-18 and a decrease in VCAM-1 concentrations was seen across pregnancy. The cytokine concentrations in cord blood differed substantially as compared to maternal levels across gestation. Some cytokines were closely correlated with each other in distinct patterns across pregnancy. Gestational age specific combination of cytokines were seen to be associated with different fetal growth parameters. CONCLUSIONS: This study for the first time provides reference concentrations for the longitudinal expression of immune markers across pregnancy in an Indian population providing a much needed baseline to compare with pregnancies leading to adverse outcomes. Growth factors showed maximum longitudinal variation with gestational age and strong correlations were identified between various cytokines at all time points across pregnancy.

Gestational weight gain trajectories in GARBH-Ini pregnancy cohort in North India and a comparative analysis with global references.

BACKGROUND: To describe the pattern of gestational weight gain (GWG), derive reference centiles for GWG specific to North Indian population, and to compare the weight gain across different periods of gestation with the INTERGROWTH-21st reference. METHODS: A prospective pregnancy (GARBH-Ini) cohort was initiated and followed between May 2015 and June 2019 in a district hospital, Gurguram, North India. GWG centile curves were modelled by Generalized Additive Models for Location, Scale and Shape method (n = 2844) and compared with INTERGROWTH-21st reference. The independent association of GWG with biological and social predictors was assessed using multivariable regression analysis. RESULTS: Percentiles (3rd, 10th, 50th, 90th and 97th) for each completed week from 18-40 weeks of gestation were derived from smoothed centile curves. The median GWG across pregnancy during specific antenatal visits was 1.29 at 18, 4.44 at 26, 5.8 at 30 and 9.06 kg at 40 weeks of gestation. Nearly 26% of participants had GWG < 10th centile at 18-20 weeks as per INTERGROWTH-21st reference and this increased to 45% at delivery. Significant predictors of GWG included maternal age, height, first trimester body mass index, parity, type of family, and use of clean fuel for cooking. CONCLUSION: These GWG percentiles will serve as a useful reference, particularly during the WHO recommended antenatal visit schedule for optimum pregnancy outcomes, for clinicians and researchers. Multiple independent biological and social predictors of GWG suggest that single interventions are unlikely to bridge the gap between general Indian population and international references.

Developing Clinical Artificial Intelligence for Obstetric Ultrasound to Improve Access in Underserved Regions: Protocol for a Computer-Assisted Low-Cost Point-of-Care UltraSound (CALOPUS) Study.

BACKGROUND: The World Health Organization recommends a package of pregnancy care that includes obstetric ultrasound scans. There are significant barriers to universal access to antenatal ultrasound, particularly because of the cost and need for maintenance of ultrasound equipment and a lack of trained personnel. As low-cost, handheld ultrasound devices have become widely available, the current roadblock is the global shortage of health care providers trained in obstetric scanning. OBJECTIVE: The aim of this study is to improve pregnancy and risk assessment for women in underserved regions. Therefore, we are undertaking the Computer-Assisted Low-Cost Point-of-Care UltraSound (CALOPUS) project, bringing together experts in machine learning and clinical obstetric ultrasound. METHODS: In this prospective study conducted in two clinical centers (United Kingdom and India), participating pregnant women were scanned and full-length ultrasounds were performed. Each woman underwent 2 consecutive ultrasound scans. The first was a series of simple, standardized ultrasound sweeps (the CALOPUS protocol), immediately followed by a routine, full clinical ultrasound examination that served as the comparator. We describe the development of a simple-to-use clinical protocol designed for nonexpert users to assess fetal viability, detect the presence of multiple pregnancies, evaluate placental location, assess amniotic fluid volume, determine fetal presentation, and perform basic fetal biometry. The CALOPUS protocol was designed using the smallest number of steps to minimize redundant information, while maximizing diagnostic information. Here, we describe how ultrasound videos and annotations are captured for machine learning. RESULTS: Over 5571 scans have been acquired, from which 1,541,751 label annotations have been performed. An adapted protocol, including a low pelvic brim sweep and a well-filled maternal bladder, improved visualization of the cervix from 28% to 91% and classification of placental location from 82% to 94%. Excellent levels of intra- and interannotator agreement are achievable following training and standardization. CONCLUSIONS: The CALOPUS study is a unique study that uses obstetric ultrasound videos and annotations from pregnancies dated from 11 weeks and followed up until birth using novel ultrasound and annotation protocols. The data from this study are being used to develop and test several different machine learning algorithms to address key clinical diagnostic questions pertaining to obstetric risk management. We also highlight some of the challenges and potential solutions to interdisciplinary multinational imaging collaboration. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/37374.

The Vaginal Microbial Signatures of Preterm Birth Delivery in Indian Women.

Background: The incidence of preterm birth (PTB) in India is around 13%. Specific bacterial communities or individual taxon living in the vaginal milieu of pregnant women is a potential risk factor for PTB and may play an important role in its pathophysiology. Besides, bacterial taxa associated with PTB vary across populations. Objective: Conduct a comparative analysis of vaginal microbiome composition and microbial genomic repertoires of women who enrolled in the Interdisciplinary Group for Advanced Research on Birth Outcomes - A DBT India Initiative (GARBH-Ini) pregnancy cohort to identify bacterial taxa associated with term birth (TB) and PTB in Indian women. Methods: Vaginal swabs were collected during all three trimesters from 38 pregnant Indian women who delivered spontaneous term (n=20) and preterm (n=18) neonates. Paired-end sequencing of V3-V4 region of 16S rRNA gene was performed using the metagenomic DNA isolated from vaginal swabs (n=115). Whole genome sequencing of bacterial species associated with birth outcomes was carried out by shotgun method. Lactobacillus species were grown anaerobically in the De Man, Rogosa and Sharpe (MRS) agar culture medium for isolation of genomic DNA and whole genome sequencing. Results: Vaginal microbiome of both term and preterm samples reveals similar alpha diversity indices. However, significantly higher abundance of Lactobacillus iners (p-value All_Trimesters<0.02), Megasphaera sp (p-value1st_Trimester <0.05), Gardnerella vaginalis (p-value2nd_Trimester= 0.01) and Sneathia sanguinegens (p-value2nd_Trimester <0.0001) were identified in preterm samples whereas higher abundance of L. gasseri (p-value3rd_Trimester =0.010) was observed in term samples by Wilcoxon rank-sum test. The relative abundance of L. iners, and Megasphaera sp. were found to be significantly different over time between term and preterm mothers. Analyses of the representative genomes of L. crispatus and L. gasseri indicate presence of secretory transcriptional regulator and several ribosomally synthesized antimicrobial peptides correlated with anti-inflammatory condition in the vagina. These findings indicate protective role of L. crispatus and L. gasseri in reducing the risk of PTB. Conclusion: Our findings indicate that the dominance of specific Lactobacillus species and few other facultative anaerobes are associated with birth outcomes.

Evaluation of spike protein antigens for SARS-CoV-2 serology.

BACKGROUND: Spike protein domains are being used in various serology-based assays to detect prior exposure to SARS-CoV-2 virus. However, there has been limited comparison of antibody titers against various spike protein antigens among COVID-19 infected patients. METHODS: We compared four spike proteins (RBD, S1, S2 and a stabilized spike trimer (ST)) representing commonly used antigens for their reactivity to human IgG antibodies using indirect ELISA in serum from COVID-19 patients and pre-2020 samples. ST ELISA was also compared against the EUROIMMUN IgG ELISA test. Further, we estimated time appropriate IgG and IgA seropositivity rates in COVID-19 patients using a panel of sera samples collected longitudinally from the day of onset of symptoms (DOS). RESULTS: Among the four spike antigens tested, the ST demonstrated the highest sensitivity (86.2 %; 95 % CI: 77.8-91.7 %), while all four antigens showed high specificity to COVID-19 sera (94.7-96.8 %). 13.8 % (13/94) of the samples did not show seroconversion in any of the four antigen-based assays. In a double-blinded head-to-head comparison, ST based IgG ELISA displayed a better sensitivity (87.5 %, 95 % CI: 76.4-93.8 %) than the EUROIMMUN IgG ELISA (67.9 %, 95 % CI: 54.8-78.6 %). Further, in ST-based assays, we found 48 % and 50 % seroconversion in the first six days (from DOS) for IgG and IgA antibodies, respectively, which increased to 84 % (IgG) and 85 % (IgA) for samples collected ≥22 days from DOS. CONCLUSIONS: Comparison of spike antigens demonstrates that spike trimer protein is a superior option as an ELISA antigen for COVID-19 serology.

Longitudinal Serology of SARS-CoV-2-Infected Individuals in India: A Prospective Cohort Study.

Clinical and epidemiological characteristics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are now widely available, but there are few data regarding longitudinal serology in large cohorts, particularly those from low-income and middle-income countries. We established an ongoing prospective cohort of 3,840 SARS-CoV-2-positive individuals according to RT-PCR in the Delhi-National Capital Region of India to document clinical and immunological characteristics during illness and convalescence. The immunoglobulin G (IgG) responses to the receptor binding domain (RBD) and nucleocapsid were assessed at 0 to 7 days, 10 to 28 days, and 6 to 10 weeks after infection. The clinical predictors of seroconversion were identified by multivariable regression analysis. The seroconversion rates during the postinfection windows of 0 to 7 days, 10 to 28 days, and 6 to 10 weeks were 46%, 84.7%, and 85.3%, respectively (N = 743). The proportion with a serological response increased with the severity of coronavirus disease 2019 (COVID-19). All participants with severe disease, 89.6% with mild to moderate infection, and 77.3% of asymptomatic participants had IgG antibodies to the RBD antigen. The threshold values for the nasopharyngeal viral RNA RT-PCR of a subset of asymptomatic and symptomatic seroconverters were comparable (P = 0.48) to those of nonseroconverters (P = 0.16) (N = 169). This is the first report of longitudinal humoral immune responses to SARS-CoV-2 over a period of 10 weeks in South Asia. The low seropositivity of asymptomatic participants and differences between assays highlight the importance of contextualizing the understanding of population serosurveys.

Screening of cardiovascular risk assessment accuracy of anthropometric indices in Indian children and adolescents.

Background: Body mass index (BMI) is the most popular anthropometric marker to define obesity and cardiometabolic risk. BMI is limited in its ability to discriminate central adiposity and other indices such as waist circumference (WC), and waist to height ratio (WHtR) could be a better choice. In this study, we aimed to evaluate the relative accuracy of these indices for the prediction of hypertension in Indian children and young adults. Methods: Anthropometric indices and blood pressure measurements were obtained in 2609 adolescent children and young adults (10-20 years) across a national residential school system. Z-scores were calculated for anthropometric parameters using the Box-Cox-Cole-Green method and World Health Organization (WHO) growth charts. Hypertension was defined using the sex, age and height specific cutoffs for systolic blood pressure. Receiver operator curve (ROC) analysis was performed to examine the predictive ability.  Results: Girls had higher BMI for age in our dataset (p < 0.001), along with higher odds for stunting (95% CI: 1.21 - 1.88) as well as central obesity (95% CI: 2.44 - 3.99). Hypertension was seen in 10.6% of the subjects, with higher age, and higher BMI or WHtR as the predictors. Prehypertension was higher in males (p <0.001). WHtR had acceptable but modest discrimination ability for hypertension (AUC > 0.6) in boys (AUC=0.62) and girls (AUC=0.66). Performance of BMI was better in boys (AUC = 0.67) but poor in girls (AUC = 0.55) Conclusion: WHtR was a better predictor of hypertension in Indian adolescent girls and could be used as an augmented parameter to BMI for a better assessment of cardiovascular risk.

Comparative evaluation of SARS-CoV-2 IgG assays in India.

INTRODUCTION: IgG immunoassays have been developed and used widely for clinical samples and serosurveys for SARS-CoV2, with most detecting antibodies against the spike/receptor-binding-domain or nucleocapsid. Limited information is available on comparative evaluation of IgG immunoassays against a clinical reference standard, i.e., RT-PCR positivity with >20 days of illness. This study addresses the need for comparing clinical performance of IgG immunoassays with respect to this alternate reference standard. METHODS: We compared the performance of three immunoassays, an in-house RBD assay, and two commercial assays, the Diasorin LIAISON SARS-CoV-2 S1/S1 IgG CLIA which detects antibodies against S1/S2 domains of the Spike protein and the Zydus Kavach assay based on inactivated virus using a well-characterized panel of sera. 379 sera and plasma samples from RTPCR positive individuals >20 days of illness in symptomatic or RT-PCR positivity in asymptomatic individuals and 184 samples collected prior to 2019 were used for assay evaluation. RESULTS: The sensitivity of the assays were 84.7 (95 %CI 80.6-88.1), 82.6 (95 %CI 78.3-86.2) and 75.7 (95 %CI 71.0-79.9) respectively for RBD, LIAISON and Kavach. Kavach and the in-house RBD ELISA showed a specificity of 99.5 % and 100 %, respectively. The RBD and LIAISON (S1/S2) assays showed high agreement (94.7 %; 95 %CI: 92.0, 96.6) and were able to correctly identify more positive sera/plasma than Kavach. CONCLUSION: Independent comparisons support the evaluation of performance characteristics of immunoassays. All three assays are suitable for serosurveillance studies, but in low prevalence sites, estimation of exposure may require adjustment based on our findings.

Salivary proteome signatures in the early and middle stages of human pregnancy with term birth outcome.

The establishment and maintenance of pregnancy in humans proceed through a continuous change of biochemical and biophysical processes. It requires a constant interaction between the fetus and the maternal system. The present prospective study aims to elucidate changes in salivary proteome from the early to middle stages of term pregnancy, and establishing an expressional trajectory for modulated proteins. To date, a comprehensive characterization of the longitudinal salivary proteome in pregnancy has not been performed and it is our immediate interest. In the discovery phase, maternal saliva (N = 20) at 6-13, 18-21, and 26-29 weeks of gestation was analyzed using level-free proteomics (SWATH-MS) approach. The expression levels of 65 proteins were found to change significantly with gestational age and distributed into two distinct clusters with a unique expression trajectory. The results revealed that altered proteins are involved in maternal immune modulation, metabolism, and host defense mechanism. Further, verification of 12 proteins was employed using targeted mass spectrometry (MRM-MS) in a separate subset of saliva (N = 14). The MRM results of 12 selected proteins confirmed a similar expression pattern as in SWATH-MS analysis. Overall, the results not only demonstrate the longitudinal maternal saliva proteome for the first time but also set the groundwork for comparative analysis between term birth and adverse pregnancy outcomes.