Transcriptomic analysis of Anopheles gambiae from Benin reveals overexpression of salivary and cuticular proteins associated with cross-resistance to pyrethroids and organophosphates.

BACKGROUND: Insecticide resistance (IR) is one of the major threats to malaria vector control programs in endemic countries. However, the mechanisms underlying IR are poorly understood. Thus, investigating gene expression patterns related to IR can offer important insights into the molecular basis of IR in mosquitoes. In this study, RNA-Seq was used to characterize gene expression in Anopheles gambiae surviving exposure to pyrethroids (deltamethrin, alphacypermethrin) and an organophosphate (pirimiphos-methyl). RESULTS: Larvae of An. gambiae s.s. collected from Bassila and Djougou in Benin were reared to adulthood and phenotyped for IR using a modified CDC intensity bottle bioassay. The results showed that mosquitoes from Djougou were more resistant to pyrethroids (5X deltamethrin: 51.7% mortality; 2X alphacypermethrin: 47.4%) than Bassila (1X deltamethrin: 70.7%; 1X alphacypermethrin: 77.7%), while the latter were more resistant to pirimiphos-methyl (1.5X: 48.3% in Bassila and 1X: 21.5% in Djougou). RNA-seq was then conducted on resistant mosquitoes, non-exposed mosquitoes from the same locations and the laboratory-susceptible An. gambiae s.s. Kisumu strain. The results showed overexpression of detoxification genes, including cytochrome P450s (CYP12F2, CYP12F3, CYP4H15, CYP4H17, CYP6Z3, CYP9K1, CYP4G16, and CYP4D17), carboxylesterase genes (COEJHE5E, COE22933) and glutathione S-transferases (GSTE2 and GSTMS3) in all three resistant mosquito groups analyzed. Genes encoding cuticular proteins (CPR130, CPR10, CPR15, CPR16, CPR127, CPAP3-C, CPAP3-B, and CPR76) were also overexpressed in all the resistant groups, indicating their potential role in cross resistance in An. gambiae. Salivary gland protein genes related to 'salivary cysteine-rich peptide' and 'salivary secreted mucin 3' were also over-expressed and shared across all resistant groups. CONCLUSION: Our results suggest that in addition to metabolic enzymes, cuticular and salivary gland proteins could play an important role in cross-resistance to multiple classes of insecticides in Benin. These genes warrant further investigation to validate their functional role in An. gambiae resistance to insecticides.

Whole transcriptomic analysis reveals overexpression of salivary gland and cuticular proteins genes in insecticide-resistant Anopheles arabiensis from Western Kenya.

BACKGROUND: Effective vector control is key to malaria prevention. However, this is now compromised by increased insecticide resistance due to continued reliance on insecticide-based control interventions. In Kenya, we have observed heterogenous resistance to pyrethroids and organophosphates in Anopheles arabiensis which is one of the most widespread malaria vectors in the country. We investigated the gene expression profiles of insecticide resistant An. arabiensis populations from Migori and Siaya counties in Western Kenya using RNA-Sequencing. Centers for Disease Control and Prevention (CDC) bottle assays were conducted using deltamethrin (DELTA), alphacypermethrin (ACYP) and pirimiphos-methyl (PMM) to determine the resistance status in both sites. RESULTS: Mosquitoes from Migori had average mortalities of 91%, 92% and 58% while those from Siaya had 85%, 86%, and 30% when exposed to DELTA, ACYP and PMM, respectively. RNA-Seq analysis was done on pools of mosquitoes which survived exposure ('resistant'), mosquitoes that were not exposed, and the insecticide-susceptible An. arabiensis Dongola strain. Gene expression profiles of resistant mosquitoes from both Migori and Siaya showed an overexpression mainly of salivary gland proteins belonging to both the short and long form D7 genes, and cuticular proteins (including CPR9, CPR10, CPR15, CPR16). Additionally, the overexpression of detoxification genes including cytochrome P450s (CYP9M1, CYP325H1, CYP4C27, CYP9L1 and CYP307A1), 2 carboxylesterases and a glutathione-S-transferase (GSTE4) were also shared between DELTA, ACYP, and PMM survivors, pointing to potential contribution to cross resistance to both pyrethroid and organophosphate insecticides. CONCLUSION: This study provides novel insights into the molecular basis of insecticide resistance in An. arabiensis in Western Kenya and suggests that salivary gland proteins and cuticular proteins are associated with resistance to multiple classes of insecticides.

The African Human Microbiome Portal: a public web portal of curated metagenomic metadata.

There is growing evidence that comprehensive and harmonized metadata are fundamental for effective public data reusability. However, it is often challenging to extract accurate metadata from public repositories. Of particular concern is the metagenomic data related to African individuals, which often omit important information about the particular features of these populations. As part of a collaborative consortium, H3ABioNet, we created a web portal, namely the African Human Microbiome Portal (AHMP), exclusively dedicated to metadata related to African human microbiome samples. Metadata were collected from various public repositories prior to cleaning, curation and harmonization according to a pre-established guideline and using ontology terms. These metadata sets can be accessed at https://microbiome.h3abionet.org/. This web portal is open access and offers an interactive visualization of 14 889 records from 70 bioprojects associated with 72 peer reviewed research articles. It also offers the ability to download harmonized metadata according to the user's applied filters. The AHMP thereby supports metadata search and retrieve operations, facilitating, thus, access to relevant studies linked to the African Human microbiome. Database URL:  https://microbiome.h3abionet.org/.

Developing Clinical Phenotype Data Collection Standards for Research in Africa.

Modern biomedical research is characterised by its high-throughput and interdisciplinary nature. Multiproject and consortium-based collaborations requiring meaningful analysis of multiple heterogeneous phenotypic datasets have become the norm; however, such analysis remains a challenge in many regions across the world. An increasing number of data harmonisation efforts are being undertaken by multistudy collaborations through either prospective standardised phenotype data collection or retrospective phenotype harmonisation. In this regard, the Phenotype Harmonisation Working Group (PHWG) of the Human Heredity and Health in Africa (H3Africa) consortium aimed to facilitate phenotype standardisation by both promoting the use of existing data collection standards (hosted by PhenX), adapting existing data collection standards for appropriate use in low- and middle-income regions such as Africa, and developing novel data collection standards where relevant gaps were identified. Ultimately, the PHWG produced 11 data collection kits, consisting of 82 protocols, 38 of which were existing protocols, 17 were adapted, and 27 were novel protocols. The data collection kits will facilitate phenotype standardisation and harmonisation not only in Africa but also across the larger research community. In addition, the PHWG aims to feed back adapted and novel protocols to existing reference platforms such as PhenX.

Assessing HLA imputation accuracy in a West African population.

The Human Leukocyte Antigen (HLA) region plays an important role in autoimmune and infectious diseases. HLA is a highly polymorphic region and thus difficult to impute. We, therefore, sought to evaluate HLA imputation accuracy, specifically in a West African population, since they are understudied and are known to harbor high genetic diversity. The study sets were selected from 315 Gambian individuals within the Gambian Genome Variation Project (GGVP) Whole Genome Sequence datasets. Two different arrays, Illumina Omni 2.5 and Human Hereditary and Health in Africa (H3Africa), were assessed for the appropriateness of their markers, and these were used to test several imputation panels and tools. The reference panels were chosen from the 1000 Genomes (1kg-All), 1000 Genomes African (1kg-Afr), 1000 Genomes Gambian (1kg-Gwd), H3Africa, and the HLA Multi-ethnic datasets. HLA-A, HLA-B, and HLA-C alleles were imputed using HIBAG, SNP2HLA, CookHLA, and Minimac4, and concordance rate was used as an assessment metric. The best performing tool was found to be HIBAG, with a concordance rate of 0.84, while the best performing reference panel was the H3Africa panel, with a concordance rate of 0.62. Minimac4 (0.75) was shown to increase HLA-B allele imputation accuracy compared to HIBAG (0.71), SNP2HLA (0.51) and CookHLA (0.17). The H3Africa and Illumina Omni 2.5 array performances were comparable, showing that genotyping arrays have less influence on HLA imputation in West African populations. The findings show that using a larger population-specific reference panel and the HIBAG tool improves the accuracy of HLA imputation in a West African population.

Analysis of Structural Variants Previously Associated With ALS in Europeans Highlights Genomic Architectural Differences in Africans.

Background and Objectives: Amyotrophic lateral sclerosis (ALS) is a degenerative condition of the brain and spinal cord in which protein-coding variants in known ALS disease genes explain a minority of sporadic cases. There is a growing interest in the role of noncoding structural variants (SVs) as ALS risk variants or genetic modifiers of ALS phenotype. In small European samples, specific short SV alleles in noncoding regulatory regions of SCAF4, SQSTM1, and STMN2 have been reported to be associated with ALS, and several groups have investigated the possible role of SMN1/SMN2 gene copy numbers in ALS susceptibility and clinical severity. Methods: Using short-read whole genome sequencing (WGS) data, we investigated putative ALS-susceptibility SCAF4 (3'UTR poly-T repeat), SQSTM1 (intron 5 AAAC insertion), and STMN2 (intron 3 CA repeat) alleles in African ancestry patients with ALS and described the architecture of the SMN1/SMN2 gene region. South African cases with ALS (n = 114) were compared with ancestry-matched controls (n = 150), 1000 Genomes Project samples (n = 2,336), and H3Africa Genotyping Chip Project samples (n = 347). Results: There was no association with previously reported SCAF4 poly-T repeat, SQSTM1 AAAC insertion, and long STMN2 CA alleles with ALS risk in South Africans (p > 0.2). Similarly, SMN1 and SMN2 gene copy numbers did not differ between South Africans with ALS and matched population controls (p > 0.9). Notably, 20% of the African samples in this study had no SMN2 gene copies, which is a higher frequency than that reported in Europeans (approximately 7%). Discussion: We did not replicate the reported association of SCAF4, SQSTM1, and STMN2 short SVs with ALS in a small South African sample. In addition, we found no link between SMN1 and SMN2 copy numbers and susceptibility to ALS in this South African sample, which is similar to the conclusion of a recent meta-analysis of European studies. However, the SMN gene region findings in Africans replicate previous results from East and West Africa and highlight the importance of including diverse population groups in disease gene discovery efforts. The clinically relevant differences in the SMN gene architecture between African and non-African populations may affect the effectiveness of targeted SMN2 gene therapy for related diseases such as spinal muscular atrophy.

MetaNovo: An open-source pipeline for probabilistic peptide discovery in complex metaproteomic datasets.

BACKGROUND: Microbiome research is providing important new insights into the metabolic interactions of complex microbial ecosystems involved in fields as diverse as the pathogenesis of human diseases, agriculture and climate change. Poor correlations typically observed between RNA and protein expression datasets make it hard to accurately infer microbial protein synthesis from metagenomic data. Additionally, mass spectrometry-based metaproteomic analyses typically rely on focused search sequence databases based on prior knowledge for protein identification that may not represent all the proteins present in a set of samples. Metagenomic 16S rRNA sequencing only targets the bacterial component, while whole genome sequencing is at best an indirect measure of expressed proteomes. Here we describe a novel approach, MetaNovo, that combines existing open-source software tools to perform scalable de novo sequence tag matching with a novel algorithm for probabilistic optimization of the entire UniProt knowledgebase to create tailored sequence databases for target-decoy searches directly at the proteome level, enabling metaproteomic analyses without prior expectation of sample composition or metagenomic data generation and compatible with standard downstream analysis pipelines. RESULTS: We compared MetaNovo to published results from the MetaPro-IQ pipeline on 8 human mucosal-luminal interface samples, with comparable numbers of peptide and protein identifications, many shared peptide sequences and a similar bacterial taxonomic distribution compared to that found using a matched metagenome sequence database-but simultaneously identified many more non-bacterial peptides than the previous approaches. MetaNovo was also benchmarked on samples of known microbial composition against matched metagenomic and whole genomic sequence database workflows, yielding many more MS/MS identifications for the expected taxa, with improved taxonomic representation, while also highlighting previously described genome sequencing quality concerns for one of the organisms, and identifying an experimental sample contaminant without prior expectation. CONCLUSIONS: By estimating taxonomic and peptide level information directly on microbiome samples from tandem mass spectrometry data, MetaNovo enables the simultaneous identification of peptides from all domains of life in metaproteome samples, bypassing the need for curated sequence databases to search. We show that the MetaNovo approach to mass spectrometry metaproteomics is more accurate than current gold standard approaches of tailored or matched genomic sequence database searches, can identify sample contaminants without prior expectation and yields insights into previously unidentified metaproteomic signals, building on the potential for complex mass spectrometry metaproteomic data to speak for itself.

Performance and accuracy evaluation of reference panels for genotype imputation in sub-Saharan African populations.

Based on evaluations of imputation performed on a genotype dataset consisting of about 11,000 sub-Saharan African (SSA) participants, we show Trans-Omics for Precision Medicine (TOPMed) and the African Genome Resource (AGR) to be currently the best panels for imputing SSA datasets. We report notable differences in the number of single-nucleotide polymorphisms (SNPs) that are imputed by different panels in datasets from East, West, and South Africa. Comparisons with a subset of 95 SSA high-coverage whole-genome sequences (WGSs) show that despite being about 20-fold smaller, the AGR imputed dataset has higher concordance with the WGSs. Moreover, the level of concordance between imputed and WGS datasets was strongly influenced by the extent of Khoe-San ancestry in a genome, highlighting the need for integration of not only geographically but also ancestrally diverse WGS data in reference panels for further improvement in imputation of SSA datasets. Approaches that integrate imputed data from different panels could also lead to better imputation.

Can we design the next generation of digital health communication programs by leveraging the power of artificial intelligence to segment target audiences, bolster impact and deliver differentiated services? A machine learning analysis of survey data from rural India.

OBJECTIVES: Direct to beneficiary (D2B) mobile health communication programmes have been used to provide reproductive, maternal, neonatal and child health information to women and their families in a number of countries globally. Programmes to date have provided the same content, at the same frequency, using the same channel to large beneficiary populations. This manuscript presents a proof of concept approach that uses machine learning to segment populations of women with access to phones and their husbands into distinct clusters to support differential digital programme design and delivery. SETTING: Data used in this study were drawn from cross-sectional survey conducted in four districts of Madhya Pradesh, India. PARTICIPANTS: Study participant included pregnant women with access to a phone (n=5095) and their husbands (n=3842) RESULTS: We used an iterative process involving K-Means clustering and Lasso regression to segment couples into three distinct clusters. Cluster 1 (n=1408) tended to be poorer, less educated men and women, with low levels of digital access and skills. Cluster 2 (n=666) had a mid-level of digital access and skills among men but not women. Cluster 3 (n=1410) had high digital access and skill among men and moderate access and skills among women. Exposure to the D2B programme 'Kilkari' showed the greatest difference in Cluster 2, including an 8% difference in use of reversible modern contraceptives, 7% in child immunisation at 10 weeks, 3% in child immunisation at 9 months and 4% in the timeliness of immunisation at 10 weeks and 9 months. CONCLUSIONS: Findings suggest that segmenting populations into distinct clusters for differentiated programme design and delivery may serve to improve reach and impact. TRIAL REGISTRATION NUMBER: NCT03576157.

Assessing HLA imputation accuracy in a West African population.

The Human Leukocyte Antigen (HLA) region plays an important role in autoimmune and infectious diseases. HLA is a highly polymorphic region and thus difficult to impute. We therefore sought to evaluate HLA imputation accuracy, specifically in a West African population, since they are understudied and are known to harbor high genetic diversity. The study sets were selected from Gambian individuals within the Gambian Genome Variation Project (GGVP) Whole Genome Sequence datasets. Two different arrays, Illumina Omni 2.5 and Human Hereditary and Health in Africa (H3Africa), were assessed for the appropriateness of their markers, and these were used to test several imputation panels and tools. The reference panels were chosen from the 1000 Genomes dataset (1kg-All), 1000 Genomes African dataset (1kg-Afr), 1000 Genomes Gambian dataset (1kg-Gwd), H3Africa dataset and the HLA Multi-ethnic dataset. HLA-A, HLA-B and HLA-C alleles were imputed using HIBAG, SNP2HLA, CookHLA and Minimac4, and concordance rate was used as an assessment metric. Overall, the best performing tool was found to be HIBAG, with a concordance rate of 0.84, while the best performing reference panel was the H3Africa panel with a concordance rate of 0.62. Minimac4 (0.75) was shown to increase HLA-B allele imputation accuracy compared to HIBAG (0.71), SNP2HLA (0.51) and CookHLA (0.17). The H3Africa and Illumina Omni 2.5 array performances were comparable, showing that genotyping arrays have less influence on HLA imputation in West African populations. The findings show that using a larger population-specific reference panel and the HIBAG tool improves the accuracy of HLA imputation in West African populations. Author Summary: For studies that associate a particular HLA type to a phenotypic trait for instance HIV susceptibility or control, genotype imputation remains the main method for acquiring a larger sample size. Genotype imputation, process of inferring unobserved genotypes, is a statistical technique and thus deals with probabilities. Also, the HLA region is highly variable and therefore difficult to impute. In view of this, it is important to assess HLA imputation accuracy especially in African populations. This is because the African genome has high diversity, and such studies have hardly been conducted in African populations. This work highlights that using HIBAG imputation tool and a larger population-specific reference panel increases HLA imputation accuracy in an African population.

A genome-wide association study identifies distinct variants associated with pulmonary function among European and African ancestries from the UK Biobank.

Pulmonary function is an indicator of well-being, and pulmonary pathologies are the third major cause of death worldwide. We analysed the UK Biobank genome-wide association summary statistics of pulmonary function for Europeans and individuals of recent African descent to identify variants associated with the trait in the two ancestries. Here, we show 627 variants in Europeans and 3 in Africans associated with three pulmonary function parameters. In addition to the 110 variants in Europeans previously reported to be associated with phenotypes related to pulmonary function, we identify 279 novel loci, including an ISX intergenic variant rs369476290 on chromosome 22 in Africans. Remarkably, we find no shared variants among Africans and Europeans. Furthermore, enrichment analyses of variants separately for each ancestry background reveal significant enrichment for terms related to pulmonary phenotypes in Europeans but not Africans. Further analysis of studies of pulmonary phenotypes reveals that individuals of European background are disproportionally overrepresented in datasets compared to Africans, with the gap widening over the past five years. Our findings extend our understanding of the different variants that modify the pulmonary function in Africans and Europeans, a promising finding for future GWASs and medical studies.

Rationale, Design, and the Baseline Characteristics of the RHDGen (The Genetics of Rheumatic Heart Disease) Network Study†.

BACKGROUND: The genetics of rheumatic heart disease (RHDGen) Network was developed to assist the discovery and validation of genetic variations and biomarkers of risk for rheumatic heart disease (RHD) in continental Africans, as a part of the global fight to control and eradicate rheumatic fever/RHD. Thus, we describe the rationale and design of the RHDGen study, comprising participants from 8 African countries. METHODS: RHDGen screened potential participants using echocardiography, thereafter enrolling RHD cases and ethnically-matched controls for whom case characteristics were documented. Biological samples were collected for conducting genetic analyses, including a discovery case-control genome-wide association study (GWAS) and a replication trio family study. Additional biological samples were also collected, and processed, for the measurement of biomarker analytes and the biomarker analyses are underway. RESULTS: Participants were enrolled into RHDGen between December 2012 and March 2018. For GWAS, 2548 RHD cases and 2261 controls (3301 women [69%]; mean age [SD], 37 [16.3] years) were available. RHD cases were predominantly Black (66%), Admixed (24%), and other ethnicities (10%). Among RHD cases, 34% were asymptomatic, 26% had prior valve surgery, and 23% had atrial fibrillation. The trio family replication arm included 116 RHD trio probands and 232 parents. CONCLUSIONS: RHDGen presents a rare opportunity to identify relevant patterns of genetic factors and biomarkers in Africans that may be associated with differential RHD risk. Furthermore, the RHDGen Network provides a platform for further work on fully elucidating the causes and mechanisms associated with RHD susceptibility and development.

Consent Codes: Maintaining Consent in an Ever-expanding Open Science Ecosystem.

We previously proposed a structure for recording consent-based data use 'categories' and 'requirements' - Consent Codes - with a view to supporting maximum use and integration of genomic research datasets, and reducing uncertainty about permissible re-use of shared data. Here we discuss clarifications and subsequent updates to the Consent Codes (v4) based on new areas of application (e.g., the neurosciences, biobanking, H3Africa), policy developments (e.g., return of research results), and further practical considerations, including developments in automated approaches to consent management.

Computational approaches for network-based integrative multi-omics analysis.

Advances in omics technologies allow for holistic studies into biological systems. These studies rely on integrative data analysis techniques to obtain a comprehensive view of the dynamics of cellular processes, and molecular mechanisms. Network-based integrative approaches have revolutionized multi-omics analysis by providing the framework to represent interactions between multiple different omics-layers in a graph, which may faithfully reflect the molecular wiring in a cell. Here we review network-based multi-omics/multi-modal integrative analytical approaches. We classify these approaches according to the type of omics data supported, the methods and/or algorithms implemented, their node and/or edge weighting components, and their ability to identify key nodes and subnetworks. We show how these approaches can be used to identify biomarkers, disease subtypes, crosstalk, causality, and molecular drivers of physiological and pathological mechanisms. We provide insight into the most appropriate methods and tools for research questions as showcased around the aetiology and treatment of COVID-19 that can be informed by multi-omics data integration. We conclude with an overview of challenges associated with multi-omics network-based analysis, such as reproducibility, heterogeneity, (biological) interpretability of the results, and we highlight some future directions for network-based integration.

A pilot study to show that asymptomatic sexually transmitted infections alter the foreskin epithelial proteome.

There is limited data on the role of asymptomatic STIs (aSTIs) on the risk of human immunodeficiency virus (HIV) acquisition in the male genital tract (MGT). The impact of foreskin removal on lowering HIV acquisition is well described, but molecular events leading to HIV acquisition are unclear. Here, in this pilot study, we show that asymptomatic urethral infection with Chlamydia trachomatis (CT) significantly impacts the foreskin proteome composition. We developed and optimized a shotgun liquid chromatography coupled tandem mass spectrometry (MS)-based proteomics approach and utilized this on foreskins collected at medical male circumcision (MMC) from 16 aSTI+ men and 10 age-matched STI- controls. We used a novel bioinformatic metaproteomic pipeline to detect differentially expressed (DE) proteins. Gene enrichment ontology analysis revealed proteins associated with inflammatory and immune activation function in both inner and outer foreskin from men with an aSTI. Neutrophil activation/degranulation and viral-evasion proteins were significantly enriched in foreskins from men with aSTI, whereas homotypic cell-cell adhesion proteins were enriched in foreskin tissue from men without an aSTI. Collectively, our data show that asymptomatic urethral sexually transmitted infections result in profound alterations in epithelial tissue that are associated with depletion of barrier integrity and immune activation.

Genome-wide SNP analysis of Plasmodium falciparum shows differentiation at drug-resistance-associated loci among malaria transmission settings in southern Mali.

Plasmodium falciparum malaria cases in Africa represent over 90% of the global burden with Mali being amongst the 11 highest burden countries that account for 70% of this annual incidence. The persistence of P. falciparum despite massive global interventions is because of its genetic diversity that drives its ability to adapt to environmental changes, develop resistance to drugs, and evade the host immune system. Knowledge on P. falciparum genetic diversity across populations and intervention landscape is thus critical for the implementation of new strategies to eliminate malaria. This study assessed genetic variation with 12,177 high-quality SNPs from 830 Malian P. falciparum isolates collected between 2007 and 2017 from seven locations. The complexity of infections remained high, varied between sites, and showed a trend toward overall decreasing complexity over the decade. Though there was no significant substructure, allele frequencies varied geographically, partly driven by temporal variance in sampling, particularly for drug resistance and antigen loci. Thirty-two mutations in known drug resistance markers (pfcrt, pfdhps, pfdhfr, pfmdr1, pfmdr2, and pfk13) attained a frequency of at least 2% in the populations. SNPs within and around the major markers of resistance to quinolines (pfmdr1 and pfcrt) and antifolates (pfdhfr and pfdhps) varied temporally and geographically, with strong linkage disequilibrium and signatures of directional selection in the genome. These geo-temporal populations also differentiated at alleles in immune-related loci, including, protein E140, pfsurfin8, pfclag8, and pfceltos, as well as pftrap, which showed signatures of haplotype differentiation between populations. Several regions across the genomes, including five known drug resistance loci, showed signatures of differential positive selection. These results suggest that drugs and immune pressure are dominant selective forces against P. falciparum in Mali, but their effect on the parasite genome varies temporally and spatially. Interventions interacting with these genomic variants need to be routinely evaluated as malaria elimination strategies are implemented.

Optimising the reach of mobile health messaging programmes: an analysis of system generated data for the Kilkari programme across 13 states in India.

Kilkari is an outbound service that makes weekly, stage-based, prerecorded calls about reproductive, maternal, neonatal and child health directly to families' mobile phones, starting from the second trimester of pregnancy and until the child is 1 year old. Since its initiation in 2012-2013, Kilkari has scaled to 13 states across India. In this analysis article, we explored the subscriber's journey from entry to programme to engagement with calls. Data sources included call data records and household survey data from the 2015 National Family Health Survey. In 2018, of the 13.6 million records received by MOTECH, the technology platform that powers Kilkari, 9.5 million (~70%) were rejected and 4.1 million new subscribers were created. Overall, 21% of pregnant women across 13 states were covered by the programme in 2018, with West Bengal and Himachal Pradesh reaching a coverage of over 50%. Among new subscriptions in 2018, 63% were subscribed during pregnancy and 37% after childbirth. Of these, over 80% were ever reached by Kilkari calls and 39% retained in the programme. The main causes for deactivation of subscribers from the system were low listenership and calls going unanswered for six continuous weeks. Globally, Kilkari is the largest maternal mobile messaging programme of its kind in terms of number of subscribers but the coverage among pregnant women remains low. While call reach appears to be on the higher side, subscriber retention is low; this highlights broader challenges with providing mobile health services at scale across India.

Strongylopus grayii tadpole blastema extract exerts cytotoxic effects on embryonal rhabdomyosarcoma cells.

Amphibians have regenerative capacity and are resistant to developing cancer. This suggests that the developing blastema, located at the tissue regeneration site, may secrete anti-cancer factors. Here, we investigate the anti-cancer potential of tadpole tail blastema extracts (TAD) from the stream frog, Strongylopus grayii, in embryonal rhabdomyosarcoma (ERMS) cells. ERMS originates in skeletal muscle tissue and is a common pediatric soft tissue sarcoma. We show using MTT assays that TAD inhibited ERMS cell viability in a concentration-dependent manner, and phase contrast/fluorescent microscopy revealed that it induced morphological markers of senescence and apoptosis. Western blotting showed that this was associated with DNA damage (γH2AX) and activation of the p38/MAPK stress signaling pathway as well as molecular markers of senescence (p16INK4a), apoptosis (cleaved PARP), and inhibition of cell cycle promoters (cyclin A, CDK2, and cyclin B1). Furthermore, proteomics followed by gene ontology analyses showed that TAD treatment inhibited known tumor promoters and proteins required for cancer cell survival. Lastly, using the LINCS drug perturbation library, we show that there is an overlap between the proteomics signature induced by TAD and common anti-cancer drugs. Taken together, this study provides novel evidence that TAD exhibits cytotoxicity in ERMS cells.

Lactic acid from vaginal microbiota enhances cervicovaginal epithelial barrier integrity by promoting tight junction protein expression.

BACKGROUND: Women with a cervicovaginal microbiota dominated by Lactobacillus spp. are at reduced risk of acquiring sexually transmitted infections including HIV, but the biological mechanisms involved remain poorly defined. Here, we performed metaproteomics on vaginal swab samples from young South African women (n = 113) and transcriptomics analysis of cervicovaginal epithelial cell cultures to examine the ability of lactic acid, a metabolite produced by cervicovaginal lactobacilli, to modulate genital epithelial barrier function. RESULTS: Compared to women with Lactobacillus-depleted microbiota, women dominated by vaginal lactobacilli exhibit higher abundance of bacterial lactate dehydrogenase, a key enzyme responsible for lactic acid production, which is independently associated with an increased abundance of epithelial barrier proteins. Physiological concentrations of lactic acid enhance epithelial cell culture barrier integrity and increase intercellular junctional molecule expression. CONCLUSIONS: These findings reveal a novel ability of vaginal lactic acid to enhance genital epithelial barrier integrity that may help prevent invasion by sexually transmitted pathogens. Video abstract.