A Decade of GigaScience: Women in Science: Past, Present, and Future

Over the last decade, women have made decisive advances in increasing equality in science, technology, engineering, and medicine (STEM), but they still do not rival that of men. Many mechanisms to reduce gender discrimination have been addressed; however, little to nothing has been done to tackle the differences in the amount of time women spend on responsibilities at home. This has never been more apparent than during the COVID-19 pandemic. After a decade of advances promoting women, the last two years have seen these advances halted, and the long-term implications for women in STEM will be substantial. Moving forward, career advancement and funding mechanisms need to be adjusted to not just help women catch up, but to become a permanent support mechanism for women in the workplace. The higher amount of responsibilities at home and lack of support for women is not reserved for times of international upheaval: it has just become more apparent.

Single-cell RNA sequencing technologies and applications: A brief overview

Single-cell RNA sequencing (scRNA-seq) technology has become the state-of-the-art approach for unravelling the heterogeneity and complexity of RNA transcripts within individual cells, as well as revealing the composition of different cell types and functions within highly organized tissues/organs/organisms. Since its first discovery in 2009, studies based on scRNA-seq provide massive information across different fields making exciting new discoveries in better understanding the composition and interaction of cells within humans, model animals and plants. In this review, we provide a concise overview about the scRNA-seq technology, experimental and computational procedures for transforming the biological and molecular processes into computational and statistical data. We also provide an explanation of the key technological steps in implementing the technology. We highlight a few examples on how scRNA-seq can provide unique information for better understanding health and diseases. One important application of the scRNA-seq technology is to build a better and high-resolution catalogue of cells in all living organism, commonly known as atlas, which is key resource to better understand and provide a solution in treating diseases. While great promises have been demonstrated with the technology in all areas, we further highlight a few remaining challenges to be overcome and its great potentials in transforming current protocols in disease diagnosis and treatment.

Composition and Dynamics of H1N1 and H7N9 Influenza A Virus Quasispecies in a Co-infected Patient Analyzed by Single Molecule Sequencing Technology

A human co-infected with H1N1 and H7N9 subtypes influenza A virus (IAV) causes a complex infectious disease. The identification of molecular-level variations in composition and dynamics of IAV quasispecies will help to understand the pathogenesis and provide guidance for precision medicine treatment. In this study, using single-molecule real-time sequencing (SMRT) technology, we successfully acquired full-length IAV genomic sequences and quantified their genotypes abundance in serial samples from an 81-year-old male co-infected with H1N1 and H7N9 subtypes IAV. A total of 26 high diversity nucleotide loci was detected, in which the A-G base transversion was the most abundant substitution type (67 and 64%, in H1N1 and H7N9, respectively). Seven significant amino acid variations were detected, such as NA:H275Y and HA: R222K in H1N1 as well as PB2:E627K and NA: K432E in H7N9, which are related to viral drug-resistance or mammalian adaptation. Furtherly, we retrieved 25 H1N1 and 22 H7N9 genomic segment haplotypes from the eight samples based on combining high-diversity nucleotide loci, which provided a more concise overview of viral quasispecies composition and dynamics. Our approach promotes the popularization of viral quasispecies analysis in a complex infectious disease, which will boost the understanding of viral infections, pathogenesis, evolution, and precision medicine.

The Dynamic Changes of Transcription Factors During the Development Processes of Human Biparental and Uniparental Embryos

Previous studies have revealed that transcription factors (TFs) play important roles in biparental (BI) early human embryogenesis. However, the contribution of TFs during early uniparental embryo development is still largely unknown. Here we systematically studied the expression profiles of transcription factors in early embryonic development and revealed the dynamic changes of TFs in human biparental and uniparental embryogenesis by single-cell RNA sequencing (scRNA-seq). In general, the TF expression model of uniparental embryos showed a high degree of conformity with biparental embryos. The detailed network analysis of three different types of embryos identified that 10 out of 17 hub TFs were shared or specifically owned, such as ZNF480, ZNF581, PHB, and POU5F1, were four shared TFs, ZFN534, GTF3A, ZNF771, TEAD4, and LIN28A, were androgenic (AG) specific TFs, and ZFP42 was the only one parthenogenetic (PG) specific TF. All the four shared TFs were validated using human embryonic stem cell (hESC) differentiation experiments; most of their target genes are responsible for stem cell maintenance and differentiation. We also found that Zf-C2H2, HMG, and MYB were three dominant transcription factor families that appeared in early embryogenesis. Altogether, our work provides a comprehensive regulatory framework and better understanding of TF function in human biparental and uniparental embryogenesis.

Association between body mass index and prevalence of multimorbidity: a cross-sectional study

OBJECTIVES

To explore the prevalence of multimorbidity in Serbia according to sex and body mass index (BMI) categories, and to examine the association between BMI and multimorbidity. In addition, this study examined the relationships between the main demographic and socio-economic characteristics of the population (age, settlement, education) and multimorbidity.

STUDY DESIGN

Secondary analysis of data from the 2013 Serbian National Health Survey.

METHODS

This study analyzed data from 13,103 participants aged ≥20 years with BMI data. The associations between BMI, age, education and multimorbidity were analyzed by multivariate logistic regression.

RESULTS

The overall prevalence of multimorbidity was higher than the overall prevalence of a single disease (26.9% vs 20.7%). The proportion of participants who reported two or more chronic diseases increased with each BMI category in both sexes, reaching the highest values in obese category III. Odds ratios (ORs) for the prevalence in all morbidity groups increased gradually with BMI category, and the highest OR values were found in obese category III. Males of obese category III were seven times more likely to have multimorbidity [OR 7.2, 95% confidence interval (CI) 4.2-12.6] than males of normal weight, whereas females of obese category III were nine and a half times more likely to have multimorbidity (OR 9.5, 95% CI 4.0-22.4) than females of normal weight. In the multivariable analysis, age (both sexes), low and middle level of education (males), and rural settlement and low level of education (females) were found to be predictors of multimorbidity.

CONCLUSIONS

This study found positive associations between obesity and multimorbidity and between overweight and multimorbidity. Recognizing these associations is of great importance from both clinical and public health perspectives because this could lead to an integrated approach for patients.