Identification of pregnancies at risk of preterm birth (PTB), the leading cause of newborn deaths, remains challenging given the syndromic nature of the disease. We report a longitudinal multi-omics study coupled with a DREAM challenge to develop predictive models of PTB. The findings indicate that whole-blood gene expression predicts ultrasound-based gestational ages in normal and complicated pregnancies (r = 0.83) and, using data collected before 37 weeks of gestation, also predicts the delivery date in both normal pregnancies (r = 0.86) and those with spontaneous preterm birth (r = 0.75). Based on samples collected before 33 weeks in asymptomatic women, our analysis suggests that expression changes preceding preterm prelabor rupture of the membranes are consistent across time points and cohorts and involve leukocyte-mediated immunity. Models built from plasma proteomic data predict spontaneous preterm delivery with intact membranes with higher accuracy and earlier in pregnancy than transcriptomic models (AUROC = 0.76 versus AUROC = 0.6 at 27–33 weeks of gestation).

Asthma is a complex, heterogeneous disorder with similar presenting symptoms but with varying underlying pathologies. Exhaled breath condensate (EBC) is a relatively unexplored matrix which reflects the signatures of respiratory epithelium, but is difficult to normalize for dilution. Here we explored whether internally normalized global NMR spectrum patterns, combined with machine learning, could be useful for diagnostics or endotype discovery. Nuclear magnetic resonance (NMR) spectroscopy of EBC was performed in 89 asthmatic subjects from a prospective cohort and 20 healthy controls. A random forest classifier was built to differentiate between asthmatics and healthy controls. Clustering of the spectra was done using k-means to identify potential endotypes. NMR spectra of the EBC could differentiate between asthmatics and healthy controls with 80% sensitivity and 75% specificity. Unsupervised clustering within the asthma group resulted in three clusters (n = 41,11, and 9). Cluster 1 patients had lower long-term exacerbation scores, when compared with other two clusters. Cluster 3 patients had lower blood eosinophils and higher neutrophils, when compared with other two clusters with a strong family history of asthma. Asthma clusters derived from NMR spectra of EBC show important clinical and chemical differences, suggesting this as a useful tool in asthma endotype-discovery.

In Ayurveda system of medicine individuals are classified into seven constitution types, “Prakriti”, for assessing disease susceptibility and drug responsiveness. Prakriti evaluation involves clinical examination including questions about physiological and behavioural traits. A need was felt to develop models for accurately predicting Prakriti classes that have been shown to exhibit molecular differences. The present study was carried out on data of phenotypic attributes in 147 healthy individuals of three extreme Prakriti types, from a genetically homogeneous population of Western India. Unsupervised and supervised machine learning approaches were used to infer inherent structure of the data, and for feature selection and building classification models for Prakriti respectively. These models were validated in a North Indian population. Unsupervised clustering led to emergence of three natural clusters corresponding to three extreme Prakriti classes. The supervised modelling approaches could classify individuals, with distinct Prakriti types, in the training and validation sets. This study is the first to demonstrate that Prakriti types are distinct verifiable clusters within a multidimensional space of multiple interrelated phenotypic traits. It also provides a computational framework for predicting Prakriti classes from phenotypic attributes. This approach may be useful in precision medicine for stratification of endophenotypes in healthy and diseased populations.

Growing evidence suggests the importance of lipid metabolism in pathogenesis of tuberculosis. Neutral lipids form the majority of lipids in a caseous granuloma, pathology characteristic of tuberculosis. Cytosolic lipid droplets (LDs) of macrophages form the store house of these lipids, and have been demonstrated to contribute to the inflammatory response to infection. The proteome of lipid droplets reflects the mechanisms of lipid metabolism active under a condition. However, infection induced changes in the proteome of these dynamic organelles remains elusive. Here we employed quantitative proteomics to identify alterations induced upon infection with live Mycobacterium tuberculosis (Mtb) in comparison with heat killed bacilli or uninfected macrophages. We found increased abundance of proteins coupled with lipid metabolism, protein synthesis and vesicular transport function in LDs upon infection with live Mtb. Using biochemical methods and microscopy, we validated ARL8B to be increased on the lipid droplet surface of live Mtb infected macrophages and that ARL8B is a bonafide LD protein. This study provides the first proteomic evidence that the dynamic responses to infection also encompass changes at the level of LDs. This information will be important in understanding how Mtb manipulates lipid metabolism and defence mechanisms of the host macrophage.

The role of the telomere repeat–binding factor 2 (TRF2) in telomere maintenance is well established. However, recent findings suggest that TRF2 also functions outside telomeres, but relatively little is known about this function. Herein, using genome-wide ChIP-Seq assays of TRF2-bound chromatin from HT1080 fibrosarcoma cells, we identified thousands of TRF2-binding sites within the extra-telomeric genome. In light of this observation, we asked how TRF2 occupancy is organized within the genome. Interestingly, we found that extra-telomeric TRF2 sites throughout the genome are enriched in potential G-quadruplex–forming DNA sequences. Furthermore, we validated TRF2 occupancy at several promoter G-quadruplex motifs, which did adopt quadruplex forms in solution. TRF2 binding altered expression from and the epigenetic state of several target promoters, indicated by histone modifications resulting in transcriptional repression of eight of nine genes investigated here. Furthermore, TRF2 occupancy and target gene expression were also sensitive to the well-known intracellular G-quadruplex–binding ligand 360A. Together, these results reveal an extensive genome-wide association of TRF2 outside telomeres and that it regulates gene expression in a G-quadruplex–dependent fashion.