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Type 2 diabetes mellitus (T2D) is a growing health problem, but little is known about its early disease stages, its effects on biological processes or the transition to clinical T2D. To understand the earliest stages of T2D better, we obtained samples from 106 healthy individuals and individuals with prediabetes over approximately four years and performed deep profiling of transcriptomes, metabolomes, cytokines, and proteomes, as well as changes in the microbiome. This rich longitudinal data set revealed many insights: first, healthy profiles are distinct among individuals while displaying diverse patterns of intra- and/or inter-personal variability. Second, extensive host and microbial changes occur during respiratory viral infections and immunization, and immunization triggers potentially protective responses that are distinct from responses to respiratory viral infections. Moreover, during respiratory viral infections, insulin-resistant participants respond differently than insulin-sensitive participants. Third, global co-association analyses among the thousands of profiled molecules reveal specific host–microbe interactions that differ between insulin-resistant and insulin-sensitive individuals. Last, we identified early personal molecular signatures in one individual that preceded the onset of T2D, including the inflammation markers interleukin-1 receptor agonist (IL-1RA) and high-sensitivity C-reactive protein (CRP) paired with xenobiotic-induced immune signalling. Our study reveals insights into pathways and responses that differ between glucose-dysregulated and healthy individuals during health and disease and provides an open-access data resource to enable further research into healthy, prediabetic and T2D states.

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Endometrial injury in the cycle preceding the stimulation cycle improved implantation and pregnancy rates during ICSI. CLINICALTRIALS.GOV: NCT02660125.

Early detection of infectious diseases is crucial for reducing transmission and facilitating early intervention. In this study, we built a real-time smartwatch-based alerting system that detects aberrant physiological and activity signals (heart rates and steps) associated with the onset of early infection and implemented this system in a prospective study. In a cohort of 3,318 participants, of whom 84 were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), this system generated alerts for pre-symptomatic and asymptomatic SARS-CoV-2 infection in 67 (80%) of the infected individuals. Pre-symptomatic signals were observed at a median of 3 days before symptom onset. Examination of detailed survey responses provided by the participants revealed that other respiratory infections as well as events not associated with infection, such as stress, alcohol consumption and travel, could also trigger alerts, albeit at a much lower mean frequency (1.15 alert days per person compared to 3.42 alert days per person for coronavirus disease 2019 cases). Thus, analysis of smartwatch signals by an online detection algorithm provides advance warning of SARS-CoV-2 infection in a high percentage of cases. This study shows that a real-time alerting system can be used for early detection of infection and other stressors and employed on an open-source platform that is scalable to millions of users.

Accurate prediction of the host phenotype from a metagenomic sample and identification of the associated microbial markers are important in understanding potential host-microbiome interactions related to disease initiation and progression. We introduce PopPhy-CNN, a novel convolutional neural network (CNN) learning framework that effectively exploits phylogenetic structure in microbial taxa for host phenotype prediction. Our approach takes an input format of a 2D matrix representing the phylogenetic tree populated with the relative abundance of microbial taxa in a metagenomic sample. This conversion empowers CNNs to explore the spatial relationship of the taxonomic annotations on the tree and their quantitative characteristics in metagenomic data. We show the competitiveness of our model compared to other available methods using nine metagenomic datasets of moderate size for binary classification. With synthetic and biological datasets, we show the superior and robust performance of our model for multiclass classification. Furthermore, we design a novel scheme for feature extraction from the learned CNN models and demonstrate improved performance when the extracted features. PopPhy-CNN is a practical deep learning framework for the prediction of host phenotype with the ability of facilitating the retrieval of predictive microbial taxa.

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Recent studies have established that the human urine contains a complex microbiome, including a virome about which little is known. Following immunosuppression in kidney transplant patients, BK polyomavirus (BKV) has been shown to induce nephropathy (BKVN), decreasing graft survival. In this study we investigated the urine virome profile of BKV+ and BKV− kidney transplant recipients. Virus-like particles were stained to confirm the presence of VLP in the urine samples. Metagenomic DNA was purified, and the virome profile was analyzed using metagenomic shotgun sequencing. While the BK virus was predominant in the BKV+ group, it was also found in the BKV− group patients. Additional viruses were also detected in all patients, notably including JC virus (JCV) and Torque teno virus (TTV) and interestingly, we detected multiple subtypes of the BKV, JCV and TTV. Analysis of the BKV subtypes showed that nucleotide polymorphisms were detected in the VP1, VP2 and Large T Antigen proteins, suggesting potential functional effects for enhanced pathogenicity. Our results demonstrate a complex urinary virome in kidney transplant patients with multiple viruses with several distinct subtypes warranting further analysis of virus subtypes in immunosuppressed hosts.