Large-Scale Meta-Longitudinal Microbiome Data with a Known Batch Factor.

Data contamination in meta-approaches where multiple biological samples are combined considerably affects the results of subsequent downstream analyses, such as differential abundance tests comparing multiple groups at a fixed time point. Little has been thoroughly investigated regarding the impact of the lurking variable of various batch sources, such as different days or different laboratories, in more complicated time series experimental designs, for instance, repeatedly measured longitudinal data and metadata. We highlight that the influence of batch factors is significant on subsequent downstream analyses, including longitudinal differential abundance tests, by performing a case study of microbiome time course data with two treatment groups and a simulation study of mimic microbiome longitudinal counts.

Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data.

Dynamic studies in time course experimental designs and clinical approaches have been widely used by the biomedical community. These applications are particularly relevant in stimuli-response models under environmental conditions, characterization of gradient biological processes in developmental biology, identification of therapeutic effects in clinical trials, disease progressive models, cell-cycle, and circadian periodicity. Despite their feasibility and popularity, sophisticated dynamic methods that are well validated in large-scale comparative studies, in terms of statistical and computational rigor, are less benchmarked, comparing to their static counterparts. To date, a number of novel methods in bulk RNA-Seq data have been developed for the various time-dependent stimuli, circadian rhythms, cell-lineage in differentiation, and disease progression. Here, we comprehensively review a key set of representative dynamic strategies and discuss current issues associated with the detection of dynamically changing genes. We also provide recommendations for future directions for studying non-periodical, periodical time course data, and meta-dynamic datasets.

Dominant ER Stress-Inducing WFS1 Mutations Underlie a Genetic Syndrome of Neonatal/Infancy-Onset Diabetes, Congenital Sensorineural Deafness, and Congenital Cataracts.

Neonatal diabetes is frequently part of a complex syndrome with extrapancreatic features: 18 genes causing syndromic neonatal diabetes have been identified to date. There are still patients with neonatal diabetes who have novel genetic syndromes. We performed exome sequencing in a patient and his unrelated, unaffected parents to identify the genetic etiology of a syndrome characterized by neonatal diabetes, sensorineural deafness, and congenital cataracts. Further testing was performed in 311 patients with diabetes diagnosed before 1 year of age in whom all known genetic causes had been excluded. We identified 5 patients, including the initial case, with three heterozygous missense mutations in WFS1 (4/5 confirmed de novo). They had diabetes diagnosed before 12 months (2 before 6 months) (5/5), sensorineural deafness diagnosed soon after birth (5/5), congenital cataracts (4/5), and hypotonia (4/5). In vitro studies showed that these WFS1 mutations are functionally different from the known recessive Wolfram syndrome-causing mutations, as they tend to aggregate and induce robust endoplasmic reticulum stress. Our results establish specific dominant WFS1 mutations as a cause of a novel syndrome including neonatal/infancy-onset diabetes, congenital cataracts, and sensorineural deafness. This syndrome has a discrete pathophysiology and differs genetically and clinically from recessive Wolfram syndrome.