The murine transcriptome reveals global aging nodes with organ-specific phase and amplitude, bioRxiv, 2019-06-07

Aging is the single greatest cause of disease and death worldwide, and so understanding the associated processes could vastly improve quality of life. While the field has identified major categories of aging damage such as altered intercellular communication, loss of proteostasis, and eroded mitochondrial function1, these deleterious processes interact with extraordinary complexity within and between organs. Yet, a comprehensive analysis of aging dynamics organism-wide is lacking. Here we performed RNA-sequencing of 17 organs and plasma proteomics at 10 ages across the mouse lifespan. We uncover previously unknown linear and non-linear expression shifts during aging, which cluster in strikingly consistent trajectory groups with coherent biological functions, including extracellular matrix regulation, unfolded protein binding, mitochondrial function, and inflammatory and immune response. Remarkably, these gene sets are expressed similarly across tissues, differing merely in age of onset and amplitude. Especially pronounced is widespread immune cell activation, detectable first in white adipose depots in middle age. Single-cell RNA-sequencing confirms the accumulation of adipose T and B cells, including immunoglobulin J-expressing plasma cells, which also accrue concurrently across diverse organs. Finally, we show how expression shifts in distinct tissues are highly correlated with corresponding protein levels in plasma, thus potentially contributing to aging of the systemic circulation. Together, these data demonstrate a similar yet asynchronous inter- and intra-organ progression of aging, thereby providing a foundation to track systemic sources of declining health at old age.

biorxiv genomics 100-200-users 2019

Genetics of 38 blood and urine biomarkers in the UK Biobank, bioRxiv, 2019-06-05

AbstractClinical laboratory tests are a critical component of the continuum of care and provide a means for rapid diagnosis and monitoring of chronic disease. In this study, we systematically evaluated the genetic basis of 38 blood and urine laboratory tests measured in 358,072 participants in the UK Biobank and identified 1,857 independent loci associated with at least one laboratory test, including 488 large-effect protein truncating, missense, and copy-number variants. We tested these loci for enrichment in specific single cell types in kidney, liver, and pancreas relevant to disease aetiology. We then causally linked the biomarkers to medically relevant phenotypes through genetic correlation and Mendelian randomization. Finally, we developed polygenic risk scores (PRS) for each biomarker and built multi-PRS models using all 38 PRSs simultaneously. We found substantially improved prediction of incidence in FinnGen (n=135,500) with the multi-PRS relative to single-disease PRSs for renal failure, myocardial infarction, liver fat percentage, and alcoholic cirrhosis. Together, our results show the genetic basis of these biomarkers, which tissues contribute to the biomarker function, the causal influences of the biomarkers, and how we can use this to predict disease.

biorxiv genetics 100-200-users 2019

A Simple Deep Learning Approach for Detecting Duplications and Deletions in Next-Generation Sequencing Data, bioRxiv, 2019-06-03

AbstractCopy number variants (CNV) are associated with phenotypic variation in several species. However, properly detecting changes in copy numbers of sequences remains a difficult problem, especially in lower quality or lower coverage next-generation sequencing data. Here, inspired by recent applications of machine learning in genomics, we describe a method to detect duplications and deletions in short-read sequencing data. In low coverage data, machine learning appears to be more powerful in the detection of CNVs than the gold-standard methods or coverage estimation alone, and of equal power in high coverage data. We also demonstrate how replicating training sets allows a more precise detection of CNVs, even identifying novel CNVs in two genomes previously surveyed thoroughly for CNVs using long read data.Available at <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpsgithub.comtomh1llldudeml>httpsgithub.comtomh1llldudeml<jatsext-link>

biorxiv bioinformatics 100-200-users 2019

Alignment and mapping methodology influence transcript abundance estimation, bioRxiv, 2019-06-03

AbstractThe accuracy of transcript quantification using RNA-seq data depends on many factors, such as the choice of alignment or mapping method and the quantification model being adopted. While the choice of quantification model has been shown to be important, considerably less attention has been given to comparing the effect of various read alignment approaches on quantification accuracy.We investigate the effect of mapping and alignment on the accuracy of transcript quantification in both simulated and experimental data, as well as the effect on subsequent differential gene expression analysis. We observe that, even when the quantification model itself is held fixed, the effect of choosing a different alignment methodology, or aligning reads using different parameters, on quantification estimates can sometimes be large, and can affect downstream analyses as well. These effects can go unnoticed when assessment is focused too heavily on simulated data, where the alignment task is often simpler than in experimentally-acquired samples. We discuss best practices regarding alignment for the purposes of quantification, and also introduce a new hybrid alignment methodology, called selective alignment (SA), to overcome the shortcomings of lightweight approaches without incurring the computational cost of traditional alignment.

biorxiv bioinformatics 100-200-users 2019

Unconventional cell division cycles from marine-derived yeasts, bioRxiv, 2019-06-02

AbstractFungi have been found in every marine habitat that has been explored, however, the diversity and functions of fungi in the ocean are poorly understood. In this study, fungi were cultured from the marine environment in the vicinity of Woods Hole, MA, USA including from plankton, sponge and coral. Our sampling resulted in 36 unique species across 20 genera. We observed many isolates by time-lapse differential interference contrast (DIC) microscopy and analyzed modes of growth and division. Several black yeasts displayed highly unconventional cell division cycles compared to those of traditional model yeast systems. Black yeasts have been found in habitats inhospitable to other life and are known for halotolerance, virulence, and stress-resistance. We find that this group of yeasts also shows remarkable plasticity in terms of cell size control, modes of cell division, and cell polarity. Unexpected behaviors include division through a combination of fission and budding, production of multiple simultaneous buds, and cell division by sequential orthogonal septations. These marine-derived yeasts reveal alternative mechanisms for cell division cycles that seem likely to expand the repertoire of rules established from classic model system yeasts.

biorxiv cell-biology 100-200-users 2019

3D RNA-seq - a powerful and flexible tool for rapid and accurate differential expression and alternative splicing analysis of RNA-seq data for biologists, bioRxiv, 2019-06-01

AbstractRNA-sequencing (RNA-seq) analysis of gene expression and alternative splicing should be routine and robust but is often a bottleneck for biologists because of different and complex analysis programs and reliance on skilled bioinformaticians to perform the analysis. To overcome these issues, we have developed the “3D RNA-seq” App, an R shiny App which provides an easy-to-use, flexible and powerful tool for the three-way differential analysis Differential Expression (DE), Differential Alternative Splicing (DAS) and Differential Transcript Usage (DTU) of RNA-seq data. The full analysis is extremely rapidand can be done within hours. The program integrates Limma, a state-of-the-art, highly rated differential expression analysis tool and adopts best practice for RNA-seq analysis. It runs the analysis through a user-friendly graphical interface, can handle complex experimental designs, allows user setting of statistical parameters, visualizes the results through graphics and tables, and generates publication quality figures such as heat-maps, expression profiles and GO enrichment plots. The utility of 3D RNA-seq is illustrated by analysis of Arabidopsis and mouse RNA-seq data. The program is designed to be run by biologists with minimal bioinformatics experience (or by bioinformaticians) allowing lab scientists to take control of the analysis of their RNA-seq data.

biorxiv bioinformatics 100-200-users 2019