Global, cell non-autonomous gene regulation drives individual lifespan among isogenic C. elegans

Across species, lifespan is highly variable among individuals within a population. Even genetically identica

Loss of Nmp4 optimizes osteogenic metabolism and secretion to enhance bone quality

A goal of osteoporosis therapy is to restore lost bone with structurally sound tissue. Mice lacking the transcription factor Nuclear Matrix Protein 4 (Nmp4, Zfp384, Ciz, ZNF384) respond to several classes of osteoporosis drugs with enhanced bone formation compared to wild type (WT) animals. Nmp4-/- mesenchymal stem/progenitor cells (MSPCs) exhibit an accelerated and enhanced mineralization during osteoblast differentiation. To address the mechanisms underlying this hyper-anabolic phenotype, we carried out RNA-sequencing and molecular and cellular analyses of WT and Nmp4-/- MSPCs during osteogenesis to define pathways and mechanisms associated with elevated matrix production. We determined that Nmp4 has a broad impact on the transcriptome during osteogenic differentiation, contributing to the expression of over 5,000 genes. Phenotypic anchoring of transcriptional data was performed for the hypothesis-testing arm through analysis of cell metabolism, protein synthesis and secretion, and bone material properties. Mechanistic studies confirmed that Nmp4-/- MSPCs exhibited an enhanced capacity for glycolytic conversion- a key step in bone anabolism. Nmp4-/- cells showed elevated collagen translation and secretion. Expression of matrix genes that contribute to bone material-level mechanical properties were elevated in Nmp4-/- cells, an observation that was supported by biomechanical testing of bone samples from Nmp4-/- and WT mice. We conclude that loss of Nmp4 increases the magnitude of glycolysis upon the metabolic switch, which fuels the conversion of the osteoblast into a super-secretor of matrix resulting in more bone with improvements in intrinsic quality

Clean Low-Biomass Procedures and Their Application to Ancient Ice Core Microorganisms

Microorganisms in glacier ice provide tens to hundreds of thousands of years archive for a changing climate and microbial responses to it. Analyzing ancient ice is impeded by technical issues, including limited ice, low biomass, and contamination. While many approaches have been evaluated and advanced to remove contaminants on ice core surfaces, few studies leverage modern sequencing to establish in silico decontamination protocols for glacier ice. Here we sought to apply such “clean” sampling techniques with in silico decontamination approaches used elsewhere to investigate microorganisms archived in ice at ~41 (D41, ~20,000 years) and ~49 m (D49, ~30,000 years) depth in an ice core (GS3) from the summit of the Guliya ice cap in the northwestern Tibetan Plateau. Four “background” controls were established – a co-processed sterile water artificial ice core, two air samples collected from the ice processing laboratories, and a blank, sterile water sample – and used to assess contaminant microbial diversity and abundances. Amplicon sequencing revealed 29 microbial genera in these controls, but quantitative PCR showed that the controls contained about 50–100-times less 16S DNA than the glacial ice samples. As in prior work, we interpreted these low-abundance taxa in controls as “contaminants” and proportionally removed them in silico from the GS3 ice amplicon data. Because of the low biomass in the controls, we also compared prokaryotic 16S DNA amplicons from pre-amplified (by re-conditioning PCR) and standard amplicon sequencing, and found the resulting microbial profiles to be repeatable and nearly identical. Ecologically, the contaminant-controlled ice microbial profiles revealed significantly different microorganisms across the two depths in the GS3 ice core, which is consistent with changing climate, as reported for other glacier ice samples. Many GS3 ice core genera, including Methylobacterium, Sphingomonas, Flavobacterium, Janthinobacterium, Polaromonas, and Rhodobacter, were also abundant in previously studied ice cores, which suggests wide distribution across glacier environments. Together these findings help further establish “clean” procedures for studying low-biomass ice microbial communities and contribute to a baseline understanding of microorganisms archived in glacier ice

AMP-Activated Protein Kinase Directly Phosphorylates and Destabilizes Hedgehog Pathway Transcription Factor GLI1 in Medulloblastoma

The Hedgehog (Hh) pathway regulates cell differen- tiation and proliferation during development by controlling the Gli transcription factors. Cell fate de- cisions and progression toward organ and tissue maturity must be coordinated, and how an energy sensor regulates the Hh pathway is not clear. AMP- activated protein kinase (AMPK) is an important sensor of energy stores and controls protein synthe- sis and other energy-intensive processes. AMPK is directly responsive to intracellular AMP levels, inhib- iting a wide range of cell activities if ATP is low and AMP is high. Thus, AMPK can affect development by influencing protein synthesis and other processes needed for growth and differentiation. Activation of AMPK reduces GLI1 protein levels and stability, thus blocking Sonic-hedgehog-induced transcrip- tional activity. AMPK phosphorylates GLI1 at serines 102 and 408 and threonine 1074. Mutation of these three sites into alanine prevents phosphorylation by AMPK. This leads to increased GLI1 protein stability, transcriptional activity, and oncogenic potency

Genetic overlap between diagnostic subtypes of ischemic stroke

Background and Purpose: Despite moderate heritability, the phenotypic heterogeneity of ischemic stroke has hampered gene discovery, motivating analyses of diagnostic subtypes with reduced sample sizes. We assessed evidence for a shared genetic basis among the 3 major subtypes: large artery atherosclerosis (LAA), cardioembolism, and small vessel disease (SVD), to inform potential cross-subtype analyses. Methods: Analyses used genome-wide summary data for 12 389 ischemic stroke cases (including 2167 LAA, 2405 cardioembolism, and 1854 SVD) and 62 004 controls from the Metastroke consortium. For 4561 cases and 7094 controls, individual-level genotype data were also available. Genetic correlations between subtypes were estimated using linear mixed models and polygenic profile scores. Meta-analysis of a combined LAA-SVD phenotype (4021 cases and 51 976 controls) was performed to identify shared risk alleles. Results: High genetic correlation was identified between LAA and SVD using linear mixed models (rg=0.96, SE=0.47, P=9×10-4) and profile scores (rg=0.72; 95% confid

Hundreds of variants clustered in genomic loci and biological pathways affect human height

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.