Nicotinamide Cooperates with Retinoic Acid and 1,25-Dihydroxyvitamin D3 to Regulate Cell Differentiation and Cell Cycle Arrest of Human Myeloblastic Leukemia Cells

Nicotinamide, the amide derivative of vitamin B3, cooperates with retinoic acid (RA), a form of vitamin A, and 1,25-dihydroxyvitamin D3 (D3), to regulate cell differentiation and proliferation of human myeloblastic leukemia cells. In human myeloblastic leukemia cells, RA or D3 are known to cause MAPK signaling leading to myeloid or monocytic differentiation and G0 cell cycle arrest. In this process, RA or D3 induces the early expression of CD38, a receptor that causes ERK signaling and propels further differentiation. Our study demonstrates that nicotinamide in combination with RA or D3 affected induced expression levels of CD38, CD11b and CD14, suggesting a cooperative function of nicotinamide and RA or D3. Nicotinamide transiently retarded the initial RA- or D3-induced expression of CD38, which subsequently reached the same nearly 100% expression. Nicotinamide induced ERK activation and further enhanced the RA-induced ERK activation, but the D3-induced ERK activation was diminished by nicotinamide, although levels still exceeded those induced by RA, suggesting lineage-specific nicotinamide responses. Nicotinamide enhanced both RA- and D3-induced CD11b expression, inducible oxidative metabolism, and G0 cell cycle arrest, accelerating their induced occurrence in all instances. Consistent with this, the RA- or D3-induced downregulation of PARP was enhanced by nicotinamide. Nicotinamide thus regulated RA- or D3-induced differentiation and G0 arrest, causing a transient delay in certain early aspects of the progression to terminal differentiation but ultimately accelerating the occurrence of terminally, functionally differentiated G0 cells

Human Rhinovirus Type 14 Gain-of-Function Mutants for oriI Utilization Define Residues of 3C(D) and 3Dpol That Contribute to Assembly and Stability of the Picornavirus VPg Uridylylation Complex▿

VPg linkage to the 5′ ends of picornavirus RNAs requires production of VPg-pUpU. VPg-pUpU is templated by an RNA stem-loop (the cre or oriI) found at different locations in picornavirus genomes. At least one adaptive mutation is required for human rhinovirus type 14 (HRV-14) to use poliovirus type 3 (PV-3) or PV-1 oriI efficiently. One mutation changes Leu-94 of 3C to Pro; the other changes Asp-406 of 3Dpol to Asn. By using an in vitro VPg uridylylation system for HRV-14 that recapitulates biological phenotypes, we show that the 3C adaptive mutation functions at the level of 3C(D) and the 3D adaptive mutation functions at the level of 3Dpol. Pro-94 3C(D) has an expanded specificity and enhanced stability relative to wild-type 3C(D) that leads to production of more processive uridylylation complexes. PV-1/HRV-14 oriI chimeras reveal sequence specificity in 3C(D) recognition of oriI that resides in the upper stem. Asn-406 3Dpol is as active as wild-type 3Dpol in RNA-primed reactions but exhibits greater VPg uridylylation activity due to more efficient recruitment to and retention in the VPg uridylylation complex. Asn-406 3Dpol from PV-1 exhibits identical behavior. These studies suggest a two-step binding mechanism in the assembly of the 3C(D)-oriI complex that leads to unwinding of at least the upper stem of oriI and provide additional support for a direct interaction between the back of the thumb of 3Dpol and 3C that is required for 3Dpol recruitment to and retention in the uridylylation complex

Leveraging Non-Targeted Metabolite Profiling via Statistical Genomics

<div><p>One of the challenges of systems biology is to integrate multiple sources of data in order to build a cohesive view of the system of study. Here we describe the mass spectrometry based profiling of maize kernels, a model system for genomic studies and a cornerstone of the agroeconomy. Using a network analysis, we can include 97.5% of the 8,710 features detected from 210 varieties into a single framework. More conservatively, 47.1% of compounds detected can be organized into a network with 48 distinct modules. Eigenvalues were calculated for each module and then used as inputs for genome-wide association studies. Nineteen modules returned significant results, illustrating the genetic control of biochemical networks within the maize kernel. Our approach leverages the correlations between the genome and metabolome to mutually enhance their annotation and thus enable biological interpretation. This method is applicable to any organism with sufficient bioinformatic resources.</p></div

Regression of kernel weight using module eigenvalues.

<p>Regression of kernel weight using module eigenvalues.</p

Genomics-assisted chemistry & chemistry-assisted genomics.

<p>This flow chart describes the process by which statistical genetics and genomics can enable metabolite profiling to have greater power and impact.</p

Sample results from genome-wide association studies.

<p>Sample results from genome-wide association studies.</p

Module eigenvalues do not obscure the importance of single compounds.

<p>MEorange was estimated from 81 molecular features, one of which was identified to be tyramine. GWAS on MEorange identified 27 significant SNPs at the FDR-corrected p<0.05 threshold. GWAS on tyramine alone identified 7 SNPs in common (red circles) with MEorange.</p

Visualization of maize grain metabolome.

<p>This node and edge projection describes the grain metabolome observed in the methanolic extract from 210 inbred line varieties of maize. This network requires a minimum degree of connectivity between any two nodes (i.e. biochemical markers detected by mass spectrometry) that exceeds four standard deviations above the mean connectivity observed between detected markers. According to this threshold, 4,102 nodes are organized into 48 modules each represented by particular color. However, some modules have separated into multiple, distinct clusters as internal connectivity may fall beneath the 4 standard deviation cutoff, such that there are 101 objects in this projection.</p