H-alpha Kinematics of the SINGS Nearby Galaxies Survey. II

This is the second part of an H-alpha kinematics follow-up survey of the Spitzer Infrared Nearby Galaxies Survey (SINGS) sample. The aim of this program is to shed new light on the role of baryons and their kinematics and on the dark/luminous matter relation in the star forming regions of galaxies, in relation with studies at other wavelengths. The data for 37 galaxies are presented. The observations were made using Fabry-Perot interferometry with the photon-counting camera FaNTOmM on 4 different telescopes, namely the Canada-France-Hawaii 3.6m, the ESO La Silla 3.6m, the William Herschel 4.2m, and the Observatoire du mont Megantic 1.6m telescopes. The velocity fields are computed using custom IDL routines designed for an optimal use of the data. The kinematical parameters and rotation curves are derived using the GIPSY software. It is shown that non-circular motions associated with galactic bars affect the kinematical parameters fitting and the velocity gradient of the rotation curves. This leads to incorrect determinations of the baryonic and dark matter distributions in the mass models derived from those rotation curves.Comment: 18 pages, 5 figures, 4 tables. Accepted for publication in MNRAS. All high-res. figures are available at http://www.astro.umontreal.ca/fantomm/singsII

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Atomic and Molecular Gas Components in Spiral Galaxies of the Virgo Cluster

Based on two models, we investigate the molecular-to-atomic gas ratio in Virgo cluster galaxies in comparison with field galaxies. We show that the enhanced metallicity for cluster members and the ram pressure stripping of atomic gas from the disk periphery cannot fully explain the observed gas component ratios. The additional environmental factors affecting the interstellar medium and leading to an increase in the molecular gas fraction should be taken into account for cluster galaxies.Comment: 11 pages, 4 figure

Gravitational stability and dynamical overheating of stellar disks of galaxies

We use the marginal stability condition for galactic disks and the stellar velocity dispersion data published by different authors to place upper limits on the disk local surface density at two radial scalelengths $R=2h$. Extrapolating these estimates, we constrain the total mass of the disks and compare these estimates to those based on the photometry and color of stellar populations. The comparison reveals that the stellar disks of most of spiral galaxies in our sample cannot be substantially overheated and are therefore unlikely to have experienced a significant merging event in their history. The same conclusion applies to some, but not all of the S0 galaxies we consider. However, a substantial part of the early type galaxies do show the stellar velocity dispersion well in excess of the gravitational stability threshold suggesting a major merger event in the past. We find dynamically overheated disks among both seemingly isolated galaxies and those forming pairs. The ratio of the marginal stability disk mass estimate to the total galaxy mass within four radial scalelengths remains within a range of 0.4---0.8. We see no evidence for a noticeable running of this ratio with either the morphological type or color index.Comment: 25 pages, 5 figures, accepted to Astronomy Letter

Functional Characterization of EngAMS, a P-Loop GTPase of Mycobacterium smegmatis

Bacterial P-loop GTPases belong to a family of proteins that selectively hydrolyze a small molecule guanosine tri-phosphate (GTP) to guanosine di-phosphate (GDP) and inorganic phosphate, and regulate several essential cellular activities such as cell division, chromosomal segregation and ribosomal assembly. A comparative genome sequence analysis of different mycobacterial species indicates the presence of multiple P-loop GTPases that exhibit highly conserved motifs. However, an exact function of most of these GTPases in mycobacteria remains elusive. In the present study we characterized the function of a P-loop GTPase in mycobacteria by employing an EngA homologue from Mycobacterium smegmatis, encoded by an open reading frame, designated as MSMEG_3738. Amino acid sequence alignment and phylogenetic analysis suggest that MSMEG_3738 (termed as EngAMS) is highly conserved in mycobacteria. Homology modeling of EngAMS reveals a cloverleaf structure comprising of α/β fold typical to EngA family of GTPases. Recombinant EngAMS purified from E. coli exhibits a GTP hydrolysis activity which is inhibited by the presence of GDP. Interestingly, the EngAMS protein is co-eluted with 16S and 23S ribosomal RNA during purification and exhibits association with 30S, 50S and 70S ribosomal subunits. Further studies demonstrate that GTP is essential for interaction of EngAMS with 50S subunit of ribosome and specifically C-terminal domains of EngAMS are required to facilitate this interaction. Moreover, EngAMS devoid of N-terminal region interacts well with 50S even in the absence of GTP, indicating a regulatory role of the N-terminal domain in EngAMS-50S interaction

A chemical biology toolbox to study protein methyltransferases and epigenetic signaling

© 2019, The Author(s). Protein methyltransferases (PMTs) comprise a major class of epigenetic regulatory enzymes with therapeutic relevance. Here we present a collection of chemical probes and associated reagents and data to elucidate the function of human and murine PMTs in cellular studies. Our collection provides inhibitors and antagonists that together modulate most of the key regulatory methylation marks on histones H3 and H4, providing an important resource for modulating cellular epigenomes. We describe a comprehensive and comparative characterization of the probe collection with respect to their potency, selectivity, and mode of inhibition. We demonstrate the utility of this collection in CD4 + T cell differentiation assays revealing the potential of individual probes to alter multiple T cell subpopulations which may have implications for T cell-mediated processes such as inflammation and immuno-oncology. In particular, we demonstrate a role for DOT1L in limiting Th1 cell differentiation and maintaining lineage integrity. This chemical probe collection and associated data form a resource for the study of methylation-mediated signaling in epigenetics, inflammation and beyond

Core module biomarker identification with network exploration for breast cancer metastasis

<p>Abstract</p> <p>Background</p> <p>In a complex disease, the expression of many genes can be significantly altered, leading to the appearance of a differentially expressed "disease module". Some of these genes directly correspond to the disease phenotype, (i.e. "driver" genes), while others represent closely-related first-degree neighbours in gene interaction space. The remaining genes consist of further removed "passenger" genes, which are often not directly related to the original cause of the disease. For prognostic and diagnostic purposes, it is crucial to be able to separate the group of "driver" genes and their first-degree neighbours, (i.e. "core module") from the general "disease module".</p> <p>Results</p> <p>We have developed COMBINER: COre Module Biomarker Identification with Network ExploRation. COMBINER is a novel pathway-based approach for selecting highly reproducible discriminative biomarkers. We applied COMBINER to three benchmark breast cancer datasets for identifying prognostic biomarkers. COMBINER-derived biomarkers exhibited 10-fold higher reproducibility than other methods, with up to 30-fold greater enrichment for known cancer-related genes, and 4-fold enrichment for known breast cancer susceptible genes. More than 50% and 40% of the resulting biomarkers were cancer and breast cancer specific, respectively. The identified modules were overlaid onto a map of intracellular pathways that comprehensively highlighted the hallmarks of cancer. Furthermore, we constructed a global regulatory network intertwining several functional clusters and uncovered 13 confident "driver" genes of breast cancer metastasis.</p> <p>Conclusions</p> <p>COMBINER can efficiently and robustly identify disease core module genes and construct their associated regulatory network. In the same way, it is potentially applicable in the characterization of any disease that can be probed with microarrays.</p

Deciphering the Catalytic Machinery in 30S Ribosome Assembly GTPase YqeH

YqeH, a circularly permuted GTPase (cpGTPase), which is conserved across bacteria and eukaryotes including humans is important for the maturation of small (30S) ribosomal subunit in Bacillus subtilis. Recently, we have shown that it binds 30S in a GTP/GDP dependent fashion. However, the catalytic machinery employed to hydrolyze GTP is not recognized for any of the cpGTPases, including YqeH. This is because they possess a hydrophobic substitution in place of a catalytic glutamine (present in Ras-like GTPases). Such GTPases were categorized as HAS-GTPases and were proposed to follow a catalytic mechanism, different from the Ras-like proteins.MnmE, another HAS-GTPase, but not circularly permuted, utilizes a potassium ion and water mediated interactions to drive GTP hydrolysis. Though the G-domain of MnmE and YqeH share only approximately 25% sequence identity, the conservation of characteristic sequence motifs between them prompted us to probe GTP hydrolysis machinery in YqeH, by employing homology modeling in conjunction with biochemical experiments. Here, we show that YqeH too, uses a potassium ion to drive GTP hydrolysis and stabilize the transition state. However, unlike MnmE, it does not dimerize in the transition state, suggesting alternative ways to stabilize switches I and II. Furthermore, we identify a potential catalytic residue in Asp-57, whose recognition, in the absence of structural information, was non-trivial due to the circular permutation in YqeH. Interestingly, when compared with MnmE, helix alpha2 that presents Asp-57 is relocated towards the N-terminus in YqeH. An analysis of the YqeH homology model, suggests that despite such relocation, Asp-57 may facilitate water mediated catalysis, similarly as the catalytic Glu-282 of MnmE. Indeed, an abolished catalysis by D57I mutant supports this inference.An uncommon means to achieve GTP hydrolysis utilizing a K(+) ion has so far been demonstrated only for MnmE. Here, we show that YqeH also utilizes a similar mechanism. While the catalytic machinery is similar in both, mechanistic differences may arise based on the way they are deployed. It appears that K(+) driven mechanism emerges as an alternative theme to stabilize the transition state and hydrolyze GTP in a subset of GTPases, such as the HAS-GTPases