Arterioles and sinusoids of the bone marrow (BM) are accompanied by stromal cells that express nerve/glial antigen 2 (NG2) and leptin receptor (LepR), and constitute specialized niches that regulate ...quiescence and proliferation of haematopoietic stem cells (HSCs). However, how niche cells differentially regulate HSC functions remains unknown. Here, we show that the effects of cytokines regulating HSC functions are dependent on the producing cell sources. Deletion of chemokine C-X-C motif ligand 12 (Cxcl12) or stem cell factor (Scf) from all perivascular cells marked by nestin-GFP dramatically depleted BM HSCs. Selective Cxcl12 deletion from arteriolar NG2
cells, but not from sinusoidal LepR
cells, caused HSC reductions and altered HSC localization in BM. By contrast, deletion of Scf in LepR
cells, but not NG2
cells, led to reductions in BM HSC numbers. These results uncover distinct contributions of cytokines derived from perivascular cells in separate vascular niches to HSC maintenance.
Genome-wide gene expression profiles, as measured with microarrays or RNA-Seq experiments, have revolutionized biological and biomedical research by providing a quantitative measure of the entire ...mRNA transcriptome. Typically, researchers set up experiments where control samples are compared to a treatment condition, and using the t-test they identify differentially expressed genes upon which further analysis and ultimately biological discovery from such experiments is based. Here we describe an alternative geometrical approach to identify differentially expressed genes. We show that this alternative method, called the Characteristic Direction, is capable of identifying more relevant genes. We evaluate our approach in three case studies. In the first two, we match transcription factor targets determined by ChIP-seq profiling with differentially expressed genes after the same transcription factor knockdown or over-expression in mammalian cells. In the third case study, we evaluate the quality of enriched terms when comparing normal epithelial cells with cancer stem cells. In conclusion, we demonstrate that the Characteristic Direction approach is much better in calling the significantly differentially expressed genes and should replace the widely currently in used t-test method for this purpose. Implementations of the method in MATLAB, Python and Mathematica are available at: http://www.maayanlab.net/CD.
As the cost of genome-wide profiling is decreasing, the possibility for using such technologies for routine diagnostics as well as for classification and stratification of patients in clinical ...settings is increasing. However, the high dimensionality of such data makes it challenging to interpret and visualize for comparing and contrasting patient samples. Here we propose two visualization methods that display unsupervised clustering of genome-wide profiling of mRNA from breast cancer tumors from patients as images that can quickly show clusters of patients based on their expression profiles with perspective of their clinical outcome. The first visualization method converts expression profiles into a sparse network, whereas the second method visualizes patient samples on a hexagonal grid. Both visualization methods use the first three coordinates from principle component analysis (PCA) applied to reduce the dimensionality of the data. Colors of nodes in the network or hexagons are based on clinical outcome or tumor estrogen receptor (ER) status. Such visualization methods could be useful for grouping patients in an unsupervised manner to predict outcome and tailor personalized therapeutics.
Much of the work on modeling the spread of viral infections utilized partial
differential equa- tions. Traveling-wave solutions to these PDEs are typically
concentrated on velocities and their ...dependence on the various parameters. Most
of the investigations into the dynamical interaction of virus and defective
interfering particles (DIP), which are incomplete forms of the virus that
replicate through co-infection, have followed the same lines. In this work we
present an agent based model of viral infection with consideration of DIP and
the negative feedback loop introduced by interferon production as part of the
host innate immune response. The model is based high resolution microscopic
images of plaques of dead cells we took from mammalian cells infected with
Sendai virus with low and high DIP. In order to investigate the effects of the
discrete stochastic microscopic mechanisms, which are responsible for virus
spreading, have on the macroscopic growth of viral plaques, we generate an
agent-based model of viral infection. The two main aims of this work are to (i)
investigate the effects of discrete microscopic randomness on the macroscopic
growth of viral plaques; and (ii) examine the dynamic interactions between the
full length virus, DIP and interferon, and interpret what may be the function
of DIP. We find that we can explain the qualitative differences between our
stochastic model and deterministic models in terms of the fractal geometry of
the resulting plaques, and that DIP have a delaying effect while the
interaction between interferon and DIP has a slowing effect on the growth of
viral plaques, potentially contributing to viral latency.
The opioid epidemic in the United States is averaging over 100 deaths per day due to overdose. The effectiveness of opioids as pain treatments, and the drug-seeking behavior of opioid addicts, leads ...physicians in the United States to issue over 200 million opioid prescriptions every year. To better understand the biomedical profile of opioid-dependent patients, we analyzed information from electronic health records (EHR) including lab tests, vital signs, medical procedures, prescriptions, and other data from millions of patients to predict opioid substance dependence.
We trained a machine learning model to classify patients by likelihood of having a diagnosis of substance dependence using EHR data from patients diagnosed with substance dependence, along with control patients with no history of substance-related conditions, matched by age, gender, and status of HIV, hepatitis C, and sickle cell disease. The top machine learning classifier using all features achieved a mean area under the receiver operating characteristic (AUROC) curve of ~ 92%, and analysis of the model uncovered associations between basic clinical factors and substance dependence. Additionally, diagnoses, prescriptions, and procedures prior to the diagnoses of substance dependence were analyzed to elucidate the clinical profile of substance-dependent patients, relative to controls.
The predictive model may hold utility for identifying patients at risk of developing dependence, risk of overdose, and opioid-seeking patients that report other symptoms in their visits to the emergency room.
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