19 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"systems biology"

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19 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"systems biology"

These pubmed results were generated on 2020/01/03

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

11 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"systems biology"

These pubmed results were generated on 2020/01/02

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

27 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"systems biology"

These pubmed results were generated on 2020/01/01

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

46 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"systems biology"

These pubmed results were generated on 2019/12/31

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

16 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"systems biology"

These pubmed results were generated on 2019/12/29

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

16 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"systems biology"

These pubmed results were generated on 2019/12/28

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

13 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"systems biology"

These pubmed results were generated on 2019/12/28

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

13 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"systems biology"

These pubmed results were generated on 2019/12/27

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Analyzing the genes and pathways related to major depressive disorder via a systems biology approach.

Brain Behav. 2019 Dec 25;:e01502

Authors: Fan T, Hu Y, Xin J, Zhao M, Wang J

Abstract
INTRODUCTION: Major depressive disorder (MDD) is a mental disorder caused by the combination of genetic, environmental, and psychological factors. Over the years, a number of genes potentially associated with MDD have been identified. However, in many cases, the role of these genes and their relationship in the etiology and development of MDD remains unclear. Under such situation, a systems biology approach focusing on the function correlation and interaction of the candidate genes in the context of MDD will provide useful information on exploring the molecular mechanisms underlying the disease.
METHODS: We collected genes potentially related to MDD by screening the human genetic studies deposited in PubMed (https://www.ncbi.nlm.nih.gov/pubmed). The main biological themes within the genes were explored by function and pathway enrichment analysis. Then, the interaction of genes was analyzed in the context of protein-protein interaction network and a MDD-specific network was built by Steiner minimal tree algorithm.
RESULTS: We collected 255 candidate genes reported to be associated with MDD from available publications. Functional analysis revealed that biological processes and biochemical pathways related to neuronal development, endocrine, cell growth and/or survivals, and immunology were enriched in these genes. The pathways could be largely grouped into three modules involved in biological procedures related to nervous system, the immune system, and the endocrine system, respectively. From the MDD-specific network, 35 novel genes potentially associated with the disease were identified.
CONCLUSION: By means of network- and pathway-based methods, we explored the molecular mechanism underlying the pathogenesis of MDD at a systems biology level. Results from our work could provide valuable clues for understanding the molecular features of MDD.

PMID: 31875662 [PubMed - as supplied by publisher]

miRNA551b-3p Activates an Oncostatin Signaling Module for the Progression of Triple-Negative Breast Cancer.

Cell Rep. 2019 Dec 24;29(13):4389-4406.e10

Authors: Parashar D, Geethadevi A, Aure MR, Mishra J, George J, Chen C, Mishra MK, Tahiri A, Zhao W, Nair B, Lu Y, Mangala LS, Rodriguez-Aguayo C, Lopez-Berestein G, Camara AKS, Liang M, Rader JS, Ramchandran R, You M, Sood AK, Kristensen VN, Mills GB, Pradeep S, Chaluvally-Raghavan P

Abstract
Genomic amplification of 3q26.2 locus leads to the increased expression of microRNA 551b-3p (miR551b-3p) in triple-negative breast cancer (TNBC). Our results demonstrate that miR551b-3p translocates to the nucleus with the aid of importin-8 (IPO8) and activates STAT3 transcription. As a consequence, miR551b upregulates the expression of oncostatin M receptor (OSMR) and interleukin-31 receptor-α (IL-31RA) as well as their ligands OSM and IL-31 through STAT3 transcription. We defined this set of genes induced by miR551b-3p as the "oncostatin signaling module," which provides oncogenic addictions in cancer cells. Notably, OSM is highly expressed in TNBC, and the elevated expression of OSM associates with poor outcome in estrogen-receptor-negative breast cancer patients. Conversely, targeting miR551b with anti-miR551b-3p reduced the expression of the OSM signaling module and reduced tumor growth, as well as migration and invasion of breast cancer cells.

PMID: 31875548 [PubMed - in process]

Patterns of Aging Biomarkers, Mortality, and Damaging Mutations Illuminate the Beginning of Aging and Causes of Early-Life Mortality.

Cell Rep. 2019 Dec 24;29(13):4276-4284.e3

Authors: Kinzina ED, Podolskiy DI, Dmitriev SE, Gladyshev VN

Abstract
An increase in the probability of death has been a defining feature of aging, yet human perinatal mortality starts high and decreases with age. Previous evolutionary models suggested that organismal aging begins after the onset of reproduction. However, we find that mortality and incidence of diseases associated with aging follow a U-shaped curve with the minimum before puberty, whereas quantitative biomarkers of aging, including somatic mutations and DNA methylation, do not, revealing that aging starts early but is masked by early-life mortality. Moreover, our genetic analyses point to the contribution of damaging mutations to early mortality. We propose that mortality patterns are governed, in part, by negative selection against damaging mutations in early life, manifesting after the corresponding genes are first expressed. Deconvolution of mortality patterns suggests that deleterious changes rather than mortality are the defining characteristic of aging and that aging begins in very early life.

PMID: 31875539 [PubMed - in process]

Evolution of vascular plants through redeployment of ancient developmental regulators.

Proc Natl Acad Sci U S A. 2019 Dec 24;:

Authors: Lu KJ, van 't Wout Hofland N, Mor E, Mutte S, Abrahams P, Kato H, Vandepoele K, Weijers D, De Rybel B

Abstract
Vascular plants provide most of the biomass, food, and feed on earth, yet the molecular innovations that led to the evolution of their conductive tissues are unknown. Here, we reveal the evolutionary trajectory for the heterodimeric TMO5/LHW transcription factor complex, which is rate-limiting for vascular cell proliferation in Arabidopsis thaliana Both regulators have origins predating vascular tissue emergence, and even terrestrialization. We further show that TMO5 evolved its modern function, including dimerization with LHW, at the origin of land plants. A second innovation in LHW, coinciding with vascular plant emergence, conditioned obligate heterodimerization and generated the critical function in vascular development in Arabidopsis In summary, our results suggest that the division potential of vascular cells may have been an important factor contributing to the evolution of vascular plants.

PMID: 31874927 [PubMed - as supplied by publisher]

Cored in the act: the use of models to understand core myopathies.

Dis Model Mech. 2019 Dec 19;12(12):

Authors: Fusto A, Moyle LA, Gilbert PM, Pegoraro E

Abstract
The core myopathies are a group of congenital myopathies with variable clinical expression - ranging from early-onset skeletal-muscle weakness to later-onset disease of variable severity - that are identified by characteristic 'core-like' lesions in myofibers and the presence of hypothonia and slowly or rather non-progressive muscle weakness. The genetic causes are diverse; central core disease is most often caused by mutations in ryanodine receptor 1 (RYR1), whereas multi-minicore disease is linked to pathogenic variants of several genes, including selenoprotein N (SELENON), RYR1 and titin (TTN). Understanding the mechanisms that drive core development and muscle weakness remains challenging due to the diversity of the excitation-contraction coupling (ECC) proteins involved and the differential effects of mutations across proteins. Because of this, the use of representative models expressing a mature ECC apparatus is crucial. Animal models have facilitated the identification of disease progression mechanisms for some mutations and have provided evidence to help explain genotype-phenotype correlations. However, many unanswered questions remain about the common and divergent pathological mechanisms that drive disease progression, and these mechanisms need to be understood in order to identify therapeutic targets. Several new transgenic animals have been described recently, expanding the spectrum of core myopathy models, including mice with patient-specific mutations. Furthermore, recent developments in 3D tissue engineering are expected to enable the study of core myopathy disease progression and the effects of potential therapeutic interventions in the context of human cells. In this Review, we summarize the current landscape of core myopathy models, and assess the hurdles and opportunities of future modeling strategies.

PMID: 31874912 [PubMed - in process]

Virtual screening identification and chemical optimization of substituted 2-arylbenzimidazoles as new non-zinc-binding MMP-2 inhibitors.

Bioorg Med Chem. 2019 Dec 09;:115257

Authors: Laghezza A, Luisi G, Caradonna A, Di Pizio A, Piemontese L, Loiodice F, Agamennone M, Tortorella P

Abstract
Matrix metalloproteinases (MMPs) are a large family of zinc-dependent endoproteases known to exert multiple regulatory roles in tumor progression and invasiveness. This encouraged over the years the approach of MMP, and particularly MMP-2, targeting for anticancer treatment. Early generations of MMP inhibitors, based on aspecific zinc binding groups (ZBGs) assembled on (pseudo)peptide scaffolds, have been discontinued due to the clinical emergence of toxicity and further drawbacks, giving the way to inhibitors with alternative zinc-chelator moieties or not binding the catalytic zinc ion. In the present paper, we continue the search for new non-zinc binding MMP-2 inhibitors: exploiting previously identified compounds, a virtual screening (VS) campaign was carried out and led to the identification of a new class of ligands. The structure-activity relationship (SAR) of the benzimidazole scaffold was explored by synthesis of several analogues whose inhibition activity was tested with enzyme inhibition assays. By performing the molecular simplification approach, we disclosed different sets of single-digit micromolar inhibitors of MMP-2, with up to a ten-fold increase in inhibitory activity and ameliorated selectivity towards off-target MMP-8, compared to selected lead compound. Molecular dynamics calculations conducted on complexes of MMP-2 with docked privileged structures confirmed that analyzed inhibitors avoid targeting the zinc ion and dip inside the S1' pocket. Present results provide a further enrichment of our insights for the design of novel MMP-2 selective inhibitors.

PMID: 31874775 [PubMed - as supplied by publisher]

scReClassify: post hoc cell type classification of single-cell rNA-seq data.

BMC Genomics. 2019 Dec 24;20(Suppl 9):913

Authors: Kim T, Lo K, Geddes TA, Kim HJ, Yang JYH, Yang P

Abstract
BACKGROUND: Single-cell RNA-sequencing (scRNA-seq) is a fast emerging technology allowing global transcriptome profiling on the single cell level. Cell type identification from scRNA-seq data is a critical task in a variety of research such as developmental biology, cell reprogramming, and cancers. Typically, cell type identification relies on human inspection using a combination of prior biological knowledge (e.g. marker genes and morphology) and computational techniques (e.g. PCA and clustering). Due to the incompleteness of our current knowledge and the subjectivity involved in this process, a small amount of cells may be subject to mislabelling.
RESULTS: Here, we propose a semi-supervised learning framework, named scReClassify, for 'post hoc' cell type identification from scRNA-seq datasets. Starting from an initial cell type annotation with potentially mislabelled cells, scReClassify first performs dimension reduction using PCA and next applies a semi-supervised learning method to learn and subsequently reclassify cells that are likely mislabelled initially to the most probable cell types. By using both simulated and real-world experimental datasets that profiled various tissues and biological systems, we demonstrate that scReClassify is able to accurately identify and reclassify misclassified cells to their correct cell types.
CONCLUSIONS: scReClassify can be used for scRNA-seq data as a post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure. It is implemented as an R package and is freely available from https://github.com/SydneyBioX/scReClassify.

PMID: 31874628 [PubMed - in process]

Using discriminative vector machine model with 2DPCA to predict interactions among proteins.

BMC Bioinformatics. 2019 Dec 24;20(Suppl 25):694

Authors: Li Z, Nie R, You Z, Cao C, Li J

Abstract
BACKGROUND: The interactions among proteins act as crucial roles in most cellular processes. Despite enormous effort put for identifying protein-protein interactions (PPIs) from a large number of organisms, existing firsthand biological experimental methods are high cost, low efficiency, and high false-positive rate. The application of in silico methods opens new doors for predicting interactions among proteins, and has been attracted a great deal of attention in the last decades.
RESULTS: Here we present a novelty computational model with the adoption of our proposed Discriminative Vector Machine (DVM) model and a 2-Dimensional Principal Component Analysis (2DPCA) descriptor to identify candidate PPIs only based on protein sequences. To be more specific, a 2DPCA descriptor is employed to capture discriminative feature information from Position-Specific Scoring Matrix (PSSM) of amino acid sequences by the tool of PSI-BLAST. Then, a robust and powerful DVM classifier is employed to infer PPIs. When applied on both gold benchmark datasets of Yeast and H. pylori, our model obtained mean prediction accuracies as high as of 97.06 and 92.89%, respectively, which demonstrates a noticeable improvement than some state-of-the-art methods. Moreover, we constructed Support Vector Machines (SVM) based predictive model and made comparison it with our model on Human benchmark dataset. In addition, to further demonstrate the predictive reliability of our proposed method, we also carried out extensive experiments for identifying cross-species PPIs on five other species datasets.
CONCLUSIONS: All the experimental results indicate that our method is very effective for identifying potential PPIs and could serve as a practical approach to aid bioexperiment in proteomics research.

PMID: 31874626 [PubMed - in process]

Dynamically characterizing individual clinical change by the steady state of disease-associated pathway.

BMC Bioinformatics. 2019 Dec 24;20(Suppl 25):697

Authors: Sun S, Yu X, Sun F, Tang Y, Zhao J, Zeng T

Abstract
BACKGROUND: Along with the development of precision medicine, individual heterogeneity is attracting more and more attentions in clinical research and application. Although the biomolecular reaction seems to be some various when different individuals suffer a same disease (e.g. virus infection), the final pathogen outcomes of individuals always can be mainly described by two categories in clinics, i.e. symptomatic and asymptomatic. Thus, it is still a great challenge to characterize the individual specific intrinsic regulatory convergence during dynamic gene regulation and expression. Except for individual heterogeneity, the sampling time also increase the expression diversity, so that, the capture of similar steady biological state is a key to characterize individual dynamic biological processes.
RESULTS: Assuming the similar biological functions (e.g. pathways) should be suitable to detect consistent functions rather than chaotic genes, we design and implement a new computational framework (ABP: Attractor analysis of Boolean network of Pathway). ABP aims to identify the dynamic phenotype associated pathways in a state-transition manner, using the network attractor to model and quantify the steady pathway states characterizing the final steady biological sate of individuals (e.g. normal or disease). By analyzing multiple temporal gene expression datasets of virus infections, ABP has shown its effectiveness on identifying key pathways associated with phenotype change; inferring the consensus functional cascade among key pathways; and grouping pathway activity states corresponding to disease states.
CONCLUSIONS: Collectively, ABP can detect key pathways and infer their consensus functional cascade during dynamical process (e.g. virus infection), and can also categorize individuals with disease state well, which is helpful for disease classification and prediction.

PMID: 31874621 [PubMed - in process]

Deforestation impacts network co-occurrence patterns of microbial communities in Amazon soils.

FEMS Microbiol Ecol. 2019 02 01;95(2):

Authors: Khan MAW, Bohannan BJM, Nüsslein K, Tiedje JM, Tringe SG, Parlade E, Barberán A, Rodrigues JLM

Abstract
Co-occurrence networks allow for the identification of potential associations among species, which may be important for understanding community assembly and ecosystem functions. We employed this strategy to examine prokaryotic co-occurrence patterns in the Amazon soils and the response of these patterns to land use change to pasture, with the hypothesis that altered microbial composition due to deforestation will mirror the co-occurrence patterns across prokaryotic taxa. In this study, we calculated Spearman correlations between operational taxonomic units (OTUs) as determined by 16S rRNA gene sequencing, and only robust correlations were considered for network construction (-0.80 ≥ P ≥ 0.80, adjusted P < 0.01). The constructed network represents distinct forest and pasture components, with altered compositional and topological features. A comparative analysis between two representative modules of these contrasting ecosystems revealed novel information regarding changes to metabolic pathways related to nitrogen cycling. Our results showed that soil physicochemical properties such as temperature, C/N and H++Al3+ had a significant impact on prokaryotic communities, with alterations to network topologies. Taken together, changes in co-occurrence patterns and physicochemical properties may contribute to ecosystem processes including nitrification and denitrification, two important biogeochemical processes occurring in tropical forest systems.

PMID: 30481288 [PubMed - indexed for MEDLINE]

25 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"systems biology"

These pubmed results were generated on 2019/12/25

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.