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Molecular classification of primary mediastinal large B-cell lymphoma using routinely available tissue specimens.

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Molecular classification of primary mediastinal large B-cell lymphoma using routinely available tissue specimens.

Blood. 2018 Sep 26;:

Authors: Mottok A, Wright G, Rosenwald A, Ott G, Ramsower C, Campo E, Braziel RM, Delabie J, Weisenburger DD, Song JY, Chan WC, Cook JR, Fu K, Greiner T, Smeland E, Holte H, Savage KJ, Glinsmann-Gibson BJ, Gascoyne RD, Staudt LM, Jaffe ES, Connors JM, Scott DW, Steidl C, Rimsza LM

Abstract
Primary mediastinal large B-cell lymphoma (PMBCL) is recognized as a distinct entity in the World Health Organization classification. Currently, diagnosis relies on consensus of histopathology, clinical variables and presentation, giving rise to diagnostic inaccuracy in routine practice. Previous studies have demonstrated that PMBCL can be distinguished from subtypes of diffuse large B-cell lymphoma (DLBCL) based on gene expression signatures. However, requirement of fresh-frozen biopsy material has precluded the transfer of gene expression-based assays to the clinic. Here, we developed a robust and accurate molecular classification assay (Lymph3Cx) for the distinction of PMBCL from DLBCL subtypes based on gene expression measurements in formalin-fixed, paraffin-embedded tissue. A probabilistic model accounting for classification error, comprising 58 gene features, was trained on 68 cases of PMBCL and DLBCL. Performance of the model was subsequently evaluated in an independent validation cohort of 158 cases and showed high agreement of the Lymph3Cx molecular classification with the clinico-pathological diagnosis of an expert panel (frank misclassification rate 3.8 %). Furthermore, we demonstrate reproducibility of the assay with 100 % concordance of subtype assignments at two independent laboratories. Future studies will determine Lymph3Cx's utility for routine diagnostic purposes and therapeutic decision making.

PMID: 30257882 [PubMed - as supplied by publisher]



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How bacterial xenogeneic silencer rok distinguishes foreign from self DNA in its resident genome.

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How bacterial xenogeneic silencer rok distinguishes foreign from self DNA in its resident genome.

Nucleic Acids Res. 2018 Sep 25;:

Authors: Duan B, Ding P, Hughes TR, Navarre WW, Liu J, Xia B

Abstract
Bacterial xenogeneic silencers play important roles in bacterial evolution by recognizing and inhibiting expression from foreign genes acquired through horizontal gene transfer, thereby buffering against potential fitness consequences of their misregulated expression. Here, the detailed DNA binding properties of Rok, a xenogeneic silencer in Bacillus subtilis, was studied using protein binding microarray, and the solution structure of its C-terminal DNA binding domain was determined in complex with DNA. The C-terminal domain of Rok adopts a typical winged helix fold, with a novel DNA recognition mechanism different from other winged helix proteins or xenogeneic silencers. Rok binds the DNA minor groove by forming hydrogen bonds to bases through N154, T156 at the N-terminal of α3 helix and R174 of wing W1, assisted by four lysine residues interacting electrostatically with DNA backbone phosphate groups. These structural features endow Rok with preference towards DNA sequences harboring AACTA, TACTA, and flexible multiple TpA steps, while rigid A-tracts are disfavored. Correspondingly, the Bacillus genomes containing Rok are rich in A-tracts and show a dramatic underrepresentation of AACTA and TACTA, which are significantly enriched in Rok binding regions. These observations suggest that the xenogeneic silencing protein and its resident genome may have evolved cooperatively.

PMID: 30252102 [PubMed - as supplied by publisher]



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Colloidal aggregation: from screening nuisance to formulation nuance.

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Colloidal aggregation: from screening nuisance to formulation nuance.

Nano Today. 2018 Apr;19:188-200

Authors: Ganesh AN, Donders EN, Shoichet BK, Shoichet MS

Abstract
It is well known that small molecule colloidal aggregation is a leading cause of false positives in early drug discovery. Colloid-formers are diverse and well represented among corporate and academic screening decks, and even among approved drugs. Less appreciated is how colloid formation by drug-like compounds fits into the wider understanding of colloid physical chemistry. Here we introduce the impact that colloidal aggregation has had on early drug discovery, and then turn to the physical and thermodynamic driving forces for small molecule colloidal aggregation, including the particulate nature of the colloids, their critical aggregation concentration-governed formation, their mechanism of protein adsorption and subsequent inhibition, and their sensitivity to detergent. We describe methods that have been used extensively to both identify aggregate-formers and to study and control their physical chemistry. While colloidal aggregation is widely recognized as a problem in early drug discovery, we highlight the opportunities for exploiting this phenomenon in biological milieus and for drug formulation.

PMID: 30250495 [PubMed]



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Generating Intracellular Modulators of E3 Ligases and Deubiquitinases from Phage-Displayed Ubiquitin Variant Libraries.

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Generating Intracellular Modulators of E3 Ligases and Deubiquitinases from Phage-Displayed Ubiquitin Variant Libraries.

Methods Mol Biol. 2018;1844:101-119

Authors: Zhang W, Sidhu SS

Abstract
Ubiquitination is a posttranslational protein modification pathway regulating diverse cellular processes that are implicated in numerous human diseases. However, targeting the enzymes in the ubiquitination cascade potently and selectively remains a major challenge. Recently we devised a methodology to generate ubiquitin-based modulators for E3 ligases and deubiquitinases, enzymes that control the specificity of protein ubiquitination and deubiquitination, respectively. Here, we describe methods to generate libraries of ubiquitin variants and perform phage display selections to isolate high-affinity binders for target proteins. Importantly, the strategy introduced here can be applied to other small protein domains mediating protein-protein interactions to engineer tools for target validation and potential therapeutic development.

PMID: 30242706 [PubMed - in process]



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Efficient and Accurate Quantitative Profiling of Alternative Splicing Patterns of Any Complexity on a Laptop.

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Efficient and Accurate Quantitative Profiling of Alternative Splicing Patterns of Any Complexity on a Laptop.

Mol Cell. 2018 Sep 10;:

Authors: Sterne-Weiler T, Weatheritt RJ, Best AJ, Ha KCH, Blencowe BJ

Abstract
Alternative splicing (AS) is a widespread process underlying the generation of transcriptomic and proteomic diversity and is frequently misregulated in human disease. Accordingly, an important goal of biomedical research is the development of tools capable of comprehensively, accurately, and efficiently profiling AS. Here, we describe Whippet, an easy-to-use RNA-seq analysis method that rapidly-with hardware requirements compatible with a laptop-models and quantifies AS events of any complexity without loss of accuracy. Using an entropic measure of splicing complexity, Whippet reveals that one-third of human protein coding genes produce transcripts with complex AS events involving co-expression of two or more principal splice isoforms. We observe that high-entropy AS events are more prevalent in tumor relative to matched normal tissues and correlate with increased expression of proto-oncogenic splicing factors. Whippet thus affords the rapid and accurate analysis of AS events of any complexity, and as such will facilitate future biomedical research.

PMID: 30220560 [PubMed - as supplied by publisher]



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Fibroblast growth factor receptor 5 (FGFR5) is a co-receptor for FGFR1 that is up-regulated in beta-cells by cytokine-induced inflammation.

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Fibroblast growth factor receptor 5 (FGFR5) is a co-receptor for FGFR1 that is up-regulated in beta-cells by cytokine-induced inflammation.

J Biol Chem. 2018 Sep 14;:

Authors: Regeenes R, Silva PN, Chang HH, Arany EJ, Shukalyuk AI, Audet J, Kilkenny DM, Rocheleau JV

Abstract
Fibroblast growth factor receptor-1 (FGFR1) activity at the plasma membrane is tightly controlled by the availability of co-receptors and competing receptor isoforms. We have previously shown that FGFR1 activity in pancreatic beta-cells modulates a wide range of processes, including lipid metabolism, insulin processing, and cell survival. More recently, we have revealed that co-expression of FGFR5, a receptor isoform that lacks a tyrosine-kinase domain, influences FGFR1 responses. We therefore hypothesized that FGFR5 is a co-receptor to FGFR1 that modulates responses to ligands by forming a receptor heterocomplex with FGFR1. We first show here increased FGFR5 expression in the pancreatic islets of non-obese diabetic (NOD) mice and also in mouse and human islets treated with proinflammatory cytokines. Using siRNA knockdown, we further report that FGFR5 and FGFR1 expression improves beta-cell survival. Co-immunoprecipitation and quantitative live-cell imaging to measure the molecular interaction between FGFR5 and FGFR1 revealed that FGFR5 forms a mixture of ligand-independent homodimers (~25%) and homotrimers (~75%) at the plasma membrane. Interestingly, co-expressed FGFR5 and FGFR1 formed hetero-complexes with a 2:1 ratio, and subsequently responded to FGF2 by forming FGFR5-FGFR1 signaling complexes with a 4:2 ratio. Taken together, our findings identify FGFR5 as a co-receptor that is up-regulated by inflammation and promotes FGFR1-induced survival, insights that reveal a potential target for intervention during beta-cell pathogenesis.

PMID: 30217817 [PubMed - as supplied by publisher]



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Single-platform 'multi-omic' profiling: unified mass spectrometry and computational workflows for integrative proteomics-metabolomics analysis.

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Single-platform 'multi-omic' profiling: unified mass spectrometry and computational workflows for integrative proteomics-metabolomics analysis.

Mol Omics. 2018 Sep 13;:

Authors: Blum BC, Mousavi F, Emili A

Abstract
The objective of omics studies is to globally measure the different classes of cellular biomolecules present in a biological specimen (e.g. proteins, metabolites) as accurately as possible in order to investigate the corresponding 'states' of biological systems. High throughput omics technologies are emerging as an increasingly powerful toolkit in the rapidly advancing field of systems biology, enabling the systematic study of dynamic molecular processes that drive core cell functions like growth, sensing, and environmental adaptation. Advances in high resolution mass spectrometry, in particular, now allow for the near comprehensive study of cellular proteins and metabolites that underlie physiological homeostasis and disease pathogenesis. Yet while the expression levels, modification states, and functional associations of diverse molecular species are now measurable, existing proteomic and metabolomic data generation and analysis workflows are often specialized and incompatible. Hence, while there are now many reports of ad hoc combinations of unimolecular proteomic and metabolomic workflows, only a limited number of multi-omic profiling approaches have been reported for obtaining different molecular measurements (proteins, metabolites, nucleic acids) in parallel from a single biological sample. Moreover, elucidating how the myriad of measured cellular components are linked together functionally within the metabolic processes, signal transduction pathways, and macromolecular interaction networks central to living systems remains a massive, complicated, and uncertain endeavor. Presented here is a review of convergent mass spectrometry-based multi-omic methodologies, with a focus on notable recent advances and remaining challenges in terms of efficient sample preparation, biochemical separations, data acquisition, and integrative computational strategies. We outline a unifying network-based integrative framework to better derive biological knowledge from integrated profiling studies with the goal of realizing the full potential of multi-omic data sets.

PMID: 30211418 [PubMed - as supplied by publisher]



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Phage-Encoded Anti-CRISPR Defenses.

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Phage-Encoded Anti-CRISPR Defenses.

Annu Rev Genet. 2018 Sep 12;:

Authors: Stanley SY, Maxwell KL

Abstract
The battle for survival between bacteria and bacteriophages (phages) is an arms race where bacteria develop defenses to protect themselves from phages and phages evolve counterstrategies to bypass these defenses. CRISPR-Cas adaptive immune systems represent a widespread mechanism by which bacteria protect themselves from phage infection. In response to CRISPR-Cas, phages have evolved protein inhibitors known as anti-CRISPRs. Here, we describe the discovery and mechanisms of action of anti-CRISPR proteins. We discuss the potential impact of anti-CRISPRs on bacterial evolution, speculate on their evolutionary origins, and contemplate the possible next steps in the CRISPR-Cas evolutionary arms race. We also touch on the impact of anti-CRISPRs on the development of CRISPR-Cas-based biotechnological tools. Expected final online publication date for the Annual Review of Genetics Volume 52 is November 23, 2018. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

PMID: 30208287 [PubMed - as supplied by publisher]



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Fc Engineering: Tailored Synthetic Human IgG1-Fc Repertoire for High-Affinity Interaction with FcRn at pH 6.0.

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Fc Engineering: Tailored Synthetic Human IgG1-Fc Repertoire for High-Affinity Interaction with FcRn at pH 6.0.

Methods Mol Biol. 2018;1827:399-417

Authors: Saxena A, Bai B, Hou SC, Jiang L, Ying T, Miersch S, Sidhu SS, Wu D

Abstract
The therapeutic efficacy of an antibody drug depends on the variable domains and on the constant crystallizable fragment (Fc). IgG variable domains have been the targets of extensive molecular engineering in search of more specific binders with higher affinities for their targets. Similarly, Fc engineering approaches have led to modulating both the immune effector responses and serum half-lives of therapeutic antibodies. A high-affinity interaction between the IgG Fc and neonatal Fc receptor (FcRn) at a slightly acidic pH can protect IgG molecules from undergoing lysosomal or serum proteinase-induced degradation. Here we describe an optimized protocol for the development of a tailored, synthetic human Fc repertoire to select Fc mutants which show highly pH-restricted FcRn binding with high affinity.

PMID: 30196509 [PubMed - in process]



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Deep learning in biomedicine.

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Deep learning in biomedicine.

Nat Biotechnol. 2018 Oct;36(9):829-838

Authors: Wainberg M, Merico D, Delong A, Frey BJ

Abstract
Deep learning is beginning to impact biological research and biomedical applications as a result of its ability to integrate vast datasets, learn arbitrarily complex relationships and incorporate existing knowledge. Already, deep learning models can predict, with varying degrees of success, how genetic variation alters cellular processes involved in pathogenesis, which small molecules will modulate the activity of therapeutically relevant proteins, and whether radiographic images are indicative of disease. However, the flexibility of deep learning creates new challenges in guaranteeing the performance of deployed systems and in establishing trust with stakeholders, clinicians and regulators, who require a rationale for decision making. We argue that these challenges will be overcome using the same flexibility that created them; for example, by training deep models so that they can output a rationale for their predictions. Significant research in this direction will be needed to realize the full potential of deep learning in biomedicine.

PMID: 30188539 [PubMed - in process]



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