Subnetwork-based analysis of chronic lymphocytic leukemia identifies pathways that associate with disease progression.

The clinical course of patients with chronic lymphocytic leukemia (CLL) is heterogeneous. Several prognostic factors have been identified that can stratify patients into groups that differ in their relative tendency for disease progression and/or survival. Here, we pursued a subnetwork-based analysis of gene expression profiles to discriminate between groups of patients with disparate risks for CLL progression. From an initial cohort of 130 patients, we identified 38 prognostic subnetworks that could predict the relative risk for disease progression requiring therapy from the time of sample collection, more accurately than established markers. The prognostic power of these subnetworks then was validated on 2 other cohorts of patients. We noted reduced divergence in gene expression between leukemia cells of CLL patients classified at diagnosis with aggressive versus indolent disease over time. The predictive subnetworks vary in levels of expression over time but exhibit increased similarity at later time points before therapy, suggesting that degenerate pathways apparently converge into common pathways that are associated with disease progression. As such, these results have implications for understanding cancer evolution and for the development of novel treatment strategies for patients with CLL.

A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors.

Cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6is) have revolutionized breast cancer therapy. However, <50% of patients have an objective response, and nearly all patients develop resistance during therapy. To elucidate the underlying mechanisms, we constructed an interpretable deep learning model of the response to palbociclib, a CDK4/6i, based on a reference map of multiprotein assemblies in cancer. The model identifies eight core assemblies that integrate rare and common alterations across 90 genes to stratify palbociclib-sensitive versus palbociclib-resistant cell lines. Predictions translate to patients and patient-derived xenografts, whereas single-gene biomarkers do not. Most predictive assemblies can be shown by CRISPR-Cas9 genetic disruption to regulate the CDK4/6i response. Validated assemblies relate to cell-cycle control, growth factor signaling and a histone regulatory complex that we show promotes S-phase entry through the activation of the histone modifiers KAT6A and TBL1XR1 and the transcription factor RUNX1. This study enables an integrated assessment of how a tumor's genetic profile modulates CDK4/6i resistance.

A multi-scale map of protein assemblies in the DNA damage response.

The DNA damage response (DDR) ensures error-free DNA replication and transcription and is disrupted in numerous diseases. An ongoing challenge is to determine the proteins orchestrating DDR and their organization into complexes, including constitutive interactions and those responding to genomic insult. Here, we use multi-conditional network analysis to systematically map DDR assemblies at multiple scales. Affinity purifications of 21 DDR proteins, with/without genotoxin exposure, are combined with multi-omics data to reveal a hierarchical organization of 605 proteins into 109 assemblies. The map captures canonical repair mechanisms and proposes new DDR-associated proteins extending to stress, transport, and chromatin functions. We find that protein assemblies closely align with genetic dependencies in processing specific genotoxins and that proteins in multiple assemblies typically act in multiple genotoxin responses. Follow-up by DDR functional readouts newly implicates 12 assembly members in double-strand-break repair. The DNA damage response assemblies map is available for interactive visualization and query (ccmi.org/ddram/).

Detection and quantification of infectious hematopoietic necrosis virus in rainbow trout (Oncorhynchus mykiss) by SYBR Green real-time reverse transcriptase-polymerase chain reaction.

A real-time reverse transcriptase-polymerase chain reaction assay using the fluorogenic dye SYBR Green I was developed for the detection and quantification of infectious hematopoietic necrosis virus (IHNV) infecting rainbow trout (Oncorhynchus mykiss). Using primers designed for the IHNV nucleocapsid (N) and surface glycoprotein (G) genes, virus was detected in liver, kidney, spleen, adipose tissue, and pectoral fin samples from fish challenged in the laboratory via either injection or immersion and in fish collected from the field. The N- and G-gene amplicons provided melting curves with a single peak at 85.5 and 86.5 degrees C, respectively. Among different tissues tested, overall the N-gene was expressed in greater abundance than the G-gene in both laboratory-challenged and field samples. Kidney, liver, and spleen tissues had higher copies of the N- and G-genes compared to adipose tissue and pectoral fin. In samples from IHNV immersion challenge fish, the virus could be detected in the pectoral fin as early as 1 day post-challenge, and the viral load appears to decline by 6 days post-challenge. To evaluate the usefulness of non-invasive tissue sampling for IHNV detection, pectoral fin samples were collected from fish that were either apparently healthy or showing clinical signs of IHNV infection from commercial operations. Among the apparently healthy fish, using SYBR Green real-time RT-PCR the N-gene was detected in 2 out of 24 (8.3%), while the G-gene was detected in 8 of 24 (33%) fish. Among the fish showing clinical signs of IHNV infection, the N-gene was detected in 15 out of 36 (42%), while the G-gene was detected in 24 of 36 (67%) fish tested. Using a viral plaque assay, virus was detected in 4 of 24 (17%) apparently healthy fish and 33 of 36 (92%) fish showing clinical sign of IHNV infection. The higher level of IHNV detection by plaque assay compared to real-time RT-PCR might be due to the presence of more than one isolate in the field samples, and the inability to detect all the IHNV isolates using the current set of primers used in real-time RT-PCR. In conclusion, we developed a real-time RT-PCR assay for the detection and quantification of IHNV by SYBR Green real-time RT-PCR. This study demonstrates the potential of using fin clip as a non-invasive tissue source for detecting IHNV and possibly other viruses infecting salmonids in commercial aquaculture and in the field.

Validation of reference genes for quantitative measurement of immune gene expression in shrimp.

To accurately measure the relative expression of a target gene, mRNA expression data is routinely normalized with reference to an internal control gene. We examined the transcriptional stability of four internal control genes, beta-actin, glyceraldehyde-3 phosphate dehydrogenase (GAPDH), elongation factor1-alpha (EF1-alpha), and 18S ribosomal RNA (18S rRNA) while measuring the mRNA expression of a gene encoding a pattern recognition protein, lipopolysaccharide and glucan binding protein (LGBP) gene, in healthy and white spot syndrome virus (WSSV) infected shrimp (Penaeus stylirostris) before and after (4, 8, 16 and 32 h) challenge using real-time RT-PCR. Here, we describe a method to rank the internal control genes based on a linear regression analysis. This analysis enables us to analyze the multivariate data set, e.g. time course study samples with control and treatment groups. Using the linear regression analysis and the WSSV-challenged time course samples, GAPDH was found to be the most stable internal control gene followed by the genes EF1-alpha, 18S rRNA and beta-actin. Using the program geNorm, GAPDH was also found to be the most stable gene followed by the genes EF1-alpha, beta-actin and 18S rRNA. Using the program NormFinder, the ranking of the internal control genes were in the order of EF1-alpha>GAPDH>18S rRNA>beta-actin. The ability to compare the healthy and WSSV infected samples in parallel by the regression analysis makes this method a very useful approach while identifying the optimal reference gene for gene expression analysis.