Substrate stiffness regulates primary hepatocyte functions†

Liver fibrosis occurs as a consequence of chronic injuries from viral infections, metabolic disorders, and alcohol abuse. Fibrotic liver microenvironment (LME) is characterized by excessive deposition and aberrant turnover of extracellular matrix proteins, which leads to increased tissue stiffness. Liver stiffness acts as a vital cue in the regulation of hepatic responses in both healthy and diseased states; however, the effect of varying stiffness on liver cells is not well understood. There is a critical need to engineer in vitro models that mimic the liver stiffness corresponding to various stages of disease progression in order to elucidate the role of individual cellular responses. Here we employed polydimethyl siloxane (PDMS) based substrates with tunable mechanical properties to investigate the effect of substrate stiffness on the behavior of primary rat hepatocytes. To recreate physiologically relevant stiffness, we designed soft substrates (2 kPa) to represent the healthy liver and stiff substrates (55 kPa) to represent the diseased liver. Tissue culture plate surface (TCPS) served as the control substrate. We observed that hepatocytes cultured on soft substrates displayed a more differentiated and functional phenotype for a longer duration as compared to stiff substrates and TCPS. We demonstrated that hepatocytes on soft substrates exhibited higher urea and albumin synthesis. Cytochrome P450 (CYP) activity, another critical marker of hepatocytes, displayed a strong dependence on substrate stiffness, wherein hepatocytes on soft substrates retained 2.7 fold higher CYP activity on day 7 in culture, as compared to TCPS. We further observed that an increase in stiffness induced downregulation of key drug transporter genes (NTCP, UGT1A1, and GSTM-2). In addition, we observed that the epithelial cell phenotype was better maintained on soft substrates as indicated by higher expression of hepatocyte nuclear factor 4α, cytokeratin 18, and connexin 32. These results indicate that the substrate stiffness plays a significant role in modulating hepatocyte behavior. Our PDMS based liver model can be utilized to investigate the signaling pathways mediating the hepatocyte-LME communication to understand the progression of liver diseases

A genetic network that suppresses genome rearrangements in Saccharomyces cerevisiae and contains defects in cancers.

Gross chromosomal rearrangements (GCRs) play an important role in human diseases, including cancer. The identity of all Genome Instability Suppressing (GIS) genes is not currently known. Here multiple Saccharomyces cerevisiae GCR assays and query mutations were crossed into arrays of mutants to identify progeny with increased GCR rates. One hundred eighty two GIS genes were identified that suppressed GCR formation. Another 438 cooperatively acting GIS genes were identified that were not GIS genes, but suppressed the increased genome instability caused by individual query mutations. Analysis of TCGA data using the human genes predicted to act in GIS pathways revealed that a minimum of 93% of ovarian and 66% of colorectal cancer cases had defects affecting one or more predicted GIS gene. These defects included loss-of-function mutations, copy-number changes associated with reduced expression, and silencing. In contrast, acute myeloid leukaemia cases did not appear to have defects affecting the predicted GIS genes

Using deep learning to detect digitally encoded DNA trigger for Trojan malware in Bio‑Cyber attacks

This article uses Deep Learning technologies to safeguard DNA sequencing against Bio-Cyber attacks. We consider a hybrid attack scenario where the payload is encoded into a DNA sequence to activate a Trojan malware implanted in a software tool used in the sequencing pipeline in order to allow the perpetrators to gain control over the resources used in that pipeline during sequence analysis. The scenario considered in the paper is based on perpetrators submitting synthetically engineered DNA samples that contain digitally encoded IP address and port number of the perpetrator’s machine in the DNA. Genetic analysis of the sample’s DNA will decode the address that is used by the software Trojan malware to activate and trigger a remote connection. This approach can open up to multiple perpetrators to create connections to hijack the DNA sequencing pipeline. As a way of hiding the data, the perpetrators can avoid detection by encoding the address to maximise similarity with genuine DNAs, which we showed previously. However, in this paper we show how Deep Learning can be used to successfully detect and identify the trigger encoded data, in order to protect a DNA sequencing pipeline from Trojan attacks. The result shows nearly up to 100% accuracy in detection in such a novel Trojan attack scenario even after applying fragmentation encryption and steganography on the encoded trigger data. In addition, feasibility of designing and synthesizing encoded DNA for such Trojan payloads is validated by a wet lab experiment

Single-cell RNA-sequencing reveals Transcriptional Changes and Clonal Architecture associated with Post-Transplant Relapse in Acute Myeloid Leukemia

"Acute myeloid leukemia (AML) is a malignancy characterized by overproduction of myeloid precursors at the expense of more differentiated, functional hematopoietic cells, resulting in anemia, thrombocytopenia, and neutropenia. Despite initial sensitivity to chemotherapy, a majority of patients with AML ultimately relapse. Among the challenges associated with relapse, post-allogeneic stem cell transplant relapse is particularly intractable because of our relative lack of understanding - and thus lack of effective treatment options - of the underlying mechanisms."--IntroductionZiheng Xu (1), Christopher A. Miller (2, 3), Sridhar N. Srivatsan (2), Catrina C. Fronick (3), Robert S. Fulton (3), Timothy J. Ley (2, 3, 4), and Allegra A. Petti (2, 3) ; 1. Washington University School of Medicine. 2. Division of Oncology, Washington University School of Medicine. 3. McDonnell Genome Institute, Washington University School of Medicine. 4. Department of Genetics, Washington University School of Medicine.Includes bibliographical reference

Understanding the Influence of Pressure and Radial Loads on Stress and Displacement Response of a Rotating Body: The Automobile Wheel

This paper highlights the use of the finite element technique for analyzing stress and displacement distributions in wheels of automotive vehicles when subject to the conjoint influence of inflation pressure and radial load. The most commonly used considerations in the design of the rotating body are elucidated. A potentially viable technique for finite element modeling of radial wheel, subjected to loading, is highlighted. The extrinsic influence of inflation pressure on performance of the rotating body, that is, the wheel, is rationalized

Genome-wide co-occupancy of AML1-ETO and N-CoR defines the t(8;21) AML signature in leukemic cells

BACKGROUND: Many leukemias result from chromosomal rearrangements. The t(8;21) chromosomal translocation produces AML1-ETO, an oncogenic fusion protein that compromises the function of AML1, a transcription factor critical for myeloid cell differentiation. Because of the pressing need for new therapies in the treatment of acute myleoid leukemia, we investigated the genome-wide occupancy of AML1-ETO in leukemic cells to discover novel regulatory mechanisms involving AML-ETO bound genes. RESULTS: We report the co-localization of AML1-ETO with the N-CoR co-repressor to be primarily on genomic regions distal to transcriptional start sites (TSSs). These regions exhibit over-representation of the motif for PU.1, a key hematopoietic regulator and member of the ETS family of transcription factors. A significant discovery of our study is that genes co-occupied by AML1-ETO and N-CoR (e.g., TYROBP and LAPTM5) are associated with the leukemic phenotype, as determined by analyses of gene ontology and by the observation that these genes are predominantly up-regulated upon AML1-ETO depletion. In contrast, the AML1-ETO/p300 gene network is less responsive to AML1-ETO depletion and less associated with the differentiation block characteristic of leukemic cells. Furthermore, a substantial fraction of AML1-ETO/p300 co-localization occurs near TSSs in promoter regions associated with transcriptionally active loci. CONCLUSIONS: Our findings establish a novel and dominant t(8;21) AML leukemia signature characterized by occupancy of AML1-ETO/N-CoR at promoter-distal genomic regions enriched in motifs for myeloid differentiation factors, thus providing mechanistic insight into the leukemic phenotype