CCAAT/Enhancer-Binding Protein Homologous (CHOP) Protein Promotes Carcinogenesis in the DEN-Induced Hepatocellular Carcinoma Model

Background and Aims C/EBP homologous protein (CHOP) plays pro-apoptotic roles in the integrated stress response. Recently, a tumor suppressive role for CHOP was demonstrated in lung cancer via regulation of tumor metabolism. To explore the role of CHOP in hepatocarcinogenesis, we induced hepatocellular carcinoma (HCC) in wild type (wt) and CHOP knockout (KO) mice using the carcinogen N-diethylnitrosamine (DEN). Results: Analysis of tumor development showed reduced tumor load, with markedly smaller tumor nodules in the CHOP KO animals, suggesting oncogenic roles of CHOP in carcinogen-induced HCC. In wt tumors, CHOP was exclusively expressed in tumor tissue, with minimal expression in normal parenchyma. Analysis of human adenocarcinomas of various origins demonstrated scattered expression of CHOP in the tumors, pointing to relevance in human pathology. Characterization of pathways that may contribute to preferential expression of CHOP in the tumor identified ATF6 as a potential candidate. ATF6, a key member of the endoplasmic reticulum stress signaling machinery, exhibited a similar pattern of expression as CHOP and strong activation in wt but not CHOP KO tumors. Because HCC is induced by chronic inflammation, we assessed whether CHOP deficiency affects tumor-immune system crosstalk. We found that the number of macrophages and levels of IFNγ and CCL4 mRNA were markedly reduced in tumors from CHOP KO relative to wt mice, suggesting a role for CHOP in modulating tumor microenvironment and macrophage recruitment to the tumor. Conclusion: Our data highlights a role for CHOP as a positive regulator of carcinogen-induced HCC progression through a complex mechanism that involves the immune system and modulation of stress signaling pathways

Use of H19 Gene Regulatory Sequences in DNA-Based Therapy for Pancreatic Cancer

Pancreatic cancer is the eighth most common cause of death from cancer in the world, for which palliative treatments are not effective and frequently accompanied by severe side effects. We propose a DNA-based therapy for pancreatic cancer using a nonviral vector, expressing the diphtheria toxin A chain under the control of the H19 gene regulatory sequences. The H19 gene is an oncofetal RNA expressed during embryo development and in several types of cancer. We tested the expression of H19 gene in patients, and found that 65% of human pancreatic tumors analyzed showed moderated to strong expression of the gene. In vitro experiments showed that the vector was effective in reducing Luciferase protein activity on pancreatic carcinoma cell lines. In vivo experiment results revealed tumor growth arrest in different animal models for pancreatic cancer. Differences in tumor size between control and treated groups reached a 75% in the heterotopic model (P = .037) and 50% in the orthotopic model (P = .007). In addition, no visible metastases were found in the treated group of the orthotopic model. These results indicate that the treatment with the vector DTA-H19 might be a viable new therapeutic option for patients with unresectable pancreatic cancer

Unique function words characterize genomic proteins

Significance The vast, mostly unknown protein universe can be explored by analyzing protein sequences as a string of domains. A broader coverage can be achieved when these domains, the essential blocks in protein evolution, are detected using sequence profiles. Using clustering to collapse redundant profiles into unique function words (UFWs), we find that over the years 2009–2016, the number of UFWs saturates while the number of sequences matched by a combination of two or more UFWs grows exponentially.</jats:p

Predicting the Trajectory of Any COVID19 Epidemic From the Best Straight Line

ABSTRACTA pipeline involving data acquisition, curation, carefully chosen graphs and mathematical models, allows analysis of COVID-19 outbreaks at 3,546 locations world-wide (all countries plus smaller administrative divisions with data available). Comparison of locations with over 50 deaths shows all outbreaks have a common feature: H(t) defined as loge(X(t)/X(t-1)) decreases linearly on a log scale, where X(t) is the total number of Cases or Deaths on day, t (we use ln for loge). The downward slopes vary by about a factor of three with time constants (1/slope) of between 1 and 3 weeks; this suggests it may be possible to predict when an outbreak will end. Is it possible to go beyond this and perform early prediction of the outcome in terms of the eventual plateau number of total confirmed cases or deaths?We test this hypothesis by showing that the trajectory of cases or deaths in any outbreak can be converted into a straight line. Specifically Y(t) ≡ −ln(ln(N / X (t)), is a straight line for the correct plateau value N, which is determined by a new method, Best-Line Fitting (BLF). BLF involves a straight-line facilitation extrapolation needed for prediction; it is blindingly fast and amenable to optimization. We find that in some locations that entire trajectory can be predicted early, whereas others take longer to follow this simple functional form. Fortunately, BLF distinguishes predictions that are likely to be correct in that they show a stable plateau of total cases or death (N value). We apply BLF to locations that seem close to a stable predicted N value and then forecast the outcome at some locations that are still growing wildly. Our accompanying web-site will be updated frequently and provide all graphs and data described here.</jats:p

Paradoxical relationship between speed and accuracy in olfactory figure-background segregation

In natural settings, many stimuli impinge on our sensory organs simultaneously. Parsing these sensory stimuli into perceptual objects is a fundamental task faced by all sensory systems. Similar to other sensory modalities, increased odor backgrounds decrease the detectability of target odors by the olfactory system. The mechanisms by which background odors interfere with the detection and identification of target odors are unknown. Here we utilized the framework of the Drift Diffusion Model (DDM) to consider possible interference mechanisms in an odor detection task. We first considered pure effects of background odors on either signal or noise in the decision-making dynamics and showed that these produce different predictions about decision accuracy and speed. To test these predictions, we trained mice to detect target odors that are embedded in random background mixtures in a two-alternative choice task. In this task, the inter-trial interval was independent of behavioral reaction times to avoid motivating rapid responses. We found that increased backgrounds reduce mouse performance but paradoxically also decrease reaction times, suggesting that noise in the decision making process is increased by backgrounds. We further assessed the contributions of background effects on both noise and signal by fitting the DDM to the behavioral data. The models showed that background odors affect both the signal and the noise, but that the paradoxical relationship between trial difficulty and reaction time is caused by the added noise.</jats:p

Paradoxical relationship between speed and accuracy in olfactory figure-background segregation

AbstractIn natural settings, many stimuli impinge on our sensory organs simultaneously. Parsing these sensory stimuli into perceptual objects is a fundamental task faced by all sensory systems. Similar to other sensory modalities, increased odor backgrounds decrease the detectability of target odors by the olfactory system. The mechanisms by which background odors interfere with the detection and identification of target odors are unknown. Here we utilized the framework of the Drift Diffusion Model (DDM) to consider possible interference mechanisms in an odor detection task. We consider effects of background odors on both signal and noise in the decision-making dynamics, and show that these produce different predictions about decision accuracy and speed. To test these predictions, we trained mice to detect target odors that are embedded in random background mixtures in a two-alternative choice task. Trial duration was independent of behavioral reaction times to avoid motivating rapid responses. We found that the behavioral data is most consistent with background odors acting by adding noise to the decision-making dynamics. The added noise decreases the correct rate, but also decreases decision times, thereby creating a paradoxical relationship between speed and accuracy of target detection, where mice make faster and less accurate decisions in the presence of background odors.</jats:p

Paradoxical relationship between speed and accuracy in olfactory figure-background segregation.

In natural settings, many stimuli impinge on our sensory organs simultaneously. Parsing these sensory stimuli into perceptual objects is a fundamental task faced by all sensory systems. Similar to other sensory modalities, increased odor backgrounds decrease the detectability of target odors by the olfactory system. The mechanisms by which background odors interfere with the detection and identification of target odors are unknown. Here we utilized the framework of the Drift Diffusion Model (DDM) to consider possible interference mechanisms in an odor detection task. We first considered pure effects of background odors on either signal or noise in the decision-making dynamics and showed that these produce different predictions about decision accuracy and speed. To test these predictions, we trained mice to detect target odors that are embedded in random background mixtures in a two-alternative choice task. In this task, the inter-trial interval was independent of behavioral reaction times to avoid motivating rapid responses. We found that increased backgrounds reduce mouse performance but paradoxically also decrease reaction times, suggesting that noise in the decision making process is increased by backgrounds. We further assessed the contributions of background effects on both noise and signal by fitting the DDM to the behavioral data. The models showed that background odors affect both the signal and the noise, but that the paradoxical relationship between trial difficulty and reaction time is caused by the added noise

S2 Fig -

Comparison of experimental and DDM predicted decisions (A), and decision times (B). Behavioral data (colored) and Model predictions (black) are shown for each mouse (column). Data and model predictions are shown separately for target A trials (blue), target B trials (purple), and no target trials (red). Reaction times are shown as median ± median absolute deviation. (TIF)</p

S1 Fig -

The number of target-on (blue) and target-off (red) trials performed by each mouse for each number of background odorants. (TIF)</p

Fig 4 -

Average drift (A) and diffusion (B) as a function of the number of background odorants. The plots depict the mean ± SE across mice. Target-on trials are shown in blue and target-off trials in red.</p