Stress response genes in the human proximal tubules

The heat shock proteins (hsps) are believed to provide protection against a variety of insults such as elevated temperature, heavy metals and chemical agents. The goal of this study was to determine if the immortalized HK-2 human proximal tubule cell line could provide a model system to study the stress response of the proximal tubule cell. Heat stress, elevated temperature at 42.5°C for 1 hr, caused a marked increase only in hsp 70 mRNA and protein, but not that of hsp 27 or hsp 60 mRNA and protein. Similar results were obtained when the cells were subjected to 100 muM sodium arsenite or 53.4 muM CdCl 2 for 4 hrs. These findings were in contrast to those found previously with mortal human proximal tubule (HPT) cells, where acute stress by all three stimuli elicited marked increases in hsp 27, hsp 60 and hsp 70 mRNA and protein. The basal levels of expression of hsp 27 and hsp 60 in the HK-2 cells were elevated compared to unstressed HPT cells. These results suggest that failure of HK-2 cells to increase hsp 27 and hsp 60 levels in response to stress is because of their elevated basal levels, indicating that the genetic events that resulted in the immortalization of the HK-2 cells also elicited a stress response for hsp 27 and hsp 60, but not for hsp 70. Thus, there are differences in the regulation of the stress response between the immortal HK-2 and mortal HPT cell lines, and as long as these differences are recognized, the HK-2 cell line should be a valuable adjunct to study the stress response of the proximal tubule in general and when exposed to environmental pollutants such as cadmium.;The metallothoneins (MT) are a family of cysteine-rich, low molecular weight (6 kD), intracellular proteins that bind transitional metals. The goal of this study was to further characterize the basal expression of MT-3 in the in situ human kidney, in mortal human proximal tubule (HPT) cell cultures and the immortalized proximal tubular cell line, HK-2. MT-3 mRNA was detected in the proximal tubule of the in situ kidney with relative expression in excess to that of the ∃-actin housekeeping gene. Human proximal tubule cells also expressed both MT-3 mRNA and protein, and were able to form domes. Exposure of HPT cells to Cd+2 resulted in a transient increase in MT-3 mRNA and protein. In contrast, the HK-2 cell line did not express MT-3 and did not form domes. Stable transfection of the HK-2 cell line with the pcDNA3.1/Hygro(+) vector containing the MT-3 gene restored MT-3 expression and dome formation to the HK-2 cells. This result demonstrated that MT-3 is involved in the transport function of a human renal cell line that retains properties of the proximal tubule

Metallothionein isoform 3 and proximal tubule vectorial active transport

Metallothionein isoform 3 and proximal tubule vectorial active transport.BackgroundMetallothionein isoform 3 (MT-3) is expressed in the proximal tubule cells of the human kidney. The goal of the present study was to further characterize the basal expression of MT-3 in the proximal tubule and to determine if MT-3 participates in the maintenance of proximal tubule cell function.MethodsExpression of MT-3 mRNA was determined in the intact proximal tubule using microdissection and reverse transcription-polymerase chain reaction (RT-PCR). Basal expression of MT-3 mRNA and protein was determined in cultured human proximal tubule (HPT) cells and an immortalized proximal tubular cell line, HK-2 cells, using RT-PCR and immunoblotting. The MT-3 gene was stably transfected into the HK-2 cell line using the pcDNA3.1/Hygro (+) vector.ResultsMT-3 mRNA was detected in the proximal tubule of the in situ kidney with relative expression in excess to that of the β-actin housekeeping gene. The mortal HPT cells were shown to express both MT-3 mRNA and protein and to form domes, while immortal HK-2 cells were shown to have no expression of MT-3 mRNA and protein nor to form domes. The stable transfection of MT-3 in HK-2 restored MT-3 expression and dome formation to the HK-2 cells.ConclusionsMT-3 mRNA is present in the human proximal tubule, and MT-3 expression is involved in the transport function of a human renal cell line that retains properties of the proximal tubule

Altering a Histone H3K4 Methylation Pathway in Glomerular Podocytes Promotes a Chronic Disease Phenotype

Methylation of specific lysine residues in core histone proteins is essential for embryonic development and can impart active and inactive epigenetic marks on chromatin domains. The ubiquitous nuclear protein PTIP is encoded by the Paxip1 gene and is an essential component of a histone H3 lysine 4 (H3K4) methyltransferase complex conserved in metazoans. In order to determine if PTIP and its associated complexes are necessary for maintaining stable gene expression patterns in a terminally differentiated, non-dividing cell, we conditionally deleted PTIP in glomerular podocytes in mice. Renal development and function were not impaired in young mice. However, older animals progressively exhibited proteinuria and podocyte ultra structural defects similar to chronic glomerular disease. Loss of PTIP resulted in subtle changes in gene expression patterns prior to the onset of a renal disease phenotype. Chromatin immunoprecipitation showed a loss of PTIP binding and lower H3K4 methylation at the Ntrk3 (neurotrophic tyrosine kinase receptor, type 3) locus, whose expression was significantly reduced and whose function may be essential for podocyte foot process patterning. These data demonstrate that alterations or mutations in an epigenetic regulatory pathway can alter the phenotypes of differentiated cells and lead to a chronic disease state

Adversarial Optimization-Based Knowledge Transfer of Layer-Wise Dense Flow for Image Classification

A deep-learning technology for knowledge transfer is necessary to advance and optimize efficient knowledge distillation. Here, we aim to develop a new adversarial optimization-based knowledge transfer method involved with a layer-wise dense flow that is distilled from a pre-trained deep neural network (DNN). Knowledge distillation transferred to another target DNN based on adversarial loss functions has multiple flow-based knowledge items that are densely extracted by overlapping them from a pre-trained DNN to enhance the existing knowledge. We propose a semi-supervised learning-based knowledge transfer with multiple items of dense flow-based knowledge extracted from the pre-trained DNN. The proposed loss function would comprise a supervised cross-entropy loss for a typical classification, an adversarial training loss for the target DNN and discriminators, and Euclidean distance-based loss in terms of dense flow. For both pre-trained and target DNNs considered in this study, we adopt a residual network (ResNet) architecture. We propose methods of (1) the adversarial-based knowledge optimization, (2) the extended and flow-based knowledge transfer scheme, and (3) the combined layer-wise dense flow in an adversarial network. The results show that it provides higher accuracy performance in the improved target ResNet compared to the prior knowledge transfer methods

Deep transfer learning for the classification of variable sources

Ongoing or upcoming surveys such as Gaia, ZTF, or LSST will observe the light curves of billions or more astronomical sources. This presents new challenges for identifying interesting and important types of variability. Collecting a sufficient amount of labeled data for training is difficult, especially in the early stages of a new survey. Here we develop a single-band light-curve classifier based on deep neural networks and use transfer learning to address the training data paucity problem by conveying knowledge from one data set to another. First we train a neural network on 16 variability features extracted from the light curves of OGLE and EROS-2 variables. We then optimize this model using a small set (e.g., 5%) of periodic variable light curves from the ASAS data set in order to transfer knowledge inferred from OGLE and EROS-2 to a new ASAS classifier. With this we achieve good classification results on ASAS, thereby showing that knowledge can be successfully transferred between data sets. We demonstrate similar transfer learning using HIPPARCO