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Nivolumab is an immune checkpoint inhibitor that targets programmed death-1 on T cells and is designed to amplify an immunologic reaction against cancer cells. However, upregulation of the immune system with checkpoint inhibition is nonspecific, and it can be associated with certain renal side effects, the best documented of which is acute tubulointerstitial nephritis. We present a unique case of a patient with acute kidney injury associated with nephrotic syndrome shortly after starting nivolumab therapy for metastatic anal carcinoma. Subsequent renal biopsy revealed membranoproliferative glomerulonephritis (MPGN). We believe this represents the first reported direct case of nivolumab-associated MPGN. As immunotherapy becomes more widely used in cancer treatment, particular attention must be paid to possible consequences of immune checkpoint inhibitors.

Generative modeling using GANs has gained traction in machine learning literature, as training does not require labeled datasets. This is perfect for applications in biological datasets, where large labeled datasets are often difficult and expensive to acquire. However, generative models offer no easy way to encode real images into feature-sets, something that is desirable for network explainability and may yield potentially informative image features. For this reason, we test a VAE-GAN architecture for label-free modeling of glomerular structural features. We show that this network can generate realistic looking synthetic images, and be used to interpolate between images. To prove the biological relevance of the network encodings, we classify small-labeled sets of encoded glomeruli by biopsy Tervaert class and for the presence of sclerosis, obtaining a Cohen's kappa values of 0.87 and 0.78 respectfully.

Diabetic Nephropathy (DN) progression is stratified into several stages with different levels of proteinuria, albuminuria, and physical characteristics as observed by pathologists. These physical changes are primarily visible within a patient's glomeruli which function as filtration units for blood returning for oxygenation. As DN stage increases, it is possible to observe the thickening of the glomerular basement membrane, expansion of the mesangium, and development of nodular sclerosis. Classification of different stages of DN by pathologists is based on semi-qualitative assessments of these characteristics on an individual glomerulus basis. Being able to probabilistically infer stage membership of individual glomeruli based on a combination of easily observable and hidden image features would be an invaluable tool for furthering our understanding of the drivers of DN progression. Markov Particle filters, included in the bnlearn package in R, were used to query a Bayesian Network (BN) constructed using the structural Hill-Climbing algorithm on a set of glomerular features. These features included both traditional characteristics such as glomerular area and number of mesangial nuclei as well as more abstract features derived from Minimum Spanning Trees (MST) to quantify spatial distribution of mesangial nuclei. Our results using images from multiple institutions suggest that these abstract features exercise a variable influence on DN stage membership over the course of disease progression. Further research incorporating clinical data will give nephrologists a "white box" visual of quantitative factors present in DN patients.

Ionising radiation (IR) is a recognised carcinogen responsible for cancer development in patients previously treated using radiotherapy, and in individuals exposed as a result of accidents at nuclear energy plants. However, the mutational signatures induced by distinct types and doses of radiation are unknown. Here, we analyse the genetic architecture of mammary tumours, lymphomas and sarcomas induced by high (56Fe-ions) or low (gamma) energy radiation in mice carrying Trp53 loss of function alleles. In mammary tumours, high-energy radiation is associated with induction of focal structural variants, leading to genomic instability and Met amplification. Gamma-radiation is linked to large-scale structural variants and a point mutation signature associated with oxidative stress. The genomic architecture of carcinomas, sarcomas and lymphomas arising in the same animals are significantly different. Our study illustrates the complex interactions between radiation quality, germline Trp53 deficiency and tissue/cell of origin in shaping the genomic landscape of IR-induced tumours.

Synthetic Zfra4-10 and WWOX7-21 peptides strongly suppress cancer growth in vivo. Hypothetically, Zfra4-10 binds to the membrane Hyal-2 of spleen Z cells and activates the Hyal-2/WWOX/SMAD4 signaling for cytotoxic Z cell activation to kill cancer cells. Stimulation of membrane WWOX in the signaling complex by a WWOX epitope peptide, WWOX7-21, is likely to activate the signaling. Here, mice receiving Zfra4-10 or WWOX7-21 peptide alone exhibited an increased binding of endogenous tumor suppressor WWOX with ERK, C1qBP, NF-κB, Iba1, p21, CD133, JNK1, COX2, Oct4, and GFAP in the spleen, brain, and/or lung which led to cancer suppression. However, when in combination, Zfra4-10 and WWOX7-21 reduced the binding of WWOX with target proteins and allowed tumor growth in vivo. In addition to Zfra4-10 and WWOX7-21 peptides, stimulating the membrane Hyal-2/WWOX complex with Hyal-2 antibody and sonicated hyaluronan (HAson) induced Z cell activation for killing cancer cells in vivo and in vitro. Mechanistically, Zfra4-10 binds to membrane Hyal-2, induces dephosphorylation of WWOX at pY33 and pY61, and drives Z cell activation for the anticancer response. Thus, Zfra4-10 and WWOX7-21 peptides, HAson, and the Hyal-2 antibody are of therapeutic potential for cancer suppression.

Methane (CH4) emissions from wetlands are likely increasing and important in global climate change assessments. However, contemporary terrestrial biogeochemical model predictions of CH4 emissions are very uncertain, at least in part due to prescribed temperature sensitivity of CH4 production and emission. While statistically consistent apparent CH4 emission temperature dependencies have been inferred from meta-Analyses across microbial to ecosystem scales, year-round ecosystem-scale observations have contradicted that finding. Here, we show that apparent CH4 emission temperature dependencies inferred from year-round chamber measurements exhibit substantial intraseasonal variability, suggesting that using static temperature relations to predict CH4 emissions is mechanistically flawed. Our model results indicate that such intra-seasonal variability is driven by substrate-mediated microbial and abiotic interactions: seasonal cycles in substrate availability favors CH4 production later in the season, leading to hysteretic temperature sensitivity of CH4 production and emission. Our findings demonstrate the uncertainty of inferring CH4 emission or production rates from temperature alone and highlight the need to represent microbial and abiotic interactions in wetland biogeochemical models.

In the clinical practice of pathology, trichrome stains are commonly used to highlight collagen and to help evaluate fibrosis. Such stains do delineate collagen deposits but are not molecularly specific and can suffer from staining inconsistencies. Moreover, performing histochemical stain evaluation requires the preparation of additional sections beyond the original hematoxylin- and eosin-stained slides, as well as additional staining steps, which together add cost, time, and workflow complications. We have developed a new microscopy approach, termed DUET (DUal-mode Emission and Transmission) that can be used to extract signals that would typically require special stains or advanced optical methods. Our preliminary analysis demonstrates the potential of using the resulting signals to generate virtual histochemical images that resemble trichrome-stained slides and can support clinical evaluation. We demonstrate advantages of this approach over images acquired from conventional trichrome-stained slides and compare them with images created using second harmonic generation microscopy.

In the clinical practice of pathology, trichrome stains are commonly used to highlight collagen and to help evaluate fibrosis. Such stains do delineate collagen deposits but are not molecularly specific and can suffer from staining inconsistencies. Moreover, performing histochemical stain evaluation requires the preparation of additional sections beyond the original hematoxylin- and eosin-stained slides, as well as additional staining steps, which together add cost, time, and workflow complications. We have developed a new microscopy approach, termed DUET (DUal-mode Emission and Transmission) that can be used to extract signals that would typically require special stains or advanced optical methods. Our preliminary analysis demonstrates the potential of using the resulting signals to generate virtual histochemical images that resemble trichrome-stained slides and can support clinical evaluation. We demonstrate advantages of this approach over images acquired from conventional trichrome-stained slides and compare them with images created using second harmonic generation microscopy.

Quantifying the temperature sensitivity of methane (CH _4 ) production is crucial for predicting how wetland ecosystems will respond to climate warming. Typically, the temperature sensitivity (often quantified as a Q _10 value) is derived from laboratory incubation studies and then used in biogeochemical models. However, studies report wide variation in incubation-inferred Q _10 values, with a large portion of this variation remaining unexplained. Here we applied observations in a thawing permafrost peatland (Stordalen Mire) and a well-tested process-rich model ( ecosys ) to interpret incubation observations and investigate controls on inferred CH _4 production temperature sensitivity. We developed a field-storage-incubation modeling approach to mimic the full incubation sequence, including field sampling at a particular time in the growing season, refrigerated storage, and laboratory incubation, followed by model evaluation. We found that CH _4 production rates during incubation are regulated by substrate availability and active microbial biomass of key microbial functional groups, which are affected by soil storage duration and temperature. Seasonal variation in substrate availability and active microbial biomass of key microbial functional groups led to strong time-of-sampling impacts on CH _4 production. CH _4 production is higher with less perturbation post-sampling, i.e. shorter storage duration and lower storage temperature. We found a wide range of inferred Q _10 values (1.2–3.5), which we attribute to incubation temperatures, incubation duration, storage duration, and sampling time. We also show that Q _10 values of CH _4 production are controlled by interacting biological, biochemical, and physical processes, which cause the inferred Q _10 values to differ substantially from those of the component processes. Terrestrial ecosystem models that use a constant Q _10 value to represent temperature responses may therefore predict biased soil carbon cycling under future climate scenarios.

ObjectiveThe Collaborative Cross (CC) is a mouse population model with diverse and reproducible genetic backgrounds used to identify novel disease models and genes that contribute to human disease. Since spontaneous tumour susceptibility in CC mice remains unexplored, we assessed tumour incidence and spectrum.DesignWe monitored 293 mice from 18 CC strains for tumour development. Genetic association analysis and RNA sequencing were used to identify susceptibility loci and candidate genes. We analysed genomes of patients with gastric cancer to evaluate the relevance of genes identified in the CC mouse model and measured the expression levels of ISG15 by immunohistochemical staining using a gastric adenocarcinoma tissue microarray. Association of gene expression with overall survival (OS) was assessed by Kaplan-Meier analysis.ResultsCC mice displayed a wide range in the incidence and types of spontaneous tumours. More than 40% of CC036 mice developed gastric tumours within 1 year. Genetic association analysis identified Nfκb1 as a candidate susceptibility gene, while RNA sequencing analysis of non-tumour gastric tissues from CC036 mice showed significantly higher expression of inflammatory response genes. In human gastric cancers, the majority of human orthologues of the 166 mouse genes were preferentially altered by amplification or deletion and were significantly associated with OS. Higher expression of the CC036 inflammatory response gene signature is associated with poor OS. Finally, ISG15 protein is elevated in gastric adenocarcinomas and correlated with shortened patient OS.ConclusionsCC strains exhibit tremendous variation in tumour susceptibility, and we present CC036 as a spontaneous laboratory mouse model for studying human gastric tumourigenesis.

Projected 21st century changes in high-latitude climate are expected to have significant impacts on permafrost thaw, which could cause substantial increases in emissions to the atmosphere of carbon dioxide (CO2) and methane (CH4, which has a global warming potential 28 times larger than CO2 over a 100-year horizon). However, predicted CH4 emission rates are very uncertain due to difficulties in modeling complex interactions among hydrological, thermal, biogeochemical, and plant processes. Methanogenic production pathways (i.e., acetoclastic [AM] and hydrogenotrophic [HM]) and the magnitude of CH4 emissions may both change as permafrost thaws, but a mechanistic analysis of controls on such shifts in CH4 dynamics is lacking. In this study, we reproduced observed shifts in CH4 emissions and production pathways with a comprehensive biogeochemical model (ecosys) at the Stordalen Mire in subarctic Sweden. Our results demonstrate that soil temperature changes differently affect AM and HM substrate availability, which regulates magnitudes of AM, HM, and thereby net CH4 emissions. We predict very large landscape-scale, vertical, and temporal variations in the modeled HM fraction, highlighting that measurement strategies for metrics that compare CH4 production pathways could benefit from model informed scale of temporal and spatial variance. Finally, our findings suggest that the warming and wetting trends projected in northern peatlands could enhance peatland AM fraction and CH4 emissions even without further permafrost degradation.

Variations in oral bacterial communities have been linked to oral cancer suggesting that the oral microbiome is an etiological factor that can influence oral cancer development. The 4-nitroquinoline 1-oxide (4-NQO)-induced murine oral and esophageal cancer model is frequently used to assess the effects of preventive and/or therapeutic agents. We used this model to assess the impact of the microbiome on tumorigenesis using axenic (germ-free) and conventionally housed mice. Increased toxicity was observed in germ-free mice, however, no difference in tumor incidence, multiplicity, and size was observed. Transcriptional profiling of liver tissue from germ-free and conventionally housed mice identified 254 differentially expressed genes including ten cytochrome p450 enzymes, the largest family of phase-1 drug metabolizing enzymes in the liver. Gene ontology revealed that differentially expressed genes were enriched for liver steatosis, inflammation, and oxidative stress in livers of germ-free mice. Our observations emphasize the importance of the microbiome in mediating chemical toxicity at least in part by altering host gene expression. Studies on the role of the microbiome in chemical-induced cancer using germ-free animal models should consider the potential difference in dose due to the microbiome-mediated changes in host metabolizing capacity, which might influence the ability to draw conclusions especially for tumor induction models that are dose dependent.

Patient-derived induced pluripotent stem cells (iPSCs) have become a promising resource for exploring genetics of complex diseases, discovering new drugs, and advancing regenerative medicine. Increasingly, laboratories are creating their own banks of iPSCs derived from diverse donors. However, there are not yet standardized guidelines for qualifying these cell lines, i.e., distinguishing between bona fide human iPSCs, somatic cells, and imperfectly reprogrammed cells. Here, we report the establishment of a panel of 30 iPSCs from CD34+ peripheral blood mononuclear cells, of which 10 were further differentiated in vitro into all three germ layers. We characterized these different cell types with commonly used pluripotent and lineage specific markers, and showed that NES, TUBB3, and OTX2 cannot be reliably used as ectoderm differentiation markers. Our work highlights the importance of marker selection in iPSC authentication, and the need for the field to establish definitive standard assays.

Intermediate-density lipoproteins (IDLs), the remnants of very-low-density lipoproteins via lipolysis, are rich in cholesteryl ester and are associated with cardiovascular disease. Despite pharmacological interest in IDLs, their three-dimensional (3D) structure is still undetermined due to their variation in size, composition, and dynamic structure. To explore the 3D structure of IDLs, we reconstructed 3D density maps from individual IDL particles using cryo-electron microscopy (cryo-EM) and individual-particle electron tomography (IPET, without averaging from different molecules). 3D reconstructions of IDLs revealed an unexpected polyhedral structure that deviates from the generally assumed spherical shape model (Frias et al., 2007; Olson, 1998; Shen et al., 1977). The polyhedral-shaped IDL contains a high-density shell formed by flat surfaces that are similar to those of very-low-density lipoproteins but have sharper dihedral angles between nearby surfaces. These flat surfaces would be less hydrophobic than the curved surface of mature spherical high-density lipoprotein (HDL), leading to a lower binding affinity of IDL to hydrophobic proteins (such as cholesteryl ester transfer protein) than HDL. This is the first visualization of the IDL 3D structure, which could provide fundamental clues for delineating the role of IDL in lipid metabolism and cardiovascular disease.

In diabetic nephropathy (DN), hyperglycemia drives a progressive thickening of glomerular filtration surfaces, increased cell proliferation as well as mesangial expansion and a constriction of capillary lumens. This leads to progressive structural changes inside the Glomeruli. In this work, we make a study of structural glomerular changes in DN from a graph-theoretic standpoint, using features extracted from Minimal Spanning Trees (MSTs) constructed over intercellular distances in order to classify the "packing signatures" of different DN stages. We further investigate the significance of the competing effects of Volume change measured here in 2Dimensional Pixel span area (Area) on one hand and increased cell proliferation on the other in determining the packing patterns. Towards that we formulate the problem as Dynamic Bayesian Network (DBN). From our preliminary results we do postulate that volume expansion caused by internal pressure as capillary lumens constriction has perhaps has a greater effect in the early stages.

Neural networks promise to bring robust, quantitative analysis to medical fields. However, their adoption is limited by the technicalities of training these networks and the required volume and quality of human-generated annotations. To address this gap in the field of pathology, we have created an intuitive interface for data annotation and the display of neural network predictions within a commonly used digital pathology whole-slide viewer. This strategy used a 'human-in-the-loop' to reduce the annotation burden. We demonstrate that segmentation of human and mouse renal micro compartments is repeatedly improved when humans interact with automatically generated annotations throughout the training process. Finally, to show the adaptability of this technique to other medical imaging fields, we demonstrate its ability to iteratively segment human prostate glands from radiology imaging data.

BackgroundDespite careful clinical examination, procurement biopsy and assessment on hypothermic machine perfusion, a significant number of potentially useable deceased donor kidneys will be discarded because they are deemed unsuitable for transplantation. Ex vivo normothermic perfusion (EVNP) may be useful as a means to further assess high-risk kidneys to determine suitability for transplantation.MethodsFrom June 2014 to October 2015, 7 kidneys (mean donor age, 54.3 years and Kidney Donor Profile Index, 79%) that were initially procured with the intention to transplant were discarded based on a combination of clinical findings, suboptimal biopsies, long cold ischemia time (CIT) and/or poor hypothermic perfusion parameters. They were subsequently placed on EVNP using oxygenated packed red blood cells and supplemental nutrition for a period of 3 hours. Continuous hemodynamic and functional parameters were assessed.ResultsAfter a mean CIT of 43.7 hours, all 7 kidneys appeared viable on EVNP with progressively increasing renal blood flow over the 3-hour period of perfusion. Five of the 7 kidneys had excellent macroscopic appearance, rapid increase in blood flow to 200 to 250 mL/min, urine output of 40 to 260 mL/h and increasing creatinine clearance.ConclusionsFavorable perfusion characteristics and immediate function after a 3-hour course of EVNP suggests that high-risk kidneys subjected to long CIT may have been considered for transplantation. The combined use of ex vivo hypothermic and normothermic perfusion may be a useful strategy to more adequately assess and preserve high-risk kidneys deemed unsuitable for transplantation. A clinical trial will be necessary to validate the usefulness of this approach.

Permafrost peatlands store large amounts of carbon potentially vulnerable to decomposition. However, the fate of that carbon in a changing climate remains uncertain in models due to complex interactions among hydrological, biogeochemical, microbial, and plant processes. In this study, we estimated effects of climate forcing biases present in global climate reanalysis products on carbon cycle predictions at a thawing permafrost peatland in subarctic Sweden. The analysis was conducted with a comprehensive biogeochemical model (ecosys) across a permafrost thaw gradient encompassing intact permafrost palsa with an ice core and a shallow active layer, partly thawed bog with a deeper active layer and a variable water table, and fen with a water table close to the surface, each with distinct vegetation and microbiota. Using in situ observations to correct local cold and wet biases found in the Global Soil Wetness Project Phase 3 (GSWP3) climate reanalysis forcing, we demonstrate good model performance by comparing predicted and observed carbon dioxide (CO 2 ) and methane (CH 4 ) exchanges, thaw depth, and water table depth. The simulations driven by the bias-corrected climate suggest that the three peatland types currently accumulate carbon from the atmosphere, although the bog and fen sites can have annual positive radiative forcing impacts due to their higher CH 4 emissions. Our simulations indicate that projected precipitation increases could accelerate CH 4 emissions from the palsa area, even without further degradation of palsa permafrost. The GSWP3 cold and wet biases for this site significantly alter simulation results and lead to erroneous active layer depth (ALD) and carbon budget estimates. Biases in simulated CO 2 and CH 4 exchanges from biased climate forcing are as large as those among the thaw stages themselves at a landscape scale across the examined permafrost thaw gradient. Future studies should thus not only focus on changes in carbon budget associated with morphological changes in thawing permafrost, but also recognize the effects of climate forcing uncertainty on carbon cycling.