Analysis of time-course microarray data: Comparison of common tools.

High-throughput time-series data have a special value for studying the dynamism of biological systems. However, the interpretation of such complex data can be challenging. The aim of this study was to compare common algorithms recently developed for the detection of differentially expressed genes in time-course microarray data. Using different measures such as sensitivity, specificity, predictive values, and related signaling pathways, we found that limma, timecourse, and gprege have reasonably good performance for the analysis of datasets in which only test group is followed over time. However, limma has the additional advantage of being able to report significance cut off, making it a more practical tool. In addition, limma and TTCA can be satisfactorily used for datasets with time-series data for all experimental groups. These findings may assist investigators to select appropriate tools for the detection of differentially expressed genes as an initial step in the interpretation of time-course big data.

In vitro versus in vivo models of kidney fibrosis: Time-course experimental design is crucial to avoid misinterpretations of gene expression data.

BACKGROUND: In vitro models are common tools in nephrology research. However, their validity has rarely been scrutinized. MATERIALS AND METHODS: Considering the critical role of transforming growth factor (TGF)-ß and hypoxia pathways in kidney fibrosis, kidney-derived cells were exposed to TGF-ß and/or hypoxic conditions and the expression levels of some genes related to these two signaling pathways were quantified in a time-course manner. Furthermore, a unilateral ureteral obstruction mouse model was generated, and the expressions of the same genes were assessed. RESULTS: In all in vitro experimental groups, the expression of the genes was noisy with no consistent pattern. However, in the animal model, TGF-ß pathway-related genes demonstrated considerable overexpression in the ureteral obstruction group compared with the sham controls. Interestingly, hypoxia pathway genes had prominent fluctuations with very similar patterns in both animal groups, suggesting a periodical pattern not affected by the intervention. CONCLUSION: The findings of this study suggest that in vitro findings should be interpreted cautiously and if possible are substituted or supported by animal models that are more consistent and reliable. Furthermore, we underscore the importance of time-course evaluation of both case and control groups in gene expression studies to avoid misconceptions caused by gene expression noise or intrinsic rhythms.

Transcriptional noise in intact and TGF-beta treated human kidney cells; the importance of time-series designs.

The transforming growth factor (TGF)-ß signaling pathway plays a key role in various cellular processes. However, insufficient knowledge about the complex and sometimes paradoxical functions of this pathway hinders its therapeutic targeting. In this study, the transcriptional profile of seven mediators and downstream elements of the TGF-ß pathway were assessed in TGF-ß treated and untreated human kidney derived cells for 2 weeks in a time course manner. As expected the up-regulation of ACTA2 and COL1A2 was evident in the treated cells. However, we observed remarkable fluctuations in gene expression, even in the supposedly steady states. The magnitude of noise was diverse in the examined genes. Our findings underscore the significance of time-course designs for gene expression analyses and clearly show that misleading data can be obtained in single point measurements. Furthermore, we propose specific considerations in the interpretation of time-course data in the context of noisy gene expression.

Zoledronic acid targets chemo-resistant polyploid giant cancer cells.

Although polyploid giant cancer cells (PGCCs) are known as a key source of failure of current therapies, sufficient drugs to target these cells are not yet introduced. Considering the similarities of polyploid cells in regeneration and cancer, we hypothesized that zoledronic acid (ZA), an osteoclast-targeting agent, might be used to eliminate PGCCs. The 5637-bladder cancer cell line was treated with various doses of cisplatin to enrich polyploid cells and the efficacy of different concentrations of ZA in reducing this population was assessed. The metabolic profile of PGCCs was investigated with gas chromatography-mass spectrometry. Lipid profiles, mitochondrial density, and ROS content were also measured to assess the response of the cells to ZA. Cancer cells surviving after three days of exposure with 6 µM cisplatin were mainly polyploid. These cells demonstrated special morphological features such as fusion with diploid or other polyploid cells and originated in daughter cells through budding. ZA could substantially eradicate PGCCs with the maximal effect observed with 50 µM which resulted in the drop of PGCC fraction from 60 ± 7.5 to 19 ± 1.7%. Enriched PGCCs after cisplatin-treatment demonstrated a drastic metabolic shift compared to untreated cancer cells with an augmentation of lipids. Further assays confirmed the high content of lipid droplets and cholesterol in these cells which were reduced after ZA administration. Additionally, the mitochondrial density and ROS increased in PGCCs both of which declined in response to ZA. Taken together, we propose that ZA is a potent inhibitor of PGCCs which alters the metabolism of PGCCs. Although this drug has been successfully exploited as adjuvant therapy for some malignancies, the current evidence on its effects on PGCCs justifies further trials to assess its potency for improving the success of current therapies for tackling tumor resistance and relapse.

Cisplatin-Resistant Ovarian Cancer Cells Reveal a Polyploid Phenotype with Remarkable Activation of Nuclear Processes.

Background: Tumor recurrence as one of the main causes of cancer death is a big barrier to cancer complete treatment. Various studies denote the possible role of therapeutics in tumor relapse. Cisplatin as one of the generally used chemotherapy agents is supposed to be the source of therapy resistance through formation of polyploid giant cancer cells (PGCCs). Nevertheless, the mechanisms by which PGCCs promote tumor relapse are not fully understood. Materials and Methods: In this study, we performed experimental and bioinformatic investigations to recognize the mechanisms related to cisplatin resistance. A2780 and SCOV-3 cell lines were treated with cisplatin for 72 hours and were evaluated for their morphology by fluorescent microscopy and DNA content analysis. Furthermore, a microarray dataset of cisplatin-resistant ovarian cancer cells was re-analyzed to determine the significantly altered genes and signaling pathways. Results: Although cisplatin led to death of considerable fraction of cells in both cell lines, a significant number of survived cells became polyploid. On the other hand, our high throughput analysis determined significant change in expression of 1930 genes which mainly related to gene regulatory mechanisms and nuclear processes. Besides, mTOR, hypoxia, Hippo, and 14-3-3 signaling pathways previously shown to have role in PGCCs were determined. Conclusion: Taken together, results of this study demonstrated some key biological mechanisms related to cisplatin-resistant polyploid cancer cells.

Regenerative potential of multinucleated cells: bone marrow adiponectin-positive multinucleated cells take the lead.

BACKGROUND: Polyploid cells can be found in a wide evolutionary spectrum of organisms. These cells are assumed to be involved in tissue regeneration and resistance to stressors. Although the appearance of large multinucleated cells (LMCs) in long-term culture of bone marrow (BM) mesenchymal cells has been reported, the presence and characteristics of such cells in native BM and their putative role in BM reconstitution following injury have not been fully investigated. METHODS: BM-derived LMCs were explored by time-lapse microscopy from the first hours post-isolation to assess their colony formation and plasticity. In addition, sub-lethally irradiated mice were killed every other day for four weeks to investigate the histopathological processes during BM regeneration. Moreover, LMCs from GFP transgenic mice were transplanted to BM-ablated recipients to evaluate their contribution to tissue reconstruction. RESULTS: BM-isolated LMCs produced mononucleated cells with characteristics of mesenchymal stromal cells. Time-series inspections of BM sections following irradiation revealed that LMCs are highly resistant to injury and originate mononucleated cells which reconstitute the tissue. The regeneration process was synchronized with a transient augmentation of adipocytes suggesting their contribution to tissue repair. Additionally, LMCs were found to be adiponectin positive linking the observations on multinucleation and adipogenesis to BM regeneration. Notably, transplantation of LMCs to myeloablated recipients could reconstitute both the hematopoietic system and BM stroma. CONCLUSIONS: A population of resistant multinucleated cells reside in the BM that serves as the common origin of stromal and hematopoietic lineages with a key role in tissue regeneration. Furthermore, this study underscores the contribution of adipocytes in BM reconstruction.

SALL4B, not targeted by IMiD, is important for SALL4-mediated tumorigenesis.

Oncofetal transcription factor SALL4 is essential for cancer cell survival. 1-5 Recently, several groups reported that immunomodulatory imide drugs (IMiDs) could degrade SALL4 in a proteasome-dependent manner. 6,7 Intriguingly, we observed that IMiDs had no effect on SALL4-positive cancer cells. Further studies demonstrated that IMiDs could only degrade SALL4A, one of the SALL4 isoforms. This finding raises the possibility that SALL4B, the isoform not affected by IMiDs, may be essential for SALL4-mediated cancer cell survival. SALL4B knockdown led to an increase in apoptosis and inhibition of cancer cell growth. SALL4B gain-of-function alone led to liver tumor formation in mice. Our observation that protein degraders can possess isoform-specific effects exemplifies the importance of delineating drug action and oncogenesis at the isoform level to develop more effective cancer therapeutics.

SALL4: An Intriguing Therapeutic Target in Cancer Treatment.

Spalt-Like Transcription Factor 4 (SALL4) is a critical factor for self-renewal ability and pluripotency of stem cells. On the other hand, various reports show tight relation of SALL4 to cancer occurrence and metastasis. SALL4 exerts its effects not only by inducing gene expression but also repressing a large cluster of genes through interaction with various epigenetic modifiers. Due to high expression of SALL4 in cancer cells and its silence in almost all adult tissues, it is an ideal target for cancer therapy. However, targeting SALL4 meets various challenges. SALL4 is a transcription factor and designing appropriate drug to inhibit this intra-nucleus component is challenging. On the other hand, due to lack of our knowledge on structure of the protein and the suitable active sites, it becomes more difficult to reach the appropriate drugs against SALL4. In this review, we have focused on approaches applied yet to target this oncogene and discuss the potential of degrader systems as new therapeutics to target oncogenes.

Olfactory receptors contribute to progression of kidney fibrosis.

Olfactory receptors (ORs) which are mainly known as odor-sensors in the olfactory epithelium are shown to be expressed in several non-sensory tissues. Despite the specified role of some of these receptors in normal physiology of the kidney, little is known about their potential effect in renal disorders. In this study, using the holistic view of systems biology, it was determined that ORs are significantly changed during the progression of kidney fibrosis. For further validation, common differentially expressed ORs resulted from reanalysis of two time-course microarray datasets were selected for experimental evaluation in a validated murine model of unilateral ureteral obstruction (UUO). Transcriptional analysis by real-time quantitative polymerase chain reaction demonstrated considerable changes in the expression pattern of Olfr433, Olfr129, Olfr1393, Olfr161, and Olfr622 during the progression of kidney fibrosis. For localization of these ORs, single-cell RNA-sequencing datasets of normal and UUO mice were reanalyzed. Results showed that Olfr433 is highly expressed in macrophages in day-2 and 7 post-injury in UUO mice and not in normal subgroups. Besides, like previous findings, Olfr1393 was shown to be expressed prominently in the proximal tubular cells of the kidney. In conclusion, our combinatorial temporal approach to the underlying mechanisms of chronic kidney disease highlighted the potential role of ORs in progression of fibrosis. The expression of Olfr433 in the macrophages provides some clue about its relation to molecular mechanisms promoted in the fibrotic kidney. The proposed ORs in this study could be the subject of further functional assessments in the future.

Circulating ß2 and α1 microglobulins predict progression of nephropathy in diabetic patients: a meta-analysis of prospective cohort studies.

AIMS: To study the association of circulating ß2 (B2M) and α1 microglobulins (A1M) with diabetic nephropathy (DN) progression, a meta-analysis was performed on the prospective cohort studies. METHODS: Up to October 2021, a comprehensive search of the PubMed, EMBASE, Scopus, Web of Science, and Cochrane Library databases was performed. The primary outcome (progression of DN) was defined as a decrease in eGFR or the occurrence of end stage renal disease or DN-related mortality. Eligible studies were included in a pooled analysis that used either fixed-effect or random-effect models to compensate for variation in measurement standards between studies. The funnel plot and Egger's test were used to assess publication bias. RESULTS: The meta-analysis included 4398 people from 9 prospective trials (8 cohorts) for B2M and 3110 people from 4 prospective trials (3 cohorts) for A1M. Diabetic individuals with higher B2M levels had an increased risk for DN (relative risk [RR]: 1.81, 95% confidence interval [CI]: 1.56-2.09). Likewise, higher A1M was associated with augmented probability of DN (RR: 1.96, 95% CI: 1.46-2.62). The funnel plot and Egger's tests indicated no publication bias for A1M. Additionally, to compensate for putative publication bias for B2M, using trim and fill analysis, four studies were filled for this marker and the results remained significant (RR: 1.62, 95% CI: 1.37-1.92). CONCLUSIONS: The elevated serum levels of B2M and A1M could be considered as potential predictors of DN progression in diabetic patients. PROTOCOL REGISTRATION: PROSPERO CRD42021278300.

Optimization of Mouse-on-Mouse Immunohistochemistry by Utilizing Fluorescent-dye Conjugated Secondary Anti-Mouse Antibody.

The application of mouse monoclonal antibody for immunostaining the mouse tissues results in a high rate of background noise because of the interaction of the secondary antibody with endogenous immunoglobulins and other immune components. The most advised blocking strategy for the mouse-on-mouse immunostaining is the use of anti-mouse Fab fragments. Nevertheless, the commercial kits containing Fab fragment are costly and unavailable in many research laboratories. In this study, we provide evidence showing the potential of the fluorescent-dye conjugated secondary anti-mouse antibody for reducing the background noise in the mouse-on-mouse immunohistochemistry. Furthermore, our findings demonstrate the inadequacy of goat serum/protein-blocking solution alone as an immunohistochemistry blocking system for reducing the background noise.

Inefficiency of SIR models in forecasting COVID-19 epidemic: a case study of Isfahan.

The multifaceted destructions caused by COVID-19 have been compared to that of World War II. What makes the situation even more complicated is the ambiguity about the duration and ultimate spread of the pandemic. It is especially critical for the governments, healthcare systems, and economic sectors to have an estimate of the future of this disaster. By using different mathematical approaches, including the classical susceptible-infected-recovered (SIR) model and its derivatives, many investigators have tried to predict the outbreak of COVID-19. In this study, we simulated the epidemic in Isfahan province of Iran for the period from Feb 14th to April 11th and also forecasted the remaining course with three scenarios that differed in terms of the stringency level of social distancing. Despite the prediction of disease course in short-term intervals, the constructed SIR model was unable to forecast the actual spread and pattern of epidemic in the long term. Remarkably, most of the published SIR models developed to predict COVID-19 for other communities, suffered from the same inconformity. The SIR models are based on assumptions that seem not to be true in the case of the COVID-19 epidemic. Hence, more sophisticated modeling strategies and detailed knowledge of the biomedical and epidemiological aspects of the disease are needed to forecast the pandemic.

Bifurcation analysis of a modular model of embryonic kidney development.

Many biological processes show switching behaviors in response to parameter changes. Although numerous surveys have been conducted on bifurcations in biological systems, they commonly focus on over-represented parts of signaling cascades, known as motifs, ignoring the multi-motif structure of biological systems and the communication links between these building blocks. In this paper, a method is proposed which partitions molecular interactions to modules based on a control theory point of view. The modules are defined so that downstream effect of one module is a regulator for its neighboring modules. Communication links between these modules are then considered as bifurcation parameters to reveal change in steady state status of each module. As a case-study, we generated a molecular interaction map of signaling molecules during the development of mammalian embryonic kidneys. The whole system was divided to modules, where each module is defined as a group of interacting molecules that result in expression of a vital downstream regulator. Bifurcation analysis was then performed on these modules by considering the communication signals as bifurcation parameters. Two-parameter bifurcation analysis was then performed to assess the effects of simultaneous input signals on each module behavior. In the case where a module had more than two inputs, a series of two parameter bifurcation diagrams were calculated each corresponding to different values of the third parameter. We detected multi-stability for RET protein as a key regulator for fate determination. This finding is in agreement with experimental data indicating that ureteric bud cells are bi-potential, able to form tip or trunk of the bud based on their RET activity level. Our findings also indicate that Glial cell-derived neurotrophic factor (GDNF), a known potent regulator of kidney development, exerts its fate-determination function on cell placement through destruction of saddle node bifurcation points in RET steady states and confining RET activity level to high activity in ureteric bud tip. In conclusion, embryonic cells usually show a huge decision making potential; the proposed modular modeling of the system in association with bifurcation analysis provides a quantitative holistic view of organ development.

The double-edged sword role of fibroblasts in the interaction with cancer cells; an agent-based modeling approach.

Fibroblasts as key components of tumor microenvironment show different features in the interaction with cancer cells. Although, Normal fibroblasts demonstrate anti-tumor effects, cancer associated fibroblasts are principal participant in tumor growth and invasion. The ambiguity of fibroblasts function can be regarded as two heads of its behavioral spectrum and can be subjected for mathematical modeling to identify their switching behavior. In this research, an agent-based model of mutual interactions between fibroblast and cancer cell was created. The proposed model is based on nonlinear differential equations which describes biochemical reactions of the main factors involved in fibroblasts and cancer cells communication. Also, most of the model parameters are estimated using hybrid unscented Kalman filter. The interactions between two cell types are illustrated by the dynamic modeling of TGFß and LIF pathways as well as their crosstalk. Using analytical and computational approaches, reciprocal effects of cancer cells and fibroblasts are constructed and the role of signaling molecules in tumor progression or prevention are determined. Finally, the model is validated using a set of experimental data. The proposed dynamic modeling might be useful for designing more efficient therapies in cancer metastasis treatment and prevention.

Cancer regeneration: Polyploid cells are the key drivers of tumor progression.

In spite of significant advancements of therapies for initial eradication of cancers, tumor relapse remains a major challenge. It is for a long time known that polyploid malignant cells are a main source of resistance against chemotherapy and irradiation. However, therapeutic approaches targeting these cells have not been appropriately pursued which could partly be due to the shortage of knowledge on the molecular biology of cell polyploidy. On the other hand, there is a rising trend to appreciate polyploid/ multinucleated cells as key players in tissue regeneration. In this review, we suggest an analogy between the functions of polyploid cells in normal and malignant tissues and discuss the idea that cell polyploidy is an evolutionary conserved source of tissue regeneration also exploited by cancers as a survival factor. In addition, polyploid cells are highlighted as a promising therapeutic target to overcome drug resistance and relapse.

Agent-based modeling and bifurcation analysis reveal mechanisms of macrophage polarization and phenotype pattern distribution.

Macrophages play a key role in tissue regeneration by polarizing to different destinies and generating various phenotypes. Recognizing the underlying mechanisms is critical in designing therapeutic procedures targeting macrophage fate determination. Here, to investigate the macrophage polarization, a nonlinear mathematical model is proposed in which the effect of IL4, IFNγ and LPS, as external stimuli, on STAT1, STAT6, and NFκB is studied using bifurcation analysis. The existence of saddle-node bifurcations in these internal key regulators allows different combinations of steady state levels which are attributable to different fates. Therefore, we propose dynamic bifurcation as a crucial built-in mechanism of macrophage polarization. Next, in order to investigate the polarization of a population of macrophages, bifurcation analysis is employed aligned with agent-based approach and a two-layer model is proposed in which the information from single cells is exploited to model the behavior in tissue level. Also, in this model, a partial differential equation describes the diffusion of secreted cytokines in the medium. Finally, the model was validated against a set of experimental data. Taken together, we have here developed a cell and tissue level model of macrophage polarization behavior which can be used for designing therapeutic interventions.

Identification of Appropriate Housekeeping Genes for Gene Expression Analysis in Long-term Hypoxia-treated Kidney Cells.

BACKGROUND: Selection of stably expressing housekeeping genes (HKGs) is a crucial step in gene expression analysis. However, there are no universal HKGs for all experiments, and they should be determined by each biologic condition. The aim of this study was to detect appropriate HKGs for kidney cells cultured in long-term hypoxia. MATERIALS AND METHODS: Based on a screening step using a microarray data available from gene expression omnibus database, a set of candidate HKGs were chosen to be assessed in human kidney cells cultured in hypoxic or normoxic conditions for about 2 weeks in a time course manner. The stability of gene expression was assessed by refFinder, a web-based tool that integrates four computational programs (geNorm, Normfinder, BestKeeper, and the comparative ΔΔCt method). RESULTS: GAPDH and ACTB were the most stable genes in hypoxia treated cells whereas, B2M and ACTB were the best HKGs in cells cultured in normoxia. When both hypoxia and normoxia treated cells from all time points were evaluated together, GAPDH and ACTB equally showed the most stability. CONCLUSION: As in relative quantification of real-time polymerase chain reaction data, the same HKGs should be selected for all groups, we believe that GAPDH and ACTB are suitable HKGs for studies on the effect of hypoxia on cultured kidney cells.

Colored petri net modeling of small interfering RNA-mediated messenger RNA degradation.

BACKGROUND: Mathematical modeling of biological systems is an attractive way for studying complex biological systems and their behaviors. Petri Nets, due to their ability to model systems with various levels of qualitative information, have been wildly used in modeling biological systems in which enough qualitative data may not be at disposal. These nets have been used to answer questions regarding the dynamics of different cell behaviors including the translation process. In one stage of the translation process, the RNA sequence may be degraded. In the process of degradation of RNA sequence, small-noncoding RNA molecules known as small interfering RNA (siRNA) match the target RNA sequence. As a result of this matching, the target RNA sequence is destroyed. MATERIALS AND METHODS: In this context, the process of matching and destruction is modeled using Colored Petri Nets (CPNs). The model is constructed using CPNs which allow tokens to have a value or type on them. Thus, CPN is a suitable tool to model string structures in which each element of the string has a different type. Using CPNs, long RNA, and siRNA strings are modeled with a finite set of colors. The model is simulated via CPN Tools. RESULTS: A CPN model of the matching between RNA and siRNA strings is constructed in CPN Tools environment. CONCLUSION: In previous studies, a network of stoichiometric equations was modeled. However, in this particular study, we modeled the mechanism behind the silencing process. Modeling this kind of mechanisms provides us with a tool to examine the effects of different factors such as mutation or drugs on the process.

Stochastic Petri Net Modeling of Hypoxia Pathway Predicts a Novel Incoherent Feed-Forward Loop Controlling SDF-1 Expression in Acute Kidney Injury.

Homing of stem cells to the sites of injury is crucial for tissue regeneration. Stromal derived factor 1 (SDF-1) is among the most important chemokines recruiting these cells. Unexpectedly, our previous experimental data on mouse models of acute kidney injury showed that SDF-1 has a declining trend following ischemic kidney insult. To describe this unforeseen observation, a stochastic Petri net model of SDF-1 regulation in the hypoxia pathway was constructed based on main related components extracted from literature. Using this strategy, predictions regarding the underlying mechanisms of SDF-1 kinetics are generated and a novel incoherent feed forward loop regulating SDF-1 expression is proposed. The computational approach suggested here can be exploited to propose novel therapies for debilitating disorders such as kidney injury.