According to FTIR and GC findings, ethanol and IPA content were found to be 43–82% and 40–69%, and 56–64% and 61–66%, respectively, whereas ethanol was labeled at 66–80% and IPA at 65–70%. FTIR and GC revealed methanol content ranging from 37 to 98 and 19 to 81%, respectively. Also, the FTIR was significantly faster than the GC. Therefore, FTIR can be used to commercially analyze the quality of hand sanitizers.•FTIR was used to identify and quantify alcohol content in commercially available hand sanitizers in a quick and easy way.•Comparing the FTIR and GC data, provided quite similar results.•Out of ten samples, five contained ethanol, three IPA, and two methanol.Since the beginning of the COVID-19 pandemic, the use and manufacture of alcohol-based hand sanitizers increased exponentially. Efficacy of hand sanitizers mainly depends on active ingredients like ethanol and isopropanol (IPA). Even though methanol is extremely hazardous to people, it is still illegally used in hand sanitizers in Bangladesh. Developing a quick and simple analytical method for detecting and quantifying ethanol/IPA/methanol is crucial. Here, Fourier transform infrared spectroscopy (FTIR) was used to identify and quantify alcohol content in commercially available hand sanitizers in a quick and easy way. Comparing the FTIR and GC data, provided quite similar results. Unlike previous studies by FTIR, C–H, CH  Specifications TableDue to COVID-19 pandemic, the World Health Organization (WHO) has encouraged the public to wash their hands regularly to avoid infection, prompting alcohol-based hand sanitizers to become increasingly popular. Therefore, these products were in short supply. To counteract this, regulatory agencies around the world, including the US Food and Drug Administration (FDA), European Union, and the Bangladesh Health Administration, issued guidelines for drug companies and pharmaceuticals to temporarily prepare these sanitizers in order to increase supply during the public health emergency. According to WHO recommendations, the formulation of hand sanitizers are as follows: formulation I: ethanol 80% v/v, glycerol 1.45% v/v, hydrogen peroxide 0.125% v/v and formulation II: isopropyl alcohol 75% v/v, glycerol 1.45% v/v, hydrogen peroxide 0.125% v/v The goal of this work was to develop an infrared spectroscopy-based analytical method for detecting and quantifying ethanol, isopropanol (IPA) or unwanted methanol in commercial hand sanitizers. The results obtained by the IR method were also compared using the gas chromatography method. In developing countries like Bangladesh, where the IR technique is available at universities, pharmaceutical companies, and law enforcement agencies, the IR-based developed method can be used as an indispensable analytical tool for analyzing hand sanitizers. Because IR spectroscopy is easier to use than GC and/or GC–MS, a miniature IR-based method for real-world applications can be developed.Ethanol, IPA, methanol and acetone were purchased from Active Fine Chemicals Ltd., Bangladesh. Commercially available hand sanitizers were purchased from local drug stores in Dhaka, Bangladesh in order to detect and quantify alcohol content . Distilled water was used throughout the experiment.−1 prior to analysis of the real sanitizer samples. About 20–30 μL of the standard was directly introduced into a capillary of the sample holder and then placed on the sample window of the FTIR instrument and scanned to record the IR spectrum. Each standard was scanned at least three times (n = 3).Calcium fluoride crystal plates were used as sample holder for liquid samples in infrared (IR) spectroscopy. A sample holder made of calcium fluoride crystals is shown in Figure S2. Calibration standards were prepared by diluting each absolute alcohol with water in the following proportions: 100/0, 80/20, 70/30, 60/40, and 50/50. Fourier transform infrared spectrophotometer (FTIR)  was used to record the IR spectra of the calibration standards and the commercial hand sanitizers within a window range of 4000 to 400 cmIn a similar way, about 20–30 μL of the relevant sanitizer sample was directly loaded into a capillary of the sample holder and placed on the sample window of the FTIR instrument and then scanned. The raw data were transferred to a desktop and edited using Excel in order to re-generate the relevant spectrum. Similarly, the IR spectrum for each sample was scanned at least three times (n = 3). The average peak heights were obtained from the spectra and used for determining the alcohol content.The content of ethanol, IPA and methanol were determined using a gas chromatography-flame ionization detector (GC-FID) system. Separation was achieved by GC column SCION-5  at a flow rate of 1.5 mL/min.To construct calibration curves, working standard solutions of ethanol, IPA, and methanol at various concentrations were prepared in toluene. Each of the hand sanitizer samples was diluted with toluene 25 times. Exactly, 0.01µL sample/standard was injected by auto injector to the column. All of these analyses were carried out in triplicate (n = 3) to ensure that the experiment was of high quality. Table S1 provides more information. R2) were measured to validate the IR spectroscopy as an analytical method R2) were greater than 0.905. The calibration curves were constructed using the average peak heights of the relevant alcohol's vibrational modes. R2) for each alcohol.Alcohol content, standard deviation (SD), relative standard deviation (RSD) and linear range correlation coefficient , Figures S4(a) and (b) , Figures S5(a) and (b)  and Figures S6(a) and (b) .The relevant analytical calibration curves are shown in Figure S3 2 bending and C−C−O symmetric stretching, respectively −1, has been identified as a promising group for quantifying ethanol, isopropanol, and even methanol in commercial hand sanitizers Figures S7(a), (b) and (c) show the IR spectra of pure ethanol, IPA and methanol, respectively. As seen from Figures S7(a), (b) and (c), red and blue circles indicate C−H and C−O stretching and the purple circle shows C-H bending while green and orange circles represent −C of 0.953 . As indicated in −1) and C−H bending (881 cm-1) vibrational modes, respectively. The height of both peaks (C−H stretching and C−H bending) decreases with increasing water content, as illustrated in R2) were 0.905 for C-H stretching and 0.980 for C−H bending vibration mode. According to the findings, using the C−H bending vibration mode to quantify ethanol in hand sanitizers could be more precise.The IR spectra of ethanol with varying water content (a) 0, (b) 20, (c) 30, (d) 40, and (e) 50 percent are shown in With the exception of sample D, the content of ethanol in commercial hand sanitizers was lower than the reported values, as revealed by the analytical curves in Figures S4(a) and (b). According to WHO formulations, hand sanitizers are supposed to contain ethanol (75–80%) or IPA (70–75%), as well as glycerol (1.45%), hydrogen peroxide (0.125%), and water. Because both water and glycerin molecules make hydrogen bonds with ethanol molecules through the C−O−H group of ethanol and H−O−H and/or C−O−H of glycerin, the C−O stretching mode of alcohol can be dramatically changed by adding polar compounds like water, glycerin, and so on. This is why the C−O stretching mode has been used to determine the amount of alcohol in hand sanitizers Polar compounds, such as water or glycerin, on the other hand, do not interact with the C−H group of alcoholic compounds and instead reduce the peak height when they are added. As a result, employing the analytical calibration curves derived by C−H bending and/or C−H stretching to quantify alcoholic substances such as ethanol, isopropanol, and/or methanol in hand sanitizers is scientifically plausible.3−C−CH3 group caused an additional peak for IPA at 952 cm−1. With the addition of water, the intensity of this peak was also reduced. As shown in Figures S5(a) and (b), C−H (2970 cm−1) and CH3−C−CH3 (952 cm−1) bending were used to construct analytical calibration curves for detecting and quantifying IPA in commercial hand sanitizers (R2) for C−H and CH3−C−CH3 bending were found to be 0.913 and 0.922, respectively. This is due to the fact that the presence of polar water molecules has a considerable impact on the C−H and CH3−C−CH3 bending vibration modes of IPA, resulting in a regular fluctuation in peak height with the addition of water.The IR spectra of isopropanol (IPA) with 0, 30, 40, 50, and 60 percent water are shown in nitizers . In the R2) was found to be less than 0.700 (data not shown). Density functional theory (DFT) calculation supported the deformation of IPA molecules in presence of water 2O)5). Unlike ethanol, IPA molecules are less polar because of presence of two methyl group. So, intensities of the both C−H and CH3−C−CH3 bending are sensible in presence of water, accordingly two analytical curves were constructed for IPA.Like ethanol, an analytical calibration curve based on C−O stretching was also constructed for IPA and correlation coefficient , (b), and (c), respectively. IPA content in hand sanitizers was estimated using the two analytical calibration curves  and (b)) and listed in C values . None of−1) and C−O stretching (1032 cm−1) vibrational modes, respectively. Unlike IPA, methanol has a characteristic C−O stretching vibration mode that appears at 1032 cm−1 ) and subsequently drops in peak height when the amount of water increases -(d)). The height of the C−H stretching (2943 cm−1) mode similarly lowers as the water content increases. In order to identify and quantify methanol in hand sanitizers, both the C−H and C−O stretching modes were used in the construction of two analytical calibration curves. Figures S6(a) and (b) illustrate the calibration curves based on C−H and C−O stretching vibrations, respectively. The correlation coefficients (R2) were found to be 0.904 and 0.996 for C−H and C−O stretching vibrations, respectively.As expected, methanol is found in two out of ten hand sanitizers in Bangladesh. Alcohol dehydrogenase converts methanol into formaldehyde, which is then transformed to formic acid by aldehyde dehydrogenase. If ingested at a high enough quantity, it can result in blindness and death. The IR spectra of methanol with 0, 20, 30, and 40% water are shown in Figure S9(a)-(d), respectively. The red and blue marks indicate how methanol/water ratios affect the heights of C−H stretching (2975 cm−1) and C−O stretching (1032 cm−1) vibrational modes, respectively, as a function of methanol/water ratios. Methanol content in I and J samples was 98 and 37–38% using the calibration curve-based on C−O stretching, respectively, while it was 98 and 39% using the analytical curve-based on C−H stretching, as shown in Figures S6(a) and (b) and The IR spectra of commercial hand sanitizers I and J are shown in Figure S10(a) and (b), respectively. The red and blue marks represent the heights of the C−H stretching . Since the concentrations of ethanol, IPA, and methanol in the samples are relatively high (30%–75%), no influence from other alcohols or volatile compounds like acetone, butanol, ethyl acetate, etc. occurs until their concentration level reaches the same order as the alcohols taken into consideration Saima Alam: Conceptualization, Data curation, Formal analysis, Investigation, Methodology. Md. Masudur Rahman Rahat: Data curation, Formal analysis, Writing – review & editing. Nusrat Jahan Upoma: Data curation, Formal analysis, Methodology. Chandan Halder: Formal analysis, Investigation, Writing – review & editing. Shyama Prosad Moulick: Methodology, Validation. Md. Monarul Islam: Methodology, Validation. Wenben Liu: Conceptualization, Writing – review & editing. Ahsan Habib: Conceptualization, Funding acquisition, Investigation, Validation, Visualization, Writing – original draft, Writing – review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


NFIX) gene encodes a ubiquitously expressed transcription factor whose mutations lead to two allelic disorders characterized by developmental, skeletal, and neural abnormalities, namely, Malan syndrome (MAL) and Marshall–Smith syndrome (MSS). NFIX mutations associated with MAL mainly cluster in exon 2 and are cleared by nonsense‐mediated decay (NMD) leading to NFIX haploinsufficiency, whereas NFIX mutations associated with MSS are clustered in exons 6–10 and escape NMD and result in the production of dominant‐negative mutant NFIX proteins. Thus, different NFIX mutations have distinct consequences on NFIX expression. To elucidate the in vivo effects of MSS‐associated NFIX exon 7 mutations, we used CRISPR‐Cas9 to generate mouse models with exon 7 deletions that comprised: a frameshift deletion of two nucleotides (Nfix Del2); in‐frame deletion of 24 nucleotides (Nfix Del24); and deletion of 140 nucleotides (Nfix Del140). Nfix+/Del2, Nfix+/Del24, Nfix+/Del140, NfixDel24/Del24, and NfixDel140/Del140 mice were viable, normal, and fertile, with no skeletal abnormalities, but NfixDel2/Del2 mice had significantly reduced viability (p < 0.002) and died at 2–3 weeks of age. Nfix Del2 was not cleared by NMD, and Del2/Del2Nfix mice, when compared to Nfix+/+ and Nfix+/Del2 mice, had: growth retardation; short stature with kyphosis; reduced skull length; marked porosity of the vertebrae with decreased vertebral and femoral bone mineral content; and reduced caudal vertebrae height and femur length. Plasma biochemistry analysis revealed NfixDel2/Del2 mice to have increased total alkaline phosphatase activity but decreased C‐terminal telopeptide and procollagen‐type‐1‐N‐terminal propeptide concentrations compared to Nfix+/+ and Nfix+/Del2 mice. NfixDel2/Del2 mice were also found to have enlarged cerebral cortices and ventricular areas but smaller dentate gyrus compared to Nfix+/+ mice. Thus, NfixDel2/Del2 mice provide a model for studying the in vivo effects of NFIX mutants that escape NMD and result in developmental abnormalities of the skeletal and neural tissues that are associated with MSS. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.The nuclear factor I/X ( A mouse model for Marshall–Smith syndrome. NFIX) gene (MIM #164005), located on chromosome 19p13.2, consists of 11 exons GCCAA‐3′ present in the promoter regions of viral and cellular genes, and a variable C‐terminal transactivation/repression domain, which can potentially provide a range of preferential interactions with other proteins to either activate or suppress transcription. NFI transcription factors play important roles in the regulation of stem cell differentiation, quiescence, and differentiation during the development of organs that include lung, kidney, liver, blood, heart, skeleton, and the nervous system.The nuclear factor I/X (#164005),2, 3ons Fig.  that encNFIX gene can lead to two rare allelic disorders, Malan syndrome  and Marshall‐Smith syndrome . MAL is an overgrowth disorder, characterized by a slender habitus, long hands and advanced bone age, moderate to severe intellectual disability, unusual facial phenotype consisting of a long, triangular face with a prominent forehead, everted lower lip and prominent chin, and behavioral problems, which are usually dominated by anxieties and, less frequently, by aggression  and lead to NFIX haploinsufficiency. MSS is characterized by short stature with skeletal abnormalities that may include kyphoscoliosis, abnormal bone maturation, craniofacial defects, and osteopenia and be associated with delays in motor and neural development that lead to moderate to severe mental retardation, limited or absent speech, and postnatal failure to thrive. In addition, MSS patients may have distinctive facial features that include a high forehead, proptosis, blue sclera, anteverted nares, small and retracted mandible, gingival hypertrophy, and hypertrichosis .1, 9) Table . The misn 2 Fig. , which eis Table . MSS patain Fig. . The difin vivo consequences of Nfix exon 2 deletion, which encodes the conserved N‐terminal DNA binding and dimerization domain, have been studied in mouse models. In one study wherein Nfix exon 2 was replaced with an in‐frame lacZ reporter gene, Nfix+/lacZ mice were reported to have normal survival, but reduced body weight, while NfixlacZ/lacZ mice developed skeletal abnormalities due to defects in ossification that resulted in kyphosis and neurological abnormalities such as partial agenesis of the corpus callosum that was associated with hydrocephalus. In other studies wherein an Nfix null allele was initially generated via Cre‐recombinase‐mediated excision of Nfix exon 2, the heterozygous Nfix+/− mice also had normal survival but with neurological abnormalities, and the homozygous Nfix−/− mice had neurological defects that included dysgenesis of the corpus callosum but did not have skeletal abnormalities. Moreover, Nfix−/− mice are reported to have severe delay in intermediate progenitor cells during forebrain development and smaller muscle fibers with impairment of muscle regeneration despite the lack of skeletal defects  Table . These NAll animal studies were approved by the Medical Research Council Harwell Institute Ethical Review Committee and were licensed under the Animal (Scientific Procedures) Act 1986, issued by the UK Government Home Office Department (PPL30/2433 and PPL30/3271). and genotyping was performed by PCR amplification using genomic DNA and confirmed by RT‐PCR using total RNA extracted, as described in Data Mice were generated using the CRISPR/Cas9 system,22 andNfix cDNA expression constructs and luciferase reporter constructs were utilized for qRT‐PCR, Western blot, and immunofluorescence analyses, as detailed in Data Murine embryonic fibroblast (MEF) cells and monkey kidney fibroblast (COS‐7) cells that were used for RNA sequencing analysis or transiently transfected with wild‐type (WT) or mutant murine  and skeletons and tissues of WT and mutant mice were prepared and used for imaging and histological analyses, as detailed in Data Blood samples were collected and used for plasma biochemical analysis,22 andp < 0.05 was considered significant for all analyses as described in Data Data are expressed as mean and standard deviation (SD) or standard errors of mean (SEM). All analyses were performed using Prism (GraphPad), and a value of NFIX gene, the CRISPR‐Cas9 system was used to target exon 7 of the murine Nfix gene. Following injection of Cas9 mRNA and Nfix guide RNA into C57BL/6J embryos, founder mice were generated from which three mutant Nfix lines comprising deletions of two nucleotides (Del2), 24 nucleotides (Del24), and 140 nucleotides (Del140) were established. More specifically, Nfix Del2 consists of a frameshift two‐nucleotide deletion from position +49,580 to +49,581 relative to the translation start site (TSS), Nfix Del24 contains an in‐frame 24‐nucleotide deletion , and Nfix Del140 contains a 140‐nucleotide deletion  and comprised 53 nucleotides from exon 7 and 87 nucleotides of intron 7  were viable and intercrossed within each line to generate WT (Nfix+/+), heterozygous  and homozygous  mice. Genotypes were confirmed and validated by PCR amplification of exon 7, Sanger DNA sequencing, and, in the case of Nfix Del2 mice, using NlaIII restriction endonuclease digestion analysis . Nfix+/Del2, Nfix+/Del24, NfixDel24/Del24, Nfix+/Del140, and NfixDel140/Del140 mice were viable, normal, and fertile, but NfixDel2/Del2 mice were subviable by 21 days post term (P21) due to early death around 2–3 weeks of age  from the expected Mendelian ratio at P21 due to early death at 2–3 weeks, indicative of reduced viability of the NfixDel2/Del2 mice. Moreover, the numbers of Nfix+/+, Nfix+/Del2, and NfixDel2/Del2 mouse embryos at day 18.5 (E18.5) did not deviate from the expected 1:2:1 Mendelian ratio , due to alternative splicing of exons 7 and 9 and the use of different transcription initiation sites (ENSMUSG00000001911.16). Thus, alternative splicing may produce WT Nfix transcripts that retain exon 7 (Nfix long isoform) or shorter conserved isoforms that lack exon 7 (Nfix ΔEx7)  and short WT ΔEx7 (194 bp) isoforms , but the NfixDel2/Del2 and NfixDel24/Del24 MEFs had mutant Nfix long isoforms of 315 and 293 bp, respectively, and the Nfix WT short isoform (ΔEx7 of 194 bp) . Nfix+/Del2 and Nfix+/Del24 MEFs were confirmed to express a WT Nfix long isoform, a mutant Nfix long isoform, and the Nfix WT ΔEx7 short isoform . In contrast, NfixDel140/Del140 MEFs had only the Nfix WT ΔEx7 short isoform, thereby suggesting that the 140‐nucleotide deletion, which comprised 53 nucleotides of the 3′ end of exon 7 along with 87 nucleotides from intron 7 that included the donor splice site, led to exon 7 skipping. Sanger DNA sequence analysis confirmed that the sequence of this Nfix short isoform from the NfixDel140/Del140 MEFs matched the consensus murine sequence of the Nfix WT ΔEx7 short isoforms . Therefore, the 140‐nucleotide deletion in the Nfix Del140 MEFs led to skipping of exon 7 and alternative splicing of exon 6 to exon 8 due to loss of a donor splice site, resulting in a frameshift and the introduction of a stop codon after 81 amino acids, which corresponded to the WT short NFIX isoforms. However, the two‐nucleotide deletion in Nfix Del2 MEFs resulted in a frameshift and the introduction of a premature stop codon after 65 amino acids, and the 24‐nucleotide in‐frame deletion in Nfix Del24 MEFs predicted the loss of eight amino acids (QGSSPRMA).The differences in viability between the homozygous x7) Fig. A. To strms Fig. B–D, butbp) Fig. B,C. Nfiorm Fig. B,C. In rms Fig. D. ThereNfix Del2 and Nfix Del24 mutations on Nfix transcription, translation, and cellular localization, in vitro expression assays in COS‐7 cells transiently transfected with N‐terminal‐FLAG‐tagged WT or mutant (Del2 or Del24) Nfix cDNA constructs that retain exon 7 were undertaken. Analysis by qRT‐PCR showed that there was no significant difference in the amount of Nfix Del2 or Nfix Del24 expression compared to Nfix WT, suggesting that these mutations affecting the C‐terminal part of the Nfix transcripts were not cleared by NMD mechanisms , as expected, compared to WT (55 kDa) , whereas that of the NFIX Del24 protein was significantly increased (p < 0.01) compared to NFIX WT, thereby revealing differences in the stabilities and likely degradations of the mutant proteins  Fig. B, thered p < 0.0 comparedd p < 0.0 comparedNfix Del2 and Nfix Del24 mutations on NFIX transcription factor function, given that NFIX is reported to activate GFAP expression, reporter constructs comprising the luciferase reporter gene downstream of the GFAP promoter were transiently cotransfected with WT or mutant Nfix cDNA constructs into COS‐7 cells. WT NFIX activated the GFAP promoter and caused an approximately ninefold increase , viscera, and plasma biochemistry.Heterozygous and homozygous NfixDel2/Del2 mice were characterized by growth retardation and short stature when compared to Nfix+/+, Nfix+/Del2, Nfix+/Del24, NfixDel24/Del24, Nfix+/Del140, and NfixDel140/Del140 mice. Thus, at P1, there was no significant difference in the weights of Nfix+/+(1.5 ± 0.1 g), Nfix+/Del2 (1.6 ± 0.0 g), and NfixDel2/Del2 (1.5 ± 0.1 g) mice . In addition, the NfixDel2/Del2 mice were visibly smaller than Nfix+/+ and Nfix+/Del2 mice at 2 weeks . In contrast, the growth rates of the Nfix+/Del24 and NfixDel24/Del24 mice, and the Nfix+/Del140 and NfixDel140/Del140 mice were not significantly different from WT mice between 2 to 12 weeks of age, irrespective of sex . Furthermore, visually, the Nfix+/Del24, NfixDel24/Del24, Nfix+/Del140, and NfixDel140/Del140 mice were indistinguishable from the Nfix+/+ mice at 12 weeks .ice Fig. , irrespeeks Fig. A. In cosex Fig. B,C. Fureks Fig. B,C.NfixDel2/Del2 mice were also significantly shorter than Nfix+/+ and Nfix+/Del2 mice . In addition, Echo‐MRI analysis revealed a significant decrease in weight , even when normalized to body weight .The tail lengths, indicative of vertebral growth, of the es Table A, even ks Table B,C.A) of skeletons of 2–3 weeks old mice revealed that >30% of NfixDel2/Del2 mice had kyphosis compared to <10% of Nfix+/+ and Nfix+/Del2 mice. Craniofacial measurements of the skulls of Nfix+/+, Nfix+/Del2, and NfixDel2/Del2 mice revealed that there was a significant reduction in the skull length of NfixDel2/Del2 mice compared to Nfix+/+ and Nfix+/Del2 mice . However, there were no significant differences in skull width, nasal bone length, and frontal bone length of NfixDel2/Del2 mice, compared to Nfix+/+ and Nfix+/Del2 mice , although parietal bone length of NfixDel2/Del2 mice was significantly different from that of Nfix+/Del2 but not Nfix+/+ mice . In contrast, radiological analyses of Nfix+/+, Nfix+/Del24, and NfixDel24/Del24 mice or Nfix+/+, Nfix+/Del140, and NfixDel140/Del140 mice revealed no skeletal abnormalities at 12 weeks . MCT analysis of the lumbar and thoracic vertebrae also revealed NfixDel2/Del2 mice to have marked porosity at 2–3 weeks  and revealed decreases in vertebral height  staining , although the lack of significant difference in the number of osteoclasts could be due to the low number of animals analyzed. To investigate whether the low BMC in NfixDel2/Del2 mice may be a result of abnormal osteoclast activity instead, the plasma concentrations of C‐terminal telopeptide (CTX), procollagen‐type‐1‐N‐terminal propeptide (P1NP), and total alkaline phosphatase (ALP) activity, which are markers of bone resorption, bone formation, and bone mineralization, respectively, were therefore measured. NfixDel2/Del2 mice, when compared to Nfix+/+ and Nfix+/Del2 mice at 2–3 weeks, had reduced CTX concentrations , thereby suggesting an overall abnormal bone turnover phenotype.Micro–computed tomography (MCT) Fig. A, Alciaice Fig. C–E, alteks Fig. B,C. MCTing Fig. C, whichP21 Fig. D,E, altNfix+/+  and inferior blade areas (p < 0.05) of the dentate gyrus .Mice homozygous for a null exon 2 te gyrus.18 Thesis Fig. N,O of nsis Fig. P,Q. Thisis Fig. R or prosis Fig. S in Nfiice Fig. N,N' but ice Fig. O,O'. Simice Fig. P,P' wereice Fig. Q,Q'. In ies Fig. B,C.NfixDel2/Del2 mice, when compared to Nfix+/+ and Nfix+/Del2 mice, had raised plasma urea  and raised total bilirubin  at 2–3 weeks, in males and females (Table A), consistent with abnormal kidney and liver function, respectively. However, there were no significant differences in the plasma concentrations of sodium, potassium, chloride, total and corrected calcium, inorganic phosphate, aspartate aminotransferase, alanine aminotransferase, albumin, creatinine, and creatine kinase in the NfixDel2/Del2 mice compared to the Nfix+/+ and Nfix+/Del2 mice (Table A). Moreover, histology of the kidneys and livers from NfixDel2/Del2 mice revealed no abnormalities compared to Nfix+/+ and Nfix+/Del2 mice . In addition, histological examination for liver inflammation  and liver fibrosis  revealed no hepatic abnormalities in the NfixDel2/Del2 mice compared to the Nfix+/+ and Nfix+/Del2 mice. The plasma biochemistry of Nfix+/+, Nfix+/Del24, NfixDel24/Del24, Nfix+/Del140, and NfixDel140/Del140 mice at 12 weeks was similar , suggesting normal kidney and liver function in these mice, and histological analysis of the liver, kidney, lung, and heart of these mice revealed no abnormalities .Plasma biochemical analysis revealed that es Table A, consice Table A. Moreoice Fig. A. In adion Fig. C.E.F ansis Fig. D,G reveies Fig. B,C.Nfix mouse models could be due to functional redundancy provided by the other members of the NFI gene family of transcription factors, we pursued RNA sequencing analysis to investigate differences in Nfia, Nfib, and Nfic gene expression in the MEFs derived from the Nfix+/+, Nfix+/Del2, NfixDel2/Del2, Nfix+/Del24, NfixDel24/Del24, Nfix+/Del140, and NfixDel140/Del140 mice. RNA sequencing analysis identified that, compared to the mean of Nfix+/+ MEFs, Nfia transcripts were significantly altered  Nfix isoforms. This suggests that different frameshift mutations or in‐frame deletions affecting the C‐terminal part of the Nfix gene have different consequences on the transcripts and activity of the resulting proteins, thereby accounting for the different phenotypes in the three mouse models.Our study reports the phenotypic characterization of three CRISPR‐Cas9 generated mouse models with three allelic variants in  Redundant paralogs that are ubiquitously expressed in a partially overlapping manner and that recognize similar motifs may provide backup for one another in case of mutation by changing their expression pattern and acquiring new regulatory capabilities in order to compensate for the mutation. For example, NFIA, NFIB, NFIC, and NFIX have the same conserved N‐terminal DNA binding and dimerization domain that enables all four related genes to recognize the same consensus sequence present in the promoter region of genes expressed in almost every organ, including the brain, lung, liver, intestine, and skeleton. Nfia−/− mice have CNS and kidney abnormalities and die perinatally,Nfib−/− mice have CNS and lung anomalies and die at birth, while Nfic−/− mice have only a mild phenotype involving abnormal tooth development of the incisors and molars. More recently, overlapping patterns of NFIA, NFIB, and NFIX expression have been reported in the brain. NFIA, NFIB, NFIC, and NFIX, which were previously shown to interact with each other as well as other cofactors, bind the same regulatory motif of promoters of genes, such as brain fatty acid binding protein (B‐FABP), GFAP, and inscuteable (INSC), and the NFIs or the ratio of the four NFIs have been shown to act either antagonistically or synergistically to regulate transcription in a promoter and context dependent manner. Moreover, knockdown of one NFI member can affect the expression levels of other NFI members, suggesting cross‐talks and possible compensation within the NFI family. NFIX was also recently shown to act sequentially after NFIA and NFIB during gliogenesis within the spinal cord, and NFIB was reported to be able to activate Nfix expression in vitro, thereby suggesting autoregulatory mechanisms within the NFI gene family. In this study, we have shown that the combination of NFI family expression might potentially influence the phenotypes of the Nfix mouse models. Nfia and Nfib, but not Nfic, change their expression pattern in order to possibly compensate for their respective Nfix Del2 and Del140 frameshift mutations in the unaffected Nfix+/Del2, Nfix+/Del140, and NfixDel140/NfixDel140 mice, while Nfia, Nfib, and Nfic expression was unaltered in the unaffected Nfix+/Del24 and NfixDel24/Del24 mice, thus suggesting that the in‐frame Nfix Del24 mutations might potentially be tolerated and are probably not as damaging as a frameshift mutation. Moreover, the lack of functional redundancy from the Nfix paralogs in the NfixDel2/Del2 mice as well as the presence of intermediate levels of aberrant mutant NFIX Del2 protein might possibly account for the more severe phenotype observed in the NfixDel2/Del2 mice.Cell autonomous, monoallelic, and stochastic variation in gene expression, as well as functionally redundant paralogs, could also account for phenotypic variability.31 RedNfixDel2/Del2 mice represent a mouse model for MSS in which patients commonly have: reduced growth rate; short stature; craniofacial defects; osteopenia with increased fracture rate and kyphosis that normally worsens in puberty and adolescence and that is possibly aggravated by decreased bone density; and anxiety and intellectual disability due to nonspecific rain abnormalities. Thus, the NfixDel2/Del2 mice had; short stature; reduced growth and TTM; kyphosis; shortened skull; marked porosity of the vertebrae; reduced BMC; shorter vertebrae height and femur length; reduced plasma CTX and P1NP concentrations but increased total ALP activity, indicative of abnormal bone function; and raised plasma urea and total bilirubin levels, suggestive of renal and hepatic dysfunction, which merits further investigation. Furthermore, NfixDel2/Del2 mice had enlarged anterior cingulate, somatosensory and retrosplenial cortices, and ventricles but reduced dentate gyrus . Moreover, the reduction in plasma CTX concentrations in the NfixDel2/Del2 mice may suggest abnormal osteoclast activity and function, which merits further investigation. Moreover, the paradoxical increased plasma ALP activity, which is a marker of bone turnover, in association with reduced plasma concentrations of CTX and P1NP, which are markers of bone resorption and bone formation respectively, in the NfixDel2/Del2 mice suggests additional extraskeletal origin for the raised ALP activity such as the kidneys or intestine, but not liver as mice, in contrast to humans, express little or no ALP in the liver, and a search for additional renal or intestinal abnormalities in MSS may be warranted. Thus, it seems possible that MSS patients may have renal, intestinal, and hepatic dysfunction, and that there may be more similarities with the NfixDel2/Del2 mice.us Table . HoweverNfixDel2/Del2 mice have similarities and differences when compared to two previous homozygous Nfix‐deficient mouse models that had targeted deletions of exon 2 (Table Nfix‐deficient mice (NfixlacZ/lacZ) were viable and had: growth retardation; an inability to fully open eyes; ataxic gait; feet‐clasping posture when lifted by their tail indicating neurological abnormalities; gastrointestinal defects; brain malformations consisting of hydrocephalus and partial agenesis of the corpus callosum; defects in endochondral ossification, reduction in trabecular bone formation and calcification; thinning of cranial bones; kyphotic deformation of the spine; and early postnatal death between 3 and 4 weeks of age. The other homozygous Nfix−/− mice showed; failure to thrive and grow when on a standard lab chow diet; delayed eye and ear opening; leg‐clasping phenotypes indicating neuroanatomical defects; increased brain weight due to expansion of the cortex and entire brain along the dorsal ventral axis; aberrant neocortex, cerebellum, hippocampus, and spinal cord formation; and an abnormal ventricular cell population due to excessive generation of Pax6‐expressing ventricular cells with hydrocephalus. Liver and kidney phenotypes were not assessed in these two previously reported Nfix‐deficient mouse models, although it is important to note that NfixlacZ/lacZ mice had gastrointestinal defects. Importantly, the NfixDel2/Del2 mice are not Nfix‐deficient but instead have aberrant Nfix transcripts that escape NMD and lead to the production of mutant truncated NFIX protein, which is representative of MSS. Interestingly, MSS patients are heterozygous for NFIX mutations, and this contrasts with Nfix+/Del2 mice, which are normal, while developmental, skeletal, cranial, neural, hepatic, and renal abnormalities are observed in NfixDel2/Del2 mice, which could account for their reduced viability. However, phenotypic differences between organisms are not uncommon and can be attributed to allelic variation, modifier genes, genetic variations, genetic background, environmental conditions, and reduced sensitivity of assays, such as behavioral assays, in animal models versus in patients. For example, the autosomal dominant disorder spondyloepimetaphyseal dysplasia, Missouri type (SEMDMO) in humans, is due to a heterozygous matrix metalloproteinase 13 (MMP13) missense F56S mutation, whereas heterozygous Mmp13+/− mice deleted for exons 3, 4, and 5 have normal growth plates, but the homozygous Mmp13−/− mice have defects in growth plate cartilage and delayed endochondral ossification.Our ) Table . Thus, hNfix mouse models with three different targeted mutations in exon 7 of the Nfix gene, which are representative of the most frequent NFIX mutations observed in MSS patients. The three mouse models, although being on the same genetic background, have differing phenotypes and viability. While the NfixDel2/Del2 mice have some similarities to previously reported Nfix deficient mouse models, they also have a number of other phenotypes that are consistent with MSS. Further studies of the NfixDel2/Del2 mice will help better understand the role of NFIX mutations that result in dominant‐negative NFIX proteins and give rise to MSS, as well as provide useful resources for testing potential future treatments.In summary, in this study we report three Kreepa G. Kooblall: Conceptualization; data curation; formal analysis; investigation; methodology; validation; writing – original draft; writing – review and editing. Mark Stevenson: Supervision; writing – original draft; writing – review and editing. Michelle Stewart: Data curation; methodology; project administration; writing – review and editing. Lachlan Harris: Conceptualization; data curation; formal analysis; investigation; methodology; validation; writing – review and editing. Oressia Zalucki: Conceptualization; data curation; formal analysis; investigation; methodology; validation; writing – review and editing. Hannah Dewhurst: Data curation; formal analysis; investigation; methodology; validation; writing – review and editing. Natalie Butterfield: Data curation; formal analysis; investigation; methodology; validation; writing – review and editing. Houfu Leng: Data curation; formal analysis; investigation; methodology; validation; writing – review and editing. Tertius A. Hough: Data curation; investigation; methodology; validation; writing – review and editing. Da Ma: Data curation; formal analysis; investigation; methodology; software; validation; writing – review and editing. Bernard Siow: Conceptualization; data curation; formal analysis; investigation; methodology; software; validation; writing – review and editing. Paul Potter: Writing – review and editing. Roger D. Cox: Writing – review and editing. Stephen D.M. Brown: Writing – review and editing. Nicole Horwood: Supervision; writing – review and editing. Benjamin Wright: Data curation; formal analysis; investigation; methodology; software; validation; writing – review and editing. Helen Lockstone: Conceptualization; data curation; formal analysis; investigation; methodology; software; validation; writing – review and editing. David Buck: Software; supervision; writing – review and editing. Tonia Vincent: Supervision; writing – review and editing. Fadil M. Hannan: Conceptualization; writing – review and editing. J.H. Duncan Bassett: Conceptualization; funding acquisition; supervision; writing – review and editing. Graham R. Williams: Conceptualization; funding acquisition; supervision; writing – review and editing. Kate E. Lines: Supervision; writing – original draft; writing – review and editing. Michael Piper: Conceptualization; funding acquisition; supervision; writing – review and editing. Sara Wells: Resources; writing – review and editing. Lydia Teboul: Conceptualization; data curation; formal analysis; investigation; methodology; resources; supervision; validation; writing – review and editing. Raoul C. Hennekam: Conceptualization; funding acquisition; supervision; writing – review and editing. Rajesh V. Thakker: Conceptualization; funding acquisition; resources; supervision; writing – original draft; writing – review and editing.https://www.webofscience.com/api/gateway/wos/peer-review/10.1002/jbm4.10739.The peer review history for this article is available at Data S1. Supporting Information.Click here for additional data file.Data S2. Supporting Information.Click here for additional data file.


There are several studies on the deregulated gene expression profiles in kidney cancer, with varying results depending on the tumor histology and other parameters. None of these, however, have identified the networks that the co-deregulated genes (co-DEGs), across different studies, create. Here, we reanalyzed 10 Gene Expression Omnibus (GEO) studies to detect and annotate co-deregulated signatures across different subtypes of kidney cancer or in single-gene perturbation experiments in kidney cancer cells and/or tissue. Using a systems biology approach, we aimed to decipher the networks they form along with their upstream regulators. Differential expression and upstream regulators, including transcription factors  and protein kinases , were computed using the Characteristic Direction, as well as GEO2Enrichr and X2K, respectively, and further subjected to GO and KEGG pathways enrichment analyses. Furthermore, using CMap, DrugMatrix and the LINCS L1000 chemical perturbation databases, we highlight putative repurposing drugs, including Etoposide, Haloperidol, BW-B70C, Triamterene, Chlorphenesin, BRD-K79459005 and β-Estradiol 3-benzoate, among others, that may reverse the expression of the identified co-DEGs in kidney cancers. Of these, the cytotoxic effects of Etoposide, Catecholamine, Cyclosporin A, BW-B70C and Lasalocid sodium were validated in vitro. Overall, we identified critical co-DEGs across different subtypes in kidney cancer, and our results provide an innovative framework for their potential use in the future. Kidney cancer accounts for 2% of all cancers worldwide and is the third most common type of urinary tract cancer, killing over 131,000 people each year , which cSeveral studies aim to find differentially expressed genes (DEGs) between kidney cancer and normal tissue without considering that the dysregulation patterns occur at multiple gene expression levels and can differ across heterogeneous kidney cancer subtypes. DEGs vary from study to study depending on tumor histology, molecular subtype, tumor stage/grade and/or other biological parameters or computational methodology. To this extent, the analysis of genes subjected to simultaneous deregulation alongside their commonly altered signaling pathways could reveal the broad pathogenetic model of kidney carcinogenesis and further aid in identifying treatment(s) for kidney tumors with diverse molecular profiles.A new multivariate method used to measure deregulated gene expression signatures is the Characteristic Direction (CD), which uses the orientation of the separating hyperplane from a linear classification scheme to define a direction that characterizes differential expression .Here, we hypothesized that specific hub proteins [transcription factors (TFs) and kinases] are the regulatory players in gene expression of co-DEGs in kidney cancer. In fact, by searching large datasets of differentially expressed genes, those modulatory links can be better understood. To investigate this further, we extracted gene expression signatures from 10 Gene Expression Omnibus (GEO) datasets and constructed the co-deregulated gene networks consisting of their upstream transcriptional regulators, such as transcription factors and kinases, and the hubs in these networks. Furthermore, we identified and evaluated in vitro the reversible effect of repurposing drugs on the expression of the proposed co-DEGs. The lists of co-deregulated gene signatures that we propose corroborate previous findings and provide new insights into the development and treatment of kidney cancer(s).p-value and the z-score. We also found the top GO- and KEGG-enriched terms for the co-DEGs and investigated the drugs that repress or increase their expression using the DrugMatrix, cMap and LINCS L1000 databases. The effects of five drugs  were validated in vitro.We analyzed the co-DEGs across 10 independent studies using a systems biology approach and classified them into two groups: (1) renal cancer vs. the normal tissue and (2) single-gene perturbation vs. the wild-type cells or tissue. We then filtered and removed genes that were found to be deregulated in a single study and concluded the co-DEGs for each of the above-mentioned groups. In specific, we found 72 co-upregulated and 74 co-downregulated genes in renal cancer(s) vs. normal renal studies, as well as 89 co-upregulated and 23 co-downregulated genes in single-gene perturbation studies. We discovered theTFs, protein–protein interactions (PPIs) and kinases driving the observed changes in gene expression. We identified the phosphorylation reactions that might be carried out by upstream regulators in particular kinases, acting as regulators of gene expression in each category. The top 10 TFs and protein kinases from both groups of co-DEGs were categorized based on the highest value of a combined score between the We looked at four different GEO datasets to see which co-DEGs were present in renal cancer tissues compared to their normal tissue. The co-upregulated genes in this category were substantially enriched “regulation of cell population proliferation”, “SRP-dependent co-translational protein targeting to membrane” and “cytoplasmic translation” ; “cadherin binding”, “Transmembrane receptor protein kinase activity” and “Semaphorin receptor binding” (GO Molecular Function); and “ribosome”, “focal adhesion” and “cell-substrate junction” (GO Cellular Component) a.We also discovered the top 10 KEGG pathways of the commonly up-regulated and down-regulated genes. The co-upregulated genes in RCC are mostly related to the pathways “Coronavirus disease”, “Adherens junction”, “Ribosome”, “HIF-1 signaling pathway”, “Focal adhesion”, “Rap1 signaling pathway”, “Regulation of actin cytoskeleton”, “Ras signaling pathway”, “Yersinia infection” and “Shigellosis” .Furthermore, the co-downregulated genes were enriched in “monocarboxylic acid catabolic process”, “aldehyde catabolic process” and “maintenance of synapse structure” ; “secondary active transmembrane transporter activity” “sodium channel regulator activity” and “N-acyltransferase activity” (GO Molecular function); and “integral component of mitochondrial inner membrane”, “lytic vacuole” and “brush border membrane” (GO Cellular Component) a.The co-downregulated genes, on the other hand, were enriched in the “Cholesterol metabolism”, “Glyoxylate and dicarboxylate metabolism”, “Glycine”, “Serine and threonine metabolism”, “Arginine and proline metabolism”, “Cysteine and methionine metabolism”, “Taurine and hypotaurine metabolism” and “Bile secretion” pathways .We then built a PPI sub-network using eXpression2Kinases (X2K) and the proteins encoded by the co-DEGs, and we ran a topological analysis on it to identify the hub proteins. Among the co-upregulated genes, we discovered 40 hub proteins, including the TFs MYC, CEBPD, RELA, SMAD4 (Mothers against decapentaplegic homolog 4) and CHD1 (Chromodomain-helicase DNA-binding 1), and the kinases CSNK2A1, MAPK14, MAPK1, CDK1 and GSK3B (Glycogen synthase kinase-3 beta), among others . These hAmong the co-downregulated genes, we highlighted the following TFs: ZMIZ1, KAR2A (KAR2 Hsp70 family ATPase), NELFE, NFE2L2 (NFE2 like bZIP transcription factor 2), RELA, CEBPB, EZH2, MYOD1 and TCF3 (Transcription factor 3), and the following kinases: CSNK2A1, MAPK14, MAPK1, CDK1, HIPK2 and GSK3B, among others .Interestingly, Casein kinase 2 alpha 1 (CSNK2A1), Mitogen-Activated Protein Kinase 14 (MAPK14) and Mitogen-Activated Protein Kinase 1 (MAPK1) were significantly ranked in both categories (co-up- and co-downregulated genes) according to the kinase enrichment analysis (KEA) b.The co-upregulated genes in kidney cancer-related single-gene perturbation studies were enriched in “cytoplasmic translation”, “SRP-dependent co-translational protein targeting to membrane” and “co-translational protein targeting to membrane” ; “RNA binding”, “mRNA binding” and “cadherin binding” (GO Molecular Function); and “cytosolic small ribosomal subunit”, “small ribosomal subunit” and “focal adhesion” (GO Cellular Component) a. We alsIn addition, the co-downregulated genes in this category were mainly enriched in “fructose metabolic process”, “calcium-mediated signaling” and “potassium ion homeostasis” ; as well as in “ATPase binding”, “MHC class II protein complex binding” and “DNA polymerase binding” (GO Molecular Function); and “cortical cytoskeleton”, “dendrite” and “rough endoplasmic reticulum membrane” (GO Cellular Component) a. In addFollowing, we built a PPI sub-network composed of the TFs and protein kinases that drive the co-deregulated genes in single-gene experiments in RCC. We prioritized the TFs MYC, KLF4, NELFE, CREB1, TAF7, ATF2 and KAT2A, as these were among the most prominent hubs across the co-upregulated genes. Similarly, the kinases CSNK2A1, CDK4, CDK1, HIPK2, AKT1, MAPK14 and MAPK1 were hubs for the co-upregulated genes a. NELFE,Following, we validated the calculated expression signatures of the co-UP or co-DOWN-regulated gene signatures across the 3 kidney cancer subtypes , as well as across 4 mRNA clusters in KIRC. Apart from the co-UP signature in KICH, the rest of the comparisons validated the results from the CD algorithm a–d.We also explored the expression of specific genes from the co-UP and co-DOWN signatures across different molecular and immune subtypes in kidney cancer. For example, from the co-UP gene signature, we found that the Kinase suppressor of ras 1 (KSR1 or RSU2) and Macrophage expressed 1 (MPEG1) are expressed higher in the C3 (inflammatory) immune subtype of KICH and KIRC and in the C2 (IFNγ-dominant) immune subtype of KIRP e. In addTwo more genes from the list of co-DOWN signatures that we further explored are Solute carrier family 16, member 9 (SLC16A9) and Claudin 2 (CLDN2). SLC16A9 is found in the cell membrane and acts as a proton-linked monocarboxylate transporter. CDLN2, on the other hand, plays a major role in tight junction-specific obliteration of the intercellular space through calcium-independent cell-adhesion activity. SLC16A9 was highly expressed in the C3 (inflammatory) and C4 (lymphocyte depleted) immune subtypes of all three different subtypes of kidney tumors  g, and itNext, we run gene set variation analysis to estimate the integrated level of expression in three TCGA datasets concerning different subtypes of kidney cancer . The gene set that we used was composed of the previously found hub genes . This analysis verified our previous findings. For example, MYC, CDK1 and CDK4 were found to be significantly up-regulated in KIRC and KIRP; whereas CSNK2A1 was significantly up-regulated in KICH.In addition, we estimated the survival ; progression-free survival (PFS); disease-specific survival (DSS); and disease-free interval (DFI)) difference between high and low gene expression groups, and we found significant associations between high CDK1 and CDK4 expression and DSS in KIRP. We also found significant association between NELFE, KAT2A, CDK4, CDK1, RELA, MAPK1, HIPK2 and KLF4 and OS (as well as PFS) in KIRC .p = 1.37 × 10−5 and CDK4, p = 6.39 × 10−3). Similar trends were also noticed for CEBPD (p = 0.02), KAT2A (p = 0.03), KLF4 (p = 5.74 × 10−6), NELFE (p = 9.68 × 10−3) and RELA (p = 6.80 × 10−3) in KICH, whereas HIPK2 (p = 0.01), ZMIZ2 (p = 3.18 × 10−3) levels significantly decreased in stage IV KICH tumors. In KIRC, we noticed an increased trend for CDK1, CDK4, CEBPD, HIPK2, KAT2A and KLF4 in tumors of higher pathologic stage. On the other hand, in KIRP, CDK1, CDK4, HIPK2 and MYC expression increased with the pathological and clinical stage of the tumor, whereas KLF4 decreased , but we also found a high percentage of activation (67%) from MYC and CDK4 of the EMT_A pathway. In addition, our data show a significant (67%) inhibitory effect of NELFE, KAT2A, CEBPD and CDK4 for the RTK_I pathway but also a high activation effect of HIPK2 (67%) for the PI3KAKT_A pathway d.p = 3.78 × 10−21) and KIRP (p = 4.57 × 10−8) tumors, but lower in KICH (p = 3.64 × 10−7), compared to their adjacent normal kidney tissues  DrugMatrix, (2) cMap and (3) LINCS L1000. We then gathered the top 10 compounds from each database targeting the co-upregulated genes with the highest p-value. In specific, we found “Etoposide”, “Cyclosporin A (5 days up)”, “Cyclosporin (1 day up)”, “Catechol”, “Microcystin-LR”, “Ifosfamide”, “Leflunomide”, “Diethystilbestrol”, “Troglitazone” and “Valproic Acid” (DrugMatrix); “haloperidol”, “levodopa”, “genistein”, “fenbufen”, “dropropizine”, “PNU-0251126”, “cinoxacin”, “mafenide”, “enilconazole” and “genistein” (cMap); as well as “BW-B70C”, “BI-2536”, “XMD16-144”, “lasalocid sodium”, “brd-k19624190”, “brd-u25771771”, “brd-k15563106”, “perphenazine”, “pf 431396” and ”brd-k6462496” (LINCS L1000 Chem pert) .On the other hand, the 10 compounds targeting the co-downregulated genes were as follows: “Triamterene”, “Naproxen”, “Sulfadimethoxine”, “Aspirin”, “Catechol”, “Phenacemide”, “Mefenamic Acid”, “Cytarabine”, “Foscarnet” and “Oxaliptain” (DrugMatrix); “chlorphenesin”, “pentetic acid” and “phenylpropanolamine” (cMap); and “BRD-K79459005”, “radicicol”, “AT 7519”, “mk 212”, “tanespimycin”, “ei-346”, “BRD-K96704748”, “idarubicin hcl” and “NVP-AUY922 (Luminespib)” (LINCS L1000).We also explored the co-DEGs across single-gene perturbation experiments in kidney cancer for repurposing drugs. The top compounds predicted to target the co-upregulated genes in this category, were as follows: “Beta-Estradiol 3-Benzoate (1 day up)”, “Gemcitabine”, “Isoprenaline”, “Rapamycin”, “Beta-Estradiol 3-Benzoate (3 days up)”, “N-Nitrosodiethylamine”, “Harringtonine”, “Methotrexate”, “4-Nitrobenzoic Acid” and “Dobutamine” (DrugMatrix); “dequalinium chloride”, “fulvestrant”, “troglitazone”, “rimexolone”, “demecolcine”, “LY-294002-5233”, “LY-294002-1007”, “phensuximide” and “testosterone” (cMap); as well as “AS-605240”, “saracatinib”, “WZ-4002”, “radicicol”, “trametinib”, “Alvocidib”, “CHIR-99021”, “AKT-inhibitor-1-2-10”, “CGP-60474” and “PD-184352” (LINCS L1000).Similarly, the top compounds predicted to target the co-downregulated genes were as follows: “Sodium Arsenite”, “Epirubicin (3 days up)”, “Nateglinide”, “4-Methylpyrazole”, “Mercuric Chloride”, “Glycidol, Epirubicin (5 days up)”, “Cisplatin”, “Zomepirac” and “Bithionol” (DrugMatrix); “arecoline”, “trimethoprim”, “monorden”, “benzamil”, “gibberellic acid”, “testosterone”, “cantharidin”, “monocrotaline”, “eucatropine” and “ascorbic acid” (cMap); and “parthenolide”, “piplartine”, “ruxolitinib”, “AT-7519”, “thiostrepton”, “BRD-K08316444”, “np-009169”, “XMD11-85H”, “WZ-3105” and “niclosamide” (LINCS L1000) .We validated the effects of six repurposed drugs on the viability of HEK-293 cells. We selected BW-B70C, Lasalocid sodium, Catecholamine, Ifosfamide, Cyclosporin A and Etoposide, all of which were predicted to target co-upregulated genes in kidney cancer. Of these, five drugs were shown to affect HEK-293 viability , whereas Ifosfamide did not show any effect in vitro.50 values of 42.30 μM (r2 = 0.8543) and 40.99 μM (r2 = 0.9368), respectively. When tested at higher concentrations, BW-B70C was found to reach a plateau of around 50% cytotoxicity. Ifosfamide was tested at concentrations up to 5 mM, but it was not found to have any cytotoxic effect against HEK-293 cells . Interestingly, Catecholamine, Cyclosporin A and Etoposide were also shown to have a cytotoxic effect in HEK-293 cells, with IC50 values of 311.8 μM (r2 = 0.842), 52.98 μM (r2 = 0.987) and 2913 μM (r2 = 0.9621), respectively  and 97 co-downregulated genes . We discovered the enriched pathways in which these co-DEGs participate. We then searched for compounds or drugs that can upregulate or downregulate these co-DEGs using three databases . In addition, we reconstructed the PPI networks to better understand the connections between co-DEGs and discovered several hub proteins that belong to either TFs or protein kinases and behave as signaling mediators.Interestingly, among the top co-upregulated TFs in kidney cancer, we prioritized the MYC proto-oncogene, CCAAT Enhancer Binding Protein Data (CEBPD) and RELA proto-oncogene, NK-kB subunit (RELA). The MYC gene is frequently altered in >30% of human malignancies ,11, spanCEBPD is a TF that belongs to the family of CCAAT/enhancer-binding proteins. Mainly, those TFs are related to biological processes, such as cell differentiation, growth arrest, cell death and motility . FurtherRELA is the most common member of the REL/Nuclear Factor-kB (NF-kB) family of dimeric transcription factors, which regulate the expression of several genes involved in differentiation, cell growth, regulation of apoptosis, neoplastic transformation and cytokine production . RelA isFrom the list of co-downregulated genes in RCC, we highlight the TF zinc finger MIZ-type containing 1 (ZMIZ1) along with lysine acetyltransferase 2A (KAT2A) and Negative Elongation Factor Complex Member E (NELFE). ZMIZ1 belongs to the family of PIAS (protein inhibitor of activated STAT) proteins and controls the activity of other transcription factors . Even more, ZMIZ1 seems to be related to translocation involving chromosomes 9 and 10 in lymphoblastic leukemia. Another study examined the link between the expression of ZMIZ1 and Wilms tumor due to the fact that ZMIZ1 is associated with cancer etiology with unknown specific function in tumor development and progression. Recently, ZMIMZ1 knockdown was shown to affect invasion and metastasis of Wilms tumor by regulating the Notch signaling pathway, thus it could be used as a new promising therapeutic strategy for the disease . FurtehrKAT2A histone acetyltransferase is a transcriptional activator, but it can also repress NK-κB through ubiquitination of its RELA subunit. KAT2A has been reported to promote carcinogenesis in colorectal cancer by interacting with RNA-binding protein PTBP1  and to mThe transcription factor Negative Elongation Factor (NELF) is part of a complex that represses transcript elongation by RNA polymerase II. It is similar to nuclear RNA-binding proteins, however, it is unknown whether it binds to RNA. NELF protein contains a tract of alternating arginine (R) and aspartic acid (D) residues. NELF, among others is involved in pancreatic cancer metastasis and gastric cancer progression . Even ifSimilar to the category of co-upregulated genes in RCC, MYC, Krupple Like Factor 4 (KLF4) and NELFE were also found among the top up-regulated TFs among the single-gene perturbation experiments. KLF4 is another transcription factor whose main purpose is to regulate cellular processes such as cell growth, proliferation and differentiation. It has been studied intensely since it is one of four factors that are obligatory for the induction of pluripotent stem cells . KLF4 isNELFE and MYC were also listed within the down-regulated genes in single-gene perturbation experiments. In this category, we also highlighted the TF Tumor Protein P63 (TP63), a transcription factor that mainly acts as a sequence-specific DNA binding transcriptional activator or repressor. TP63 can induce the expression of various proteins using different promoters or splicing, DNA-binding or functioning through their transactivation in squamous cell carcinoma (SCC), lung cancer and other cancer types ,34. TP63Furthermore, among the co-DEGs acting as protein kinases, we highlighted the hubs Casein Kinase 2 alpha 1 (CSNK2A1), Mitogen-activated protein kinase 14 (MAPK14) and Mitogen-activated protein kinase 1 (MAPK1). In specific, CSNK2A1 is a protein kinase that phosphorylates various proteins, including sirtuins (SIRT1), and relates to a number of cellular processes, including apoptosis, circadian rhythm and cell cycle control. CSNK2A1 has been previoulsy reported to be a predictive biomarker and a therapeutic target in different types of cancer . Bae et MAPK14 is a protein kinase that belongs to the large family of MAP kinases. Similar to others kinases, it is related to a large variety of cellular processes, including differentiation, proliferation, development and transcription regulation. MAPK14 acts as an integration point for several biochemicals signals. It is mainly triggered by proinflammatory cytokines and different environmental stresses, and it is activated with phosphorylation or autophosporylation. MAPK14 has been described either as a cancer inducer or suppressor in different studies. In a study by Liu et al. , the proIn addition, MAPK1 is another protein kinase belonging to the extracellular signal-regulated kinase (ERKs) family. Similar to MAPK14, MAPK1 needs phosphorylation from other upstream kinases for its activation and is related to a variety of cellular processes. After its activation, it translocates into the nucleus of stimulated cells, where it phosphorylates various nuclear targets, including Aurora-A kinase gene (AURKA) . Fei et The hubs CSNK2A1, Cyclin-dependent kinase 4 (CDK4) and Cyclin-dependent kinase 1 (CDK1) were identified as the most relevant kinase proteins among the up-regulated genes with a single-gene perturbation. CDK4 belongs to the Ser/Thr protein kinase family and acts in the G1-S phase of the cell cycle, where it phosphorylates the retinoblastoma gene (RB) product. Importantly, various mutations in this kinase are associated with cancer. INK4 family members bind CDK4 and inhibit its kinase activity. In kidney cancer patients, Sun et al.  showed tSimilar to CDK4, CDK1 belongs to the Ser/Thr protein kinase family and is a catalytic subunit of M-phase promoting factor (MPF) complex. The latter is important for the transition of the eukaryotic cell cycle from the G1 to the S phase and from the G2 phase to mitosis. For its activation, CDK1 binds to B-type cyclins. The expression of CDK1 and related genes could efficiency be used as part of a prognostication scoring system in combination with other genes for renal cancer. Genes of the prognostication scoring system were deregulated and, specifically, CDK1 expression was notably low . A studyWe finally highlight the hubs Homeodomain Interacting Protein Kinase 2 (HIPK2) and Extracellular-signal regulated kinase (ERK1) among the most connected protein kinases in the co-downregulated genes in single-gene perturbation studies. HIPK2 is part of the homeodomain-interacting protein kinase family and acts as another serine/threonine kinase that interrelates mainly with homeodomain transcription factors and could be either a co-repressor or co-activator contingent on the transcription factor. HIPK2 has been reported to be overexpressed in cases of cervical cancer. Furtermore, the expression of HIPK2 kinase was meaningfully lower in cases of cervical cancer in stage I than cases of cervical cancer in stage II or III, showing a connection between HIPK2 and cancer development . HIPK2 cERK1 is a protein-serine/threonine kinase connected with ERK2 protein. They both contribute to the RAS/RAF/MEK/ERK cascade, which adjusts a number of cell processes, such as cell cycle progression, cell survival, cell adhesion, cell proliferation, cell differentiation and others. ERK1/2 is able to phosphorylate a large number of substrates, cytoplasmic and nuclear, such as transcription factors and regulatory molecules. An important notice is the high activity of the RAS-MEK-ERK cascade in one-third of all human tumor types . TherefoFurthermore, we predicted potential repurposing drugs that could target the above-mentioned co-DEGs or hubs using in silico analysis of data from three different databases . Importantly, the cytotoxic effects of BW-B70C, Lasalocid sodium, Catecholamine, Cyclosporin A and Etoposide were validated in vitro using the HEK-293 cell line, whereas Ifosfamide did not show any effect on the viability of the cells. Among these drugs, we discuss below Etoposide (DrugMatrix), Haloperidol (cMap down) and BW-B70C (LINCS L1000 Chem Pert down), which were predicted to down-regulate the co-upregulated genes in kidney cancer. Of these, Etoposide and BW-B70C were also proven to reduce HEK-203 cellular viability. We further discuss Triamterene (DrugMatrix), Chlorphenesin (cMap down) and BRD-K79459005 (LINCS L1000 Chem Pert down), which were predicted to induce the co-downregulated genes.The advantage of HEK293 cells that we used is that they are very easy to grow and to maintain, with high reproducibility, which makes them preferable over other less-robust and slow-growing cell lines. In addition, they are very efficient at protein production and accessible for transfection. They have also been widely used to analyze the effect of drugs on sodium channels and the interactions between different proteins and to produce proteins and drugs. Their disadvantage is that, if extensively cultured, their growth rate and translation efficiency is affected, although high passages increase their tumorigenicity. In addition, they display some cytogenetic instability.Etoposide is already used as a treatment against testicular and small cell lung tumors. This compound works as an inhibitor of DNA synthesis by forming a complex with DNA and topoisomerase II and inhibiting repair from the latter. As a result, cell death is promoted due to the large number of DNA breaks and the suspension of cell division ,58. A stHaloperidol is mainly used as a treatment for schizophrenia and other psychoses. It is commonly used to reduce nausea and vomiting in patients with tumors without any harmful actions on patients’ health ,61. SuppBW-B70C is a selective inhibitor of arachidonic acid 5-lipoxygenase . InteresTriamterene is a compound usually used as potassium-sparing diuretic, and its main purpose is to control hypertension . It has Chlorphenesin is a compound being used in different pharmaceutical industry sectors. Two compounds are known by the name Chlorphenesin, the first is often being used in the cosmetic industry and the other, known as Chlorphenesin carbamate, is frequently being used for muscle relaxation . Other uBRD-K79459005 is a lactam and azamacrocyclen. Unfortunately, there is no remarkable information on this compound as an anticancer drug.Beta-Estradiol 3-benzoate  is a derivative from the sex hormone estradiol. Mainly, it is branded for its capacity as a blood coagulability inducer. Estradiol benzoate has been used therapeutically against breast cancer in combination with other compounds, and some patients exhibited improvement for a short period after treatment. In any case, the results are encouraging, but still further examination is needed for the use of Estradiol benzoate as part of treatment against kidney cancer.Dequalinium chloride is a compound with frequent use as an antiseptic drug, but also as an antineoplastic and antifungal agent. It is is an inhibitor of XIAP and can also act as a mitochondrial targeting agent, which renders it as a promising therapeutic agent against malignant glioma . TherefoAS-605240 is an active inhibitor of PI3Kγ. By blocking the PI3Kγ kinase through phosphorylation, the tumor shrinks. An additional benefit of the of the phosphorylation of PI3Kγ kinase is the obviation of anthracyclines cardiotoxicity . This paSodium arsenite, among others, is an antineoplastic agent. Its derivative, sodium metaarsenite, is a possible anticancer compound due to its ability to stop the action of telomerase, a critical enzyme with high activity in the majority of cancer cells. The inhibition of the telomerase results in the induction of cancer cell death and the inhibition of cancer cell development. Nevertheless, more studies on this compound are required, especially in kidney cancer.Arecoline is an alkaloid from the betel nut (Areca catechu), the fruit of palm trees. In line with our findings demonstrating that Arecoline could be used as a drug against kidney cancer, previous studies also show that this compound acts as a tumor inhibitor. In specific, this compound is known to act as an inhibitor of p53 and a repressor of DNA repair ,75.Finally, Parthenolide is a sesquiterpene lactone that is found in Tanacetum and has anticancer chemotherapeutic, anti-metastatic, anti-angiogenic, anti-inflammatory and antinociceptive properties. Parthenolide acts as a partial agonist and desensitizes transient receptor potential ankyrin 1 (TRPA1) channels, preventing the release of calcitonin gene-related peptide (CGRP). Parthenolide also impedes the ATPase activity of NLRP3 and the protease activity of caspase 1. Parthenolide inhibits cell migration and tubule formation by decreasing NF-B, VEGF and IL-6 expression and increasing IB kinase expression in multiple myeloma cells. Parthenolide reduces MCL-1 levels while increasing MAIP-1 levels in non-small cell lung cancer (NSCLC) cells, causing ER stress and inducing cell cycle arrest and apoptosis. Parthenolide activates NADPH oxidase and increases ROS generation in breast cancer cells, increasing p-JNK levels while decreasing NF-κB, VEGF and MMP2/9. In vivo, parthenolide inhibits the growth and metastasis of tumors. In addition, this compound has been identified as an agonist of adiponectin receptor 2 . RecentlWe gathered data from 10 different studies and divided them into two groups: (1) studies focusing on gene expression differences between renal cancer and adjacent normal tissue (4 GEO studies) and (2) studies focusing on a single-gene perturbation  vs. wild-type cells or tissues (6 studies). Gene expression signatures were extracted using GEO2Enrichr . We begaThe GEO datasets that we selected for each category were as follows: GDS507, containing 8 normal samples  and 9 renal cell carcinoma samples; GDS1344, containing 22 molecular class type 1  samples  and 13 molecular class type 2  samples ; GDS2881, having 10 wild-type samples and 10 ccRCCs (5 stage I and 5 stage II ccRCCs); GDS3274, containing 9 oncocytomas and 9 chroRCCs; GDS1748, with 4 controls (2 E14.5 and 2 E18.5) and 4 Lim1 null mutant samples (2 E14.5 and 2 E18.5), where the terms E14.5 and E18.5 described the embryonic day; GDS2010, with 2 control and 2 WTAP knockdown samples; GDS4282, having 22 normal cortex BAP1 wild type/PBRM1 wild type and 57 gene perturbation samples . We looked at four different GEO datasets to see which co-DEGs were present in kidney cancer tissues compared to their normal tissue. The co-upregulated genes in this category were substantially enriched tumograft_p1 2 BAP1 wild type/PBRM1 loss 1 BAP1 wild type/PBRM1 wild type, tumorgraft_p2 2 BAP1 wild type/PPBRM1 loss, 1 BAP1 wild type/PBRM1 wild type, tumorgraft_p3 BAP1 wild type PBRM1 wild type, BAP loss/PBRM1 wild type, tumorgraft_p4 2 BAP 1 loss/PBRM1 wild type, 1 BAP1 wild type/PBRM1 wild type, tumorgraft_p7 1 BAP1 wild type/PBRM1 wild type, tymorgraft_p8 2 BAP1 loss/PBRM1 wild type, tumorgraft_p9 1 BAP1 wild type/PBRM1 wild type and tumorgraft_p1_from metastasis 1 BAP1 wild type/PBRM1 loss; GDS4802, containing two control samples and two WTX overexpression samples; GDS4864, with three control samples, four VHL knockout samples, five induced acute kidney injury and four VHL knockout + induced acute kidney injury; and GDS5415, having four controls  and four Lin28-derived tumors .We re-processed the extracted expression signatures to filter their quality and check data integrity, as previously explained . In detaWe also checked for batch effects across the different GEO studies and improved the quality of the gene expression signatures. We first obtained the “scan date” from the raw microarray CEL files and assumed that the experiments were performed on the same dates that were listed within the experimental batch. We then quantified the batch effect using principal variation component analysis (PCVA), which attributes the variation in the gene expression data to known sources such as batches and experimental conditions. Batch effects were corrected using the surrogate variable analysis (SVA) algorithm with default parameters .We used the Characteristic Direction (CD) algorithm  to calcuThe Expression2Kinases (X2K) method was used to deduce the upstream regulatory networks from a list of co-DEG signatures in the two categories on which we concentrated our efforts . Using Enrichr, we inferred the gene networks that are anticipated to govern the expression of the co-DEGs by combining transcription factors (TF) enrichment analysis, protein–protein interaction (PPI) network expansion and kinase enrichment analysis (KEA) .https://maayanlab.cloud/X2K/; 1 January 2021), we received enrichment lists of TFs by recognizing proteins that interact with them. We also presented a ball-and-stick diagram for each category as a subnetwork of the TFs and their interacting proteins.The PPI networks that were created included TFs, protein kinases and intermediate proteins for each class of co-DEGs. Using X2K  are more centralized in the network and have a stronger capacity of modulating adjacent genes.p-values, and an adjusted p-value (adj-p) of 0.05 was selected as threshold of statistical significance. Uniform Manifold Approximation and Projection (UMAP) was used to cluster similar gene sets together. Clusters were computed using the Leiden algorithm [The biological properties, molecular function and cellular components of the top co-DEGs were investigated using GO and KEGG pathway enrichment analysis. The hypergeometric test was employed to uncover GO terms that were considerably enriched when compared to the entire human genome. The Fisher’s exact test was used to determine statistical significance. Benjamini–Hochberg (BH) was used to correct lgorithm . Points https://portal.gdc.cancer.gov/; 15 May 2022). We then normalized the read counts to log2(TPM + 1) values, as previously described [2FC = 1 and q-value = 0.01. The UP and DOWN gene signatures were explored across KICH, KIRC and KIRP, as well as across four mRNA clusters in KIRC .We validated the calculated signatures in kidney cancer after extracting the read counts of RNA-seq data from the TCGA-KIRC (523 tumor samples and 72 controls), TCGA-KIRP (286 tumor samples and 32 controls) and TCGA-KICH (66 tumor samples and 25 controls) datasets using the Genomic Data Commons data portal   and immune subtypes in KIRP, kidney chromophobe (KICH) and kidney renal clear-cell carcinoma (KIRC) , as previously described by Thorsson et al. .Gene Set Cancer Analysis (GSCA) was used to investigate and explore the gene set of the hub proteins that we identified above across kidney cancers  in the TCGA dataset. We validated the mRNA expression profiles of the key hub genes across different stages in KICH, KIRC and KIRP tumors and explored the pathway activity between high and low mRNA expression of these genes in kidney cancer. In addition, gene set variation analysis (GSVA) scores were calculated for the designated hub genes in KICH, KIRC and KIRP tumors .ntp.niehs.nih.gov/drugmatrix/; 1 April 2021).DrugMatrix is the world’s largest molecular toxicology reference database and informatics system, containing the comprehensive results of thousands of highly controlled and standardized toxicological experiments in which rats or primary rat hepatocytes were systematically treated with >600 therapeutic, industrial or environmental chemicals at both non-toxic and toxic doses. It contains various gene expression datasets created by extracting RNA from the toxicologically relevant organs and tissues, the analysis of which is performed with the GE Codelink rat array and the Affymetrix whole genome 230 2.0 rat GeneChip array  was emphttps://lincsproject.org/, 15 June 2021) [The LINCS L1000 project collects gene expression profiles for thousands of perturbagens at a variety of time points, doses and cell lines. The LINCS L1000 Chem Pert  library was used to detect repurposing drugs for each gene set (ne 2021) .3 or 75 cm3 cell culture flasks loosely capped  at 37 °C in 5% CO2 and 95% humidity. The flasks were left to reach adequate confluence before conducting each experiment.HEK-293 cells were routinely maintained in Dulbecco’s Modified Eagle’s medium (DMEM/Ham’s F-12 with L-Glutamine) supplemented with 10% heat-inactivated fetal bovine serum (FBS) and 1% antibiotic cocktail mix. The cells were grown to confluence in 25 cm3 cells were seeded in normal medium in a 96-well plate and left to attach overnight. The cells were then treated with increasing concentrations of the drugs  or vehicle for 48 h. Then, 15 μL of MTT solution (5 mg/mL in PBS) was added for 4 h, the medium was removed, and 100 μL of DMSO was used to completely dissolve the purple formazan crystals. The absorbance of the test plate was read at 570 nm. The absorbance for each compound was subsequently normalized to the vehicle-treated cells prior to plotting. Data were statistically analyzed using non-linear regression (curve fit) to calculate the half-maximal inhibitory concentration  of each drug against HEK-293 cells in GraphPad Prism (v8.4.2).The MTT assay was conducted as described previously . BrieflyOverall, we used a systems biology approach to investigate the co-deregulated genes in studies with kidney cancer or in studies that used single-gene perturbations in kidney cancer cells. We identified the important hub genes in each group acting as transcriptional regulators for the co-deregulated genes (transcription factors and kinases), and we examined the molecular networks generated by these co-DEGs. Apart from identifying the key genes involved in kidney cancers, we further proposed five repurposed drugs , which could be used as a potential treatment against the disease. Overall, our findings validate previously published results and reveal the dynamic processes that take place during kidney carcinogenesis.


Furthermore, the article explored the refractive index sensing capabilities of this structure by introducing a 1 mm analyte layer on top of the patch structure. Through refractive index sensing analysis, we've determined that this absorber-based sensor yields an impressive high-quality factor value of 84.5, highlighting its remarkable sensitivity and precision. A more profound comprehension of the physical mechanisms in action has been attained by examining the distribution of surface currents. Furthermore, the behavior of the absorber has been investigated under varying polarization and incident angle conditions, ranging from zero degrees to sixty degrees. The thorough characterization establishes this absorber as a promising choice for microwave sensing applications.This work has developed and simulated a planar complementary Archimedes-based metamaterial absorber with the goal of its application in refractive index sensing. Unlike designs that employ multiple layers or numerous resonators within a single unit cell, our proposed absorber adopts a more streamlined approach. It consists of three layers, with an FR4 dielectric substrate sandwiched between two copper layers. It's important to note that the absorption characteristics of this design are polarization-dependent. This polarization dependence arises from the asymmetrical resonance behavior observed in both the x and y directions. The absorber exhibits impressive absorption rates at various resonance frequencies, namely  PMMAs have been extensively developed across various electromagnetic (EM) spectrum ranges, including microwave3, terahertz11, infrared16, and visible regions20. The advantage of PMMA surface current distribution lies in its geometric scalability, allowing it to operate at different EM spectrum ranges without being limited by the quarter-wavelength thickness. PMMAs have found applications in thermal imaging12, sensors23, solar cells19, thermal emitters24, and more. Typically, a PMMA consists of three layers: a patterned metallic structure serving as the EM resonator, a dielectric or magnetic spacer, and a continuous metal film or metal wire as the ground layer. By adjusting the geometric parameters and properties of these layers, the PMMA can achieve near-unity absorption based on the fundamental resonance of the EM resonator.Since the groundbreaking work of Landy et al.32. Two design strategies have been employed: combining multiple sub-units within a super-unit resonant structure8, or constructing alternating patterned metallic structures and dielectric layers with different geometric parameters31. However, both strategies have limitations, such as increased fabrication cost, angular dependence, and neglecting high-order EM responses. High-order resonances of metamaterials (MMs) are often overlooked in PMMA designs, but they can play a crucial role in achieving multi-band absorption. Several studies have demonstrated the combination of fundamental and high-order resonance modes in a single patterned metallic structure35, resulting in multi-band absorption.Efforts have been made to achieve multi-band or broadband high-level absorption for EM waves21. Essentially, when alterations occur in the dielectric substrate or geometric structure due to external influences, the electromagnetic properties of the metamaterial change accordingly. This leads to variations in resonance frequency and absorption intensity. If there's a direct and proportional relationship between these frequency or absorption changes and external influences, metamaterial absorbers can effectively identify changes in those influences. This versatility extends to parameters like dielectric constants, substrate thickness, biological features, and more31.Metamaterial absorbers are predominantly reliant on their robust resonance characteristics, which offer a significant advantage. These sensors can generate a powerful and easily measurable signal through a resonance absorption peak. This signal is potent enough to accurately monitor shifts in absorption spectra31. Conversely, investigations into the microwave waveband have lagged somewhat behind, limiting their broader applicability. Moreover, many microwave and metamaterial absorber-based sensors often necessitate specific samples that have undergone destructive treatments or involve liquid samples. This requirement is due to their restricted sensing areas, which also diminishes their suitability for non-destructive sensing applications.However, it’s important to note that research on the sensing applications of metamaterials has predominantly concentrated on the terahertz and infrared bandsThis paper presents a simple and effective design of an ultrathin seven-band polarization-sensitive PMMA in the X-band microwave region. The design consists of an array of Complementary Archimedes Resonator CAR and a copper ground plane separated by a thin FR4 dielectric film. The PMMA exhibits seven narrow absorption bands with resonance peaks exceeding 77.1–99.9%. Compared to previous PMMA designs, our approach offers compact unit size, novel resonance mechanism, polarization sensitivity, and higher Q factors. The simplicity and effectiveness of this design hold potential for applications in biological sensing, material detection, thermal imaging, and microwave communications.εr = 4.6, loss tangent tan δ = 0.015, and thickness h = 2 mm. The front face of substrate is planar complementary archimedes based on patch circle. And the back side is metallic plane. The metallic layers are copper with a thickness is tc = 35 µm and electrical conductivity constant is σ = 5.8 × 107 S/m. The structure parameters are listed in Table The artificial metamaterials absorption characteristic depends on some factors including the electromagnetic properties of dielectric substrate and structure topology of the unit-cell. In this project, the substrate is FR4 with permittivity The simulations have been performed by using computer simulation technology (CST) Microwave Studio commercial software, which is based on frequency domain solver and finite element analysis. The boundary condition of unit cell is employed to simulate the infinite periodic structure in x- and y-directions, and open (add space) boundary is used in z-direction. The absorber is excited by an incident plane wave along the negative direction of the z-axis by using the Floquet port. Figure g to Eq. .3\documef1, f2, f3, f4, f5, f6 and f7). At these resonant frequencies , the absorbance A(ω) is approximately 98.5%, 77.1%, 88.7%, 98.2%, 99.7%, 83.4% and 99.9%, respectively  is approximately 0.056λ1, 0.059λ2, 0.062λ3, 0.066λ4, 0.071λ5, 0.077λ6 and 0.082λ7, respectively. Thus, the designed PMMA exhibits an ultrathin thickness compared to the operating wavelength. The PMMA demonstrates frequency selectivity, with narrow bandwidths of perfect absorption. The high-level absorption at each resonant frequency is attributed to the dipole resonances of the CAR structure. The absorption frequency band of the seven-peak PMMA is relatively narrow compared to previous PMMAs. This design is expected to have significantly higher Q factors than previous designs. The Q factor, defined as the ratio of the central frequency to the full width at half maximum (FWHM) bandwidth, is calculated for each resonance. Consequently, the corresponding Q factors are approximately Q1 = 41.2, Q2 = 80.73, Q3 = 84.5, Q4 = 38, Q5 = 35.3, Q6 = 13 and Q7 = 30.6. These results indicate that the high-level absorption with high Q factors occurs only at resonant frequencies. Compared to previous MM structures used in sensing applications (Q factor < 20), the proposed PMMA exhibits relatively higher Q factors, making it highly suitable for sensitive sensing applications such as phase imaging of prohibited drugs, detection of combustible, toxic, and harmful gases, and biological sensing. Additionally, the proposed PMMA structure is expected to be sensitive to the polarization state of incident waves due to its geometric rotational asymmetry.The results and discussion of the proposed PMMA are presented in this section. Figure 0°, 15°, 30°, 45° and 60°). The findings indicate that when waves under normal incidence, absorbance do not remain constant across different polarization angles in both TE and TM modes. This indicates that PMMA exhibits polarization sensitivity, making it suitable for polarization-independent applications. Notably, the absorbance for the first and seven resonances remains steady for both TE and TM modes when the incident wave angle is below 60°. However, as the incident angle increases (θ > 45°), the performance of higher-resonant frequencies deteriorates due to the impact of higher-order multipolar plasmon resonance.The polarization angle dependence of the PMMA was assessed for TE and TM waves under normal incidence, as depicted in Fig. In order to gain a comprehensive understanding of the physical mechanisms underpinning the seven-band absorptions, an examination of the current distribution on the metallic structures, as depicted in Fig. This section presents a comprehensive parametric investigation aimed to understand the behavior of the proposed structure. During the design phase, the Archimedes structure's performance is affected by changes in key factors like the Archimedes resonator width, the number of loops, and the substrate thickness.11) of PMMA for five distinct width values . Figure In particular, it has been focused on the analysis of the Archimedes width, as it plays a pivotal role in shaping the behavior of the metamaterial Resonator. Also, it has been examined the reflection coefficient , effective permeability (μeff), ‘ni’, and wave impedance (z). These parameters play a pivotal role in characterizing the structure’s response to the analyte layer. They are calculated using various techniques, such as transmission-reflection (TR), Lossy-Drude, and Nicolson-Ross-Weir methods. Specifically, for Lossy-Drude and Nicolson-Ross-Weir techniques, ‘n’ is determined as follows: n = cos–1 ((1–S11+S22)/(2S21))/kδ, and ‘z’ is derived as: z = sqr[(1+S11)2–S22]/[(1–S11)2–S22].To gain deeper insights into the behavior of the material, a delve into the essential parameters, including the effective permittivity  values within the range of 1 to 1.9 see Fig. The experimental results indicate that as we vary the refractive index (n2, the sensor offers a frequency band of 2.4–4.2, a Q-factor of 30, and an analyte layer thickness of 3. However, in3, these values are 2–6, 60, and 1.6, respectively. Reference4 demonstrates an impressive Q-factor, although it is accompanied by a considerably thick analyte layer. A similar issue arises in21 and22. Notably, the proposed sensor exhibits an overall performance that surpasses that of the other sensors mentioned in the literature.Table In conclusion, our study has successfully developed and simulated a planar complementary Archimedes-based metamaterial absorber, with a specific focus on its potential application in refractive index sensing. What sets our design apart is its simplicity, as it comprises just three layers: an FR4 dielectric substrate sandwiched between two copper layers. An important characteristic to highlight is its polarization-dependent absorption behavior, a result of the distinct resonance patterns observed in both the x and y directions. Furthermore, the resonator has explored the refractive index sensing potential of this structure by introducing a 1 mm analyte layer atop the patch structure. Our refractive index sensing analysis reveals a high-quality factor value of 84.5, highlighting the sensor's precision and sensitivity. Taken together, these findings position our absorber as a highly promising candidate for a wide range of microwave sensing applications, with its simplicity and exceptional performance making it a valuable asset in the field of metamaterial-based sensor technology.