Pseudotyped virus infection of multiplexed ACE2 libraries reveals SARS-CoV-2 variant shifts in receptor usage.

Pairwise compatibility between virus and host proteins can dictate the outcome of infection. During transmission, both inter- and intraspecies variabilities in receptor protein sequences can impact cell susceptibility. Many viruses possess mutable viral entry proteins and the patterns of host compatibility can shift as the viral protein sequence changes. This combinatorial sequence space between virus and host is poorly understood, as traditional experimental approaches lack the throughput to simultaneously test all possible combinations of protein sequences. Here, we created a pseudotyped virus infection assay where a multiplexed target-cell library of host receptor variants can be assayed simultaneously using a DNA barcode sequencing readout. We applied this assay to test a panel of 30 ACE2 orthologs or human sequence mutants for infectability by the original SARS-CoV-2 spike protein or the Alpha, Beta, Gamma, Delta, and Omicron BA1 variant spikes. We compared these results to an analysis of the structural shifts that occurred for each variant spike's interface with human ACE2. Mutated residues were directly involved in the largest shifts, although there were also widespread indirect effects altering interface structure. The N501Y substitution in spike conferred a large structural shift for interaction with ACE2, which was partially recreated by indirect distal substitutions in Delta, which does not harbor N501Y. The structural shifts from N501Y greatly influenced the set of animal orthologs the variant spike was capable of interacting with. Out of the thirteen non-human orthologs, ten exhibited unique patterns of variant-specific compatibility, demonstrating that spike sequence changes during human transmission can toggle ACE2 compatibility and potential susceptibility of other animal species, and cumulatively increase overall compatibilities as new variants emerge. These experiments provide a blueprint for similar large-scale assessments of protein compatibility during entry by diverse viruses. This dataset demonstrates the complex compatibility relationships that occur between variable interacting host and virus proteins.

LRIG1 engages ligand VISTA and impairs tumor-specific CD8+ T cell responses.

Immune checkpoint blockade is a promising approach to activate antitumor immunity and improve the survival of patients with cancer. V-domain immunoglobulin suppressor of T cell activation (VISTA) is an immune checkpoint target; however, the downstream signaling mechanisms are elusive. Here, we identify leucine-rich repeats and immunoglobulin-like domains 1 (LRIG1) as a VISTA binding partner, which acts as an inhibitory receptor by engaging VISTA and suppressing T cell receptor signaling pathways. Mice with T cell-specific LRIG1 deletion developed superior antitumor responses because of expansion of tumor-specific cytotoxic T lymphocytes (CTLs) with increased effector function and survival. Sustained tumor control was associated with a reduction of quiescent CTLs (TCF1+ CD62Lhi PD-1low) and a reciprocal increase in progenitor and memory-like CTLs (TCF1+ PD-1+). In patients with melanoma, elevated LRIG1 expression on tumor-infiltrating CD8+ CTLs correlated with resistance to immunotherapies. These results delineate the role of LRIG1 as an inhibitory immune checkpoint receptor and propose a rationale for targeting the VISTA/LRIG1 axis for cancer immunotherapy.

Pseudotyped virus infection of multiplexed ACE2 libraries reveals SARS-CoV-2 variant shifts in receptor usage.

Pairwise compatibility between virus and host proteins can dictate the outcome of infection. During transmission, both inter- and intraspecies variabilities in receptor protein sequences can impact cell susceptibility. Many viruses possess mutable viral entry proteins and the patterns of host compatibility can shift as the viral protein sequence changes. This combinatorial sequence space between virus and host is poorly understood, as traditional experimental approaches lack the throughput to simultaneously test all possible combinations of protein sequences. Here, we created a pseudotyped virus infection assay where a multiplexed target-cell library of host receptor variants can be assayed simultaneously using a DNA barcode sequencing readout. We applied this assay to test a panel of 30 ACE2 orthologs or human sequence mutants for infectability by the original SARS-CoV-2 spike protein or the Alpha, Beta, Gamma, Delta, and Omicron BA1 variant spikes. We compared these results to an analysis of the structural shifts that occurred for each variant spike's interface with human ACE2. Mutated residues were directly involved in the largest shifts, although there were also widespread indirect effects altering interface structure. The N501Y substitution in spike conferred a large structural shift for interaction with ACE2, which was partially recreated by indirect distal substitutions in Delta, which does not harbor N501Y. The structural shifts from N501Y greatly influenced the set of animal orthologs the variant spike was capable of interacting with. Out of the thirteen non-human orthologs, ten exhibited unique patterns of variant-specific compatibility, demonstrating that spike sequence changes during human transmission can toggle ACE2 compatibility and potential susceptibility of other animal species, and cumulatively increase overall compatibilities as new variants emerge. These experiments provide a blueprint for similar large-scale assessments of protein compatibility during entry by diverse viruses. This dataset demonstrates the complex compatibility relationships that occur between variable interacting host and virus proteins.

Novel CAF-identifiers via transcriptomic and protein level analysis in HNSC patients.

Cancer-associated fibroblasts (CAFs), a prominent component of the tumor microenvironment, play an important role in tumor development, invasion, and drug resistance. The expression of distinct "CAF-markers" which separates CAFs from normal fibroblasts and epithelial cells, have traditionally been used to identify them. These commonly used CAF-markers have been reported to differ greatly across different CAF subpopulations, even within a cancer type. Using an unbiased -omic approach from public data and in-house RNAseq data from patient derived novel CAF cells, TIMP-1, SPARC, COL1A2, COL3A1 and COL1A1 were identified as potential CAF-markers by differential gene expression analysis using publicly available single cell sequencing data and in-house RNAseq data to distinguish CAF populations from tumor epithelia and normal oral fibroblasts. Experimental validation using qPCR and immunofluorescence revealed CAF-specific higher expression of TIMP-1 and COL1A2 as compared to other markers in 5 novel CAF cells, derived from patients of diverse gender, habits and different locations of head and neck squamous cell carcinoma (HNSC). Upon immunohistochemical (IHC) analysis of FFPE blocks however, COL1A2 showed better differential staining between tumor epithelia and tumor stroma. Similar data science driven approach utilizing single cell sequencing and RNAseq data from stabilized CAFs can be employed to identify CAF-markers in various cancers.

Performance-based protocol for selection of economical portable sensor for air quality measurement.

An effective micro-level air quality management plan requires high-resolution monitoring of pollutants. India has already developed a vast network of air quality monitoring stations, both manual and real time, located primarily in urban areas, including megacities. The air quality monitoring network consists of conventional manual stations and real time Continuous Ambient Air Quality Monitoring Stations (CAAQMS) which comprise state-of-the-art analysers and instruments. India is currently in the early stages of developing and adopting economical portable sensor (EPS) in air quality monitoring systems. Protocols need to be established for field calibration and testing. The present research work is an attempt to develop a performance-based assessment framework for the selection of EPS for air quality monitoring. The two-stage selection protocol includes a review of the factory calibration data and a comparison of EPS data with a reference monitor, i.e. a portable calibrated monitor and a CAAQMS. Methods deployed include calculation of central tendency, dispersion around a central value, calculation of statistical parameters for data comparison, and plotting pollution rose and diurnal profile (peak and non-peak pollution measurement). Four commercially available EPS were tested blind, out of which, data from EPS 2 (S2) and EPS 3 (S3) were closer to reference stations at both locations. The selection was made by evaluating monitoring results, physical features, measurement range, and frequency along with examining capital cost. This proposed approach can be used to increase the usability of EPS in the development of micro-level air quality management strategies, other than regulatory compliance. For regulatory compliance, additional research is needed, including field calibration and evaluating EPS performance through additional variables. This proposed framework may be used as starting point, for such experiments, in order to develop confidence in the use of EPS.

Towards Understanding the Key Signature Pathways Associated from Differentially Expressed Gene Analysis in an Indian Prostate Cancer Cohort.

Prostate cancer (PCa) is one of the most prevalent cancers among men in India. Although studies on PCa have dealt with genetics, genomics, and the environmental influence in the causality of PCa, not many studies employing the Next Generation Sequencing (NGS) approaches of PCa have been carried out. In our previous study, we identified some causal genes and mutations specific to Indian PCa using Whole Exome Sequencing (WES). In the recent past, with the help of different cancer consortiums such as The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC), along with differentially expressed genes (DEGs), many cancer-associated novel non-coding RNAs have been identified as biomarkers. In this work, we attempt to identify differentially expressed genes (DEGs) including long non-coding RNAs (lncRNAs) associated with signature pathways from an Indian PCa cohort using the RNA-sequencing (RNA-seq) approach. From a cohort of 60, we screened six patients who underwent prostatectomy; we performed whole transcriptome shotgun sequencing (WTSS)/RNA-sequencing to decipher the DEGs. We further normalized the read counts using fragments per kilobase of transcript per million mapped reads (FPKM) and analyzed the DEGs using a cohort of downstream regulatory tools, viz., GeneMANIA, Stringdb, Cytoscape-Cytohubba, and cbioportal, to map the inherent signatures associated with PCa. By comparing the RNA-seq data obtained from the pairs of normal and PCa tissue samples using our benchmarked in-house cuffdiff pipeline, we observed some important genes specific to PCa, such as STEAP2, APP, PMEPA1, PABPC1, NFE2L2, and HN1L, and some other important genes known to be involved in different cancer pathways, such as COL6A1, DOK5, STX6, BCAS1, BACE1, BACE2, LMOD1, SNX9, CTNND1, etc. We also identified a few novel lncRNAs such as LINC01440, SOX2OT, ENSG00000232855, ENSG00000287903, and ENST00000647843.1 that need to be characterized further. In comparison with publicly available datasets, we have identified characteristic DEGs and novel lncRNAs implicated in signature PCa pathways in an Indian PCa cohort which perhaps have not been reported. This has set a precedent for us to validate candidates further experimentally, and we firmly believe this will pave a way toward the discovery of biomarkers and the development of novel therapies.

DNA methylation profiling of meningiomas highlights clinically distinct molecular subgroups.

BACKGROUND: Introduction of the classification of brain tumours based on DNA methylation profile has significantly changed the diagnostic approach. Due to the paucity of data on the molecular profiling of meningiomas and their clinical implications, no effective therapies and new treatments have been implemented. METHODS: DNA methylation profiling, copy number analysis, targeted sequencing and H3K27me3 expression was performed on 35 meningiomas and 5 controls. RESULTS: Unsupervised hierarchical clustering (UHC) analysis revealed four distinct molecular subgroups: Malignant; Intermediate; Benign A, and Benign B. Molecular heterogeneity was observed within the same grade as the Intermediate, Benign A, and Benign B subgroups were composed of WHO grade 1 as well as grade 2 cases. There was association of mutations with distinct methylation subgroups (NF2, AKT1, SMO, TRAF7 and pTERT). Loss of chromosome 22q was observed across all subgroups. 1p/14q co-deletion was seen in 50% of malignant and intermediate while CDKN2A loss was predominantly observed in malignant subgroup (50%). Majority of malignant (75%) and a small proportion of other subgroups (Intermediate: 25%, Benign A: 38.5%, and Benign B: 20%) harboured H3K27me3 loss. 38,734 genes were dysregulated amongst the four subgroups. DKFZ classified 71% cases with acceptable score. On survival analysis, methylation profiling had significant impact on progression-free-survival in WHO grade1 and 2 meningiomas (p = 0.0051). CONCLUSION: Genome-wide DNA methylation profiling highlights clinically distinct molecular subgroups and heterogeneity within the same grade of meningiomas. Molecular profiling can usher in a paradigm shift in meningioma classification, prognostic prediction, and treatment strategy.

Identification of Plausible Candidates in Prostate Cancer Using Integrated Machine Learning Approaches.

Background: Currently, prostate-specific antigen (PSA) is commonly used as a prostate cancer (PCa) biomarker. PSA is linked to some factors that frequently lead to erroneous positive results or even needless biopsies of elderly people. Objectives: In this pilot study, we undermined the potential genes and mutations from several databases and checked whether or not any putative prognostic biomarkers are central to the annotation. The aim of the study was to develop a risk prediction model that could help in clinical decision-making. Methods: An extensive literature review was conducted, and clinical parameters for related comorbidities, such as diabetes, obesity, as well as PCa, were collected. Such parameters were chosen with the understanding that variations in their threshold values could hasten the complicated process of carcinogenesis, more particularly PCa. The gathered data was converted to semi-binary data (-1, -0.5, 0, 0.5, and 1), on which machine learning (ML) methods were applied. First, we cross-checked various publicly available datasets, some published RNA-seq datasets, and our whole-exome sequencing data to find common role players in PCa, diabetes, and obesity. To narrow down their common interacting partners, interactome networks were analysed using GeneMANIA and visualised using Cytoscape, and later cBioportal was used (to compare expression level based on Z scored values) wherein various types of mutation w.r.t their expression and mRNA expression (RNA seq FPKM) plots are available. The GEPIA 2 tool was used to compare the expression of resulting similarities between the normal tissue and TCGA databases of PCa. Later, top-ranking genes were chosen to demonstrate striking clustering coefficients using the Cytoscape-cytoHubba module, and GEPIA 2 was applied again to ascertain survival plots. Results: Comparing various publicly available datasets, it was found that BLM is a frequent player in all three diseases, whereas comparing publicly available datasets, GWAS datasets, and published sequencing findings, SPFTPC and PPIMB were found to be the most common. With the assistance of GeneMANIA, TMPO and FOXP1 were found as common interacting partners, and they were also seen participating with BLM. Conclusion: A probabilistic machine learning model was achieved to identify key candidates between diabetes, obesity, and PCa. This, we believe, would herald precision scale modeling for easy prognosis.

Expanded ACE2 dependencies of diverse SARS-like coronavirus receptor binding domains.

Viral spillover from animal reservoirs can trigger public health crises and cripple the world economy. Knowing which viruses are primed for zoonotic transmission can focus surveillance efforts and mitigation strategies for future pandemics. Successful engagement of receptor protein orthologs is necessary during cross-species transmission. The clade 1 sarbecoviruses including Severe Acute Respiratory Syndrome-related Coronavirus (SARS-CoV) and SARS-CoV-2 enter cells via engagement of angiotensin converting enzyme-2 (ACE2), while the receptor for clade 2 and clade 3 remains largely uncharacterized. We developed a mixed cell pseudotyped virus infection assay to determine whether various clades 2 and 3 sarbecovirus spike proteins can enter HEK 293T cells expressing human or Rhinolophus horseshoe bat ACE2 proteins. The receptor binding domains from BtKY72 and Khosta-2 used human ACE2 for entry, while BtKY72 and Khosta-1 exhibited widespread use of diverse rhinolophid ACE2s. A lysine at ACE2 position 31 appeared to be a major determinant of the inability of these RBDs to use a certain ACE2 sequence. The ACE2 protein from Rhinolophus alcyone engaged all known clade 3 and clade 1 receptor binding domains. We observed little use of Rhinolophus ACE2 orthologs by the clade 2 viruses, supporting the likely use of a separate, unknown receptor. Our results suggest that clade 3 sarbecoviruses from Africa and Europe use Rhinolophus ACE2 for entry, and their spike proteins appear primed to contribute to zoonosis under the right conditions.

Long Non-coding RNA and mRNA Co-expression Network Reveals Novel Players in Pleomorphic Xanthoastrocytoma.

Histological interpretation of the rare pleomorphic xanthoastrocytoma (PXA) has been the holy grail for treatment options. However, no stand-alone clinical interventions have been developed owing to the lack of gene expression profiling data in PXA/APXA patients. We first time report the comprehensive analyses of the coding as well as long non-coding RNA (lncRNA) signatures of PXA/APXA patients. Several genes such as IGFBP2, NF1, FOS, ERBB2, and lncRNAs such as NEAT1, HOTAIRM1, and GAS5 known to play crucial roles in glioma patients were also deregulated in PXA patients suggesting the commonality in the molecular signatures. PPI network, co-expression, and lncRNA-mRNA interaction studies unraveled hub genes (such as ERBB2, FOS, RPA1) and networks that may play a critical role in PXA biology. The most enriched pathways based on gene profiles were related to TLR, chemokine, MAPK, Rb, and PI3K-Akt signaling pathways. The lncRNA targets were enriched in glucuronidation, adipogenesis, TGF-beta signaling, EGF/EGFR signaling, and cell cycle pathways. Interestingly, several mRNAs like PARVG, and ABI2 were found to be targeted by multiple lncRNAs suggesting a tight control of their levels. Some of the most prominent lncRNA-mRNA pairs were LOC728730: MRPL9, XLOC_l2_011987: ASIC2, lnc-C1QTNF5-1: RNF26. Notably, several lncRNAs such as lnc-CETP-1, lnc-XRCC3-1, lnc-RPL31-1, lnc-USP13-1, and MAPKAPK5-AS1, and genes such as RPA1, NTRK3, and CNRP1 showed strong correlation to the progression-free survival of PXA patients suggesting their potential as novel biomarkers. Overall, the findings of this study may facilitate the development of a new realm of RNA biology in PXA that may have clinical significance in the future.

Gene expression based profiling of pleomorphic xanthoastrocytoma highlights two prognostic subgroups.

BACKGROUND: Pleomorphic xanthoastrocytomas (PXAs) are rare, accounting for less than 1% of astrocytomas, and commonly occur in young patients. The majority are WHO grade II. A fraction of tumors that present or recur with malignant change are classified as anaplastic (APXA, grade III). Limited data are available on their molecular characteristics. METHODOLOGY: Genome-wide expression profiling of 14 PXA and 6 APXAs was performed by microarray. Among differentially expressed genes (DEGs), Cyclin-Dependent Kinase 14 (CDK14) and Mitochondrial Fission Process 1 (MTFP1) were validated by qRT PCR. RESULT: Unsupervised hierarchical clustering revealed two distinct molecular clusters (Cluster 1: 10 PXA, 3 APXA and Cluster 2: 4 PXA, 3 APXA) with grade II and III tumors distributed in both highlighting molecular heterogeneity within the same grade. There was an insignificant difference in age, sex, immunohistochemical profile, frequency of BRAF mutation, or CDKN2A deletion among the two clusters. Significantly, worse progression-free survival was observed in cluster 2 (P=0.003). mRNA profiling-based prediction of recurrence was superior to and independent of histological grade, BRAF mutation, or CDKN2A deletion status. A total of 10 upregulated and 418 downregulated genes were identified between the PXA clusters. qRT-PCR validation of CDK14 (upregulated in cluster 2) and MTFP1 (upregulated in cluster 1) showed strong concordance with expression array data. CONCLUSION: This is the first comprehensive study highlighting distinct molecular subgroups of PXA. The differentially expressed genes between two clusters may potentially be used for developing histology independent classification schemes, prognostication and may serve as prospective therapeutic targets for PXA patients.

Integrated support vector machine and pharmacophore based virtual screening driven identification of thiophene carboxamide scaffold containing compound as potential PARP1 inhibitor.

Poly (ADP-ribose) polymerase-1 (PARP1) inhibition strategy for cancer treatment is gaining advantage particularly in patients having a mutation in BRCA1/BRCA2 gene. To date, four drugs have obtained FDA approval and some inhibitors are in clinical trials. To identify more potent PARP1 inhibitors extensive research is going on to enrich the library of PARP1 inhibitors with compounds belonging to different classes. We employed an integrated virtual screening approach to identify potential PARP1 inhibitors. The sequential support vector machine (SVM) and pharmacophore model based virtual screening was carried out on the Maybridge library. The obtained hits were docked in the binding site of the PARP1 catalytic domain and nine drug-like compounds showing good ADME properties and form critical molecular interactions with the binding site residues were considered for the in vitro PARP1 inhibition assay. MD simulations were performed to decipher the stability of the PARP1-ligand complexes. Hydrogen bond interactions were also probed for their stability during MD simulations. We have identified three compounds (BTB02767, GK01172, and KM09200) showing 50% inhibition of PARP1 enzyme activity at 25 µM. BTB02767 and KM09200 have phthalazinone scaffold, while GK01172 bears a thiophene carboxamide scaffold, which could be a new chemotype of PARP1 inhibitors. In conclusion, GK01172 may serve as an important compound for further development of PARP1 inhibitors containing thiophene carboxamide scaffold.Communicated by Ramaswamy H. Sarma.

Innate and adaptive resistance to RNAi: a major challenge and hurdle to the development of double stranded RNA-based pesticides.

RNA interference (RNAi) is a post-transcriptional gene silencing process where short interfering RNAs degrade targeted mRNA. Exploration of gene function through reverse genetics is the major achievement of RNAi discovery. Besides, RNAi can be used as a potential strategy for the control of insect pests. This has led to the idea of developing RNAi-based pesticides. Differential RNAi efficiency in the different insect orders is the biggest biological obstacle in developing RNAi-based pesticides. dsRNA stability, the sensitivity of core RNAi machinery, uptake of dsRNA and amplification and spreading of the RNAi signal are the key factors responsible for RNAi efficiency in insects. This review discusses the physiological and adaptive factors responsible for reduced RNAi in insects that pose a major challenge in developing dsRNA- based pesticides.

The Omic Insights on Unfolding Saga of COVID-19.

The year 2019 has seen an emergence of the novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease of 2019 (COVID-19). Since the onset of the pandemic, biological and interdisciplinary research is being carried out across the world at a rapid pace to beat the pandemic. There is an increased need to comprehensively understand various aspects of the virus from detection to treatment options including drugs and vaccines for effective global management of the disease. In this review, we summarize the salient findings pertaining to SARS-CoV-2 biology, including symptoms, hosts, epidemiology, SARS-CoV-2 genome, and its emerging variants, viral diagnostics, host-pathogen interactions, alternative antiviral strategies and application of machine learning heuristics and artificial intelligence for effective management of COVID-19 and future pandemics.

SARS-CoV-2 transgressing LncRNAs uncovers the known unknowns.

SARS-CoV-2 harbors many known unknown regions in the form of hypothetical open reading frames (ORFs). Although the mechanisms underlying the disease pathogenesis are not clearly understood, molecules such as long noncoding RNAs (lncRNAs) play a key regulatory role in the viral pathogenesis from endocytosis. We asked whether or not the lncRNAs in the host are associated with the viral proteins and argue that lncRNA-mRNAs molecules related to viral infection may regulate SARS-CoV-2 pathogenesis. Toward the end of the perspective, we provide challenges and insights into investigating these transgression pathways.

Age-related prescription medication utilization for the -management of sickle cell disease among Texas Medicaid patients.

INTRODUCTION: Sickle cell disease (SCD) is associated with recurrent complications and healthcare burden. Although SCD management guidelines differ based on age groups, little is known regarding actual utilization of preventative (hydroxyurea) and palliative therapies (opioid and nonopioid analgesics) to manage complications. This study assessed whether there were agerelated differences in SCD index therapy type and SCD-related medication utilization. DESIGN AND PATIENTS: Texas Medicaid prescription claims from September 1, 2011 to August 31, 2016 were retrospectively analyzed for SCD patients aged 2-63 years who received one or more SCD-related medications (hydroxyurea, opioid, or nonopioid analgesics). OUTCOME MEASURES: The primary outcomes were SCD index drug type and medication utilization: hydroxyurea adherence, and days' supply of opioid, and nonopioid analgesics. Chi-square, analysis of variance, and Kruskal-Wallis tests were used. RESULTS: Index therapy percentages for included patients (N = 2,339) were the following: opioids (45.7 percent), nonopioids (36.6 percent), dual therapy-opioids and nonopioids (11.2 percent), and hydroxyurea (6.5 percent), and they differed by age-groups (χ2 = 243.0, p < 0.0001). Hydroxyurea as index therapy was higher among children (2-12:9.1 percent) compared to adults (26-40:3.7 percent; 41-63:2.9 percent). Opioids as index therapy were higher among adults (18-25:48.0 percent; 26-40:54.9 percent; 41-63:65.2 percent) compared to children (2-12:36.6 percent). Mean hydroxyurea adherence was higher (p < 0.0001) for younger ages, and opioid days' supply was higher for older ages. CONCLUSIONS: Texas Medicaid SCD patients had low hydroxyurea utilization and adherence across all age groups. Interventions to increase the use of hydroxyurea and newer preventative therapies could result in better management of SCD-related complications and reduce the frequency of pain crises, which may reduce the need for opioid use.

Mechanism of interaction between autophagy and apoptosis in cancer.

The mechanisms of two programmed cell death pathways, autophagy, and apoptosis, are extensively focused areas of research in the context of cancer. Both the catabolic pathways play a significant role in maintaining cellular as well as organismal homeostasis. Autophagy facilitates this by degradation and elimination of misfolded proteins and damaged organelles, while apoptosis induces canonical cell death in response to various stimuli. Ideally, both autophagy and apoptosis have a role in tumor suppression, as autophagy helps in eliminating the tumor cells, and apoptosis prevents their survival. However, as cancer proceeds, autophagy exhibits a dual role by enhancing cancer cell survival in response to stress conditions like hypoxia, thereby promoting chemoresistance to the tumor cells. Thus, any inadequacy in either of their levels can lead to tumor progression. A complex array of biomarkers is involved in maintaining coordination between the two by acting as either positive or negative regulators of one or both of these pathways of cell death. The resulting crosstalk between the two and its role in influencing the survival or death of malignant cells makes it quintessential, among other challenges facing chemotherapeutic treatment of cancer. In view of this, the present review aims to highlight some of the factors involved in maintaining their diaphony and stresses the importance of inhibition of cytoprotective autophagy and deletion of the intermediate pathways involved to facilitate tumor cell death. This will pave the way for future prospects in designing drug combinations facilitating the synergistic effect of autophagy and apoptosis in achieving cancer cell death.

Mutants of human ACE2 differentially promote SARS-CoV and SARS-CoV-2 spike mediated infection.

SARS-CoV and SARS-CoV-2 encode spike proteins that bind human ACE2 on the cell surface to enter target cells during infection. A small fraction of humans encode variants of ACE2, thus altering the biochemical properties at the protein interaction interface. These and other ACE2 coding mutants can reveal how the spike proteins of each virus may differentially engage the ACE2 protein surface during infection. We created an engineered HEK 293T cell line for facile stable transgenic modification, and expressed the major human ACE2 allele or 28 of its missense mutants, 24 of which are possible through single nucleotide changes from the human reference sequence. Infection with SARS-CoV or SARS-CoV-2 spike pseudotyped lentiviruses revealed that high ACE2 cell-surface expression could mask the effects of impaired binding during infection. Drastically reducing ACE2 cell surface expression revealed a range of infection efficiencies across the panel of mutants. Our infection results revealed a non-linear relationship between soluble SARS-CoV-2 RBD binding to ACE2 and pseudovirus infection, supporting a major role for binding avidity during entry. While ACE2 mutants D355N, R357A, and R357T abrogated entry by both SARS-CoV and SARS-CoV-2 spike proteins, the Y41A mutant inhibited SARS-CoV entry much more than SARS-CoV-2, suggesting differential utilization of the ACE2 side-chains within the largely overlapping interaction surfaces utilized by the two CoV spike proteins. These effects correlated well with cytopathic effects observed during SARS-CoV-2 replication in ACE2-mutant cells. The panel of ACE2 mutants also revealed altered ACE2 surface dependencies by the N501Y spike variant, including a near-complete utilization of the K353D ACE2 variant, despite decreased infection mediated by the parental SARS-CoV-2 spike. Our results clarify the relationship between ACE2 abundance, binding, and infection, for various SARS-like coronavirus spike proteins and their mutants, and inform our understanding for how changes to ACE2 sequence may correspond with different susceptibilities to infection.

Long Non-Coding RNAs in Insects.

Only a small subset of all the transcribed RNAs are used as a template for protein translation, whereas RNA molecules that are not translated play a very important role as regulatory non-coding RNAs (ncRNAs). Besides traditionally known RNAs (ribosomal and transfer RNAs), ncRNAs also include small non-coding RNAs (sncRNAs) and long non-coding RNAs (lncRNAs). The lncRNAs, which were initially thought to be junk, have gained a great deal attention because of their regulatory roles in diverse biological processes in animals and plants. Insects are the most abundant and diverse group of animals on this planet. Recent studies have demonstrated the role of lncRNAs in almost all aspects of insect development, reproduction, and genetic plasticity. In this review, we describe the function and molecular mechanisms of the mode of action of different insect lncRNAs discovered up to date.

Genome-editing approaches and applications: a brief review on CRISPR technology and its role in cancer.

The development of genome-editing technologies in 1970s has discerned a new beginning in the field of science. Out of different genome-editing approaches such as Zing-finger nucleases, TALENs, and meganucleases, clustered regularly interspaced short palindromic repeats-CRISPR-associated protein 9 (CRISPR/Cas9) is a recent and versatile technology that has the ability of making changes to the genome of different organisms with high specificity. Cancer is a complex process that is characterized by multiple genetic and epigenetic changes resulting in abnormal cell growth and proliferation. As cancer is one of the leading causes of deaths worldwide, a large number of studies are done to understand the molecular mechanisms underlying the development of cancer. Because of its high efficiency and specificity, CRISPR/Cas9 has emerged as a novel and powerful tool in the field of cancer research. CRISPR/Cas9 has the potential to accelerate cancer research by dissecting tumorigenesis process, generating animal and cellular models, and identify drug targets for chemotherapeutic approaches. However, despite having tremendous potential, there are certain challenges associated with CRISPR/Cas9 such as safe delivery to the target, potential off-target effects and its efficacy which needs to be addressed prior to its clinical application. In this review, we give a gist of different genome-editing technologies with a special focus on CRISPR/Cas9 development, its mechanism of action and its applications, especially in different type of cancers. We also highlight the importance of CRISPR/Cas9 in generating animal models of different cancers. Finally, we present an overview of the clinical trials and discuss the challenges associated with translating CRISPR/Cas9 in clinical use.