Necroptosis is a lytic, inflammatory cell death pathway that is dysregulated in many human pathologies. The pathway is executed by a core machinery comprising the RIPK1 and RIPK3 kinases, which assemble into necrosomes in the cytoplasm, and the terminal effector pseudokinase, MLKL. RIPK3-mediated phosphorylation of MLKL induces oligomerization and translocation to the plasma membrane where MLKL accumulates as hotspots and perturbs the lipid bilayer to cause death. The precise choreography of events in the pathway, where they occur within cells, and pathway differences between species, are of immense interest. However, they have been poorly characterized due to a dearth of validated antibodies for microscopy studies. Here, we describe a toolbox of antibodies for immunofluorescent detection of the core necroptosis effectors, RIPK1, RIPK3 and MLKL, and their phosphorylated forms, in human and mouse cells. By comparing reactivity with endogenous proteins in wild-type cells and knockout controls in basal and necroptosis-inducing conditions, we characterise the specificity of frequently-used commercial and recently-developed antibodies for detection of necroptosis signaling events. Importantly, our findings demonstrate that not all frequently-used antibodies are suitable for monitoring necroptosis by immunofluorescence microscopy, and methanol-is preferable to paraformaldehyde-fixation for robust detection of specific RIPK1, RIPK3 and MLKL signals..

Humanity has seen numerous pandemics during its course of evolution. The list includes several incidents from the past, such as measles, Ebola, severe acute respiratory syndrome (SARS), and Middle East respiratory syndrome (MERS), etc. The latest edition to this is coronavirus disease 2019 (COVID-19), caused by the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of August 18, 2020, COVID-19 has affected over 21 million people from 180 + countries with 0.7 million deaths across the globe. Genomic technologies have enabled us to understand the genomic constitution of pathogens, their virulence, evolution, and rate of mutation, etc. To date, more than 83,000 viral genomes have been deposited in public repositories, such as GISAID and NCBI. While we are writing this, India is the third most affected country by COVID-19, with 2.7 million cases and > 53,000 deaths. Gujarat is the 11th highest affected state with a 3.48% death rate compared to the national average of 1.91%. In this study, a total of 502 SARS-CoV-2 genomes from Gujarat were sequenced and analyzed to understand its phylogenetic distribution and variants against global and national sequences. Further variants were analyzed from diseased and recovered patients from Gujarat and the world to understand its role in pathogenesis. Among the missense mutations present in the Gujarat SARS-CoV-2 genomes, C28854T (Ser194Leu) had an allele frequency of 47.62 and 7.25% in deceased patients from the Gujarat and global datasets, respectively. In contrast, the allele frequency of 35.16 and 3.20% was observed in recovered patients from the Gujarat and global datasets, respectively. It is a deleterious mutation present in the nucleocapsid (N) gene and is significantly associated with mortality in Gujarat patients with a p-value of 0.067 and in the global dataset with a p-value of 0.000924. The other deleterious variant identified in deceased patients from Gujarat (p-value of 0.355) and the world (p-value of 2.43E-06) is G25563T, which is located in Orf3a and plays a potential role in viral pathogenesis. SARS-CoV-2 genomes from Gujarat are forming distinct clusters under the GH clade of GISAID. This study will shed light on the viral haplotype in SARS-CoV-2 samples from Gujarat, India.

Humanity has seen numerous pandemics during its course of evolution. The list includes many such as measles, Ebola, SARS, MERS, etc. Latest edition to this pandemic list is COVID-19, caused by the novel coronavirus, SARS-CoV-2. As of 4th July 2020, COVID-19 has affected over 10 million people from 170+ countries, and 5,28,364 deaths. Genomic technologies have enabled us to understand the genomic constitution of the pathogens, their virulence, evolution, rate of mutations, etc. To date, more than 60,000 virus genomes have been deposited in the public depositories like GISAID and NCBI. While we are writing this, India is the 3rd most-affected country with COVID-19 with 0.6 million cases, and >18000 deaths. Gujarat is the fourth highest affected state with , 5.44 percent death rate compared to national average of 2.8 percent. Here, 361 SARS-CoV-2 genomes from across Gujarat have been sequenced and analyzed in order to understand its phylogenetic distribution and variants against global and national sequences. Further, variants were analyzed from diseased and recovered patients from Gujarat and the World to understand its role in pathogenesis. From missense mutations, found from Gujarat SARS-CoV-2 genomes, C28854T, deleterious mutation in nucleocapsid (N) gene was found to be significantly associated with mortality in patients. The other significant deleterious variant found in diseased patients from Gujarat and the world is G25563T, which is located in Orf3a and has a potential role in viral pathogenesis. SARS-CoV-2 genomes from Gujarat are forming distinct cluster under GH clade of GISAID.

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BackgroundBreast and ovarian cancers are the most prevalent cancers and one of the leading causes of death in Indian women. The healthcare burden of breast and ovarian cancers and the rise in mortality rate are worrying and stress the need for early detection and treatment.MethodsWe performed amplicon sequencing of 144 cases who had breast/ovarian cancer disease (total 137 cases are patients and seven are tested for BRCA1/2 carrier) Using our custom designed gene panel consisting of 14 genes, that are associated with high to moderate risk of breast and ovarian cancers. Variants were called using Torrent Variant Caller and were annotated using ThermoFisher’s Ion Reporter software. Classification of variants and their clinical significance were identified by searching the variants against ClinVar database.ResultsFrom a total of 144 cases, we were able to detect 42 pathogenic mutations in [40/144] cases. Majority of pathogenic mutations (30/41) were detected in BRCA1 gene, while (7/41) pathogenic mutations were detected in BRCA2 gene, whereas, (2/41) pathogenic mutations were detected in TP53 gene and BRIP1, PALB2, and ATM genes respectively. So, BRCA genes contributed 88.09% of pathogenic mutations, whereas non-BRCA genes contributed 11.91% of pathogenic mutations. We were also able to detect 25 VUS which were predicted to be damaging by in silico prediction tools.ConclusionEarly detection of cancers in the Indian population can be done by genetic screening using customized multi-gene panels. Indications of our findings show that in the Indian population, apart from the common BRCA genes, there are other genes that are also responsible for the disease. High frequency mutations detected in the study and variants of uncertain significance predicted to be damaging by in silico pathogenicity prediction tools can be potential biomarkers of hereditary breast and ovarian cancer in Indian HBOC patients.