SARS-CoV-2, an infectious agent behind the ongoing COVID-19 pandemic, induces high levels of cytokines such as IL-1, IL-2, IL-4, IL-6, IL-10, TNF-α, IFN-γ etc in infected individuals that play a role in the underlying patho-physiology. Nonetheless, exact association and contribution of every cytokine towards COVID-19 pathology remains poorly understood. Delineation of the roles of cytokines during COVID-19 holds the key to efficient patient management in clinics. This study performed a comprehensive meta-analysis to establish association between induced cytokines and COVID-19 disease severity to help in prognosis and clinical care. Main methods: Scientific literature was searched to identify 13 cytokines (IL-

Objectives: SARS-CoV-2, an infectious agent behind the ongoing COVID-19 pandemic, induces high levels of cytokines in patients contributing to the disease patho-physiology. Nonetheless, exact association and contribution of particular cytokines towards COVID-19 pathology remains poorly understood. This study performed a comprehensive meta-analysis to establish association between induced cytokines and COVID-19 disease severity to help in prognosis and clinical management. Methods: Scientific literature was searched to identify 18 clinical studies. Standardized mean difference (SMD) for cytokines IL-2, IL-4, IL-6, IL-10, TNF-α and IFN-γ between severe and non-severe COVID-19 patient groups were summarized using random effects model. A classifier was built using logistic regression model with cytokines having significant SMD as covariates. Results: Out of the 6 cytokines, IL-6 and IL-10 showed statistically significant SMD across the studies synthesized. Classifier with mean values of both IL-6 and IL-10 as covariates performed well with accuracy of ~ 92% that was significantly higher than accuracy reported in literature with IL-6 and IL-10 as individual covariates. Conclusions: Simple panel proposed by us with only two cytokine markers can be used as predictors for fast diagnosis of patients with higher risk of COVID-19 disease deterioration and thus can be managed well for a favourable prognosis.

Background Cotton is one of the most important commercial crops as the source of natural fiber, oil and fodder. To protect it from harmful pest populations number of newer transgenic lines have been developed. For quick expression checks in successful agriculture qPCR (quantitative polymerase chain reaction) have become extremely popular. The selection of appropriate reference genes plays a critical role in the outcome of such experiments as the method quantifies expression of the target gene in comparison with the reference. Traditionally most commonly used reference genes are the “house-keeping genes”, involved in basic cellular processes. However, expression levels of such genes often vary in response to experimental conditions, forcing the researchers to validate the reference genes for every experimental platform. This study presents a data science driven unbiased genome-wide search for the selection of reference genes by assessing variation of > 50,000 genes in a publicly available RNA-seq dataset of cotton species Gossypium hirsutum. Result Five genes (TMN5, TBL6, UTR5B, AT1g65240 and CYP76B6) identified by data-science driven analysis, along with two commonly used reference genes found in literature (PP2A1 and UBQ14) were taken through qPCR in a set of 33 experimental samples consisting of different tissues (leaves, square, stem and root), different stages of leaf (young and mature) and square development (small, medium and large) in both transgenic and non-transgenic plants. Expression stability of the genes was evaluated using four algorithms - geNorm, BestKeeper, NormFinder and RefFinder. Conclusion Based on the results we recommend the usage of TMN5 and TBL6 as the optimal candidate reference genes in qPCR experiments with normal and transgenic cotton plant tissues. AT1g65240 and PP2A1 can also be used if expression study includes squares. This study, for the first time successfully displays a data science driven genome-wide search method followed by experimental validation as a method of choice for selection of stable reference genes over the selection based on function alone.

Dysregulation of BCL2 is a pathophysiology observed in haematological malignancies. For implementation of available treatment-options it is preferred to know the relative quantification of BCL2 mRNA with appropriate reference genes. For the choice of reference genes-(i) Reference Genes were selected by assessing variation of >60,000 genes from 4 RNA-seq datasets of haematological malignancies followed by filtering based on their GO biological process annotations and proximity of their chromosomal locations to known disease translocations. Selected genes were experimentally validated across various haematological malignancy samples followed by stability comparison using geNorm, NormFinder, BestKeeper and RefFinder. (ii) 43 commonly used Reference Genes were obtained from literature through extensive systematic review. Levels of BCL2 mRNA was assessed by qPCR normalized either by novel reference genes from this study or GAPDH, the most cited reference gene in literature and compared. The analysis showed PTCD2, PPP1R3B and FBXW9 to be the most unregulated genes across lymph-nodes, bone marrow and PBMC samples unlike the Reference Genes used in literature. BCL2 mRNA level shows a consistent higher expression in haematological malignancy patients when normalized by these novel Reference Genes as opposed to GAPDH, the most cited Reference Gene. These reference genes should also be applicable in qPCR platforms using Taqman probes and other model systems including cell lines and rodent models. Absence of sample from healthy-normal individual in diagnostic cases call for careful selection of Reference Genes for relative quantification of a biomarker by qPCR.BCL2 can be used as molecular diagnostics only if normalized with a set of reference genes with stable yet low levels of expression across different types of haematological malignancies.

Background: Cotton is one of the most important commercial crops as the source of natural fiber, oil and fodder. To protect it from harmful pest populations number of newer transgenic lines have been developed. For quick expression checks in successful agriculture qPCR (quantitative polymerase chain reaction) have become extremely popular. The selection of appropriate reference genes plays a critical role in the outcome of such experiments as the method quantifies expression of the target gene in comparison with the reference. Traditionally most commonly used reference genes are the “ house-keeping genes”, involved in basic cellular processes. However, expression levels of such genes often vary in response to experimental conditions, forcing the researchers to validate the reference genes for every experimental platform. This study presents a data science driven unbiased genome-wide search for the selection of reference genes by assessing variation of >50,000 genes in a publicly available RNA- seq dataset of cotton species Gossypium hirsutum . Result: Five genes ( TMN5, TBL6, UTR5B, AT1g65240 and CYP76B6 ) identified by data-science driven analysis, along with two commonly used reference genes found in literature ( PP2A1 and UBQ14 ) were taken through qPCR in a set of 33 experimental samples consisting of different tissues (leaves, square, stem and root), different stages of leaf (young and mature) and square development (small, medium and large) in both transgenic and non-transgenic plants. Expression stability of the genes was evaluated using four algorithms - geNorm , BestKeeper , NormFinder and RefFinder. Conclusion: Based on the results we recommend the usage of TMN5 and TBL6 as the optimal candidate reference genes in qPCR experiments with normal and transgenic cotton plant tissues. AT1g65240 and PP2A1 can also be used if expression study includes squares. This study, for the first time successfully displays a data science driven genome-wide search method followed by experimental validation as a method of choice for selection of stable reference genes over the selection based on function alone.

Background: With the advent of newer breeds and transgenic varieties of commercial crops, qPCR (quantitative polymerase chain reaction) experiments have become extremely popular for quick expression checks. Selection of appropriate reference genes plays a critical role in quantifying the expression of target gene. Most commonly used reference genes in expression studies are the “house-keeping genes”, involved in basic cellular processes. However, expression levels of such genes often vary in response to experimental conditions, forcing the researchers to validate the reference genes in every experiment. This study presents a data science driven unbiased genome-wide search results for selection of reference genes by assessing variation of >50,000 genes in a publicly available RNA-seq dataset of cotton species Gossypium hirsutum. Selected candidate genes were validated experimentally across 33 samples from normal and transgenic G. hirsutum plants, harvested from different areas of the plant at different time points under various developmental conditions. Experimental validation also includes commonly used genes from literature to suggest the most stable set of 5 genes to be used for assessment of quantitative expression in cotton plants (Fig.1). Result: Five genes (TMN5, TBL6, UTR5B, AT1g65240, CYP76B6) identified by data-driven analysis, along with two commonly used reference genes for cotton found in literature (GhPP2A1 and GhuBQ14) were validated using qPCR in a set of 33 experimental samples consisting of different tissues (leaves, square, stem and root), different stages of leaf (young and mature) and square development (small, medium and large) in both transgenic and non-transgenic plants. Expression stability of the genes was evaulated using four different algorithms - DeltaCT, Genorm, BestKeeper and Normfinder. GhPP2A1 and TMN5 were identified as the most stable genes, followed by GhuBQ14 across all the samples tested. Conclusion: This study, for the first time successfully displays a data science driven genome-wide search method followed by experimental validation as a method of choice for selection of stable reference genes for experiment with cotton species. Based on the results we recommend use of GhPP2A1, TMN5 and GhuBQ14 as the optimal candidate reference genes in qPCR experiments with normal or transgenic cotton plant tissues.