Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Clonal Evolution Dynamics and Tumor Microenvironment Composition of Chronic Lymphocytic Leukemia

Chronic lymphocytic leukemia (CLL) is the most common adult leukemia in western world. This disease, with an indolent course and patients responding heterogeneously to recommended therapies, remains incurable. The Eµ-TCL1 mouse model, is a known useful tool for preclinical studies of CLL. In this thesis, I present a detailed in-silico view of CLL specific clonal heterogeneity and T cell tumor microenvironment (TME) as observed in spleen of EµTCL1 mouse and patient lymph nodes during the course of the disease. In the first part, I present clonal evolution orchestrated by dynamics of B cell receptor (BCR) rearrangements and somatic variations, using whole exome sequencing (WES) of serially transplanted Eµ-TCL1 mouse tumors. Low allele frequency mutations that were nonoverlapping between mouse tumors were identified. 10 out of 13 tumors were identified to be oligoclonal. In addition, three distinct patterns of evolving SNV-defined and BCR clonotypes emerged as the disease aggressed from primary to secondary tumor. Interestingly, I identified stereotypic CLL mouse BCRs having Ighv11 and Ighv12 genes that are known to undergo chronic stimulation in response to autoantigens, hence potentially contributing to CLL pathogenesis. These observations signified the importance of clonotype information for accurate interpretation of CLL disease course and drug efficacy, especially during time-series experiments involving adoptively transferred Eµ-TCL1 mouse tumors. Also, trisomy 15 was observed, hypothesizing involvement of Myc overexpression during CLL development in EµTCL1 mouse. It could be stated that, not just the overexpression of Tcl1 gene but other factors also contribute to CLL malignancy in mice. Following this, I investigated genetic (WES) and transcriptomic (RNA-seq) changes in monoclonal Eµ-TCL1 AT (adoptive transfer) mouse tumors, acquired as a result of ibrutinib resistance. Ibrutinib is widely used as a frontline treatment for CLL patients, some of which acquire resistance to the drug after showing an initial response. In mouse tumors, loss of therapeutic efficacy followed by uncontrolled tumor growth was observed at 6 weeks of treatment initiation. Ibrutinib was not able to inflict an observable selection pressure on BCR clonality as well as mutation profile of these tumors in 6 weeks. However, the transcriptional profile of ibrutinib resistant tumors was unique in contrast to untreated ones. From top upregulated genes identified to be putatively involved in ibrutinib resistance, Tbet gene, is currently being followed up for in-vivo studies as a therapeutic target. In the second part of the thesis, I present subpopulations of CD3+ T cell compartment characteristically differentially expressed in the CLL TME as compared to that of controls. This analysis was the first of its kind to have utilized CLL patient lymph nodes (LN) for probing TME at single cell level. Additionally, the patient’s bone marrow (BM) and peripheral blood (PB); as well as the spleens from Eµ-TCL1 AT mice were investigated for CLL infiltrating T cell subpopulations. Single cell (sc) CyTOF (mass cytometry) analysis using a panel of 32 surface protein markers revealed an increased abundance of exhausted phenotype in patient LNs as compared to BM and PB samples from the same patient. This observation raised uncertainty of PB and BM as the tissue of choice for studying CLL linked T cell exhaustion. Intriguingly, Eµ-TCL1 mouse T cell compartments showed presence of IFN-responders, absent from patient CD4+ cell type. 7 out of 12 mouse Cd4+ subpopulations showed expression of Tcytotoxic markers, which could indicate activated subpopulations. The results presented in this thesis provide a detailed view of heterogeneity manifested by 1) Eµ-TCL1 mouse tumors in course of disease progression; 2) the transformed CLL TME in patients and mouse. These findings would prove valuable during mechanistic and drug treatment studies in Eµ-TCL1 mouse and to evaluate their translational potency in CLL clinical setting under the influence of CLL specific tumor niche

Gene Prioritization by Integrated Analysis of Protein Structural and Network Topological Properties for the Protein-Protein Interaction Network of Neurological Disorders

Neurological disorders are known to show similar phenotypic manifestations like anxiety, depression, and cognitive impairment. There is a need to identify shared genetic markers and molecular pathways in these diseases, which lead to such comorbid conditions. Our study aims to prioritize novel genetic markers that might increase the susceptibility of patients affected with one neurological disorder to other diseases with similar manifestations. Identification of pathways involving common candidate markers will help in the development of improved diagnosis and treatments strategies for patients affected with neurological disorders. This systems biology study for the first time integratively uses 3D-structural protein interface descriptors and network topological properties that characterize proteins in a neurological protein interaction network, to aid the identification of genes that are previously not known to be shared between these diseases. Results of protein prioritization by machine learning have identified known as well as new genetic markers which might have direct or indirect involvement in several neurological disorders. Important gene hubs have also been identified that provide an evidence for shared molecular pathways in the neurological disease network

DNA methylation signatures for 2016 WHO classification subtypes of diffuse gliomas

Abstract Background Glioma is the most common of all primary brain tumors with poor prognosis and high mortality. The 2016 World Health Organization classification of the tumors of central nervous system uses molecular parameters in addition to histology to redefine many tumor entities. The new classification scheme divides diffuse gliomas into low-grade glioma (LGG) and glioblastoma (GBM) as per histology. LGGs are further divided into isocitrate dehydrogenase (IDH) wild type or mutant, which is further classified into either oligodendroglioma that harbors 1p/19q codeletion or diffuse astrocytoma that has an intact 1p/19q loci but enriched for ATRX loss and TP53 mutation. GBMs are divided into IDH wild type that corresponds to primary or de novo GBMs and IDH mutant that corresponds to secondary or progressive GBMs. To make the 2016 WHO subtypes of diffuse gliomas more robust, we carried out Prediction Analysis of Microarrays (PAM) to develop DNA methylation signatures for these subtypes. Results In this study, we applied PAM on a training set of diffuse gliomas derived from The Cancer Genome Atlas (TCGA) and identified DNA methylation signatures to classify LGG IDH wild type from LGG IDH mutant, LGG IDH mutant with 1p/19q codeletion from LGG IDH mutant with intact 1p/19q loci and GBM IDH wild type from GBM IDH mutant with an accuracy of 99–100%. The signatures were validated using the test set of diffuse glioma samples derived from TCGA with an accuracy of 96 to 99%. In addition, we also carried out additional validation of all three signatures using independent LGG and GBM cohorts. Further, the methylation signatures identified a fraction of samples as discordant, which were found to have molecular and clinical features typical of the subtype as identified by methylation signatures. Conclusions Thus, we identified methylation signatures that classified different subtypes of diffuse glioma accurately and propose that these signatures could complement 2016 WHO classification scheme of diffuse glioma

Additional file 2: of DNA methylation signatures for 2016 WHO classification subtypes of diffuse gliomas

Has ten additional figures and their corresponding figure legends. (PPTX 830 kb

Additional file 2: Table S2. of PI3 kinase pathway regulated miRNome in glioblastoma: identification of miR-326 as a tumour suppressor miRNA

List of miRNAs regulated upon PI3 kinase pathway inhibition and their regulation in GBM. (XLSX 12 kb

Additional file 6: Table S5. of PI3 kinase pathway regulated miRNome in glioblastoma: identification of miR-326 as a tumour suppressor miRNA

Gene regulated by miR-326 overexpression at 48 h interval. (XLSX 44 kb

Additional file 5: Table S4. of PI3 kinase pathway regulated miRNome in glioblastoma: identification of miR-326 as a tumour suppressor miRNA

List of transcription factors regulated by PI3 kinase pathway that can potentially regulate ARRB1 locus. (XLSX 13 kb

Additional file 8: Table S7. of PI3 kinase pathway regulated miRNome in glioblastoma: identification of miR-326 as a tumour suppressor miRNA

List of genes downregulated upon miR-326 overexpression, upregulated in GBM and bearing binding sites for miR-326. (XLSX 12 kb

Additional file 7: Table S6. of PI3 kinase pathway regulated miRNome in glioblastoma: identification of miR-326 as a tumour suppressor miRNA

Gene regulated by miR-326 overexpression at 72 h interval. (XLSX 22 kb