Job engagement as the mediator on the relationship between leadership styles, organizational structure, and organizational performance

The aim of this research is to investigate the relationship between leadership styles (i.e. transformational, transactional, and passive avoidant), organizational structure and organizational performance through the mediating role of job engagement in government-owned mobile phone company in Bangladesh. The study adopted the survey method for data collection and a total of 213 questionnaires were analyzed giving a response rate of 38.31 percent. This study used convenience sampling for sample selection. The respondents of the study were from the mid-level position e.g. senior executive, assistant manager, deputy manager, manager, and deputy general manager of Teletalk mobile phone company. The collected data were analyzed using SPSS version 20, and Partial Least Squares-Structural Equation Modeling (PLS-SEM) was used to test the study hypotheses. With regard to leadership styles, the study revealed that the relationship between transformational leadership style and organizational performance is statistically significant; whereas, the relationship between transactional and passive-avoidant leadership style with organizational performance is not significant. Similarly, the relationship between transformational leadership style and job engagement is significant; but the relationship between transactional and passive-avoidant leadership style with job engagement is not significant. Relating to organizational structure, the relationship between organizational structure with organizational performance and job engagement is found statistically significant. Job engagement is also significant with organizational performance. In terms of mediation effects, job engagement mediates the relationship between transformational, passive-avoidant leadership style and organizational structure with organizational performance partially, while the relationship between transactional leadership style and organizational performance is fully mediated by job engagement. Finally, the study implications, limitations as well suggestions are discussed accordingly

Multi-study inference of regulatory networks for more accurate models of gene regulation.

Gene regulatory networks are composed of sub-networks that are often shared across biological processes, cell-types, and organisms. Leveraging multiple sources of information, such as publicly available gene expression datasets, could therefore be helpful when learning a network of interest. Integrating data across different studies, however, raises numerous technical concerns. Hence, a common approach in network inference, and broadly in genomics research, is to separately learn models from each dataset and combine the results. Individual models, however, often suffer from under-sampling, poor generalization and limited network recovery. In this study, we explore previous integration strategies, such as batch-correction and model ensembles, and introduce a new multitask learning approach for joint network inference across several datasets. Our method initially estimates the activities of transcription factors, and subsequently, infers the relevant network topology. As regulatory interactions are context-dependent, we estimate model coefficients as a combination of both dataset-specific and conserved components. In addition, adaptive penalties may be used to favor models that include interactions derived from multiple sources of prior knowledge including orthogonal genomics experiments. We evaluate generalization and network recovery using examples from Bacillus subtilis and Saccharomyces cerevisiae, and show that sharing information across models improves network reconstruction. Finally, we demonstrate robustness to both false positives in the prior information and heterogeneity among datasets

Mapping Transcription Factor Networks and Elucidating Their Biological Determinants

A central goal in systems biology is to accurately map the transcription factor (TF) network of a cell. Such a network map is a key component for many downstream applications, from developmental biology to transcriptome engineering, and from disease modeling to drug discovery. Building a reliable network map requires a wide range of data sources including TF binding locations and gene expression data after direct TF perturbations. However, we are facing two roadblocks. First, rich resources are available only for a few well-studied systems and cannot be easily replicated for new organisms or cell types. Second, when TF binding and TF- perturbation response data are available, they rarely converge on a common set of direct and functional targets for a TF. This dissertation explores and validates the best combination of experimental and analytic techniques to map TF networks. First, we introduce an unsupervised inference algorithm that maps TF networks by exploiting only gene expression and genome sequence data. We show that our “data light” method is more accurate at identifying direct targets of TFs than other similar methods. Second, we develop an optimization method to search for a convergent set of target genes that are independently identified by binding locations and perturbation responses of each TF. Combining this method with network inference greatly expanded the high-confidence network maps, especially when applied on datasets obtained by using recently developed experimental methods. Third, we describe a framework for predicting each gene’s responsiveness to a TF perturbation from genomic features. Using this framework, we identified properties of each gene that are independent of the perturbed TF as the major determinants of TF-perturbation responsiveness. This may lead to improvements in network mapping algorithms that exploit TF perturbation responses. Overall, this dissertation provides a scalable framework for mapping high-quality TF networks for a variety of organisms and cell types

Investigating bifidobacteria-host interactions in the gut using organoid models and network biology approaches

Despite numerous studies indicating that Bifidobacterium species exert beneficial effects a range of diseases, current knowledge about the specific modulating factors is limited. One mechanism is represented by autophagy, mediating key processes in intestinal epithelial cells, and which is often disrupted in gut disorders such as inflammatory bowel disease. In this regard, intestinal organoids represent a useful model to investigate these processes, allowing to study the effect of microbial-derived molecules on host epithelial cell function in a high-throughput and representative manner. The goal of this PhD thesis is to combine experimental and computational approaches, including intestinal organoids and network biology methods, to identify specific mechanisms by which Bifidobacterium-derived metabolites affect intestinal epithelial cell function, exerting a beneficial effect on the host. To achieve these goals, mouse and human intestinal organoid models were developed, and in parallel with existing colon cancer cell lines, their culture conditions were further characterised to allow their co-culture with Bifidobacterium-derived metabolites. Subsequently, downstream applications were optimised to assess modulation of host intestinal barrier, cytokine release, autophagy, and gene expression changes. Host transcriptomics data from organoids was further integrated with a priori knowledge to build regulatory and molecular interaction networks, whose analysis can reveal specific mechanisms modulated by bifidobacteria. This work resulted in the development and further characterisation of novel experimental models to investigate apical host-microbe interactions, including organoids with reversed polarity or organoid-derived monolayers. Furthermore, exposure of epithelial cultures to Bifidobacterium strains highlighted the ability of bifidobacterial metabolites to improve intestinal barrier function and modulate autophagy in epithelial cells. Transcriptomics analysis of human colonic organoids exposed to Bifidobacterium metabolites also revealed positive modulation of the immune response, epithelial differentiation and tight junctions through epigenetics mechanisms, and the downregulation of cholesterol biosynthesis. Overall, this work has increased the understanding of the effects of bifidobacteria on the intestinal epithelium, while showing how a combination of experimental and network biology approaches can be used for these types of studies. Once further validated, results of this thesis will help unravel the beneficial effects of probiotics such as bifidobacteria in the gut, further aiding the development of management strategies for inflammatory diseases of the gut