A computational approach to generate highly conserved gene co-expression networks with RNA-seq data.

We describe a consensus approach for network construction based on fully conserved gene-gene interactions from randomly downsampled data subsets for an unbiased differential analysis of gene co-expression networks. The pipeline allows users to identify network nodes lost, conserved, and acquired in cancer as well as interpret the functional significance of these network changes. For proof of concept, the protocol is used to leverage RNA-seq data of tumor samples from TCGA and healthy tissue samples from the GTEx database. For complete details on the use and execution of this protocol, please refer to Arshad and McDonald (2021).

Changes in gene-gene interactions associated with cancer onset and progression are largely independent of changes in gene expression.

Recent findings indicate that changes underlying cancer onset and progression are not only attributable to changes in DNA structure and expression of individual genes but to changes in interactions among these genes as well. We examined co-expression changes in gene-network structure occurring during the onset and progression of nine different cancer types. Network complexity is generally reduced in the transition from normal precursor tissues to corresponding primary tumors. Cross-tissue cancer network similarity generally increases in early-stage cancers followed by a subsequent loss in cross-tissue cancer similarity as tumors reacquire cancer-specific network complexity. Gene-gene connections remaining stable through cancer development are enriched for "housekeeping" gene functions, whereas newly acquired interactions are associated with established cancer-promoting functions. Surprisingly, >90% of changes in gene-gene network interactions in cancers are not associated with changes in the expression of network genes relative to normal precursor tissues.

Treatment of leachate through constructed wetlands using Typha angustifolia in combination with catalytic ozonation on Fe-zeolite A.

Leachate control and management is a major challenge faced during solid waste management as it may pollute surface and groundwaters. In the current research, constructed wetlands (CWs) vegetated with Typha angustifolia plant in combination with catalytic ozonation by ferrous (Fe)-coated zeolite A was studied for the treatment of leachate. The CWs treatment with 9 days detention reduced the chemical oxygen demand (COD) and biochemical oxygen demand (BOD) up to 75.81% and 69.84%, respectively. Moreover, total suspended solids (TSS), total dissolved solids (TDS), and total kjeldahl nitrogen (TKN) removal of 91.16%, 33.33%, and 25.22% were achieved, respectively. The Fe-coated zeolite A catalytic ozonation further reduced the COD up to 90.7%. Comparison of the processes showed the effective performance of the combined process (CW/O3/Fe-zeolite) with 97.76% COD reduction of leachate. It is, therefore, concluded that the studied combined process (CW/O3/Fe-zeolite A) was more efficient as compared with single ozonation and CW alone, hence it can be implied for the leachate treatment in real conditions.

ATLANTIS - Attractor Landscape Analysis Toolbox for Cell Fate Discovery and Reprogramming.

Boolean modelling of biological networks is a well-established technique for abstracting dynamical biomolecular regulation in cells. Specifically, decoding linkages between salient regulatory network states and corresponding cell fate outcomes can help uncover pathological foundations of diseases such as cancer. Attractor landscape analysis is one such methodology which converts complex network behavior into a landscape of network states wherein each state is represented by propensity of its occurrence. Towards undertaking attractor landscape analysis of Boolean networks, we propose an Attractor Landscape Analysis Toolbox (ATLANTIS) for cell fate discovery, from biomolecular networks, and reprogramming upon network perturbation. ATLANTIS can be employed to perform both deterministic and probabilistic analyses. It has been validated by successfully reconstructing attractor landscapes from several published case studies followed by reprogramming of cell fates upon therapeutic treatment of network. Additionally, the biomolecular network of HCT-116 colorectal cancer cell line has been screened for therapeutic evaluation of drug-targets. Our results show agreement between therapeutic efficacies reported by ATLANTIS and the published literature. These case studies sufficiently highlight the in silico cell fate prediction and therapeutic screening potential of the toolbox. Lastly, ATLANTIS can also help guide single or combinatorial therapy responses towards reprogramming biomolecular networks to recover cell fates.