Pan-genomic analysis of transcriptional modules across Salmonella Typhimurium reveals the regulatory landscape of different strains

Salmonella enterica Typhimurium is a serious pathogen that is involved in human nontyphoidal infections. Tackling Typhimurium infections is difficult due to the species' dynamic adaptation to its environment, which is dictated by a complex transcriptional regulatory network (TRN). While traditional biomolecular methods provide characterizations of specific regulators, it is laborious to construct the global TRN structure from this bottom-up approach. Here, we used a machine learning technique to understand the transcriptional signatures of S. enterica Typhimurium from the top down, as a whole and in individual strains. Furthermore, we conducted cross-strain comparison of 6 strains in serovar Typhimurium to investigate similarities and differences in their TRNs with pan-genomic analysis. By decomposing all the publicly available RNA-Seq data of Typhimurium with independent component analysis (ICA), we obtained over 400 independently modulated sets of genes, called iModulons. Through analysis of these iModulons, we 1) discover three transport iModulons linked to antibiotic resistance, 2) describe concerted responses to cationic antimicrobial peptides (CAMPs), 3) uncover evidence towards new regulons, and 4) identify two iModulons linked to bile responses in strain ST4/74. We extend this analysis across the pan-genome to show that strain-specific iModulons 5) reveal different genetic signatures in pathogenicity islands that explain phenotypes and 6) capture the activity of different phages in the studied strains. Using all high-quality publicly-available RNA-Seq data to date, we present a comprehensive, data-driven Typhimurium TRN. It is conceivable that with more high-quality datasets from more strains, the approach used in this study will continue to guide our investigation in understanding the pan-transcriptome of Typhimurium. Interactive dashboards for all gene modules in this project are available at https://imodulondb.org/ to enable browsing for interested researchers.

Legal and Regulatory Landscape of Blockchain Technology in Various Countries

Blockchain technology can be leveraged to record information securely, ranging from public sector data to private records. It has the potential of being ubiquitous due to its far-reaching use cases and revolutionary features. The deployment of blockchain technology can radically transform corporate and government operations and services. The blockchain legislative landscape is rapidly evolving, and an in-depth analysis is provided to offer a legal and contextual perspective of the regulatory trends across the globe. Part I explores the widespread use of blockchain technology for various industries and business applications. It also outlines two types of legislation that can be enacted, namely enabling and prohibitive legislation, to advance the policy objectives of a country. Part II examines the regulatory responses of various countries relating to blockchain use cases and applications.

Comparative Review of the Regulatory Framework of Cryptocurrency in Selected Jurisdictions

This chapter compares the current legal and regulatory landscape of cryptocurrency regulations of selected countries. Countries have adopted distinct and disparate regulatory approaches in regulating cryptocurrency. Countries such as Gibraltar, Malta, Switzerland, Singapore, and certain states in the United States have enacted proactive, enabling, and industry-specific laws to regulate cryptocurrency. The Philippines and Denmark are relatively forward-looking in their endeavour to regulate cryptocurrency by allowing its utilization and/or trade but with a restrictive and cautious approach. Certain countries have imposed rigorous restrictions or banned the usage or trade of cryptocurrency. With the rapid evolution and emergence of cryptocurrency markets, policymakers are adopting different trajectories to develop a suitable regulatory framework to regulate cryptocurrency. Countries around the world should harness the capabilities of cryptocurrency by devising favourable regulations rather than inhibit the application of cryptocurrency.

Efficient pre-processing of Single-cell ATAC-seq data

The primary tool currently used to pre-process 10X Chromium single-cell ATAC-seq data is Cell Ranger, which can take very long to run on standard datasets. To facilitate rapid pre-processing that enables reproducible workflows, we present a suite of tools called scATAK for pre-processing single-cell ATAC-seq data that is 18 times faster than Cell Ranger on human samples, and that uses 33% less RAM when 8 CPU threads are used. Our tool can also calculate chromatin interaction potential matrices, and generate open chromatin signals and interaction traces for cell groups. We demonstrate the utility of scATAK in an exploration of the chromatin regulatory landscape of a healthy adult human brain and show that it can reveal cell-type-specific features. scATAK is available at https://pachterlab.github.io/scATAK/.

Biosimilar Monoclonal Antibodies in Latin America

In the last decade, the expiration of patents protecting therapeutic monoclonal antibodies opened an opportunity for the development and approval of biosimilar versions of these drugs. The complexity of these biologic molecules required the imposition of strict regulations to establish robust comparability with the antibody of reference in physicochemical, analytical, biological and, when deemed necessary, clinical data. Accordingly, this period coincides with the updating of the requirements and guidelines for the manufacture and approval of biologics in Latin American countries by their respective regulatory agencies. Although the term “biosimilar” does not appear in the official regulatory provisions in most of the countries, it is of general use in Latin America, and several biosimilars of therapeutic monoclonal antibodies were approved based on comparative quality, nonclinical and clinical data that demonstrate similarity to a licensed biological reference registered before in a Regulatory Health Authority of reference. Here, we provide an overview of how the complexities of therapeutic monoclonal antibodies shaped the regulatory landscape of similar biologics, the current status of biosimilar monoclonal antibodies in Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, México, Paraguay, Perú and Uruguay and their potential to reduce the cost of antibody therapies in this region.

Cell-specific alterations in Pitx1 regulatory landscape activation caused by the loss of a single enhancer

Developmental genes are frequently controlled by multiple enhancers sharing similar specificities. As a result, deletions of such regulatory elements have often failed to reveal their full function. Here, we use the Pitx1 testbed locus to characterize in detail the regulatory and cellular identity alterations following the deletion of one of its enhancers (Pen). By combining single cell transcriptomics and an in-embryo cell tracing approach, we observe an increased fraction of Pitx1 non/low-expressing cells and a decreased fraction of Pitx1 high-expressing cells. We find that the over-representation of Pitx1 non/low-expressing cells originates from a failure of the Pitx1 locus to coordinate enhancer activities and 3D chromatin changes. This locus mis-activation induces a localized heterochrony and a concurrent loss of irregular connective tissue, eventually leading to a clubfoot phenotype. This data suggests that, in some cases, redundant enhancers may be used to locally enforce a robust activation of their host regulatory landscapes.

FLI1 and FRA1 transcription factors drive the transcriptional regulatory networks characterizing muscle invasive bladder cancer

Bladder cancer is mostly present in the form of urothelium carcinoma, causing over 150.000 deaths each year. Its histopathological classification as muscle invasive (MIBC) and non-muscle invasive (NMIBC) is the most prominent aspect, affecting the prognosis and progression of this disease. In this study, we defined the active regulatory landscape of MIBC and NMIBC cell lines using H3K27ac-seq and used an integrative data approach to combine our findings with existing data. Our analysis revealed FRA1 and FLI1 as the two critical transcription factors differentially regulating MIBC regulatory landscape. Importantly, we show that FRA1 and FLI1 regulate the genes involved in epithelial cell migration and cell junction organization. Knock-down of FRA1 and FLI1 in MIBC revealed the downregulation of several EMT-related genes such as MAP4K4 and FLOT1. Further, ChIP-SICAP performed for FRA1 and FLI1 enabled us to infer chromatin binding partners of these two transcription factors and link this information with their target genes, providing a comprehensive regulatory circuit for the genes implicated in invasive ability of the bladder cancer cells. Finally, for the first time we show that knock-down of FRA1 and FRA1-FLI1 double knock-down results in significant reduction of invasion capacity of MIBC cells towards muscle microenvironment using IC-CHIP assays. Our results collectively highlight the role of these two transcription factors in invasive characteristics of bladder cancer in selection and design of targeted options for treatment of MIBC.

Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants

The majority of genetic variants detected in genome wide association studies (GWAS) exert their effects on phenotypes through gene regulation. Motivated by this observation, we propose a multi-omic integration method that models the cascading effects of genetic variants from epigenome to transcriptome and eventually to the phenome in identifying target genes influenced by risk alleles. This cascading epigenomic analysis for GWAS, which we refer to as CEWAS, comprises two types of models: one for linking cis genetic effects to epigenomic variation and another for linking cis epigenomic variation to gene expression. Applying these models in cascade to GWAS summary statistics generates gene level statistics that reflect genetically-driven epigenomic effects. We show on sixteen brain-related GWAS that CEWAS provides higher gene detection rate than related methods, and finds disease relevant genes and gene sets that point toward less explored biological processes. CEWAS thus presents a novel means for exploring the regulatory landscape of GWAS variants in uncovering disease mechanisms.