Structural and Functional Disparities within the Human Gut Virome in Terms of Genome Topology and Representative Genome Selection.

Circularity confers protection to viral genomes where linearity falls short, thereby fulfilling the form follows function aphorism. However, a shift away from morphology-based classification toward the molecular and ecological classification of viruses is currently underway within the field of virology. Recent years have seen drastic changes in the International Committee on Taxonomy of Viruses' operational definitions of viruses, particularly for the tailed phages that inhabit the human gut. After the abolition of the order Caudovirales, these tailed phages are best defined as members of the class Caudoviricetes. To determine the epistemological value of genome topology in the context of the human gut virome, we designed a set of seven experiments to assay the impact of genome topology and representative viral selection on biological interpretation. Using Oxford Nanopore long reads for viral genome assembly coupled with Illumina short-read polishing, we showed that circular and linear virus genomes differ remarkably in terms of genome quality, GC skew, transfer RNA gene frequency, structural variant frequency, cross-reference functional annotation (COG, KEGG, Pfam, and TIGRfam), state-of-the-art marker-based classification, and phage-host interaction. Furthermore, the disparity profile changes during dereplication. In particular, our phage-host interaction results demonstrated that proportional abundances cannot be meaningfully compared without due regard for genome topology and dereplication threshold, which necessitates the need for standardized reporting. As a best practice guideline, we recommend that comparative studies of the human gut virome always report the ratio of circular to linear viral genomes along with the dereplication threshold so that structural and functional metrics can be placed into context when assessing biologically relevant metagenomic properties such as proportional abundance.

ReDisX, a machine learning approach, rationalizes rheumatoid arthritis and coronary artery disease patients uniquely upon identifying subpopulation differentiation markers from their genomic data.

Diseases originate at the molecular-genetic layer, manifest through altered biochemical homeostasis, and develop symptoms later. Hence, symptomatic diagnosis is inadequate to explain the underlying molecular-genetic abnormality and individual genomic disparities. The current trends include molecular-genetic information relying on algorithms to recognize the disease subtypes through gene expressions. Despite their disposition toward disease-specific heterogeneity and cross-disease homogeneity, a gap still exists in describing the extent of homogeneity within the heterogeneous subpopulation of different diseases. They are limited to obtaining the holistic sense of the whole genome-based diagnosis resulting in inaccurate diagnosis and subsequent management. Addressing those ambiguities, our proposed framework, ReDisX, introduces a unique classification system for the patients based on their genomic signatures. In this study, it is a scalable machine learning algorithm deployed to re-categorize the patients with rheumatoid arthritis and coronary artery disease. It reveals heterogeneous subpopulations within a disease and homogenous subpopulations across different diseases. Besides, it identifies granzyme B (GZMB) as a subpopulation-differentiation marker that plausibly serves as a prominent indicator for GZMB-targeted drug repurposing. The ReDisX framework offers a novel strategy to redefine disease diagnosis through characterizing personalized genomic signatures. It may rejuvenate the landscape of precision and personalized diagnosis and a clue to drug repurposing.

Cis-Regulatory Logic Produces Gene-Expression Noise Describing Phenotypic Heterogeneity in Bacteria.

Gene transcriptional process is random. It occurs in bursts and follows single-molecular kinetics. Intermittent bursts are measured based on their frequency and size. They influence temporal fluctuations in the abundance of total mRNA and proteins by generating distinct transcriptional variations referred to as "noise". Noisy expression induces uncertainty because the association between transcriptional variation and the extent of gene expression fluctuation is ambiguous. The promoter architecture and remote interference of different cis-regulatory elements are the crucial determinants of noise, which is reflected in phenotypic heterogeneity. An alternative perspective considers that cellular parameters dictating genome-wide transcriptional kinetics follow a universal pattern. Research on noise and systematic perturbations of promoter sequences reinforces that both gene-specific and genome-wide regulation occur across species ranging from bacteria and yeast to animal cells. Thus, deciphering gene-expression noise is essential across different genomics applications. Amidst the mounting conflict, it is imperative to reconsider the scope, progression, and rational construction of diversified viewpoints underlying the origin of the noise. Here, we have established an indication connecting noise, gene expression variations, and bacterial phenotypic variability. This review will enhance the understanding of gene-expression noise in various scientific contexts and applications.

A review of computational tools for generating metagenome-assembled genomes from metagenomic sequencing data.

Metagenomic sequencing provides a culture-independent avenue to investigate the complex microbial communities by constructing metagenome-assembled genomes (MAGs). A MAG represents a microbial genome by a group of sequences from genome assembly with similar characteristics. It enables us to identify novel species and understand their potential functions in a dynamic ecosystem. Many computational tools have been developed to construct and annotate MAGs from metagenomic sequencing, however, there is a prominent gap to comprehensively introduce their background and practical performance. In this paper, we have thoroughly investigated the computational tools designed for both upstream and downstream analyses, including metagenome assembly, metagenome binning, gene prediction, functional annotation, taxonomic classification, and profiling. We have categorized the commonly used tools into unique groups based on their functional background and introduced the underlying core algorithms and associated information to demonstrate a comparative outlook. Furthermore, we have emphasized the computational requisition and offered guidance to the users to select the most efficient tools. Finally, we have indicated current limitations, potential solutions, and future perspectives for further improving the tools of MAG construction and annotation. We believe that our work provides a consolidated resource for the current stage of MAG studies and shed light on the future development of more effective MAG analysis tools on metagenomic sequencing.

Weight changes in hypertensive patients with phlegm-dampness syndrome: an integrated proteomics and metabolomics approach.

BACKGROUND: Hypertension (HTN) patients who have phlegm-dampness syndrome (PDS) tend to be obese and have worse outcomes. However, the association of body weight (BW) changes and mechanisms underlying the pathophysiology of HTN-PDS are not well elucidated. This study aims to identify the longitudinal observations associated with the circulating markers discriminating BW changes of individuals with HTN-PDS. METHODS: An integrative approach relying on metabolomics and proteomics was applied to serum samples from HTN-PDS patients in a prospective cohort to identify the plausible mechanistic pathways underpinning HTN-PDS pathophysiology. Study participants were determined to have experienced a weight change if they showed a 5%-15% increase/reduction in BW at the end of the follow-up period. The joint pathway analysis and network analysis were performed using Ingenuity Pathway Analysis (IPA®) on the serum samples obtained from the participants over the period. RESULTS: The study involved 22 HTN-PDS patients who were overweight initially and were able to lose enough weight and 24 HTN-PDS individuals who developed overweight from normal BMI during a one-year follow-up. Our analysis suggested three types of phosphatidylcholine (PC) were altered. PC (22:2(13Z,16Z)/24:1(15Z)) and LysoPC (16:1(9Z)) were decreased in Queryweight gain samples, whereas the levels of PC (14:0/16:0) were increased in weight loss samples. The metabolomic analysis suggested 24 metabolites associated with HTN-PDS. Of them, 13 were up-regulated and 11 were down-regulated. The two-dimensional difference gel electrophoresis (2D DIGE) identified 45 phosphorylated proteins got altered in the HTN-PDS patients, wherein 23 were up-regulated and 22 were down-regulated. Integrated proteomic and metabolomics analyse acknowledged biomarkers PC, Complement C3, C4a/C4b, A2M and SERPINF1 as strong predictors for BW changes in HTN-PDS patients. CONCLUSION: The combined serum proteomic and metabolomic profiling reveals a link between BW change and the complement system activity, altered phosphatidylcholine metabolism in HTN-PDS patients. Future studies with larger cohorts are required to strengthen and validate these findings.

Identifying Transcription Factor Combinations to Modulate Circadian Rhythms by Leveraging Virtual Knockouts on Transcription Networks.

The mammalian circadian systems consist of indigenous, self-sustained 24-h rhythm generators. They comprise many genes, molecules, and regulators. To decode their systematic controls, a robust computational approach was employed. It integrates transcription-factor-occupancy and time-series gene-expression data as input. The model equations were constructed and solved to determine the transcriptional regulatory logics in the mouse transcriptome network. This hypothesizes to explore the underlying mechanisms of combinatorial transcriptional regulations for circadian rhythms in mouse. We reconstructed the quantitative transcriptional-regulatory networks for circadian gene regulation at a dynamic scale. Transcriptional-simulations with virtually knocked-out mutants were performed to estimate their influence on networks. The potential transcriptional-regulators-combinations modulating the circadian rhythms were identified. Of them, CLOCK/CRY1 double knockout preserves the highest modulating capacity. Our quantitative framework offers a quick, robust, and physiologically relevant way to characterize the druggable targets to modulate the circadian rhythms at a dynamic scale effectively.

Understanding Quantitative Circadian Regulations Are Crucial Towards Advancing Chronotherapy.

Circadian rhythms have a deep impact on most aspects of physiology. In most organisms, especially mammals, the biological rhythms are maintained by the indigenous circadian clockwork around geophysical time (~24-h). These rhythms originate inside cells. Several core components are interconnected through transcriptional/translational feedback loops to generate molecular oscillations. They are tightly controlled over time. Also, they exert temporal controls over many fundamental physiological activities. This helps in coordinating the body's internal time with the external environments. The mammalian circadian clockwork is composed of a hierarchy of oscillators, which play roles at molecular, cellular, and higher levels. The master oscillation has been found to be developed at the hypothalamic suprachiasmatic nucleus in the brain. It acts as the core pacemaker and drives the transmission of the oscillation signals. These signals are distributed across different peripheral tissues through humoral and neural connections. The synchronization among the master oscillator and tissue-specific oscillators offer overall temporal stability to mammals. Recent technological advancements help us to study the circadian rhythms at dynamic scale and systems level. Here, we outline the current understanding of circadian clockwork in terms of molecular mechanisms and interdisciplinary concepts. We have also focused on the importance of the integrative approach to decode several crucial intricacies. This review indicates the emergence of such a comprehensive approach. It will essentially accelerate the circadian research with more innovative strategies, such as developing evidence-based chronotherapeutics to restore de-synchronized circadian rhythms.