A global metagenomic map of urban microbiomes and antimicrobial resistance.

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.

Annotating unknown species of urban microorganisms on a global scale unveils novel functional diversity and local environment association.

In urban ecosystems, microbes play a key role in maintaining major ecological functions that directly support human health and city life. However, the knowledge about the species composition and functions involved in urban environments is still limited, which is largely due to the lack of reference genomes in metagenomic studies comprises more than half of unclassified reads. Here we uncovered 732 novel bacterial species from 4728 samples collected from various common surface with the matching materials in the mass transit system across 60 cities by the MetaSUB Consortium. The number of novel species is significantly and positively correlated with the city population, and more novel species can be identified in the skin-associated samples. The in-depth analysis of the new gene catalog showed that the functional terms have a significant geographical distinguishability. Moreover, we revealed that more biosynthetic gene clusters (BGCs) can be found in novel species. The co-occurrence relationship between BGCs and genera and the geographical specificity of BGCs can also provide us more information for the synthesis pathways of natural products. Expanded the known urban microbiome diversity and suggested additional mechanisms for taxonomic and functional characterization of the urban microbiome. Considering the great impact of urban microbiomes on human life, our study can also facilitate the microbial interaction analysis between human and urban environment.

Biological networks in Parkinson's disease: an insight into the epigenetic mechanisms associated with this disease.

BACKGROUND: Parkinson's disease (PD) is the second most prevalent neurodegenerative disorders in the world. Studying PD from systems biology perspective involving genes and their regulators might provide deeper insights into the complex molecular interactions associated with this disease. RESULT: We have studied gene co-expression network obtained from a PD-specific microarray data. The co-expression network identified 11 hub genes, of which eight genes are not previously known to be associated with PD. Further study on the functionality of these eight novel hub genes revealed that these genes play important roles in several neurodegenerative diseases. Furthermore, we have studied the tissue-specific expression and histone modification patterns of the novel hub genes. Most of these genes possess several histone modification sites those are already known to be associated with neurodegenerative diseases. Regulatory network namely mTF-miRNA-gene-gTF involves microRNA Transcription Factor (mTF), microRNA (miRNA), gene and gene Transcription Factor (gTF). Whereas long noncoding RNA (lncRNA) mediated regulatory network involves miRNA, gene, mTF and lncRNA. mTF-miRNA-gene-gTF regulatory network identified a novel feed-forward loop. lncRNA-mediated regulatory network identified novel lncRNAs of PD and revealed the two-way regulatory pattern of PD-specific miRNAs where miRNAs can be regulated by both the TFs and lncRNAs. SNP analysis of the most significant genes of the co-expression network identified 20 SNPs. These SNPs are present in the 3' UTR of known PD genes and are controlled by those miRNAs which are also involved in PD. CONCLUSION: Our study identified eight novel hub genes which can be considered as possible candidates for future biomarker identification studies for PD. The two regulatory networks studied in our work provide a detailed overview of the cellular regulatory mechanisms where the non-coding RNAs namely miRNA and lncRNA, can act as epigenetic regulators of PD. SNPs identified in our study can be helpful for identifying PD at an earlier stage. Overall, this study may impart a better comprehension of the complex molecular interactions associated with PD from systems biology perspective.

The conserved phylogeny of blood microbiome.

The proliferation and intensification of diseases have forced every researcher to take actions for a robust understanding of the organisms. This demands deep knowledge about the cells and tissues in an organ and its entire surroundings, more precisely the microbiome community which involves viruses, bacteria, archaea, among others. They play an important role in the function of our body, and act both as a deterrent as well as shelter for diseases. Therefore, it is pertinent to study the relation within the microbiome in a human body. In this work, we analyze the sequence data provided through the Human Microbiome Project to explore evolutionary relations within blood microbiome. The objective is to analyze the common proteins present in the different microbes in the blood and find their phylogeny. The analysis of the phylogenetic relation between these species provides important insights about the conservedness of phylogeny of blood microbiome. Interestingly, the co-existence of five of those common proteins is observed in human too.

Identifying significant microRNA-mRNA pairs associated with breast cancer subtypes.

MicroRNAs (miRNAs) are small non-coding RNAs that help in post-transcriptional gene silencing. These endogenous RNAs develop a post-transcriptional gene-regulatory network by binding to complementary sequences of target mRNAs and essentially degrade them. Cancer is a class of diseases that is caused by the uncontrolled cell growth, thereby resulting into a gradual degradation of cell structure. Earlier researches have shown that miRNAs have significant biological involvement in cancer. Prolonged research in this genre has led to the identification of the functions of numerous miRNAs in cancer development. Studying the differential expression profiles of miRNAs and mRNAs together could help us in recognizing the significant miRNA-mRNA pairs from cancer samples. In this paper, we have analyzed the simultaneous over-expression of miRNAs and under-expression of mRNAs and vice versa to establish their association with cancer. This study focuses on breast tumor samples and the miRNA-mRNA target pairs that have a visible signature in such breast tumor samples. We have been able to identify the differentially expressed miRNAs and mRNAs, and further established relations between them to extract the miRNA-mRNA pairs that might be significant in the breast cancer types. This gives us the clue about the potential biomarkers for the breast cancer subtypes that can further help in understanding the progression of each of the subtypes separately. This might be helpful for the joint miRNA-mRNA biomarker identification.

Prevalence of long COVID symptoms in Haryana, India: a cross-sectional follow-up study.

Background: Emerging research indicates growing concern over long COVID globally, although there have been limited studies that estimate population burden. We aimed to estimate the burden of long COVID in three districts of Haryana, India, using an opportunity to link a seroprevalence study to follow-up survey of symptoms associated with long COVID. Methods: We used a population-based seroprevalence survey for COVID-19 conducted in September 2021 across Haryana, India. Adults from three purposively selected districts (Rohtak, Gurugram, and Jhajjar) were eligible to participate; 2205 of 3213 consented to participate in a survey on health status. Trained investigators administered a structured questionnaire that included demographic characteristics, self-reported symptoms of illness in the last six months before the survey, mental health, and history of COVID-19. Findings: Unadjusted regression estimates indicated positive correlations between symptomatic complaints and COVID-19 exposure, suggesting lingering effects of COVID-19 in this population. The overall physical morbidity index was higher among those who tested positive for COVID-19, as was the incidence of new cases. However, both morbidity and incidence became statistically insignificant after adjustment for multiple comparisons. Cough emerged as the only statistically significant individual persistent symptom. Sex-stratified analyses indicated significant estimates only for physical morbidity in women. Interpretation: This study is one of the first from India that uses a large population-based sample to examine longer term repercussions of COVID infections. The burden of long COVID should primarily be addressed in clinical settings, where specialised treatment for individual cases continues to evolve. Our analyses also provide insight into the size and nature of studies required to assess the population-level burden of long COVID. Funding: This paper was produced under the auspices of the Lancet COVID 19 Commission India Task Force, which was supported financially by the Reliance Foundation. The Lancet COVID 19 Commission was set up in July 2020 and submitted its final report by October 2022. This report by the India Task Force was prepared during the same period.

Correction: The importance of investing in data, models, experiments, team science, and public trust to help policymakers prepare for the next pandemic.

[This corrects the article DOI: 10.1371/journal.pgph.0002601.].

MicroRNA transcription start site prediction with multi-objective feature selection.

MicroRNAs (miRNAs) are non-coding, short (21-23nt) regulators of protein-coding genes that are generally transcribed first into primary miRNA (pri-miR), followed by the generation of precursor miRNA (pre-miR). This finally leads to the production of the mature miRNA. A large amount of information is available on the pre- and mature miRNAs. However, very little is known about the pri-miRs, due to a lack of knowledge about their transcription start sites (TSSs). Based on the genomic loci, miRNAs can be categorized into two types --intragenic (intra-miR) and intergenic (inter-miR). While it is already an established fact that intra-miRs are commonly transcribed in conjunction with their host genes, the transcription machinery of inter-miRs is poorly understood. Although it is assumed that miRNA promoters are similar in structure to gene promoters, since both are transcribed by RNA polymerase II (Pol II), computational validations exhibit poor performance of gene promoter prediction methods on miRNAs. In this paper, we concentrate on the problem of TSS prediction for miRNAs. The present study begins with the identification of positive and negative promoter samples from recently published data stemming from RNA-sequencing studies. From these samples of experimentally validated miRNA TSSs, a number of standard sequence features are extracted. Furthermore, to account for potential footprints related to promoter regulation by CpG dinucleotide targeted DNA methylation, a number of novel features are defined. We develop a support vector machine (SVM) with RBF kernel for the prediction of miRNA TSSs trained on human miRNA promoters. A novel feature reduction technique based on archived multi-objective simulated annealing (AMOSA) identifies the final set of features. The resulting model trained on miRNA promoters shows improved performance over the one trained on protein-coding gene promoters in terms of classification accuracy, sensitivity and specificity. Results are also reported for a completely independent biologically validated test set. In a part of the investigation, the proposed approach is used to predict protein-coding gene TSSs. It shows a significantly improved performance when compared to previously published gene TSS prediction methods.

The importance of investing in data, models, experiments, team science, and public trust to help policymakers prepare for the next pandemic.

The COVID-19 pandemic has brought about valuable insights regarding models, data, and experiments. In this narrative review, we summarised the existing literature on these three themes, exploring the challenges of providing forecasts, the requirement for real-time linkage of health-related datasets, and the role of 'experimentation' in evaluating interventions. This literature review encourages us to broaden our perspective for the future, acknowledging the significance of investing in models, data, and experimentation, but also to invest in areas that are conceptually more abstract: the value of 'team science', the need for public trust in science, and in establishing processes for using science in policy. Policy-makers rely on model forecasts early in a pandemic when there is little data, and it is vital to communicate the assumptions, limitations, and uncertainties (theme 1). Linked routine data can provide critical information, for example, in establishing risk factors for adverse outcomes but are often not available quickly enough to make a real-time impact. The interoperability of data resources internationally is required to facilitate sharing across jurisdictions (theme 2). Randomised controlled trials (RCTs) provided timely evidence on the efficacy and safety of vaccinations and pharmaceuticals but were largely conducted in higher income countries, restricting generalisability to low- and middle-income countries (LMIC). Trials for non-pharmaceutical interventions (NPIs) were almost non-existent which was a missed opportunity (theme 3). Building on these themes from the narrative review, we underscore the importance of three other areas that need investment for effective evidence-driven policy-making. The COVID-19 response relied on strong multidisciplinary research infrastructures, but funders and academic institutions need to do more to incentivise team science (4). To enhance public trust in the use of scientific evidence for policy, researchers and policy-makers must work together to clearly communicate uncertainties in current evidence and any need to change policy as evidence evolves (5). Timely policy decisions require an established two-way process between scientists and policy makers to make the best use of evidence (6). For effective preparedness against future pandemics, it is essential to establish models, data, and experiments as fundamental pillars, complemented by efforts in planning and investment towards team science, public trust, and evidence-based policy-making across international communities. The paper concludes with a 'call to actions' for both policy-makers and researchers.

Topological Analysis on Multi-scenario Graphs: Applications Toward Discerning Variability in SARS-CoV-2 and Topic Similarity in Research.

A network is often an obvious choice for modeling real-life interconnected systems, where the nodes represent interacting objects and the edges represent their associations. There has been immense progress in complex network analysis with methods and tools that can provide important insights into the respective scenario. In the advancement of information technology and globalization, the amount of data is increasing day by day, and it is indeed incomprehensible without the help of network science. This work highlights how we can model multiple interaction scenarios under a single umbrella to uncover novel insights. We show that a varying scenario gets reflected by the change of topological patterns in interaction networks. We construct multi-scenario graphs, a novel framework proposed by us, from real-life environments followed by topological analysis. We focus on two different application areas: analyzing geographical variations in SARS-CoV-2 and studying topic similarity in citation patterns.

Supervised Machine Learning Enables Geospatial Microbial Provenance.

The recent increase in publicly available metagenomic datasets with geospatial metadata has made it possible to determine location-specific, microbial fingerprints from around the world. Such fingerprints can be useful for comparing microbial niches for environmental research, as well as for applications within forensic science and public health. To determine the regional specificity for environmental metagenomes, we examined 4305 shotgun-sequenced samples from the MetaSUB Consortium dataset-the most extensive public collection of urban microbiomes, spanning 60 different cities, 30 countries, and 6 continents. We were able to identify city-specific microbial fingerprints using supervised machine learning (SML) on the taxonomic classifications, and we also compared the performance of ten SML classifiers. We then further evaluated the five algorithms with the highest accuracy, with the city and continental accuracy ranging from 85-89% to 90-94%, respectively. Thereafter, we used these results to develop Cassandra, a random-forest-based classifier that identifies bioindicator species to aid in fingerprinting and can infer higher-order microbial interactions at each site. We further tested the Cassandra algorithm on the Tara Oceans dataset, the largest collection of marine-based microbial genomes, where it classified the oceanic sample locations with 83% accuracy. These results and code show the utility of SML methods and Cassandra to identify bioindicator species across both oceanic and urban environments, which can help guide ongoing efforts in biotracing, environmental monitoring, and microbial forensics (MF).

A history of the MetaSUB consortium: Tracking urban microbes around the globe.

The MetaSUB Consortium, founded in 2015, is a global consortium with an interdisciplinary team of clinicians, scientists, bioinformaticians, engineers, and designers, with members from more than 100 countries across the globe. This network has continually collected samples from urban and rural sites including subways and transit systems, sewage systems, hospitals, and other environmental sampling. These collections have been ongoing since 2015 and have continued when possible, even throughout the COVID-19 pandemic. The consortium has optimized their workflow for the collection, isolation, and sequencing of DNA and RNA collected from these various sites and processing them for metagenomics analysis, including the identification of SARS-CoV-2 and its variants. Here, the Consortium describes its foundations, and its ongoing work to expand on this network and to focus its scope on the mapping, annotation, and prediction of emerging pathogens, mapping microbial evolution and antibiotic resistance, and the discovery of novel organisms and biosynthetic gene clusters.

The nature, cause and consequence of COVID-19 panic among social media users in India.

The recent pandemic of COVID-19 has not only shaken the healthcare but also economic structure around the world. In addition to these direct effects, it has also brought in some indirect difficulties owing to the information epidemic (hereafter termed as infodemic) on social media. We aimed to understand the nature of panic social media users in India are experiencing due to the flow of (mis)information. We further extend this investigation to other countries. We performed a cross-sectional study on 1075 social media users from India and 29 other countries. This revealed a significant increase in social media usage and the rise of panic (symbolizing a sense of alarm and/or fear) over time in India. Several of these behaviors are unique to social media users in India possibly because of later outbreak of COVID-19 and a prolonged uninterrupted lockdown. The amount of social media usage might not be causal but has a significant role in generating panic among the people in India. As multiple countries are entering into the second phase of lockdown, this study focused on India might provide a unique perspective of how various factors, including infodemic, affect the mental state of individuals around the globe. SUPPLEMENTARY INFORMATION: The online version supplementary material available at 10.1007/s13278-021-00750-2.

PuTmiR: a database for extracting neighboring transcription factors of human microRNAs.

BACKGROUND: Some of the recent investigations in systems biology have revealed the existence of a complex regulatory network between genes, microRNAs (miRNAs) and transcription factors (TFs). In this paper, we focus on TF to miRNA regulation and provide a novel interface for extracting the list of putative TFs for human miRNAs. A putative TF of an miRNA is considered here as those binding within the close genomic locality of that miRNA with respect to its starting or ending base pair on the chromosome. Recent studies suggest that these putative TFs are possible regulators of those miRNAs. DESCRIPTION: The interface is built around two datasets that consist of the exhaustive lists of putative TFs binding respectively in the 10 kb upstream region (USR) and downstream region (DSR) of human miRNAs. A web server, named as PuTmiR, is designed. It provides an option for extracting the putative TFs for human miRNAs, as per the requirement of a user, based on genomic locality, i.e., any upstream or downstream region of interest less than 10 kb. The degree distributions of the number of putative TFs and miRNAs against each other for the 10 kb USR and DSR are analyzed from the data and they explore some interesting results. We also report about the finding of a significant regulatory activity of the YY1 protein over a set of oncomiRNAs related to the colon cancer. CONCLUSION: The interface provided by the PuTmiR web server provides an important resource for analyzing the direct and indirect regulation of human miRNAs. While it is already an established fact that miRNAs are regulated by TFs binding to their USR, this database might possibly help to study whether an miRNA can also be regulated by the TFs binding to their DSR.

Analyzing miRNA co-expression networks to explore TF-miRNA regulation.

BACKGROUND: Current microRNA (miRNA) research in progress has engendered rapid accumulation of expression data evolving from microarray experiments. Such experiments are generally performed over different tissues belonging to a specific species of metazoan. For disease diagnosis, microarray probes are also prepared with tissues taken from similar organs of different candidates of an organism. Expression data of miRNAs are frequently mapped to co-expression networks to study the functions of miRNAs, their regulation on genes and to explore the complex regulatory network that might exist between Transcription Factors (TFs), genes and miRNAs. These directions of research relating miRNAs are still not fully explored, and therefore, construction of reliable and compatible methods for mining miRNA co-expression networks has become an emerging area. This paper introduces a novel method for mining the miRNA co-expression networks in order to obtain co-expressed miRNAs under the hypothesis that these might be regulated by common TFs. RESULTS: Three co-expression networks, configured from one patient-specific, one tissue-specific and a stem cell-based miRNA expression data, are studied for analyzing the proposed methodology. A novel compactness measure is introduced. The results establish the statistical significance of the sets of miRNAs evolved and the efficacy of the self-pruning phase employed by the proposed method. All these datasets yield similar network patterns and produce coherent groups of miRNAs. The existence of common TFs, regulating these groups of miRNAs, is empirically tested. The results found are very promising. A novel visual validation method is also proposed that reflects the homogeneity as well as statistical properties of the grouped miRNAs. This visual validation method provides a promising and statistically significant graphical tool for expression analysis. CONCLUSION: A heuristic mining methodology that resembles a clustering motivation is proposed in this paper. However, there remains a basic difference between the mining method and a clustering approach. The heuristic approach can produce priority modules (PM) from an miRNA co-expression network, by employing a self-pruning phase, which are analyzed for statistical and biological significance. The mining algorithm minimizes the space/time complexity of the analysis, and also handles noise in the data. In addition, the mining method reveals promising results in the unsupervised analysis of TF-miRNA regulation.

Exploring the Missing Links Between Dietary Habits and Diseases.

Disease dietomics is an emerging area of systems biology that attempts to explore the connections between the dietary habits and diseases. Some of the topical studies highlight that foods might have different impacts over an organism either in progressing a disease (negative association) or in fighting against it (positive association). The association of foods with different diseases can be put together to build a network that might provide a global view of the entire system. Again, such disease-food networks might emerge in a more complex form while considering the disease subtypes individually. Some foods might have positive association with a particular subtype of a disease, whereas it might have no association or negative association with another subtype of the same disease. Therefore, the subtypes might have completely different network patterns. On the other hand, the same food may be helpful for a disease and harmful for another disease or even for a subtype. Analyzing such disease-food networks in different forms might give us important information about the relations between different diseases. In this paper, we have analyzed a large-scale disease-food network comprising 162 different diseases and 455 types of foods for gaining knowledge about the connection between these diseases and their subtypes. We have measured the similarity between diseases based on their patterns of association with foods. In addition to observing a high similarity between several disease subtypes, particularly for cancer, we have found strong relations between constipation-dysphagia and cancer-cardiovascular disease, which are rarely known. Tendency of occurrence of different diseases can be predicted based on such information.

MicroRNA signatures highlight new breast cancer subtypes.

MicroRNAs (miRNAs) are a kind of short non-coding RNAs, of about 22 nucleotides in length, which modulate and sometimes degrade the target mRNAs thereby regulating a number of cellular functions. Recent research in this area establishes the involvement of miRNAs in various disease progressions, including certain types of cancer development. Further, genome-wide expression profiling of miRNAs has been proven to be useful for differentiating various cancer types. In this paper, we have used miRNA expression profiles over a large set of breast cancer tumor samples for identifying subtypes of breast cancers. The experimental results demonstrate that miRNAs carry a unique signature that distinguishes cancer subtypes and reveal new cancer subtypes. Additional survival analyses based on clinical data also strengthen this claim.

Studying the system-level involvement of microRNAs in Parkinson's disease.

BACKGROUND: Parkinson's Disease (PD) is a progressive neurologic disorder that affects movement and balance. Recent studies have revealed the importance of microRNA (miR) in PD. However, the detailed role of miR and its regulation by Transcription Factor (TF) remain unexplored. In this work for the first time we have studied TF-miR-mRNA regulatory network as well as miR co-expression network in PD. RESULT: We compared the 204 differentially expressed miRs from microarray data with 73 PD related miRs obtained from literature, Human MicroRNA Disease Database and found a significant overlap of 47 PD related miRs (p-value<0.05). Functional enrichment analyses of these 47 common (Group1) miRs and the remaining 157 (Group2) miRs revealed similar kinds of over-representative GO Biological Processes and KEGG pathways. This strengthens the possibility that some of the Group 2 miRs can have functional roles in PD progression, hitherto unidentified in any study. In order to explore the cross talk between TF, miR and target mRNA, regulatory networks were constructed. Study of these networks resulted in 14 Inter-Regulatory hub miRs whereas miR co-expression network revealed 18 co-expressed hub miRs. Of these 32 hub miRs, 23 miRs were previously unidentified with respect to their association with PD. Hierarchical clustering analysis further strengthens the roles of these novel miRs in different PD pathways. Furthermore hsa-miR-92a appeared as novel hub miR in both regulatory and co-expression network indicating its strong functional role in PD. High conservation patterns were observed for most of these 23 novel hub miRs across different species including human. Thus these 23 novel hub miRs can be considered as potential biomarkers for PD. CONCLUSION: Our study identified 23 novel miR markers which can open up new avenues for future studies and shed lights on potential therapeutic targets for PD.

Studying the differential co-expression of microRNAs reveals significant role of white matter in early Alzheimer's progression.

MicroRNAs (miRNAs) are a class of short non-coding RNAs, which show tissue-specific regulatory activity on genes. Expression profiling of miRNAs is an important step for understanding the pathology of Alzheimer's disease (AD), a neurodegenerative disorder originating in the brain. Recent studies highlight that miRNAs enriched in gray matter (GM) and white matter (WM) of AD brains show differential expression. However, no in-depth study has yet been conducted on analysing the differential co-expression of pairs of miRNAs over GM and WM. Two genes (or miRNAs) are said to be co-expressed if their expression profiles change similarly over a number of samples. A pair of co-expressed genes under a condition type (or phenotype) may not remain co-expressed, or get contra-expressed, under another condition. Such pairs of genes are referred to as differentially co-expressed. Such an investigation in the early stage of AD is reported in this article. A network of differentially co-expressed miRNAs in GM and WM is first built. Analysis of the differential co-expression property reveals that such a network can not have any cycle. We use the notion of switching to distinguish two distinct types of differential co-expression patterns - a pair of miRNAs that are highly co-expressed in GM but does not remain so in WM, and vice versa. Based on this, we find the substructures, referred to as differentially co-expressed switching tree (DCST), that throughout have similar pattern of switching. The miR-423-5p emerges as a hub of the network. We extract subtrees of these DCSTs that have similar switching pattern throughout. These substructures are found to be both statistically and biologically significant. A large number of miRNAs obtained from the DCSTs are found to have association with AD, most of which are enriched in WM. This computational study therefore indicates a significant role of WM in early AD progression, a hitherto less acknowledged fact.