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Article ; Online: Short-chain fatty acids of various lengths differentially inhibit

Chang, Kai Chirng / Nagarajan, Niranjan / Gan, Yunn-Hwen

mSphere

2024 Volume 9, Issue 2, Page(s) e0078123

Abstract: The gut microbiota is inextricably linked to human health and disease. It can confer colonization resistance against invading pathogens either through niche occupation and nutrient competition or via its secreted metabolites. Short-chain fatty acids ( ... ...

Abstract	The gut microbiota is inextricably linked to human health and disease. It can confer colonization resistance against invading pathogens either through niche occupation and nutrient competition or via its secreted metabolites. Short-chain fatty acids (SCFA) are the primary metabolites in the gut as a result of dietary fiber fermentation by the gut microbiota. In this work, we demonstrate that the interaction of single-species gut commensals on solid media is insufficient for pathogen inhibition, but supernatants from monocultures of these commensal bacteria enriched in acetate confer inhibition against anaerobic growth of the enteric pathogen
MeSH term(s)	Humans ; Enterobacteriaceae ; Klebsiella pneumoniae ; Fatty Acids, Volatile/metabolism ; Bacteria/metabolism ; Anti-Bacterial Agents/pharmacology ; Acetates
Chemical Substances	Fatty Acids, Volatile ; Anti-Bacterial Agents ; Acetates
Language	English
Publishing date	2024-02-02
Publishing country	United States
Document type	Journal Article
ISSN	2379-5042
ISSN (online)	2379-5042
DOI	10.1128/msphere.00781-23
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Article ; Online: MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes.

Peres da Silva, Rafael / Suphavilai, Chayaporn / Nagarajan, Niranjan

BMC bioinformatics

2024 Volume 25, Issue Suppl 1, Page(s) 153

Abstract: Background: With the rapid increase in throughput of long-read sequencing technologies, recent studies have explored their potential for taxonomic classification by using alignment-based approaches to reduce the impact of higher sequencing error rates. ... ...

Abstract	Background: With the rapid increase in throughput of long-read sequencing technologies, recent studies have explored their potential for taxonomic classification by using alignment-based approaches to reduce the impact of higher sequencing error rates. While alignment-based methods are generally slower, k-mer-based taxonomic classifiers can overcome this limitation, potentially at the expense of lower sensitivity for strains and species that are not in the database. Results: We present MetageNN, a memory-efficient long-read taxonomic classifier that is robust to sequencing errors and missing genomes. MetageNN is a neural network model that uses short k-mer profiles of sequences to reduce the impact of distribution shifts on error-prone long reads. Benchmarking MetageNN against other machine learning approaches for taxonomic classification (GeNet) showed substantial improvements with long-read data (20% improvement in F1 score). By utilizing nanopore sequencing data, MetageNN exhibits improved sensitivity in situations where the reference database is incomplete. It surpasses the alignment-based MetaMaps and MEGAN-LR, as well as the k-mer-based Kraken2 tools, with improvements of 100%, 36%, and 23% respectively at the read-level analysis. Notably, at the community level, MetageNN consistently demonstrated higher sensitivities than the previously mentioned tools. Furthermore, MetageNN requires < 1/4th of the database storage used by Kraken2, MEGAN-LR and MMseqs2 and is > 7× faster than MetaMaps and GeNet and > 2× faster than MEGAN-LR and MMseqs2. Conclusion: This proof of concept work demonstrates the utility of machine-learning-based methods for taxonomic classification using long reads. MetageNN can be used on sequences not classified by conventional methods and offers an alternative approach for memory-efficient classifiers that can be optimized further.
MeSH term(s)	Animals ; Viverridae ; Metagenomics/methods ; Neural Networks, Computer ; Metagenome ; Machine Learning ; High-Throughput Nucleotide Sequencing/methods ; Sequence Analysis, DNA/methods
Language	English
Publishing date	2024-04-16
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-024-05760-3
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Article ; Online: Comparative analysis of metagenomic classifiers for long-read sequencing datasets.

Marić, Josip / Križanović, Krešimir / Riondet, Sylvain / Nagarajan, Niranjan / Šikić, Mile

BMC bioinformatics

2024 Volume 25, Issue 1, Page(s) 15

Abstract: Background: Long reads have gained popularity in the analysis of metagenomics data. Therefore, we comprehensively assessed metagenomics classification tools on the species taxonomic level. We analysed kmer-based tools, mapping-based tools and two ... ...

Abstract	Background: Long reads have gained popularity in the analysis of metagenomics data. Therefore, we comprehensively assessed metagenomics classification tools on the species taxonomic level. We analysed kmer-based tools, mapping-based tools and two general-purpose long reads mappers. We evaluated more than 20 pipelines which use either nucleotide or protein databases and selected 13 for an extensive benchmark. We prepared seven synthetic datasets to test various scenarios, including the presence of a host, unknown species and related species. Moreover, we used available sequencing data from three well-defined mock communities, including a dataset with abundance varying from 0.0001 to 20% and six real gut microbiomes. Results: General-purpose mappers Minimap2 and Ram achieved similar or better accuracy on most testing metrics than best-performing classification tools. They were up to ten times slower than the fastest kmer-based tools requiring up to four times less RAM. All tested tools were prone to report organisms not present in datasets, except CLARK-S, and they underperformed in the case of the high presence of the host's genetic material. Tools which use a protein database performed worse than those based on a nucleotide database. Longer read lengths made classification easier, but due to the difference in read length distributions among species, the usage of only the longest reads reduced the accuracy. The comparison of real gut microbiome datasets shows a similar abundance profiles for the same type of tools but discordance in the number of reported organisms and abundances between types. Most assessments showed the influence of database completeness on the reports. Conclusion: The findings indicate that kmer-based tools are well-suited for rapid analysis of long reads data. However, when heightened accuracy is essential, mappers demonstrate slightly superior performance, albeit at a considerably slower pace. Nevertheless, a combination of diverse categories of tools and databases will likely be necessary to analyse complex samples. Discrepancies observed among tools when applied to real gut datasets, as well as a reduced performance in cases where unknown species or a significant proportion of the host genome is present in the sample, highlight the need for continuous improvement of existing tools. Additionally, regular updates and curation of databases are important to ensure their effectiveness.
MeSH term(s)	Sequence Analysis, DNA ; High-Throughput Nucleotide Sequencing ; Metagenome ; Metagenomics ; Databases, Protein ; Nucleotides
Chemical Substances	Nucleotides
Language	English
Publishing date	2024-01-11
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-024-05634-8
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Article ; Online: The skin microbiome in pediatric atopic dermatitis and food allergy.

Tham, Elizabeth Huiwen / Chia, Minghao / Riggioni, Carmen / Nagarajan, Niranjan / Common, John E A / Kong, Heidi H

Allergy

2024

Abstract: The skin microbiome is an extensive community of bacteria, fungi, mites, viruses and archaea colonizing the skin. Fluctuations in the composition of the skin microbiome have been observed in atopic dermatitis (AD) and food allergy (FA), particularly in ... ...

Abstract	The skin microbiome is an extensive community of bacteria, fungi, mites, viruses and archaea colonizing the skin. Fluctuations in the composition of the skin microbiome have been observed in atopic dermatitis (AD) and food allergy (FA), particularly in early life, established disease, and associated with therapeutics. However, AD is a multifactorial disease characterized by skin barrier aberrations modulated by genetics, immunology, and environmental influences, thus the skin microbiome is not the sole feature of this disease. Future research should focus on mechanistic understanding of how early-life skin microbial shifts may influence AD and FA onset, to guide potential early intervention strategies or as microbial biomarkers to identify high-risk infants who may benefit from possible microbiome-based biotherapeutic strategies. Harnessing skin microbes as AD biotherapeutics is an emerging field, but more work is needed to investigate whether this approach can lead to sustained clinical responses.
Language	English
Publishing date	2024-02-03
Publishing country	Denmark
Document type	Journal Article ; Review
ZDB-ID	391933-x
ISSN	1398-9995 ; 0105-4538
ISSN (online)	1398-9995
ISSN	0105-4538
DOI	10.1111/all.16044
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Article ; Online: Metagenomics-enabled microbial surveillance.

Ko, Karrie K K / Chng, Kern Rei / Nagarajan, Niranjan

Nature microbiology

2022 Volume 7, Issue 4, Page(s) 486–496

Abstract: Lessons learnt from the COVID-19 pandemic include increased awareness of the potential for zoonoses and emerging infectious diseases that can adversely affect human health. Although emergent viruses are currently in the spotlight, we must not forget the ... ...

Abstract	Lessons learnt from the COVID-19 pandemic include increased awareness of the potential for zoonoses and emerging infectious diseases that can adversely affect human health. Although emergent viruses are currently in the spotlight, we must not forget the ongoing toll of morbidity and mortality owing to antimicrobial resistance in bacterial pathogens and to vector-borne, foodborne and waterborne diseases. Population growth, planetary change, international travel and medical tourism all contribute to the increasing frequency of infectious disease outbreaks. Surveillance is therefore of crucial importance, but the diversity of microbial pathogens, coupled with resource-intensive methods, compromises our ability to scale-up such efforts. Innovative technologies that are both easy to use and able to simultaneously identify diverse microorganisms (viral, bacterial or fungal) with precision are necessary to enable informed public health decisions. Metagenomics-enabled surveillance methods offer the opportunity to improve detection of both known and yet-to-emerge pathogens.
MeSH term(s)	Animals ; COVID-19 ; Humans ; Metagenomics/methods ; Pandemics ; Viruses/genetics ; Zoonoses
Language	English
Publishing date	2022-04-01
Publishing country	England
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ISSN	2058-5276
ISSN (online)	2058-5276
DOI	10.1038/s41564-022-01089-w
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Article ; Online: TUGDA: task uncertainty guided domain adaptation for robust generalization of cancer drug response prediction from in vitro to in vivo settings.

Peres da Silva, Rafael / Suphavilai, Chayaporn / Nagarajan, Niranjan

Bioinformatics (Oxford, England)

2021 Volume 37, Issue Supplement_1, Page(s) i76–i83

Abstract: Motivation: Large-scale cancer omics studies have highlighted the diversity of patient molecular profiles and the importance of leveraging this information to deliver the right drug to the right patient at the right time. Key challenges in learning ... ...

Abstract	Motivation: Large-scale cancer omics studies have highlighted the diversity of patient molecular profiles and the importance of leveraging this information to deliver the right drug to the right patient at the right time. Key challenges in learning predictive models for this include the high-dimensionality of omics data and heterogeneity in biological and clinical factors affecting patient response. The use of multi-task learning techniques has been widely explored to address dataset limitations for in vitro drug response models, while domain adaptation (DA) has been employed to extend them to predict in vivo response. In both of these transfer learning settings, noisy data for some tasks (or domains) can substantially reduce the performance for others compared to single-task (domain) learners, i.e. lead to negative transfer (NT). Results: We describe a novel multi-task unsupervised DA method (TUGDA) that addresses these limitations in a unified framework by quantifying uncertainty in predictors and weighting their influence on shared feature representations. TUGDA's ability to rely more on predictors with low-uncertainty allowed it to notably reduce cases of NT for in vitro models (94% overall) compared to state-of-the-art methods. For DA to in vivo settings, TUGDA improved over previous methods for patient-derived xenografts (9 out of 14 drugs) as well as patient datasets (significant associations in 9 out of 22 drugs). TUGDA's ability to avoid NT thus provides a key capability as we try to integrate diverse drug-response datasets to build consistent predictive models with in vivo utility. Availabilityand implementation: https://github.com/CSB5/TUGDA. Supplementary information: Supplementary data are available at Bioinformatics online.
Language	English
Publishing date	2021-05-17
Publishing country	England
Document type	Journal Article
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btab299
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Article ; Online: CREAMMIST: an integrative probabilistic database for cancer drug response prediction.

Yingtaweesittikul, Hatairat / Wu, Jiaxi / Mongia, Aanchal / Peres, Rafael / Ko, Karrie / Nagarajan, Niranjan / Suphavilai, Chayaporn

Nucleic acids research

2022 Volume 51, Issue D1, Page(s) D1242–D1248

Abstract: Extensive in vitro cancer drug screening datasets have enabled scientists to identify biomarkers and develop machine learning models for predicting drug sensitivity. While most advancements have focused on omics profiles, cancer drug sensitivity scores ... ...

Abstract	Extensive in vitro cancer drug screening datasets have enabled scientists to identify biomarkers and develop machine learning models for predicting drug sensitivity. While most advancements have focused on omics profiles, cancer drug sensitivity scores precalculated by the original sources are often used as-is, without consideration for variabilities between studies. It is well-known that significant inconsistencies exist between the drug sensitivity scores across datasets due to differences in experimental setups and preprocessing methods used to obtain the sensitivity scores. As a result, many studies opt to focus only on a single dataset, leading to underutilization of available data and a limited interpretation of cancer pharmacogenomics analysis. To overcome these caveats, we have developed CREAMMIST (https://creammist.mtms.dev), an integrative database that enables users to obtain an integrative dose-response curve, to capture uncertainty (or high certainty when multiple datasets well align) across five widely used cancer cell-line drug-response datasets. We utilized the Bayesian framework to systematically integrate all available dose-response values across datasets (>14 millions dose-response data points). CREAMMIST provides easy-to-use statistics derived from the integrative dose-response curves for various downstream analyses such as identifying biomarkers, selecting drug concentrations for experiments, and training robust machine learning models.
MeSH term(s)	Humans ; Antineoplastic Agents/pharmacology ; Antineoplastic Agents/therapeutic use ; Bayes Theorem ; Biomarkers ; Machine Learning ; Neoplasms/drug therapy ; Neoplasms/genetics ; Databases, Factual
Chemical Substances	Antineoplastic Agents ; Biomarkers
Language	English
Publishing date	2022-10-18
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkac911
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Article ; Online: No evidence for a common blood microbiome based on a population study of 9,770 healthy humans.

Tan, Cedric C S / Ko, Karrie K K / Chen, Hui / Liu, Jianjun / Loh, Marie / Chia, Minghao / Nagarajan, Niranjan

Nature microbiology

2023 Volume 8, Issue 5, Page(s) 973–985

Abstract: Human blood is conventionally considered sterile but recent studies suggest the presence of a blood microbiome in healthy individuals. Here we characterized the DNA signatures of microbes in the blood of 9,770 healthy individuals using sequencing data ... ...

Abstract	Human blood is conventionally considered sterile but recent studies suggest the presence of a blood microbiome in healthy individuals. Here we characterized the DNA signatures of microbes in the blood of 9,770 healthy individuals using sequencing data from multiple cohorts. After filtering for contaminants, we identified 117 microbial species in blood, some of which had DNA signatures of microbial replication. They were primarily commensals associated with the gut (n = 40), mouth (n = 32) and genitourinary tract (n = 18), and were distinct from pathogens detected in hospital blood cultures. No species were detected in 84% of individuals, while the remainder only had a median of one species. Less than 5% of individuals shared the same species, no co-occurrence patterns between different species were observed and no associations between host phenotypes and microbes were found. Overall, these results do not support the hypothesis of a consistent core microbiome endogenous to human blood. Rather, our findings support the transient and sporadic translocation of commensal microbes from other body sites into the bloodstream.
MeSH term(s)	Humans ; Microbiota/genetics ; Mouth ; Symbiosis ; DNA
Chemical Substances	DNA (9007-49-2)
Language	English
Publishing date	2023-03-30
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2058-5276
ISSN (online)	2058-5276
DOI	10.1038/s41564-023-01350-w
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Article ; Online: Boosting natural history research via metagenomic clean-up of crowdsourced feces.

Srivathsan, Amrita / Nagarajan, Niranjan / Meier, Rudolf

PLoS biology

2019 Volume 17, Issue 11, Page(s) e3000517

Abstract: Biodiversity is in crisis due to habitat destruction and climate change. The conservation of many noncharismatic species is hampered by the lack of data. Yet, natural history research-a major source of information on noncharismatic species-is in decline. ...

Abstract	Biodiversity is in crisis due to habitat destruction and climate change. The conservation of many noncharismatic species is hampered by the lack of data. Yet, natural history research-a major source of information on noncharismatic species-is in decline. We here suggest a remedy for many mammal species, i.e., metagenomic clean-up of fecal samples that are "crowdsourced" during routine field surveys. Based on literature data, we estimate that this approach could yield natural history information for circa 1,000 species within a decade. Metagenomic analysis would simultaneously yield natural history data on diet and gut parasites while enhancing our understanding of host genetics, gut microbiome, and the functional interactions between traditional and new natural history data. We document the power of this approach by carrying out a "metagenomic clean-up" on fecal samples collected during a single night of small mammal trapping in one of Alfred Wallace's favorite collecting sites.
MeSH term(s)	Animals ; Bacteria ; Biodiversity ; Conservation of Natural Resources ; Crowdsourcing ; Feces/chemistry ; Feces/microbiology ; Feces/parasitology ; Gastrointestinal Microbiome ; Mammals ; Metagenome ; Metagenomics ; Natural History/methods ; Sequence Analysis, DNA
Language	English
Publishing date	2019-11-07
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2126776-5
ISSN	1545-7885 ; 1544-9173
ISSN (online)	1545-7885
ISSN	1544-9173
DOI	10.1371/journal.pbio.3000517
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Article ; Online: Comparison of

Ding, Yichen / Er, Shuan / Tan, Abel / Gounot, Jean-Sebastien / Saw, Woei-Yuh / Tan, Linda Wei Lin / Teo, Yik Ying / Nagarajan, Niranjan / Seedorf, Henning

Microbiology spectrum

2024 Volume 12, Issue 4, Page(s) e0396923

Abstract: ... Recently ... ...

Abstract	Recently discovered
MeSH term(s)	Humans ; Tigecycline ; Healthy Volunteers ; Metagenome ; Anti-Bacterial Agents/pharmacology ; Gammaproteobacteria ; Plasmids/genetics ; Microbial Sensitivity Tests
Chemical Substances	Tigecycline (70JE2N95KR) ; Anti-Bacterial Agents
Language	English
Publishing date	2024-03-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	2807133-5
ISSN	2165-0497 ; 2165-0497
ISSN (online)	2165-0497
ISSN	2165-0497
DOI	10.1128/spectrum.03969-23
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