Jijakli, K. | bioRxiv Science

bioRxiv2025

MillionFull enables massive, full-length enzyme sequence-fitness data collection at low cost for machine learning-guided enzyme engineering

Machine learning holds great promise for accelerating enzyme optimization, but its power is fundamentally constrained by the limited availability of sequence-fitness data. Here, we introduce MillionFull, a low-cost method that enables high-throughput full-length sequence- fitness mapping for enzymes of arbitrary length. Each run yields on the order of 10-10 data points, capturing sequence-function relationships at unprecedented scale. By overcoming the data bottleneck, MillionFull provides a foundation for dramatically advancing AI-driven enzyme engineering.

synthetic biology↗

bioRxiv2023

The unique microbial diversity, community composition and networks of Pacific Islander endocervical and vaginal microbiomes in the presence or absence of Chlamydia trachomatis infection using metagenomic shotgun sequencing

BackgroundPacific Islanders are a vulnerable population with a high prevalence of Chlamydia trachomatis (Ct) sexually transmitted infections (STIs) and remain underrepresented in research. Here, 258 vaginal and 92 paired endocervical samples from women of diverse ethnicities in Fiji were evaluated using metagenomics to characterize microbial relative abundance, composition and networks including associations with Neisseria gonorrhoeae, human papilloma virus (HPV), Mycoplasma genitalium, Candida and bacterial vaginosis (BV). ResultsPacific Islander ethnicities and age <25 years were significantly associated with Ct infection. Using VALENCIA, a sub-community state type (subCST) classifier, 93 (36.04%) vaginal and 38 (41.30%) endocervical microbiomes did not match reference subCSTs. Four unique subCSTs were developed to better classify Pacific Islander microbiomes: IV-D0, dominated by Gardnerella vaginalis; IV-D1, high/moderate G. vaginalis relative abundance with Prevotella spp.; IV-D2, high/moderate G. vaginalis with Lactobacillus iners; and IV-E, moderate Prevotella spp. Ct positive endocervical and paired vaginal microbiomes were significantly more likely to have differential species relative abundance (81.58%) than Ct negative pairs (35.84%; AOR: 7.93; 95% CI: 2.93-21.93; P<0.0001). A significantly higher alpha diversity was found for iTaukei ethnicity, all subCST-IV types, BV, and Ct for vaginal microbiomes. For endocervical microbiomes, higher diversity was significant for subCST-IV-A, subCST-IV-D1, and subCST-IV-E, and high-risk HPV types. Overall, there was a significantly higher diversity for the endocervix in paired microbiomes. Vaginal microbiomes showed significant divergence in community composition as above and for Candida. Endocervical composition varied significantly by subCST type and Ct status. Gut and BV-associated bacterial clusters were present in Ct positive and negative paired endocervical and vaginal microbiome networks but were smaller with fewer bacterial and no Lactobacillus spp. interactions in Ct-infected endocervical networks where G. vaginalis generated polymicrobial biofilms along with Ct likely influence pathogenicity. ConclusionsFijian endocervical and vaginal microbiomes represent divergent microbial abundance and compositions, especially for Pacific Islander ethnicities, with distinct subCSTs compared to other global populations. The higher microbial diversity of the endocervix with prevalent G. vaginalis, L. iners and Prevotella spp. suggest that these microbiomes/networks may predispose to and/or promote chlamydial and HPV pathogenesis. Prospective studies are needed to further define causal associations to develop successful interventions.

microbiology↗

bioRxiv2018

Metabolic modeling of Streptococcus mutans reveals complex nutrient requirements of an oral pathogen

Streptococcus mutans is a Gram positive bacterium that thrives under acidic conditions and is a primary cause of tooth decay (dental caries). To better understand the metabolism of S. mutans on a systematic level, we manually constructed a genome-scale metabolic model of the S. mutans type strain UA159. The model, called iSMU, contains 656 reactions involving 514 metabolites and the products of 488 genes.\n\nWe interrogated S. mutans nutrient requirements using model simulations and nutrient removal experiments in defined media. The iSMU model matched experimental results in greater than 90% of the conditions tested. We also simulated effects of single gene deletions. The models predictions agreed with 78.1% and 84.4% of the gene essentiality predictions from two experimental datasets. Our manually curated model is more accurate than S. mutans models generated from automated reconstruction pipelines. We believe the iSMU model is an important resource for understanding how metabolism enables the cariogenicity of S. mutans.

systems biology↗