BACKGROUND: The mammalian immune system is able to generate antibodies against a huge variety of antigens, including bacteria, viruses, and toxins. The ultradeep DNA sequencing of rearranged immunoglobulin genes has considerable potential in furthering our understanding of the immune response, but it is limited by the lack of a high-throughput, sequence-based method for predicting the antigen(s) that a given immunoglobulin recognizes.
OBJECTIVE: As a step toward the prediction of antibody-antigen binding from sequence data alone, we aimed to compare a range of machine learning approaches that were applied to a collated data set of antibody-antigen pairs in order to predict antibody-antigen binding from sequence data.
METHODS: Data for training and testing were extracted from the Protein Data Bank and the Coronavirus Antibody Database, and additional antibody-antigen pair data were generated by using a molecular docking protocol. Several machine learning methods, including the weighted nearest neighbor method, the nearest neighbor method with the BLOSUM62 matrix, and the random forest method, were applied to the problem.
RESULTS: The final data set contained 1157 antibodies and 57 antigens that were combined in 5041 antibody-antigen pairs. The best performance for the prediction of interactions was obtained by using the nearest neighbor method with the BLOSUM62 matrix, which resulted in around 82% accuracy on the full data set. These results provide a useful frame of reference, as well as protocols and considerations, for machine learning and data set creation in the prediction of antibody-antigen binding.
CONCLUSIONS: Several machine learning approaches were compared to predict antibody-antigen interaction from protein sequences. Both the data set (in CSV format) and the machine learning program (coded in Python) are freely available for download on GitHub.

A 21-year-old man who was diagnosed with Ollier disease at the age of 1 year developed incidental multiple gliomas at the age of 15 years. Subsequently, the multiple gliomas enlarged and the patient underwent three surgical removals. Genetic analysis revealed the IDH1 p.R132C mutation in the gliomas, and histopathology showed malignant transformation. Despite multimodality treatment, the gliomas could not be controlled, and the patient died at the age of 23 years. Ollier disease is a rare disease with IDH1/2 mutations and is often associated with gliomas. However, there are very few reports on genetic analysis of IDH1/2 mutations and long-term follow-up in Ollier disease-related gliomas. Genetic analysis of IDH mutations may contribute to the elucidation of its pathogenesis. The cross-departmental collaboration is required for long-term follow-up of Ollier disease-related gliomas.

UNASSIGNED: To analyze the composition of abnormal hemoglobin and the relationship between genotype and phenotype by screening abnormal hemoglobin in a subpopulation of Guizhou, China.
UNASSIGNED: Routine blood evaluation, capillary electrophoresis of hemoglobin, and mutation of α - and β - thalassemia genes were evaluated in 19,976 individuals for thalassemia screening in Guizhou. Sanger sequencing of HBA1, HBA2 and HBB genes was performed in samples with abnormal bands or unexplained increases of normal bands. The types of abnormal hemoglobin were obtained by sequence analysis.
UNASSIGNED: Abnormal hemoglobin was detected in 84 individuals (detection rate, 0.42%). Ten types each of α and β globin chain variants were detected, including most commonly Hb E, Hb New York and Hb Port Phillip. In this study, the abnormal Hb Mizuho was identified for the first time in a Chinese population, and a novel abnormal hemoglobin Hb Guiyang (HBA2: c.151C > A) was detected for the first time. Except for Hb Mizuho, other abnormal hemoglobin heterozygotes without thalassemia or iron deficiency had no significant hematological changes.
UNASSIGNED: This study enriched the molecular epidemiological data of abnormal hemoglobin in Guizhou, China and provided reference data for genetic counseling and prenatal diagnosis of abnormal hemoglobin.

BACKGROUND: Chronic enteropathy associated with SLCO2A1 gene (CEAS) results from loss-of-function variants in SLCO2A1, which encodes the prostaglandin transporter (PGT). CEAS follows an autosomal recessive inheritance pattern. To date, approximate 30 pathogenic variants have been reported in CEAS.
METHODS: We performed whole exome sequencing (WES) to screen for potential pathogenic variants in a patient suspected of having CEAS, and confirmed a variant in SLCO2A1 using Sanger sequencing. We established an in vitro minigene model to compare splicing between wild type (WT) and mutant transcripts. Quantitative polymerase chain reaction (qPCR) was used to evaluate SLCO2A1 transcription in the stomach and colon tissues from the patient and a healthy control (HC). The transcripts were further cloned and sequenced.
RESULTS: The patient had a novel, homozygous, recessive c.929A > G variant in exon 7 of SLCO2A1, which has not been previously reported in CEAS or PHO. This variant altered splicing, resulting in an exon 7-truncated transcript lacking 16 bases. No normal transcript was detected in the patient\'s stomach or colon tissue. qPCR also showed significantly decreased SLCO2A1 transcription compared to HC.
CONCLUSIONS: A previously unreported variant caused defective SLCO2A1 splicing and reduced mRNA levels in a patient with CEAS and PHO. This research enhances understanding of CEAS and PHO pathophysiology and aids genetic counseling and diagnosis.

The genome of a halophilic bacterium Halomonas salifodinae IM328 was completely sequenced in order to offer convenience for the research such as the synthesis of compatible solutes. The genome contains a circular chromosome which was sequenced by PacBio system.

DNA sequencing with the quantum tunneling technique heralds a paradigm shift in genetic analysis, promising rapid and accurate identification for diverging applications ranging from personalized medicine to security issues. However, the widespread distribution of molecular conductance, conduction orbital alignment for resonant transport, and decoding crisscrossing conductance signals of isomorphic nucleotides have been persistent experimental hurdles for swift and precise identification. Herein, we have reported a machine learning (ML)-driven quantum tunneling study with solid-state model nanogap to determine nucleotides at single-base resolution. The optimized ML basecaller has demonstrated a high predictive basecalling accuracy of all four nucleotides from seven distinct data pools, each containing complex transmission readouts of their different dynamic conformations. ML classification of quaternary, ternary, and binary nucleotide combinations is also performed with high precision, sensitivity, and F1 score. ML explainability unravels the evidence of how extracted normalized features within overlapped nucleotide signals contribute to classification improvement. Moreover, electronic fingerprints, conductance sensitivity, and current readout analysis of nucleotides have promised practical applicability with significant sensitivity and distinguishability. Through this ML approach, our study pushes the boundaries of quantum sequencing by highlighting the effectiveness of single nucleotide basecalling with promising implications for advancing genomics and molecular diagnostics.

In many countries, some form of genetic screening is offered to all or part of the population, either in the form of well-organized screening programs or in a less formalized way. Screening can be offered at different phases of life, such as preconception, prenatal, neonatal and later in life. Screening should only be offered if the advantages outweigh the disadvantages. Technical innovations in testing and treatment are driving changes in the field of prenatal and neonatal screening, where many jurisdictions have organized population-based screening programs. As a result, a greater number and wider range of conditions are being added to the programs, which can benefit couples\' reproductive autonomy (preconception and prenatal screening) and improve early diagnosis to prevent irreversible health damage in children (neonatal screening) and in adults (cancer and cascade screening). While many developments in screening are technology-driven, citizens may also express a demand for innovation in screening, as was the case with non-invasive prenatal testing. Relatively new emerging issues for genetic screening, especially if testing is performed using DNA sequencing, relate to organization, data storage and interpretation, benefit-harm ratio and distributive justice, information provision and follow-up, all connected to acceptability in current healthcare systems.

The high prevalence of dental caries and periodontal disease place a significant burden on society, both socially and economically. Recent advances in genomic technologies have linked both diseases to shifts in the oral microbiota - a community of >700 bacterial species that live within the mouth. The development of oral microbiome transplantation draws on the success of fecal microbiome transplantation for the treatment of gut pathologies associated with disease. Many current in vitro oral biofilm models have been developed but do not fully capture the complexity of the oral microbiome which is required for successful OMT. To address this, we developed an in vitro biofilm system that maintained an oral microbiome with 252 species on average over 14 days. Six human plaque samples were grown in 3D printed flow cells on hydroxyapatite discs using artificial saliva medium (ASM). Biofilm composition and growth were monitored by high throughput sequencing and confocal microscopy/SEM, respectively. While a significant drop in bacterial diversity occurred, up to 291 species were maintained in some flow cells over 14 days with 70% viability grown with ASM. This novel in vitro biofilm model represents a marked improvement on existing oral biofilm systems and provides new opportunities to develop oral microbiome transplant therapies.

Seven public water systems in Minnesota, USA were analyzed from one to five times over a two-year period to assess temporal changes in the concentrations of total bacteria, Legionella spp., and Legionella pneumophila from source (i.e., raw water) through the water treatment process to the end water user. Bacterial biomass was collected by filtering large volumes of raw water (12 to 425 L, median: 38 L) or finished and tap water (27 to 1205 L, median: 448 L) using ultrafiltration membrane modules. Quantitative PCR (qPCR) was then used to enumerate all bacteria (16S rRNA gene fragments), all Legionella spp. (ssrA), and Legionella pneumophila (mip). Total coliforms, Escherichia coli, and L. pneumophila also were quantified in the water samples via cultivation. Median concentrations of total bacteria and Legionella spp. (ssrA) in raw water (8.5 and 4.3 log copies/L, respectively) decreased by about 2 log units during water treatment. The concentration of Legionella spp. (ssrA) in water collected from distribution systems inversely correlated with the total chlorine concentration for chloraminated systems significantly (p = 0.03). Although only 8 samples were collected from drinking water distribution systems using free chlorine as a residual disinfectant, these samples had significantly lower concentrations of Legionella spp. (ssrA) than samples collected from the chloraminated systems (p = 5 × 10-4). There was considerable incongruity between the results obtained via cultivation-independent (qPCR) and cultivation-dependent assays. Numerous samples were positive for L. pneumophila via cultivation, none of which tested positive for L. pneumophilia (mip) via qPCR. Conversely, a single sample tested positive for L. pneumophilia (mip) via qPCR, but this sample tested negative for L. pneumophilia via cultivation. Overall, the results suggest that conventional treatment is effective at reducing, but not eliminating, Legionella spp. from surface water supplies and that residual disinfection is effective at suppressing these organisms within drinking water distribution systems.

Bulk DNA sequencing of multiple samples from the same tumor is becoming common, yet most methods to infer copy-number aberrations (CNAs) from this data analyze individual samples independently. We introduce HATCHet2, an algorithm to identify haplotype- and clone-specific CNAs simultaneously from multiple bulk samples. HATCHet2 extends the earlier HATCHet method by improving identification of focal CNAs and introducing a novel statistic, the minor haplotype B-allele frequency (mhBAF), that enables identification of mirrored-subclonal CNAs. We demonstrate HATCHet2\'s improved accuracy using simulations and a single-cell sequencing dataset. HATCHet2 analysis of 10 prostate cancer patients reveals previously unreported mirrored-subclonal CNAs affecting cancer genes.