We systemically identified tuberculosis (TB)-related DNA methylation biomarkers and further constructed classifiers for TB diagnosis. TB-related DNA methylation datasets were searched through October 3, 2020. Limma and DMRcate were employed to identify differentially methylated probes (DMPs) and regions (DMRs). Machine learning methods were used to construct classifiers. The performance of the classifiers was evaluated in discovery datasets and a prospective independent cohort. Eighty-nine DMPs and 24 DMRs were identified based on 67 TB patients and 45 healthy controls from 4 datasets. Nine and three DMRs were selected by elastic net regression and logistic regression, respectively. Among the selected DMRs, two regions (chr3: 195635643–195636243 and chr6: 29691631–29692475) were differentially methylated in the independent cohort (p = 4.19 × 10 −5 and 0.024, respectively). Among the ten classifiers, the 3-DMR logistic regression classifier exhibited the strongest performance. The sensitivity, specificity, and area under the curve were, respectively, 79.1%, 84.4%, and 0.888 in the discovery datasets and 64.5%, 90.3%, and 0.838 in the independent cohort. The differential diagnostic ability of this classifier was also assessed. Collectively, these data showed that DNA methylation might be a promising TB diagnostic biomarker. The 3-DMR logistic regression classifier is a potential clinical tool for TB diagnosis, and further validation is needed.

Context.— Minimal/measurable residual disease (MRD) measured by molecular and multiparametric flow cytometry (MFC) has been proven to be predictive of relapse and survival in patients with B-cell acute lymphoblastic leukemia (B-ALL). A universally applicable antibody panel at a low cost but without compromising sensitivity and power of prognosis prediction in adult B-ALL remains unestablished. Objective.— To report our experience of using a single-tube 8-color MFC panel to measure the MRD status as a prognostic indicator in adult B-ALL patients. Design.— We retrospectively analyzed the characteristics, MRD status, and prognosis of adult B-ALL based on a large real-world cohort of 486 patients during a 10-year period. Results.— MRD assessed by MFC and polymerase chain reaction (PCR) assays for BCR-ABL+ patients showed concordant results in 74.2% of cases. MRD− status by our MFC panel could clearly predict a favorable relapse-free survival (RFS) and overall survival (OS) both at the end of induction and at the end of 1 consolidation course. Patients with continuous MRD− and with at least 1 MRD− result showed a favorable RFS and OS compared with those with at least 1 MRD+ result and continuous MRD+, respectively. Conclusions.— The single-tube 8-color MFC panel demonstrated a low cost, decent sensitivity, and comparability with polymerase chain reaction–MRD but an excellent performance in predicting RFS and OS, and thus could potentially be taken as a routine indicator in the evaluation of the treatment response for adult patients with B-ALL.

Alternative splicing (AS) is an important approach for pathogens and hosts to remodel transcriptome. However, tuberculosis (TB)-related AS has not been sufficiently explored. Here we presented the first landscape of TB-related AS by long-read sequencing, and screened four AS events (S100A8-intron1-retention intron, RPS20-exon1-alternaitve promoter, KIF13B-exon4-skipping exon (SE) and UBE2B-exon7-SE) as potential biomarkers in an in-house cohort-1. The validations in an in-house cohort-2 (2274 samples) and public datasets (1557 samples) indicated that the latter three AS events are potential promising biomarkers for TB diagnosis, but not for TB progression and prognosis. The excellent performance of classifiers further underscored the diagnostic value of these three biomarkers. Subgroup analyses indicated that UBE2B-exon7-SE splicing was not affected by confounding factors and thus had relatively stable performance. The splicing of UBE2B-exon7-SE can be changed by heat-killed mycobacterium tuberculosis through inhibiting SRSF1 expression. After heat-killed mycobacterium tuberculosis stimulation, 231 ubiquitination proteins in macrophages were differentially expressed, and most of them are apoptosis-related proteins. Taken together, we depicted a global TB-associated splicing profile, developed TB-related AS biomarkers, demonstrated an optimal application scope of target biomarkers and preliminarily elucidated mycobacterium tuberculosis-host interaction from the perspective of splicing, offering a novel insight into the pathophysiology of TB.

Alternative splicing (AS) is an evolutionarily conserved mechanism that removes introns and ligates exons to generate mature messenger RNAs (mRNAs), extremely improving the richness of transcriptome and proteome. Both mammal hosts and pathogens require AS to maintain their life activities, and inherent physiological heterogeneity between mammals and pathogens makes them adopt different ways to perform AS. Mammals and fungi conduct a two-step transesterification reaction by spliceosomes to splice each individual mRNA (named cis-splicing). Parasites also use spliceosomes to splice, but this splicing can occur among different mRNAs (named trans-splicing). Bacteria and viruses directly hijack the host's splicing machinery to accomplish this process. Infection-related changes are reflected in the spliceosome behaviors and the characteristics of various splicing regulators (abundance, modification, distribution, movement speed, and conformation), which further radiate to alterations in the global splicing profiles. Genes with splicing changes are enriched in immune-, growth-, or metabolism-related pathways, highlighting approaches through which hosts crosstalk with pathogens. Based on these infection-specific regulators or AS events, several targeted agents have been developed to fight against pathogens. Here, we summarized recent findings in the field of infection-related splicing, including splicing mechanisms of pathogens and hosts, splicing regulation and aberrant AS events, as well as emerging targeted drugs. We aimed to systemically decode host–pathogen interactions from a perspective of splicing. We further discussed the current strategies of drug development, detection methods, analysis algorithms, and database construction, facilitating the annotation of infection-related splicing and the integration of AS with disease phenotype.

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