HervD Atlas: a curated knowledgebase of associations between human endogenous retroviruses and diseases.

Human endogenous retroviruses (HERVs), as remnants of ancient exogenous retrovirus infected and integrated into germ cells, comprise ∼8% of the human genome. These HERVs have been implicated in numerous diseases, and extensive research has been conducted to uncover their specific roles. Despite these efforts, a comprehensive source of HERV-disease association still needs to be added. To address this gap, we introduce the HervD Atlas (https://ngdc.cncb.ac.cn/hervd/), an integrated knowledgebase of HERV-disease associations manually curated from all related published literature. In the current version, HervD Atlas collects 60 726 HERV-disease associations from 254 publications (out of 4692 screened literature), covering 21 790 HERVs (21 049 HERV-Terms and 741 HERV-Elements) belonging to six types, 149 diseases and 610 related/affected genes. Notably, an interactive knowledge graph that systematically integrates all the HERV-disease associations and corresponding affected genes into a comprehensive network provides a powerful tool to uncover and deduce the complex interplay between HERVs and diseases. The HervD Atlas also features a user-friendly web interface that allows efficient browsing, searching, and downloading of all association information, research metadata, and annotation information. Overall, the HervD Atlas is an essential resource for comprehensive, up-to-date knowledge on HERV-disease research, potentially facilitating the development of novel HERV-associated diagnostic and therapeutic strategies.

Comparative transcriptome analysis of SARS-CoV-2, SARS-CoV, MERS-CoV, and HCoV-229E identifying potential IFN/ISGs targets for inhibiting virus replication.

Introduction: Since its outbreak in December 2019, SARS-CoV-2 has spread rapidly across the world, posing significant threats and challenges to global public health. SARS-CoV-2, together with SARS-CoV and MERS-CoV, is a highly pathogenic coronavirus that contributes to fatal pneumonia. Understanding the similarities and differences at the transcriptome level between SARS-CoV-2, SARS-CoV, as well as MERS-CoV is critical for developing effective strategies against these viruses. Methods: In this article, we comparatively analyzed publicly available transcriptome data of human cell lines infected with highly pathogenic SARS-CoV-2, SARS-CoV, MERS-CoV, and lowly pathogenic HCoV-229E. The host gene expression profiles during human coronavirus (HCoV) infections were generated, and the pathways and biological functions involved in immune responses, antiviral efficacy, and organ damage were intensively elucidated. Results: Our results indicated that SARS-CoV-2 induced a stronger immune response versus the other two highly pathogenic HCoVs. Specifically, SARS-CoV-2 induced robust type I and type III IFN responses, marked by higher upregulation of type I and type III IFNs, as well as numerous interferon-stimulated genes (ISGs). Further Ingenuity Pathway Analysis (IPA) revealed the important role of ISGs for impeding SARS-CoV-2 infection, and the interferon/ISGs could be potential targets for therapeutic interventions. Moreover, our results uncovered that SARS-CoV-2 infection was linked to an enhanced risk of multi-organ toxicity in contrast to the other two highly pathogenic HCoVs. Discussion: These findings provided valuable insights into the pathogenic mechanism of SARS-CoV-2, which showed a similar pathological feature but a lower fatality rate compared to SARS-CoV and MERS-CoV.

Spatiotemporal single-cell transcriptomic profiling reveals inflammatory cell states in a mouse model of diffuse alveolar damage.

Diffuse alveolar damage (DAD) triggers neutrophilic inflammation in damaged tissues of the lung, but little is known about the distinct roles of tissue structural cells in modulating the recruitment of neutrophils to damaged areas. Here, by combining single-cell and spatial transcriptomics, and using quantitative assays, we systematically analyze inflammatory cell states in a mouse model of DAD-induced neutrophilic inflammation after aerosolized intratracheal inoculation with ricin toxin. We show that homeostatic resident fibroblasts switch to a hyper-inflammatory state, and the subsequent occurrence of a CXCL1-CXCR2 chemokine axis between activated fibroblasts (AFib) as the signal sender and neutrophils as the signal receiver triggers further neutrophil recruitment. We also identify an anatomically localized inflamed niche (characterized by a close-knit spatial intercellular contact between recruited neutrophils and AFib) in peribronchial regions that facilitate the pulmonary inflammation outbreak. Our findings identify an intricate interplay between hyper-inflammatory fibroblasts and neutrophils and provide an overarching profile of dynamically changing inflammatory microenvironments during DAD progression.

Proteomic analyses of smear-positive/negative tuberculosis patients uncover differential antigen-presenting cell activation and lipid metabolism.

Background: Tuberculosis (TB) remains a major global health concern, ranking as the second most lethal infectious disease following COVID-19. Smear-Negative Pulmonary Tuberculosis (SNPT) and Smear-Positive Pulmonary Tuberculosis (SPPT) are two common types of pulmonary tuberculosis characterized by distinct bacterial loads. To date, the precise molecular mechanisms underlying the differences between SNPT and SPPT patients remain unclear. In this study, we aimed to utilize proteomics analysis for identifying specific protein signatures in the plasma of SPPT and SNPT patients and further elucidate the molecular mechanisms contributing to different disease pathogenesis. Methods: Plasma samples from 27 SPPT, 37 SNPT patients and 36 controls were collected and subjected to TMT-labeled quantitative proteomic analyses and targeted GC-MS-based lipidomic analysis. Ingenuity Pathway Analysis (IPA) was then performed to uncover enriched pathways and functionals of differentially expressed proteins. Results: Proteomic analysis uncovered differential protein expression profiles among the SPPT, SNPT, and Ctrl groups, demonstrating dysfunctional immune response and metabolism in both SPPT and SNPT patients. Both groups exhibited activated innate immune responses and inhibited fatty acid metabolism, but SPPT patients displayed stronger innate immune activation and lipid metabolic inhibition compared to SNPT patients. Notably, our analysis uncovered activated antigen-presenting cells (APCs) in SNPT patients but inhibited APCs in SPPT patients, suggesting their critical role in determining different bacterial loads/phenotypes in SNPT and SPPT. Furthermore, some specific proteins were detected to be involved in the APC activation/acquired immune response, providing some promising therapeutic targets for TB. Conclusion: Our study provides valuable insights into the differential molecular mechanisms underlying SNPT and SPPT, reveals the critical role of antigen-presenting cell activation in SNPT for effectively clearing the majority of Mtb in bodies, and shows the possibility of APC activation as a novel TB treatment strategy.

Genomic epidemiology and heterogeneity of Providencia and their blaNDM-1-carrying plasmids.

Providencia as an opportunistic pathogen can cause serious infection, and moreover the emergence of multi-drug-resistant Providencia strains poses a potentially life-threatening risk to public health. However, a comprehensive genomic study to reveal the population structure and dissemination of Providencia is still lacking. In this study, we conducted a genomic epidemiology analysis on the 580 global sequenced Providencia isolates, including 257 ones sequenced in this study (42 ones were fully sequenced). We established a genome sequence-based species classification scheme for Providencia, redefining the conventional 11 Providencia species into seven genocomplexes that were further divided into 18 genospecies, providing an extensively updated reference for Providencia species discrimination based on the largest Providencia genome dataset to date. We then dissected the profile of antimicrobial resistance genes and the prevalence of multi-drug-resistant Providencia strains among these genocomplexes/genospecies, disclosing the presence of diverse and abundant antimicrobial resistance genes and high resistance ratios against multiple classes of drugs in Providencia. We further dissected the genetic basis for the spread of blaNDM-1 in Providencia. blaNDM-1 genes were mainly carried by five incompatible (Inc) groups of plasmids: IncC, IncW, IncpPROV114-NR, IncpCHS4.1-3, and IncpPrY2001, and the last three were newly designated in this study. By tracking the spread of blaNDM-1-carrying plasmids, IncC, IncpPROV114-NR, IncpCHS4.1-3, and IncpPrY2001 plasmids were found to be highly involved in parallel horizontal transfer or vertical clonal expansion of blaNDM-1 among Providencia. Overall, our study provided a comprehensive genomic view of species differentiation, antimicrobial resistance prevalence, and plasmid-mediated blaNDM-1 dissemination in Providencia.

Comparative genomic analysis reveals differential genomic characteristics and featured genes between rapid- and slow-growing non-tuberculous mycobacteria.

Introduction: Non-tuberculous mycobacteria (NTM) is a major category of environmental bacteria in nature that can be divided into rapidly growing mycobacteria (RGM) and slowly growing mycobacteria (SGM) based on their distinct growth rates. To explore differential molecular mechanisms between RGM and SGM is crucial to understand their survival state, environmental/host adaptation and pathogenicity. Comparative genomic analysis provides a powerful tool for deeply investigating differential molecular mechanisms between them. However, large-scale comparative genomic analysis between RGM and SGM is still uncovered. Methods: In this study, we screened 335 high-quality, non-redundant NTM genome sequences covering 187 species from 3,478 online NTM genomes, and then performed a comprehensive comparative genomic analysis to identify differential genomic characteristics and featured genes/protein domains between RGM and SGM. Results: Our findings reveal that RGM has a larger genome size, more genes, lower GC content, and more featured genes/protein domains in metabolism of some main substances (e.g. carbohydrates, amino acids, nucleotides, ions, and coenzymes), energy metabolism, signal transduction, replication, transcription, and translation processes, which are essential for its rapid growth requirements. On the other hand, SGM has a smaller genome size, fewer genes, higher GC content, and more featured genes/protein domains in lipid and secondary metabolite metabolisms and cellular defense mechanisms, which help enhance its genome stability and environmental adaptability. Additionally, orthogroup analysis revealed the important roles of bacterial division and bacteriophage associated genes in RGM and secretion system related genes for better environmental adaptation in SGM. Notably, PCoA analysis of the top 20 genes/protein domains showed precision classification between RGM and SGM, indicating the credibility of our screening/classification strategies. Discussion: Overall, our findings shed light on differential underlying molecular mechanisms in survival state, adaptation and pathogenicity between RGM and SGM, show the potential for our comparative genomic pipeline to investigate differential genes/protein domains at whole genomic level across different bacterial species on a large scale, and provide an important reference and improved understanding of NTM.

Impact of TP53 Mutations on EGFR-Tyrosine Kinase Inhibitor Efficacy and Potential Treatment Strategy.

BACKGROUND: We investigated the impact of factors that influence TP53 mutations on the efficacy of EGFR-tyrosine kinase inhibitors and potential treatment strategies. MATERIALS AND METHODS: Tumor samples were collected to screen gene mutations by next-generation sequencing, as well as the patients' baseline characteristics. The overall response to treatment with TKIs was evaluated based on interval computed tomography scans at each follow-up time point. A Fisher's exact test and log-rank test were used to determine the statistical differences in this study. RESULTS: A total of 1134 clinical samples were collected from NSCLC patients, and TP53mut was identified in 644 cases and EGFRmut in 622 cases. A low frequency of TP53mut or more than 50% EGFR co-mutation rate were related to the prognosis of TKI-treated patients. In addition, TP53mut in the region outside of the DB domain had the strongest correlation with TKI resistance, whereas various types of mutations in the DB domain only had an impact on PFS. A grouping study of EGFR-TKI-based treatment revealed that EGFR-TKIs with chemotherapy were associated with more significant survival benefits for patients with prognostic TP53mut, whereas EGFR-TKI therapy was favorable for TP53wt patients. Furthermore, TP53mut could shorten the time to the relapse of postoperative patients, who will also likely respond well to EGFR-TKIs with chemotherapy. CONCLUSION: Various characteristics of TP53mut affect the prognosis of TKI-treated patients to varying degrees. EGFR-TKIs with chemotherapy were benefit for patients' survival with prognostic TP53mut, which provides an important reference for treatment management of EGFRmut patients.

RNA expression profiling from the liquid fraction of synovial fluid in knee joint osteoarthritis patients.

OBJECTIVE: To investigate the RNA profile of synovial fluid (SF) from osteoarthritis (OA) patients and carry out cluster analysis of OA-related genes. METHODS: RNA of SF from OA patients was isolated using RNA-specific Trizol. A cDNA library was built and subjected to the second-generation sequencing using HisSeq4000 with a data size of 8G. The sequencing reads were aligned to the UCSC human reference genome (hg38) using Tophat with default parameters. Gene function enrichment was generated using DAVID. RESULTS: The minimum weight 0.096 µg RNA of SF sample was used for sequencing analysis, which produced 66,154,562 clean reads, 91.28% of which were matched to the reference with 2,682 genes identified. Some of the unmatchable reads matched RNAs of bacteria, mainly Pseudomonas. The detected human RNAs in samples fell into different categories of genes, including protein-coding ones, processed and unprocessed pseudogenes, and long noncoding, antisense and miscellaneous RNAs that mediate various biological functions. Interestingly, 80% of the expressed genes belonged to the mitochondrial genome. CONCLUSION: These results suggest that less than 0.1 µg RNA is sufficient for establishing a cDNA library and deep sequencing, and that the liquid fraction of SF contains a whole RNA repertoire that may reflect a history of previous microorganism infections.

Serum Proteomic Analysis for New Types of Long-Term Persistent COVID-19 Patients in Wuhan.

The emergence of a new type of COVID-19 patients, who were retested positive after hospital discharge with long-term persistent SARS-CoV-2 infection but without COVID-19 clinical symptoms (hereinafter, LTPPs), poses novel challenges to COVID-19 treatment and prevention. Why was there such a contradictory phenomenon in LTPPs? To explore the mechanism underlying this phenomenon, we performed quantitative proteomic analyses using the sera of 12 LTPPs (Wuhan Pulmonary Hospital), with the longest carrying history of 132 days, and mainly focused on 7 LTPPs without hypertension (LTPPs-NH). The results showed differential serum protein profiles between LTPPs/LTPPs-NH and health controls. Further analysis identified 174 differentially-expressed-proteins (DEPs) for LTPPs, and 165 DEPs for LTPPs-NH, most of which were shared. GO and KEGG analyses for these DEPs revealed significant enrichment of "coagulation" and "immune response" in both LTPPs and LTPPs-NH. A unity of contradictory genotypes in the 2 aspects were then observed: some DEPs showed the same dysregulated expressed trend as that previously reported for patients in the acute phase of COVID-19, which might be caused by long-term stimulation of persistent SARS-CoV-2 infection in LTPPs, further preventing them from complete elimination; in contrast, some DEPs showed the opposite expression trend in expression, so as to retain control of COVID-19 clinical symptoms in LTPPs. Overall, the contrary effects of these DEPs worked together to maintain the balance of LTPPs, further endowing their contradictory steady-state with long-term persistent SARS-CoV-2 infection but without symptoms. Additionally, our study revealed some potential therapeutic targets of COVID-19. Further studies on these are warranted. IMPORTANCE This study reported a new type of COVID-19 patients and explored the underlying molecular mechanism by quantitative proteomic analyses. DEPs were significantly enriched in "coagulation" and "immune response". Importantly, we identified 7 "coagulation system"- and 9 "immune response"-related DEPs, the expression levels of which were consistent with those previously reported for patients in the acute phase of COVID-19, which appeared to play a role in avoiding the complete elimination of SARS-CoV-2 in LTPPs. On the contrary, 6 "coagulation system"- and 5 "immune response"-related DEPs showed the opposite trend in expression. The 11 inconsistent serum proteins seem to play a key role in the fight against long-term persistent SARS-CoV-2 infection, further retaining control of COVID-19 clinical symptom of LTPPs. The 26 proteins can serve as potential therapeutic targets and are thus valuable for the treatment of LTPPs; further studies on them are warranted.

[Application of metagenomic and culturomic technologies in fecal microbiota transplantation: a review].

Fecal microbiota transplantation (FMT) refers to using the intestinal microorganisms present in the feces or processed feces from healthy people for treating various types of diseases, such as digestive and metabolic diseases. The rapid development of metagenomic and culturomic technologies in gut microbiome analysis provides powerful tools for the FMT research and its clinical applications. Metagenomics technologies comprehensively revealed the diversity and functions of gut microbiota under health and disease conditions, while culturomics technologies helped isolation and identification of "unculturable" bacteria in the human gut under conventional culture conditions. The combination of these two technologies not only enabled us better understand the FMT regularities of cause and effect in clinical practices, but also effectively promoted its applications. Considering the above advantages, this article summarized the applications of metagenomics and culturomics technologies in FMT and prospected its future development trend.

A bla SIM-1 and mcr-9.2 harboring Klebsiella michiganensis strain reported and genomic characteristics of Klebsiella michiganensis.

As a newly emerging Klebsiella pathogen, more and more Klebsiella michiganensis drug resistant strains have been reported in recent years, which posed serious threats to public health. Here we first reported a multidrug-resistant K. michiganensis strain 12084 with two bla SIM-1 and one mcr-9.2 genes isolated from the sputum specimen of a patient in the Second Affiliated Hospital of Zhejiang University School of Medicine and analyzed its genetic basis and drug-resistance phenotypes. Genetic analysis showed that this strain harbored three different incompatibility groups (IncHI2, IncHI5, and IncFIIpKPHS2:IncFIB-4.1) of plasmids (p12084-HI2, p12084-HI5, and p12084-FII). A total of 26 drug-resistance genes belonging to 12 classes of antibiotics were identified, most of which (24) were located on two plasmids (p12084-HI2 and p12084-HI5). Interestingly, two bla SIM-1 genes were identified to locate on p12084-HI2 and p12084-HI5, respectively, both of which were embedded in In630, indicating their genetic homogeny. It was noting that one bla SIM-1 gene was situated in a novel unit transposon (referred to as Tn6733) on the p12084-HI5 plasmid. We also discovered an mcr-9.2 gene on the p12084-HI2 plasmid. To the best of our knowledge, this is the first report of a bla SIM-1 and mcr-9.2 harboring K. michiganensis strain. We then investigated the population structure/classification, and antibiotic resistance for all 275 availably global K. michiganensis genomes. Population structure revealed that K. michiganensis could be divided into two main clades (Clade 1 and Clade 2); the most popular ST29 was located in Clade 1, while other common STs (such as ST50, ST27, and ST43) were located in Clade 2. Drug-resistance analysis showed 25.5% of the K. michiganensis strains (70/275) harboring at least one carbapenemase gene, indicating severe drug resistance of K. michiganensis beyond our imagination; this is a dangerous trend and should be closely monitored, especially for ST27 K. michiganensis with the most drug-resistant genes among all the STs. Overall, we reported a bla SIM-1 and mcr-9.2 harboring K. michiganensis strain, and further revealed the population structure/classification, and drug-resistance of K. michiganensis, which provided an important framework, reference, and improved understanding of K. michiganensis.

Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis.

BACKGROUND: Tuberculosis (TB) had been the leading lethal infectious disease worldwide for a long time (2014-2019) until the COVID-19 global pandemic, and it is still one of the top 10 death causes worldwide. One important reason why there are so many TB patients and death cases in the world is because of the difficulties in precise diagnosis of TB using common detection methods, especially for some smear-negative pulmonary tuberculosis (SNPT) cases. The rapid development of metabolome and machine learning offers a great opportunity for precision diagnosis of TB. However, the metabolite biomarkers for the precision diagnosis of smear-positive and smear-negative pulmonary tuberculosis (SPPT/SNPT) remain to be uncovered. In this study, we combined metabolomics and clinical indicators with machine learning to screen out newly diagnostic biomarkers for the precise identification of SPPT and SNPT patients. METHODS: Untargeted plasma metabolomic profiling was performed for 27 SPPT patients, 37 SNPT patients and controls. The orthogonal partial least squares-discriminant analysis (OPLS-DA) was then conducted to screen differential metabolites among the three groups. Metabolite enriched pathways, random forest (RF), support vector machines (SVM) and multilayer perceptron neural network (MLP) were performed using Metaboanalyst 5.0, "caret" R package, "e1071" R package and "Tensorflow" Python package, respectively. RESULTS: Metabolomic analysis revealed significant enrichment of fatty acid and amino acid metabolites in the plasma of SPPT and SNPT patients, where SPPT samples showed a more serious dysfunction in fatty acid and amino acid metabolisms. Further RF analysis revealed four optimized diagnostic biomarker combinations including ten features (two lipid/lipid-like molecules and seven organic acids/derivatives, and one clinical indicator) for the identification of SPPT, SNPT patients and controls with high accuracy (83-93%), which were further verified by SVM and MLP. Among them, MLP displayed the best classification performance on simultaneously precise identification of the three groups (94.74%), suggesting the advantage of MLP over RF/SVM to some extent. CONCLUSIONS: Our findings reveal plasma metabolomic characteristics of SPPT and SNPT patients, provide some novel promising diagnostic markers for precision diagnosis of various types of TB, and show the potential of machine learning in screening out biomarkers from big data.

Genomic Epidemiology of Carbapenemase-producing Klebsiella pneumoniae in China.

The rapid spread of carbapenemase-producing Klebsiella pneumoniae (cpKP) poses serious threats to public health; however, the underlying genetic basis for its dissemination is still unknown. We conducted a comprehensive genomic epidemiology analysis on 420 cpKP isolates collected from 70 hospitals in 24 provinces/autonomous regions/municipalities of China during 2009-2017 by short-/long-read sequencing. The results showed that most cpKP isolates were categorized into clonal group 258 (CG258), in which ST11 was the dominant clone. Phylogenetic analysis revealed three major clades including the top one of Clade 3 for CG258 cpKP isolates. Additionally, carbapenemase gene analysis indicated that blaKPC was dominant in the cpKP isolates, and most blaKPC genes were located in five major incompatibility (Inc) groups of blaKPC-harboring plasmids. Importantly, three advantageous combinations of host-blaKPC-carrying plasmid (Clade 3.1+3.2-IncFIIpHN7A8, Clade 3.1+3.2-IncFIIpHN7A8:IncR, and Clade 3.3-IncFIIpHN7A8:IncpA1763-KPC) were identified to confer cpKP isolates the advantages in both genotypes (strong correlation/coevolution) and phenotypes (resistance/growth/competition) to facilitate the nationwide spread of ST11/CG258 cpKP. Intriguingly, Bayesian skyline analysis illustrated that the three advantageous combinations might be directly associated with the strong population expansion during 2007-2008 and subsequent maintenance of the population of ST11/CG258 cpKP after 2008. We then examined drug resistance profiles of these cpKP isolates and proposed combination treatment regimens for CG258/non-CG258 cpKP infections. Thus, the findings of our systematical analysis shed light on the molecular epidemiology and genetic basis for the dissemination of ST11/CG258 cpKP in China, and much emphasis should be given to the close monitoring of advantageous cpKP-plasmid combinations.

Precision Methylome and In Vivo Methylation Kinetics Characterization of Klebsiella pneumoniae.

Klebsiella pneumoniae (K. pneumoniae) is an important pathogen that can cause severe hospital- and community-acquired infections. To systematically investigate its methylation features, we determined the whole-genome sequences of 14 K. pneumoniae strains covering varying serotypes, multilocus sequence types, clonal groups, viscosity/virulence, and drug resistance. Their methylomes were further characterized using Pacific Biosciences single-molecule real-time and bisulfite technologies. We identified 15 methylation motifs [13 N6-methyladenine (6mA) and two 5-methylcytosine (5mC) motifs], among which eight were novel. Their corresponding DNA methyltransferases were also validated. Additionally, we analyzed the genomic distribution of GATC and CCWGG methylation motifs shared by all strains, and identified differential distribution patterns of some hemi-/un-methylated GATC motifs, which tend to be located within intergenic regions (IGRs). Specifically, we characterized the in vivo methylation kinetics at single-base resolution on a genome-wide scale by simulating the dynamic processes of replication-mediated passive demethylation and MTase-catalyzed re-methylation. The slow methylation of the GATC motifs in the replication origin (oriC) regions and IGRs implicates the epigenetic regulation of replication initiation and transcription. Our findings illustrate the first comprehensive dynamic methylome map of K. pneumoniae at single-base resolution, and provide a useful reference to better understand epigenetic regulation in this and other bacterial species.

DANMEL: A manually curated reference database for analyzing mobile genetic elements associated with bacterial drug resistance.

We have developed a manually curated online reference database, DANMEL (http://124.239.252.254/danmel/), that addresses the lack of accurate dissection and annotation of the genetic structures of mobile genetic elements (MGEs) with genes for drug resistance. DANMEL contains accurately annotated and genetically dissected reference MGEs covering 5 categories and 135 subcategories/subfamilies of MGEs. Further, DANMEL provides a detailed guide on how to precisely annotate MGEs. DANMEL also provides SEARCH/BLAST functions to facilitate finding reference MGEs. Overall, DANMEL will aid researchers to conduct in-depth genetic analysis of sequenced bacterial MGEs with drug-resistance genes and further facilitate a better understanding of bacterial MGEs associated with drug resistance at a genomic level.

Combining comparative genomic analysis with machine learning reveals some promising diagnostic markers to identify five common pathogenic non-tuberculous mycobacteria.

Non-tuberculous mycobacteria (NTM) can cause various respiratory diseases and even death in severe cases, and its incidence has increased rapidly worldwide. To date, it's difficult to use routine diagnostic methods and strain identification to precisely diagnose various types of NTM infections. We combined systematic comparative genomics with machine learning to select new diagnostic markers for precisely identifying five common pathogenic NTMs (Mycobacterium kansasii, Mycobacterium avium, Mycobacterium intracellular, Mycobacterium chelonae, Mycobacterium abscessus). A panel including six genes and two SNPs (nikA, benM, codA, pfkA2, mpr, yjcH, rrl C2638T, rrl A1173G) was selected to simultaneously identify the five NTMs with high accuracy (> 90%). Notably, the panel only containing the six genes also showed a good classification effect (accuracy > 90%). Additionally, the two panels could precisely differentiate the five NTMs from M. tuberculosis (accuracy > 99%). We also revealed some new marker genes/SNPs/combinations to accurately discriminate any one of the five NTMs separately, which provided the possibility to diagnose one certain NTM infection precisely. Our research not only reveals novel promising diagnostic markers to promote the development of precision diagnosis in NTM infectious, but also provides an insight into precisely identifying various genetically close pathogens through comparative genomics and machine learning.

A special prognostic indicator: tumor mutation burden combined with immune infiltrates in lung adenocarcinoma with TP53 mutation.

BACKGROUND: TP53 mutation (TP53 mut) is significantly associated with immunotherapy response in lung adenocarcinoma (LUAD), but not an ideal independent prognostic predictor for it. Here, we investigated a novel potential biomarker and constructed a model for prognostic prediction in LUAD TP53 mut patients. METHODS: 469 LUAD samples retrieved from The Cancer Genome Atlas database were divided into TP53 wt (wild-type TP53) and TP53 mut groups. TMB values were calculated based on the number of variants/exon lengths, and high- and low-TMB groups were divided by the median value. Differentially expressed genes (DEGs) between the two TMB groups were identified using "limma" package, and functional analyses were performed by Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and Gene Set Enrichment Analysis. The infiltration ratio of 22 immune cells were calculated with the CIBERSORT algorithm. Survival analyses were estimated by Kaplan-Meier with the log-rank test. Finally a TMB prognostic index (TMBPI) with receiver operating characteristic (ROC) curve was constructed and calculated to evaluate the predictive value in TP53 mut LUAD. RESULTS: There were diverse mutation types in 100% of TP53 mutants, while mutations were present in 86.5% of cases with TP53 wt. TP53 mut patients had higher TMB levels than TP53 wt patients. Overall survival in TP53 mut patients with low-TMB levels was significantly shorter than that in high-TMB TP53 mut patients. High-TMB patients had higher levels of CD8 T cell and effector B cell, while lower levels of resting memory CD4 T cells, monocytes, activated dendritic cells, etc. than low-TMB patients. Poor survival outcome in TP53 mut patients was correlated with lower effector B cell infiltration and higher activated dendritic cell. Survival risk analyses of 121 DEGs showed that good survival outcomes correlated positively with FBXO36 and KLHL35 expression levels, but correlated negatively with that of LINC0054. TMBPI analysis of the TP53 mut patients showed that high-TMBPI patients had worse survival outcomes than low-TMBPI patients. CONCLUSIONS: Our findings suggest that the TMB value with immune infiltrates is a novel potential biomarker for prognostic prediction of TP53 mut patients. The TMBPI combined with detection of TP53 mutation can be used as a better predictor of prognosis in LUAD.

Hepatic Transcriptome Analysis Revealing the Molecular Pathogenesis of Type 2 Diabetes Mellitus in Zucker Diabetic Fatty Rats.

Around 9% of the adult population in the world (463 million) suffer from diabetes mellitus. Most of them (~90%) belong to type 2 diabetes mellitus (T2DM), which is a common chronic metabolic disorder, and the number of cases has been reported to increase each year. Zucker diabetic fatty (ZDF) rat provides a successful animal model to study the pathogenesis of T2DM. Although previous hepatic transcriptome studies revealed some novel genes associated with the occurrence and development of T2DM, there still lacks the comprehensive transcriptomic analysis for the liver tissues of ZDF rats. We performed comparative transcriptome analyses between the liver tissues of ZDF rats and healthy ZCL rats and also evaluated several clinical indices. We could identify 214 and 104 differentially expressed genes (DEGs) and lncRNAs in ZDF rats, respectively. Pathway and biofunction analyses showed a synergistic effect between mRNAs and lncRNAs. By comprehensively analyzing transcriptomic data and clinical indices, we detected some typical features of T2DM in ZDF rats, such as upregulated metabolism (significant increased lipid absorption/transport/utilization, gluconeogenesis, and protein hydrolysis), increased inflammation, liver injury and increased endoplasmic reticulum (ER) stress. In addition, of the 214 DEGs, 114 were known and 100 were putative T2DM-related genes, most of which have been associated with substance metabolism (particularly degradation), inflammation, liver injury and ER stress biofunctions. Our study provides an important reference and improves understanding of molecular pathogenesis of obesity-associated T2DM. Our data can also be used to identify potential diagnostic markers and therapeutic targets, which should strengthen the prevention and treatment of T2DM.