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.

Emergence of a Multidrug-Resistant Hypervirulent Klebsiella pneumoniae Sequence Type 23 Strain with a Rare blaCTX-M-24-Harboring Virulence Plasmid.

Here, we report a multidrug-resistant hypervirulent Klebsiella pneumoniae (MDR-HvKP) strain of sequence type 23 (ST23) with a rare hybrid plasmid harboring virulence genes and blaCTX-M-24, and we analyze the genetic basis for relationship between genotypes and MDR-hypervirulence phenotypes. Further analysis indicates that the hybrid plasmid is formed by IS903D-mediated intermolecular transposition of the blaCTX-M-24 gene into the virulence plasmid. The emergence of MDR-HvKP strains, especially those carrying drug-resistant virulent plasmids, poses unprecedented threats/challenges to public health. This is a dangerous trend and should be closely monitored.

Circular RNA profiling provides insights into their subcellular distribution and molecular characteristics in HepG2 cells.

Circular RNA (circRNA) is a novel RNA molecule that has become a research focus recently. Although some research indicated that the circRNAs in different subcellular compartments could execute different regulatory functions, a panoramic analysis of the subcellular distribution and the transport mechanism of circRNA is still required. In this study, we comprehensively analyzed the subcellular distribution/characteristics and the transport mechanism, through systemically investigating the circRNA profiles among the subcellular fractions of HepG2 cell (nucleus, cytoplasm, mitochondria, ribosome, cytosol and exosome). CircRNAs were widely distributed among the subcellular fractions except in the mitochondria, with differences in the subcellular distribution/characteristics in terms of classification, length, GC content, alternative circularization and parental gene function. Further analysis indicated this might be due to the selective transportation mediated by the transport-related RNA binding proteins (RBPs). The circRNAs may follow the same transportation mechanism of linear RNAs, in which the RBPs specially recognize/transport the RNAs with the corresponding binding motifs. Interestingly, we found that the exosome could selectively package the circRNAs containing the purine-rich 5'-GMWGVWGRAG-3' motif, with the characteristic of 'garbage dumping' and 'intercellular signaling' functions. Besides, although we observed numerous circRNAs enriched in the ribosome, we did not reliably identify any unique-peptides from circRNAs using 3D-LC-MS/MS strategy. This suggests that circRNAs rarely function as translation templates in vivo like lincRNA. Our findings not only indicates the differential distributions/characteristics among the subcellular fractions, but also reveals the possible transportation mechanism. This provides an improved understanding of the life history and molecular behavior of circRNA in cells.

Precision methylome characterization of Mycobacterium tuberculosis complex (MTBC) using PacBio single-molecule real-time (SMRT) technology.

Tuberculosis (TB) remains one of the most common infectious diseases caused by Mycobacterium tuberculosis complex (MTBC). To panoramically analyze MTBC's genomic methylation, we completed the genomes of 12 MTBC strains (Mycobacterium bovis; M. bovis BCG; M. microti; M. africanum; M. tuberculosis H37Rv; H37Ra; and 6 M. tuberculosis clinical isolates) belonging to different lineages and characterized their methylomes using single-molecule real-time (SMRT) technology. We identified three (m6)A sequence motifs and their corresponding methyltransferase (MTase) genes, including the reported mamA, hsdM and a newly discovered mamB. We also experimentally verified the methylated motifs and functions of HsdM and MamB. Our analysis indicated the MTase activities varied between 12 strains due to mutations/deletions. Furthermore, through measuring 'the methylated-motif-site ratio' and 'the methylated-read ratio', we explored the methylation status of each modified site and sequence-read to obtain the 'precision methylome' of the MTBC strains, which enabled intricate analysis of MTase activity at whole-genome scale. Most unmodified sites overlapped with transcription-factor binding-regions, which might protect these sites from methylation. Overall, our findings show enormous potential for the SMRT platform to investigate the precise character of methylome, and significantly enhance our understanding of the function of DNA MTase.

Gender-Driven English Speech Emotion Recognition with Genetic Algorithm.

Speech emotion recognition based on gender holds great importance for achieving more accurate, personalized, and empathetic interactions in technology, healthcare, psychology, and social sciences. In this paper, we present a novel gender-emotion model. First, gender and emotion features were extracted from voice signals to lay the foundation for our recognition model. Second, a genetic algorithm (GA) processed high-dimensional features, and the Fisher score was used for evaluation. Third, features were ranked by their importance, and the GA was improved through novel crossover and mutation methods based on feature importance, to improve the recognition accuracy. Finally, the proposed algorithm was compared with state-of-the-art algorithms on four common English datasets using support vector machines (SVM), and it demonstrated superior performance in accuracy, precision, recall, F1-score, the number of selected features, and running time. The proposed algorithm faced challenges in distinguishing between neutral, sad, and fearful emotions, due to subtle vocal differences, overlapping pitch and tone variability, and similar prosodic features. Notably, the primary features for gender-based differentiation mainly involved mel frequency cepstral coefficients (MFCC) and log MFCC.

Advanced differential evolution for gender-aware English speech emotion recognition.

Speech emotion recognition (SER) technology involves feature extraction and prediction models. However, recognition efficiency tends to decrease because of gender differences and the large number of extracted features. Consequently, this paper introduces a SER system based on gender. First, gender and emotion features are extracted from speech signals to develop gender recognition and emotion classification models. Second, according to gender differences, distinct emotion recognition models are established for male and female speakers. The gender of speakers is determined before executing the corresponding emotion model. Third, the accuracy of these emotion models is enhanced by utilizing an advanced differential evolution algorithm (ADE) to select optimal features. ADE incorporates new difference vectors, mutation operators, and position learning, which effectively balance global and local searches. A new position repairing method is proposed to address gender differences. Finally, experiments on four English datasets demonstrate that ADE is superior to comparison algorithms in recognition accuracy, recall, precision, F1-score, the number of used features and execution time. The findings highlight the significance of gender in refining emotion models, while mel-frequency cepstral coefficients are important factors in gender differences.

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.

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.

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.

[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.

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.

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.

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.

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.

Altered gut microbiota in Parkinson's disease patients/healthy spouses and its association with clinical features.

INTRODUCTION: Increasing evidence shows that gut microbiota dysbiosis may play important roles in the occurrence and progression of Parkinson's disease (PD), but the findings are inconsistent. Besides, the effect of family environment on gut microbiota dysbiosis remains unclear. METHODS: We characterized the gut microbial compositions of 63 PD patients, 63 healthy spouses (HS) and 74 healthy people (HP) using 16S rRNA sequencing. Clinical phenotypes and microbial composition were analyzed comprehensively. RESULTS: There were markedly different microbial compositions among PD, HS and HP samples by alpha/beta diversity. We also found differential microbial compositions among Hoehn & Yahr stage/disease duration. Eight inflammation-associated microbial genera shared a continuously increase trend with increased Hoehn & Yahr stage and disease duration, indicating characteristic bacteria associated with deterioration in PD. Additionally, seven bacterial markers were identified for accurately differentiating PD patients from the controls (area under the curve [AUC]: 0.856). CONCLUSIONS: Our study shows altered gut microbiota in PD patients. Importantly, inflammation-associated microbial genera may play roles in PD progression. Differential microbial compositions in HS and HP samples demonstrate that the gut microbiota are also affected by family environment. Disease-associated metagenomics studies should consider the family environmental factor. Our research provides an important reference and improves the understanding of gut microbiota in PD patients.

A rare carbapenem-resistant hypervirulent K1/ST1265 Klebsiella pneumoniae with an untypeable blaKPC-harboured conjugative plasmid.

OBJECTIVE: We investigated the pathogenesis of a patient with severe acute pancreatitis by comprehensively analysing a rare carbapenem-resistant hypervirulent K1/ST1265 Klebsiella pneumoniae (CR-HvKP) strain. METHODS: We conducted virulence and multidrug-resistance phenotypic characterization and identified a CR-HvKP strain from the patient. It was subjected to Pacbio sequencing, and subsequent analysis of virulence, resistance genes and mobile genetic elements. RESULTS: We described the phenotype and genotype of a rare CR-HvKP strain with an untypeable blaKPC-harboured conjugative plasmid and a pLVPK-like virulent plasmid. Resistance gene analysis showed that the untypeable blaKPC-2-harboured plasmid was formed by IS26-mediated recombination of blaKPC-embedded transposon Tn6500 into pCN061p4 from Escherichia coli. Interestingly, it had an R-M system that might protect plasmid from cleavage. This may facilitate the stabilization of plasmids in bacteria in the event of missing CR-plasmid during transmission. Virulence gene analysis indicated 78 virulence genes on the genome, including 67 on the chromosome (37 in high-pathogenic island) and 11 on the pLVPK-like virulence plasmid (harbouring rmpA/rmpA2). Further phylogenetic analysis revealed that the CR-HvKP evolved from HvKP through acquiring an antimicrobial-resistance plasmid. CONCLUSION: Our research, to our knowledge, first reported a ST1265/K1 CR-HvKP strain with an untypeable blaKPC-harboured plasmid. The trend that HvKP could evolve into CR-HvKP by obtaining stabilized/conjugative blaKPC-carrying plasmids is a considerable threat to public health and should be closely supervised.

Small RNA Profiles of Serum Exosomes Derived From Individuals With Latent and Active Tuberculosis.

Tuberculosis (TB) has been the leading lethal infectious disease worldwide since 2014, and about one third of the world's population has a latent TB infection (LTBI). This is largely attributed to the difficulties in diagnosis and treatment of TB and LTBI patients. Exosomes offer a new perspective on investigation of the process of TB infection. In this study, we performed small RNA sequencing to explore small RNA profiles of serum exosomes derived from LTBI and TB patients and healthy controls (HC). Our results revealed distinct miRNA profile of the exosomes in the three groups. We screened 250 differentially expressed miRNAs including 130 specifically expressed miRNAs. Some miRNAs were further validated to be specifically expressed in LTBI (hsa-let-7e-5p, hsa-let-7d-5p, hsa-miR-450a-5p, and hsa-miR-140-5p) and TB samples (hsa-miR-1246, hsa-miR-2110, hsa-miR-370-3P, hsa-miR-28-3p, and hsa-miR-193b-5p). Additionally, we demonstrated four expression panels in LTBI and TB groups, and six expression patterns among the three groups. These specifically expressed miRNAs and differentially expressed miRNAs in different panels and patterns provide potential biomarkers for detection/diagnosis of latent and active TB using exosomal miRNAs. Additionally, we also discovered plenty of small RNAs derived from genomic repetitive sequences, which might play roles in host immune responses along with Mtb infection progresses. Overall, our findings provide important reference and an improved understanding about miRNAs and repetitive region-derived small RNAs in exosomes during the Mtb infectious process, and facilitate the development of potential molecular targets for detection/diagnosis of latent and active tuberculosis.