Antibiotic class with potent in vivo activity targeting lipopolysaccharide synthesis in Gram-negative bacteria.

Here, we describe the identification of an antibiotic class acting via LpxH, a clinically unexploited target in lipopolysaccharide synthesis. The lipopolysaccharide synthesis pathway is essential in most Gram-negative bacteria and there is no analogous pathway in humans. Based on a series of phenotypic screens, we identified a hit targeting this pathway that had activity on efflux-defective strains of Escherichia coli. We recognized common structural elements between this hit and a previously published inhibitor, also with activity against efflux-deficient bacteria. With the help of X-ray structures, this information was used to design inhibitors with activity on efflux-proficient, wild-type strains. Optimization of properties such as solubility, metabolic stability and serum protein binding resulted in compounds having potent in vivo efficacy against bloodstream infections caused by the critical Gram-negative pathogens E. coli and Klebsiella pneumoniae. Other favorable properties of the series include a lack of pre-existing resistance in clinical isolates, and no loss of activity against strains expressing extended-spectrum-ß-lactamase, metallo-ß-lactamase, or carbapenemase-resistance genes. Further development of this class of antibiotics could make an important contribution to the ongoing struggle against antibiotic resistance.

FLUXestimator: a webserver for predicting metabolic flux and variations using transcriptomics data.

Quantitative assessment of single cell fluxome is critical for understanding the metabolic heterogeneity in diseases. Unfortunately, laboratory-based single cell fluxomics is currently impractical, and the current computational tools for flux estimation are not designed for single cell-level prediction. Given the well-established link between transcriptomic and metabolomic profiles, leveraging single cell transcriptomics data to predict single cell fluxome is not only feasible but also an urgent task. In this study, we present FLUXestimator, an online platform for predicting metabolic fluxome and variations using single cell or general transcriptomics data of large sample-size. The FLUXestimator webserver implements a recently developed unsupervised approach called single cell flux estimation analysis (scFEA), which uses a new neural network architecture to estimate reaction rates from transcriptomics data. To the best of our knowledge, FLUXestimator is the first web-based tool dedicated to predicting cell-/sample-wise metabolic flux and metabolite variations using transcriptomics data of human, mouse and 15 other common experimental organisms. The FLUXestimator webserver is available at http://scFLUX.org/, and stand-alone tools for local use are available at https://github.com/changwn/scFEA. Our tool provides a new avenue for studying metabolic heterogeneity in diseases and has the potential to facilitate the development of new therapeutic strategies.

A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data.

The metabolic heterogeneity and metabolic interplay between cells are known as significant contributors to disease treatment resistance. However, with the lack of a mature high-throughput single-cell metabolomics technology, we are yet to establish systematic understanding of the intra-tissue metabolic heterogeneity and cooperative mechanisms. To mitigate this knowledge gap, we developed a novel computational method, namely, single-cell flux estimation analysis (scFEA), to infer the cell-wise fluxome from single-cell RNA-sequencing (scRNA-seq) data. scFEA is empowered by a systematically reconstructed human metabolic map as a factor graph, a novel probabilistic model to leverage the flux balance constraints on scRNA-seq data, and a novel graph neural network-based optimization solver. The intricate information cascade from transcriptome to metabolome was captured using multilayer neural networks to capitulate the nonlinear dependency between enzymatic gene expressions and reaction rates. We experimentally validated scFEA by generating an scRNA-seq data set with matched metabolomics data on cells of perturbed oxygen and genetic conditions. Application of scFEA on this data set showed the consistency between predicted flux and the observed variation of metabolite abundance in the matched metabolomics data. We also applied scFEA on five publicly available scRNA-seq and spatial transcriptomics data sets and identified context- and cell group-specific metabolic variations. The cell-wise fluxome predicted by scFEA empowers a series of downstream analyses including identification of metabolic modules or cell groups that share common metabolic variations, sensitivity evaluation of enzymes with regards to their impact on the whole metabolic flux, and inference of cell-tissue and cell-cell metabolic communications.

A multisite study to develop and validate first trimester, circulating microparticle biomarkers for tiered risk stratification of spontaneous preterm birth in nulliparas.

BACKGROUND: Despite much research, advances in early prediction of spontaneous preterm birth (sPTB) has been slow. The evolving field of circulating microparticle (CMP) biology may identify novel blood-based, and clinically useful, biomarkers. OBJECTIVE: To test the ability of a previously identified, 7-marker set of CMP-derived proteins from the first trimester of pregnancy, in the form of an in vitro diagnostic multivariate index assay (IVDMIA), to stratify pregnant patients according to their risk for sPTB. STUDY DESIGN: We employed a previously validated set of CMP protein biomarkers, utilizing mass spectrometry assays and a nested case-control design in a subset of participants from the Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be (nuMoM2b). We evaluated these biomarkers in the form of an IVDMIA to predict risk for sPTB at different gestational ages. Plasma samples collected at 9- to 13-weeks' gestation were analyzed. The IVDMIA assigned subjects to 1 of 3 sPTB risk categories: low risk (LR), moderate risk (MR), or high risk (HR). Independent validation on a set-aside set confirmed the IVDMIA's performance in risk stratification. RESULTS: Samples from 400 participants from the nuMoM2b cohort were used for the study; of these, 160 delivered<37 weeks and 240 delivered at term. Through Monte Carlo simulation in which the validation results were adjusted based on actual weekly sPTB incidence rates in the nuMoM2b cohort, the IVDMIA stratifications demonstrated statistically significant differences among the risk groups in time-to-event (birth) analysis (P<.0001). The incidence-rate adjusted cumulative risks of sPTB at ≤32 weeks' gestation were 0.4%, 1.6%, and 7.5%, respectively for the LR, MR, and HR groups, respectively. Compared to the LR group, the corresponding risk ratios of the IVDMIA assigned MR and HR group were 4.25 (95% confidence interval [CI] 2.2-7.9) and 19.92 (95% CI 10.4-37.4), respectively. CONCLUSION: A first trimester CMP protein biomarker panel can be used to stratify risk for sPTB at different gestational ages. Such a multitiered stratification tool could be used to assess risk early in pregnancy to enable timely clinical management and interventions, and, ultimately, to enable the development of tailored care pathways for sPTB prevention.

Positive Selection during Niche Adaptation Results in Large-Scale and Irreversible Rearrangement of Chromosomal Gene Order in Bacteria.

Analysis of bacterial genomes shows that, whereas diverse species share many genes in common, their linear order on the chromosome is often not conserved. Whereas rearrangements in gene order could occur by genetic drift, an alternative hypothesis is rearrangement driven by positive selection during niche adaptation (SNAP). Here, we provide the first experimental support for the SNAP hypothesis. We evolved Salmonella to adapt to growth on malate as the sole carbon source and followed the evolutionary trajectories. The initial adaptation to growth in the new environment involved the duplication of 1.66 Mb, corresponding to one-third of the Salmonella chromosome. This duplication is selected to increase the copy number of a single gene, dctA, involved in the uptake of malate. Continuing selection led to the rapid loss or mutation of duplicate genes from either copy of the duplicated region. After 2000 generations, only 31% of the originally duplicated genes remained intact and the gene order within the Salmonella chromosome has been significantly and irreversibly altered. These results experientially validate predictions made by the SNAP hypothesis and show that SNAP can be a strong driving force for rearrangements in chromosomal gene order.

Supervised clustering of high-dimensional data using regularized mixture modeling.

Identifying relationships between genetic variations and their clinical presentations has been challenged by the heterogeneous causes of a disease. It is imperative to unveil the relationship between the high-dimensional genetic manifestations and the clinical presentations, while taking into account the possible heterogeneity of the study subjects.We proposed a novel supervised clustering algorithm using penalized mixture regression model, called component-wise sparse mixture regression (CSMR), to deal with the challenges in studying the heterogeneous relationships between high-dimensional genetic features and a phenotype. The algorithm was adapted from the classification expectation maximization algorithm, which offers a novel supervised solution to the clustering problem, with substantial improvement on both the computational efficiency and biological interpretability. Experimental evaluation on simulated benchmark datasets demonstrated that the CSMR can accurately identify the subspaces on which subset of features are explanatory to the response variables, and it outperformed the baseline methods. Application of CSMR on a drug sensitivity dataset again demonstrated the superior performance of CSMR over the others, where CSMR is powerful in recapitulating the distinct subgroups hidden in the pool of cell lines with regards to their coping mechanisms to different drugs. CSMR represents a big data analysis tool with the potential to resolve the complexity of translating the clinical representations of the disease to the real causes underpinning it. We believe that it will bring new understanding to the molecular basis of a disease and could be of special relevance in the growing field of personalized medicine.

SSMD: a semi-supervised approach for a robust cell type identification and deconvolution of mouse transcriptomics data.

Deconvolution of mouse transcriptomic data is challenged by the fact that mouse models carry various genetic and physiological perturbations, making it questionable to assume fixed cell types and cell type marker genes for different data set scenarios. We developed a Semi-Supervised Mouse data Deconvolution (SSMD) method to study the mouse tissue microenvironment. SSMD is featured by (i) a novel nonparametric method to discover data set-specific cell type signature genes; (ii) a community detection approach for fixing cell types and their marker genes; (iii) a constrained matrix decomposition method to solve cell type relative proportions that is robust to diverse experimental platforms. In summary, SSMD addressed several key challenges in the deconvolution of mouse tissue data, including: (i) varied cell types and marker genes caused by highly divergent genotypic and phenotypic conditions of mouse experiment; (ii) diverse experimental platforms of mouse transcriptomics data; (iii) small sample size and limited training data source and (iv) capable to estimate the proportion of 35 cell types in blood, inflammatory, central nervous or hematopoietic systems. In silico and experimental validation of SSMD demonstrated its high sensitivity and accuracy in identifying (sub) cell types and predicting cell proportions comparing with state-of-the-arts methods. A user-friendly R package and a web server of SSMD are released via https://github.com/xiaoyulu95/SSMD.

ADAM8 is expressed widely in breast cancer and predicts poor outcome in hormone receptor positive, HER-2 negative patients.

BACKGROUND: Breast malignancies are the predominant cancer-related cause of death in women. New methods of diagnosis, prognosis and treatment are necessary. Previously, we identified the breast cancer cell surface protein ADAM8 as a marker of poor survival, and a driver of Triple-Negative Breast Cancer (TNBC) growth and spread. Immunohistochemistry (IHC) with a research-only anti-ADAM8 antibody revealed 34.0% of TNBCs (17/50) expressed ADAM8. To identify those patients who could benefit from future ADAM8-based interventions, new clinical tests are needed. Here, we report on the preclinical development of a highly specific IHC assay for detection of ADAM8-positive breast tumors. METHODS: Formalin-fixed paraffin-embedded sections of ADAM8-positive breast cell lines and patient-derived xenograft tumors were used in IHC to identify a lead antibody, appropriate staining conditions and controls. Patient breast cancer samples (n = 490) were used to validate the assay. Cox proportional hazards models assessed association between survival and ADAM8 expression. RESULTS: ADAM8 staining conditions were optimized, a lead anti-human ADAM8 monoclonal IHC antibody (ADP2) identified, and a breast staining/scoring control cell line microarray (CCM) generated expressing a range of ADAM8 levels. Assay specificity, reproducibility, and appropriateness of the CCM for scoring tumor samples were demonstrated. Consistent with earlier findings, 36.1% (22/61) of patient TNBCs expressed ADAM8. Overall, 33.9% (166/490) of the breast cancer population was ADAM8-positive, including Hormone Receptor (HR) and Human Epidermal Growth Factor Receptor-2 (HER2) positive cancers, which were tested for the first time. For the most prevalent HR-positive/HER2-negative subtype, high ADAM8 expression identified patients at risk of poor survival. CONCLUSIONS: Our studies show ADAM8 is widely expressed in breast cancer and provide support for both a diagnostic and prognostic value of the ADP2 IHC assay. As ADAM8 has been implicated in multiple solid malignancies, continued development of this assay may have broad impact on cancer management.

PLUS: Predicting cancer metastasis potential based on positive and unlabeled learning.

Metastatic cancer accounts for over 90% of all cancer deaths, and evaluations of metastasis potential are vital for minimizing the metastasis-associated mortality and achieving optimal clinical decision-making. Computational assessment of metastasis potential based on large-scale transcriptomic cancer data is challenging because metastasis events are not always clinically detectable. The under-diagnosis of metastasis events results in biased classification labels, and classification tools using biased labels may lead to inaccurate estimations of metastasis potential. This issue is further complicated by the unknown metastasis prevalence at the population level, the small number of confirmed metastasis cases, and the high dimensionality of the candidate molecular features. Our proposed algorithm, called Positive and unlabeled Learning from Unbalanced cases and Sparse structures (PLUS), is the first to use a positive and unlabeled learning framework to account for the under-detection of metastasis events in building a classifier. PLUS is specifically tailored for studying metastasis that deals with the unbalanced instance allocation as well as unknown metastasis prevalence, which are not considered by other methods. PLUS achieves superior performance on synthetic datasets compared with other state-of-the-art methods. Application of PLUS to The Cancer Genome Atlas Pan-Cancer gene expression data generated metastasis potential predictions that show good agreement with the clinical follow-up data, in addition to predictive genes that have been validated by independent single-cell RNA-sequencing datasets.

A Lesson of Immunosuppression in Renal Transplant: Retreat or Hold?

Background: Aiming at understanding whether there are cases of near-tolerance among long-term surviving kidney transplant recipients in our center, or even operant tolerance can be attempted based on their immune status, we analyzed changes of immune cell subsets and cytokines in various groups, and evaluated immune status of long-term survival recipients. Methods: A real-world, observational, retrospective cohort study was conducted in our hospital. Twenty-eight long-term recipients were selected as study subjects, 15 recent postoperative stable recipients, and 15 healthy subjects as controls. T and B lymphocyte subsets, MDSCs, and cytokines were detected and analyzed. Results: Treg/CD4 T cells, total B and B10 cells in long-term and recent renal recipients were lower than healthy controls (HC). The level of IFN-γ and IL-17A in long-term survival patients was obviously higher than that in recent postoperative stable recipients and HC, while TGF-ß1 level was significantly lower in long-term survival group than in short-term postoperative group and HC. Notably, compared with short-term recipients, it has been found that the IL-6 level in both positive and negative HLA groups were obviously lower (all P<0.05). In the long-term survival group, 43% of recipients were positive for urinary protein and 50% were positive for HLA antibody. Conclusion: This "real-world" study validates the findings of real status of long-term survival recipients observed in clinical trials. Contrary to a state of proper tolerance as expected, the group recipients in long-term survival were accompanied by the increased indicators of immune response, while those related to immune tolerance were not significantly increased. Long-term survival recipients with stable renal function may be in an immune equilibrium state where immunosuppression and rejection coexist under the action of low-intensity immune agents. If immunosuppressive agents are reduced or even removed, rejection may occur.

SCI: A Bayesian adaptive phase I/II dose-finding design accounting for semi-competing risks outcomes for immunotherapy trials.

An immunotherapy trial often uses the phase I/II design to identify the optimal biological dose, which monitors the efficacy and toxicity outcomes simultaneously in a single trial. The progression-free survival rate is often used as the efficacy outcome in phase I/II immunotherapy trials. As a result, patients developing disease progression in phase I/II immunotherapy trials are generally seriously ill and are often treated off the trial for ethical consideration. Consequently, the happening of disease progression will terminate the toxicity event but not vice versa, so the issue of the semi-competing risks arises. Moreover, this issue can become more intractable with the late-onset outcomes, which happens when a relatively long follow-up time is required to ascertain progression-free survival. This paper proposes a novel Bayesian adaptive phase I/II design accounting for semi-competing risks outcomes for immunotherapy trials, referred to as the dose-finding design accounting for semi-competing risks outcomes for immunotherapy trials (SCI) design. To tackle the issue of the semi-competing risks in the presence of late-onset outcomes, we re-construct the likelihood function based on each patient's actual follow-up time and develop a data augmentation method to efficiently draw posterior samples from a series of Beta-binomial distributions. We propose a concise curve-free dose-finding algorithm to adaptively identify the optimal biological dose using accumulated data without making any parametric dose-response assumptions. Numerical studies show that the proposed SCI design yields good operating characteristics in dose selection, patient allocation, and trial duration.

Mutant RNA polymerase can reduce susceptibility to antibiotics via ppGpp-independent induction of a stringent-like response.

BACKGROUND: Mutations in RNA polymerase (RNAP) can reduce susceptibility to ciprofloxacin in Escherichia coli, but the mechanism of transcriptional reprogramming responsible is unknown. Strains carrying ciprofloxacin-resistant (CipR) rpoB mutations have reduced growth fitness and their impact on clinical resistance development is unclear. OBJECTIVES: To assess the potential for CipRrpoB mutations to contribute to resistance development by estimating the number of distinct alleles. To identify fitness-compensatory mutations that ameliorate the fitness costs of CipRrpoB mutations. To understand how CipRrpoB mutations reprogramme RNAP. METHODS: E. coli strains carrying five different CipRrpoB alleles were evolved with selection for improved fitness and characterized for acquired mutations, relative fitness and MICCip. The effects of dksA mutations and a ppGpp0 background on growth and susceptibility phenotypes associated with CipRrpoB alleles were determined. RESULTS: The number of distinct CipRrpoB mutations was estimated to be >100. Mutations in RNAP genes and in dksA can compensate for the fitness cost of CipRrpoB mutations. Deletion of dksA reduced the MICCip for strains carrying CipRrpoB alleles. A ppGpp0 phenotype had no effect on drug susceptibility. CONCLUSIONS: CipRrpoB mutations induce an ppGpp-independent stringent-like response. Approximately half of the reduction in ciprofloxacin susceptibility is caused by an increased affinity of RNAP to DksA while the other half is independent of DksA. Stringent-like response activating mutations might be the most diverse class of mutations reducing susceptibility to antibiotics.

QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data.

MOTIVATION: The biclustering of large-scale gene expression data holds promising potential for detecting condition-specific functional gene modules (i.e. biclusters). However, existing methods do not adequately address a comprehensive detection of all significant bicluster structures and have limited power when applied to expression data generated by RNA-Sequencing (RNA-Seq), especially single-cell RNA-Seq (scRNA-Seq) data, where massive zero and low expression values are observed. RESULTS: We present a new biclustering algorithm, QUalitative BIClustering algorithm Version 2 (QUBIC2), which is empowered by: (i) a novel left-truncated mixture of Gaussian model for an accurate assessment of multimodality in zero-enriched expression data, (ii) a fast and efficient dropouts-saving expansion strategy for functional gene modules optimization using information divergency and (iii) a rigorous statistical test for the significance of all the identified biclusters in any organism, including those without substantial functional annotations. QUBIC2 demonstrated considerably improved performance in detecting biclusters compared to other five widely used algorithms on various benchmark datasets from E.coli, Human and simulated data. QUBIC2 also showcased robust and superior performance on gene expression data generated by microarray, bulk RNA-Seq and scRNA-Seq. AVAILABILITY AND IMPLEMENTATION: The source code of QUBIC2 is freely available at https://github.com/OSU-BMBL/QUBIC2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Bayesian adaptive phase I/II clinical trial design with late-onset competing risk outcomes.

Early-phase dose-finding clinical trials are often subject to the issue of late-onset outcomes. In phase I/II clinical trials, the issue becomes more intractable because toxicity and efficacy can be competing risk outcomes such that the occurrence of the first outcome will terminate the other one. In this paper, we propose a novel Bayesian adaptive phase I/II clinical trial design to address the issue of late-onset competing risk outcomes. We use the continuation-ratio model to characterize the trinomial response outcomes and the cause-specific hazard rate method to model the competing-risk survival outcomes. We treat the late-onset outcomes as missing data and develop a Bayesian data augmentation method to impute the missing data from the observations. We also propose an adaptive dose-finding algorithm to allocate patients and identify the optimal biological dose during the trial. Simulation studies show that the proposed design yields desirable operating characteristics.

LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data.

A key challenge in modeling single-cell RNA-seq data is to capture the diversity of gene expression states regulated by different transcriptional regulatory inputs across individual cells, which is further complicated by largely observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model, from the kinetic relationships of the transcriptional regulatory inputs, mRNA metabolism and abundance in single cells. LTMG infers the expression multi-modalities across single cells, meanwhile, the dropouts and low expressions are treated as left truncated. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of scRNA-seq data, comparing to three other state-of-the-art models. Our biological assumption of the low non-zero expressions, rationality of the multimodality setting, and the capability of LTMG in extracting expression states specific to cell types or functions, are validated on independent experimental data sets. A differential gene expression test and a co-regulation module identification method are further developed. We experimentally validated that our differential expression test has higher sensitivity and specificity, compared with other five popular methods. The co-regulation analysis is capable of retrieving gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.

TSNP: A two-stage nonparametric phase I/II clinical trial design for immunotherapy.

We develop a transparent and efficient two-stage nonparametric (TSNP) phase I/II clinical trial design to identify the optimal biological dose (OBD) of immunotherapy. We propose a nonparametric approach to derive the closed-form estimates of the joint toxicity-efficacy response probabilities under the monotonic increasing constraint for the toxicity outcomes. These estimates are then used to measure the immunotherapy's toxicity-efficacy profiles at each dose and guide the dose finding. The first stage of the design aims to explore the toxicity profile. The second stage aims to find the OBD, which can achieve the optimal therapeutic effect by considering both the toxicity and efficacy outcomes through a utility function. The closed-form estimates and concise dose-finding algorithm make the TSNP design appealing in practice. The simulation results show that the TSNP design yields superior operating characteristics than the existing Bayesian parametric designs. User-friendly computational software is freely available to facilitate the application of the proposed design to real trials. We provide comprehensive illustrations and examples about implementing the proposed design with associated software.

Operon Concatenation Is an Ancient Feature That Restricts the Potential to Rearrange Bacterial Chromosomes.

The last common ancestor of the Gammaproteobacteria carried an important 40-kb chromosome section encoding 51 proteins of the transcriptional and translational machinery. These genes were organized into eight contiguous operons (rrnB-tufB-secE-rpoBC-str-S10-spc-alpha). Over 2 Gy of evolution, in different lineages, some of the operons became separated by multigene insertions. Surprisingly, in many Enterobacteriaceae, much of the ancient organization is conserved, indicating a strong selective force on the operons to remain colinear. Here, we show for one operon pair, tufB-secE in Salmonella, that an interruption of contiguity significantly reduces growth rate. Our data show that the tufB-secE operons are concatenated by an interoperon terminator-promoter overlap that plays a significant role regulating gene expression. Interrupting operon contiguity interferes with this regulation, reducing cellular fitness. Six operons of the ancestral chromosome section remain contiguous in Salmonella (tufB-secE-rpoBC and S10-spc-alpha) and, strikingly, each of these operon pairs is also connected by an interoperon terminator-promoter overlap. Accordingly, we propose that operon concatenation is an ancient feature that restricts the potential to rearrange bacterial chromosomes and can select for the maintenance of a colinear operon organization over billions of years.

Structure-activity relationship studies on the inhibition of the bacterial translation of novel Odilorhabdins analogues.

A structure-activity relationship (SAR) study of NOSO-95179, a nonapeptide from the Odilorhabdin class of antibacterials, was performed by systematic variations of amino acids in positions 2 and 5 of the peptide. A series of non-proteinogenic amino acids was synthesized in high enantiomeric purity from Williams' chiral diphenyloxazinone by highly diastereoselective alkylation or by aldol-type reaction. NOSO-95179 analogues for SAR studies were prepared using solid-phase peptide synthesis. Inhibition of bacterial translation by each of the synthesized Odilorhabdin analogues was measured using an in vitro test. For the most efficient analogues, antibacterial efficacy was measured against two wild-type Enterobacteriaceae (Escherichia coli and Klebsiella pneumoniae) and against an efflux defective E. coli strain (ΔtolC) to evaluate the impact of efflux on the antibacterial activity.

M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data.

BACKGROUND: Various statistical models have been developed to model the single cell RNA-seq expression profiles, capture its multimodality, and conduct differential gene expression test. However, for expression data generated by different experimental design and platforms, there is currently lack of capability to determine the most proper statistical model. RESULTS: We developed an R package, namely Multi-Modal Model Selection (M3S), for gene-wise selection of the most proper multi-modality statistical model and downstream analysis, useful in a single-cell or large scale bulk tissue transcriptomic data. M3S is featured with (1) gene-wise selection of the most parsimonious model among 11 most commonly utilized ones, that can best fit the expression distribution of the gene, (2) parameter estimation of a selected model, and (3) differential gene expression test based on the selected model. CONCLUSION: A comprehensive evaluation suggested that M3S can accurately capture the multimodality on simulated and real single cell data. An open source package and is available through GitHub at https://github.com/zy26/M3S.

Co-evolution with recombination affects the stability of mobile genetic element insertions within gene families of Salmonella.

Bacteria can have multiple copies of a gene at separate locations on the same chromosome. Some of these gene families, including tuf (translation elongation factor EF-Tu) and rrl (ribosomal RNA), encode functions critically important for bacterial fitness. Genes within these families are known to evolve in concert using homologous recombination to transfer genetic information from one gene to another. This mechanism can counteract the detrimental effects of nucleotide sequence divergence over time. Whether such mechanisms can also protect against the potentially lethal effects of mobile genetic element insertion is not well understood. To address this we constructed two different length insertion cassettes to mimic mobile genetic elements and inserted these into various positions of the tuf and rrl genes. We measured rates of recombinational repair that removed the inserted cassette and studied the underlying mechanism. Our results indicate that homologous recombination can protect the tuf and rrl genes from inactivation by mobile genetic elements, but for insertions within shorter gene sequences the efficiency of repair is very low. Intriguingly, we found that physical distance separating genes on the chromosome directly affects the rate of recombinational repair suggesting that relative location will influence the ability of homologous recombination to maintain homogeneity.