The heterogeneous genetic architectures of orofacial clefts.

Orofacial clefts (OFCs) are common, affecting 1:1000 live births. OFCs occur across a phenotypic spectrum - including cleft lip (CL), cleft lip and palate (CLP), or cleft palate (CP) - and can be further subdivided based on laterality, severity, or specific structures affected. Herein we review what is known about the genetic architecture underlying each of these subtypes, considering both shared and subtype-specific risks. While there are more known genetic similarities between CL and CLP than CP, recent research supports both shared and subtype-specific genetic risk factors within and between phenotypic classifications of OFCs. Larger sample sizes and deeper phenotyping data will be of increasing importance for the discovery of novel genetic risk factors for OFCs and various subtypes going forward.

Interpretation of 10 years of Alzheimer's disease genetic findings in the perspective of statistical heterogeneity.

Common genetic variants and susceptibility loci associated with Alzheimer's disease (AD) have been discovered through large-scale genome-wide association studies (GWAS), GWAS by proxy (GWAX) and meta-analysis of GWAS and GWAX (GWAS+GWAX). However, due to the very low repeatability of AD susceptibility loci and the low heritability of AD, these AD genetic findings have been questioned. We summarize AD genetic findings from the past 10 years and provide a new interpretation of these findings in the context of statistical heterogeneity. We discovered that only 17% of AD risk loci demonstrated reproducibility with a genome-wide significance of P < 5.00E-08 across all AD GWAS and GWAS+GWAX datasets. We highlighted that the AD GWAS+GWAX with the largest sample size failed to identify the most significant signals, the maximum number of genome-wide significant genetic variants or maximum heritability. Additionally, we identified widespread statistical heterogeneity in AD GWAS+GWAX datasets, but not in AD GWAS datasets. We consider that statistical heterogeneity may have attenuated the statistical power in AD GWAS+GWAX and may contribute to explaining the low repeatability (17%) of genome-wide significant AD susceptibility loci and the decreased AD heritability (40-2%) as the sample size increased. Importantly, evidence supports the idea that a decrease in statistical heterogeneity facilitates the identification of genome-wide significant genetic loci and contributes to an increase in AD heritability. Collectively, current AD GWAX and GWAS+GWAX findings should be meticulously assessed and warrant additional investigation, and AD GWAS+GWAX should employ multiple meta-analysis methods, such as random-effects inverse variance-weighted meta-analysis, which is designed specifically for statistical heterogeneity.

Effective bet-hedging through growth rate dependent stability.

Microbes in the wild face highly variable and unpredictable environments and are naturally selected for their average growth rate across environments. Apart from using sensory regulatory systems to adapt in a targeted manner to changing environments, microbes employ bet-hedging strategies where cells in an isogenic population switch stochastically between alternative phenotypes. Yet, bet-hedging suffers from a fundamental trade-off: Increasing the phenotype-switching rate increases the rate at which maladapted cells explore alternative phenotypes but also increases the rate at which cells switch out of a well-adapted state. Consequently, it is currently believed that bet-hedging strategies are effective only when the number of possible phenotypes is limited and when environments last for sufficiently many generations. However, recent experimental results show that gene expression noise generally decreases with growth rate, suggesting that phenotype-switching rates may systematically decrease with growth rate. Such growth rate dependent stability (GRDS) causes cells to be more explorative when maladapted and more phenotypically stable when well-adapted, and we show that GRDS can almost completely overcome the trade-off that limits bet-hedging, allowing for effective adaptation even when environments are diverse and change rapidly. We further show that even a small decrease in switching rates of faster-growing phenotypes can substantially increase long-term fitness of bet-hedging strategies. Together, our results suggest that stochastic strategies may play an even bigger role for microbial adaptation than hitherto appreciated.

Robust use of phenotypic heterogeneity at drug target genes for mechanistic insights: Application of cis-multivariable Mendelian randomization to GLP1R gene region.

Phenotypic heterogeneity at genomic loci encoding drug targets can be exploited by multivariable Mendelian randomization to provide insight into the pathways by which pharmacological interventions may affect disease risk. However, statistical inference in such investigations may be poor if overdispersion heterogeneity in measured genetic associations is unaccounted for. In this work, we first develop conditional F statistics for dimension-reduced genetic associations that enable more accurate measurement of phenotypic heterogeneity. We then develop a novel extension for two-sample multivariable Mendelian randomization that accounts for overdispersion heterogeneity in dimension-reduced genetic associations. Our empirical focus is to use genetic variants in the GLP1R gene region to understand the mechanism by which GLP1R agonism affects coronary artery disease (CAD) risk. Colocalization analyses indicate that distinct variants in the GLP1R gene region are associated with body mass index and type 2 diabetes (T2D). Multivariable Mendelian randomization analyses that were corrected for overdispersion heterogeneity suggest that bodyweight lowering rather than T2D liability lowering effects of GLP1R agonism are more likely contributing to reduced CAD risk. Tissue-specific analyses prioritized brain tissue as the most likely to be relevant for CAD risk, of the tissues considered. We hope the multivariable Mendelian randomization approach illustrated here is widely applicable to better understand mechanisms linking drug targets to diseases outcomes, and hence to guide drug development efforts.

Phenotypic Heterogeneity Analysis of APC-Mutant Colon Cancer by Proteomics and Phosphoproteomics Identifies RAI14 as a Key Prognostic Determinant in East Asians and Westerners.

Adenomatous polyposis coli (APC) is an important tumor suppressor and is mostly linked to the regulation of the Wnt/ß-catenin signaling pathway. APC mutation has been identified as an early event in more than 80% of sporadic colorectal cancers (CRCs). Moreover, prognostic differences are observed in CRC patients with APC mutations. Although previous genomics studies have investigated the roles of concomitant gene mutations in determining the phenotypic heterogeneity of APC-mutant tumors, valuable prognostic determinants for APC-mutant CRC patients are still lacking. Based on the proteome and phosphoproteome data, we classified APC-mutant colon cancer patients and revealed genomic, proteomic, and phosphoproteomic heterogeneity in APC-mutant tumors. More importantly, we identified RAI14 as a key prognostic determinant for APC-mutant but not APC-wildtype colon cancer patients. The heterogeneity and the significance of prognostic biomarkers in APC-mutant tumors were further validated in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) colon cancer cohort. In addition, we found that colon cancer patients with high expression of RAI14 were less responsive to chemotherapy. Knockdown of RAI14 in cell lines led to reduced cell migration and changes in epithelial-mesenchymal transition (EMT)-related markers. Mechanistically, knockdown of RAI14 remodeled the phosphoproteome associated with cell adhesion, which might affect EMT marker expression and promote F-actin degradation. Collectively, this work describes the phenotypic heterogeneity of APC-mutant tumors and identifies RAI14 as an important prognostic determinant for APC-mutant colon cancer patients. The prognostic utility of RAI14 in APC-mutant colon cancer will provide early warning and increase the chance of successful treatment.

Multiscale X-ray study of Bacillus subtilis biofilms reveals interlinked structural hierarchy and elemental heterogeneity.

Biofilms are multicellular microbial communities that encase themselves in an extracellular matrix (ECM) of secreted biopolymers and attach to surfaces and interfaces. Bacterial biofilms are detrimental in hospital and industrial settings, but they can be beneficial, for example, in agricultural as well as in food technology contexts. An essential property of biofilms that grants them with increased survival relative to planktonic cells is phenotypic heterogeneity, the division of the biofilm population into functionally distinct subgroups of cells. Phenotypic heterogeneity in biofilms can be traced to the cellular level; however, the molecular structures and elemental distribution across whole biofilms, as well as possible linkages between them, remain unexplored. Mapping X-ray diffraction across intact biofilms in time and space, we revealed the dominant structural features in Bacillus subtilis biofilms, stemming from matrix components, spores, and water. By simultaneously following the X-ray fluorescence signal of biofilms and isolated matrix components, we discovered that the ECM preferentially binds calcium ions over other metal ions, specifically, zinc, manganese, and iron. These ions, remaining free to flow below macroscopic wrinkles that act as water channels, eventually accumulate and may possibly lead to sporulation. The possible link between ECM properties, regulation of metal ion distribution, and sporulation across whole, intact biofilms unravels the importance of molecular-level heterogeneity in shaping biofilm physiology and development.

Phenotypic plasticity: The role of a phosphatase family Rap in the genetic regulation of Bacilli.

In the last two decades, an increasing number of bacterial species have been recognized that are able to generate a phenotypically diverse population that shares an identical genotype. This ability is dependent on a complex genetic regulatory network that includes cellular and environmental signals, as well as stochastic elements. Among Bacilli, a broadly distributed family of Rap (Response-regulator aspartyl phosphate) phosphatases is known to modulate the function of the main phenotypic heterogeneity regulators by controlling their phosphorylation. Even more, their related extracellular Phr (Phosphatase regulator) peptides function as signals, creating a cell-cell communication network that regulates the phenotypic development of the entire population. In this review, we examine the role that the Rap phosphatases and their Phr peptides play in the regulation of Bacillus subtilis phenotypic differentiation, and in other members of the Bacillus genus. We also highlight the contribution of these regulatory elements to the fitness of bacterial cells and mobile genetic elements, for example, prophages and conjugative vectors.

Expanding the spectrum of phenotypes for MPDZ: Report of four unrelated families and review of the literature.

MPDZ, a gene with diverse functions mediating cell-cell junction interactions, receptor signaling, and binding multivalent scaffold proteins, is associated with a spectrum of clinically heterogeneous phenotypes with biallelic perturbation. Despite its clinical relevance, the mechanistic underpinnings of these variants remain elusive, underscoring the need for extensive case series and functional investigations. In this study, we conducted a systematic review of cases in the literature through two electronic databases following the PRISMA guidelines. We selected nine studies, including 18 patients, with homozygous or compound heterozygous variants in MPDZ and added five patients from four unrelated families with novel MPDZ variants. To evaluate the role of Mpdz on hearing, we analyzed available auditory electrophysiology data from a knockout murine model (Mpdzem1(IMPC)J/em1(IMPC)J) generated by the International Mouse Phenotyping Consortium. Using exome and genome sequencing, we identified three families with compound heterozygous variants, and one family with a homozygous frameshift variant. MPDZ-related disease is clinically heterogenous with hydrocephaly, vision impairment, hearing impairment and cardiovascular disease occurring most frequently. Additionally, we describe two unrelated patients with spasticity, expanding the phenotypic spectrum. Our murine analysis of the Mpdzem1(IMPC)J/em1(IMPC)J allele showed severe hearing impairment. Overall, we expand understanding of MPDZ-related phenotypes and highlight hearing impairment and spasticity among the heterogeneous phenotypes.

Phenotypic heterogeneity associated with KIF21A: Two new cases and review of the literature.

Our understanding of genetic and phenotypic heterogeneity associated with the clinical spectrum of rare diseases continues to expand. Thorough phenotypic descriptions and model organism functional studies are valuable tools in dissecting the biology of the disease process. Kinesin genes are well known to be associated with specific disease phenotypes and a subset of kinesin genes, including KIF21A, have been associated with more than one disease. Here we report two patients with KIF21A variants identified by exome sequencing; one with biallelic variants, supporting a novel KIF21A related syndrome with recessive inheritance and the second report of this condition, and another with a heterozygous de novo variant allele representing a phenotypic expansion of the condition described to date. We provide detailed phenotypic information on both families, including a novel neuropathology finding of neuroaxonal dystrophy associated with biallelic variants in KIF21A. Additionally, we studied the dominant variant in Saccharomyces cerevisiae to assess variant pathogenicity and found that this variant appears to impair protein function. KIF21A associated disease has mounting evidence for phenotypic heterogeneity; further patients and study of an allelic series are required to define the phenotypic spectrum and further explore the molecular etiology for each of these conditions.

Decomposed FDG PET-based phenotypic heterogeneity predicting clinical prognosis and decision-making in temporal lobe epilepsy patients.

OBJECTIVE: This study utilized a data-driven Bayesian model to automatically identify distinct latent disease factors represented by overlapping glucose metabolism patterns from 18F-Fluorodeoxyglucose PET (18F-FDG PET) to analyze heterogeneity among patients with TLE. METHODS: We employed unsupervised machine learning to estimate latent disease factors from 18F-FDG PET scans, representing whole-brain glucose metabolism patterns in seventy patients with TLE. We estimated the extent to which multiple distinct factors were expressed within each participant and analyzed their relevance to epilepsy burden, including seizure onset, duration, and frequency. Additionally, we established a predictive model for clinical prognosis and decision-making. RESULTS: We identified three latent disease factors: hypometabolism in the unilateral temporal lobe and hippocampus (factor 1), hypometabolism in bilateral prefrontal lobes (factor 2), and hypometabolism in bilateral temporal lobes (factor 3), variably co-expressed within each patient. Factor 3 demonstrated the strongest negative correlation with the age of onset and duration (r = - 0.33, - 0.38 respectively, P < 0.05). The supervised classifier, trained on latent disease factors for predicting patient-specific antiepileptic drug (AED) responses, achieved an area under the curve (AUC) of 0.655. For post-surgical seizure outcomes, the AUC was 0.857, and for clinical decision-making, it was 0.965. CONCLUSIONS: Decomposing 18F-FDG PET-based phenotypic heterogeneity facilitates individual-level predictions relevant to disease monitoring and personalized therapeutic strategies.

Personalised modelling of clinical heterogeneity between medium-chain acyl-CoA dehydrogenase patients.

BACKGROUND: Monogenetic inborn errors of metabolism cause a wide phenotypic heterogeneity that may even differ between family members carrying the same genetic variant. Computational modelling of metabolic networks may identify putative sources of this inter-patient heterogeneity. Here, we mainly focus on medium-chain acyl-CoA dehydrogenase deficiency (MCADD), the most common inborn error of the mitochondrial fatty acid oxidation (mFAO). It is an enigma why some MCADD patients-if untreated-are at risk to develop severe metabolic decompensations, whereas others remain asymptomatic throughout life. We hypothesised that an ability to maintain an increased free mitochondrial CoA (CoASH) and pathway flux might distinguish asymptomatic from symptomatic patients. RESULTS: We built and experimentally validated, for the first time, a kinetic model of the human liver mFAO. Metabolites were partitioned according to their water solubility between the bulk aqueous matrix and the inner membrane. Enzymes are also either membrane-bound or in the matrix. This metabolite partitioning is a novel model attribute and improved predictions. MCADD substantially reduced pathway flux and CoASH, the latter due to the sequestration of CoA as medium-chain acyl-CoA esters. Analysis of urine from MCADD patients obtained during a metabolic decompensation showed an accumulation of medium- and short-chain acylcarnitines, just like the acyl-CoA pool in the MCADD model. The model suggested some rescues that increased flux and CoASH, notably increasing short-chain acyl-CoA dehydrogenase (SCAD) levels. Proteome analysis of MCADD patient-derived fibroblasts indeed revealed elevated levels of SCAD in a patient with a clinically asymptomatic state. This is a rescue for MCADD that has not been explored before. Personalised models based on these proteomics data confirmed an increased pathway flux and CoASH in the model of an asymptomatic patient compared to those of symptomatic MCADD patients. CONCLUSIONS: We present a detailed, validated kinetic model of mFAO in human liver, with solubility-dependent metabolite partitioning. Personalised modelling of individual patients provides a novel explanation for phenotypic heterogeneity among MCADD patients. Further development of personalised metabolic models is a promising direction to improve individualised risk assessment, management and monitoring for inborn errors of metabolism.

Understanding Functional Neurological Disorder: Recent Insights and Diagnostic Challenges.

Functional neurological disorder (FND), formerly called conversion disorder, is a condition characterized by neurological symptoms that lack an identifiable organic purpose. These signs, which can consist of motor, sensory, or cognitive disturbances, are not deliberately produced and often vary in severity. Its diagnosis is predicated on clinical evaluation and the exclusion of other medical or psychiatric situations. Its treatment typically involves a multidisciplinary technique addressing each of the neurological symptoms and underlying psychological factors via a mixture of medical management, psychotherapy, and supportive interventions. Recent advances in neuroimaging and a deeper exploration of its epidemiology, pathophysiology, and clinical presentation have shed new light on this disorder. This paper synthesizes the current knowledge on FND, focusing on its epidemiology and underlying mechanisms, neuroimaging insights, and the differentiation of FND from feigning or malingering. This review highlights the phenotypic heterogeneity of FND and the diagnostic challenges it presents. It also discusses the significant role of neuroimaging in unraveling the complex neural underpinnings of FND and its potential in predicting treatment response. This paper underscores the importance of a nuanced understanding of FND in informing clinical practice and guiding future research. With advancements in neuroimaging techniques and growing recognition of the disorder's multifaceted nature, the paper suggests a promising trajectory toward more effective, personalized treatment strategies and a better overall understanding of the disorder.

The vulnerable primed cancer stem cells in disguise: demystifying the role of Maspin.

Epithelial-specific Maspin is widely known as a tumor suppressor. However, while the level of maspin expression is inversely correlated with tumor grade and stage, emerging clinical evidence shows a correlation between seemingly better differentiated tumor cells that express Maspin in both the nucleus and the cytoplasm, (n + c)Maspin, with a poor prognosis of many types of cancer. Biological studies demonstrate that Maspin plays an essential role in stem cell differentiation. In light of the recently established characterization of primed stem cells (P-SCs) in development, we propose, for the first time, that cancer stem cells (CSCs) also need to undergo priming (P-CSCs) before their transition to various progeny phenotypes. We envisage major differences in the steady state kinetics between P-SCs and P-CSCs. We further propose that P-CSCs of carcinoma are both marked and regulated by (n + c)Maspin. The concept of P-CSCs helps explain the apparent dichotomous relationships of (n + c)Maspin expression with cancer diagnosis and prognosis, and is supported by the evidence from mechanistic studies. We believe that the potential utility of (n + c)Maspin as a molecular marker of P-CSCs may significantly accelerate the advancement in our understanding of the genesis of tumor phenotypic plasticity in response to changes of tumor microenvironments (TME) or drug treatments. The vulnerabilities of the cellular state of (n + c)Maspin-expressing P-CSCs are also discussed as the rationale for future development of P-CSC-targeted chemotherapeutic and immunotherapeutic strategies.

High-Resolution Bacterial Cytological Profiling Reveals Intrapopulation Morphological Variations upon Antibiotic Exposure.

Phenotypic heterogeneity is crucial to bacterial survival and could provide insights into the mechanism of action (MOA) of antibiotics, especially those with polypharmacological actions. Although phenotypic changes among individual cells could be detected by existing profiling methods, due to the data complexity, only population average data were commonly used, thereby overlooking the heterogeneity. In this study, we developed a high-resolution bacterial cytological profiling method that can capture morphological variations of bacteria upon antibiotic treatment. With an unprecedented single-cell resolution, this method classifies morphological changes of individual cells into known MOAs with an overall accuracy above 90%. We next showed that combinations of two antibiotics induce altered cell morphologies that are either unique or similar to that of an antibiotic in the combinations. With these combinatorial profiles, this method successfully revealed multiple cytological changes caused by a natural product-derived compound that, by itself, is inactive against Acinetobacter baumannii but synergistically exerts its multiple antibacterial activities in the presence of colistin. The findings have paved the way for future single-cell profiling in bacteria and have highlighted previously underappreciated intrapopulation variations caused by antibiotic perturbation.

Molecular analysis of phenotypic heterogeneity in JAK2V617F-positive myeloproliferative neoplasms reveals a potential target for therapy.

JAK2V617F is the most frequent mutation in BCR-ABL-negative myeloproliferative neoplasms (MPNs). It is an important but not the only determinant of MPN phenotype. We performed high-throughput sequencing on JAK2V617F+ essential thrombocythaemia (ET) and polycythaemia vera (PV) patient samples to unveil factors involved in phenotypic heterogeneity and to identify novel therapeutic targets for MPN. Two concurrent mutations that may affect phenotype were identified, including mutations in SH2B3, which is primarily prevalent in PV, and SF3B1, which is more commonly mutated in ET. Next, we conducted transcriptomic analysis at the haematopoietic stem cell (HSC) and megakaryocyte (MK)-erythroid progenitor (MEP) levels. Inflammatory signalling pathways were elevated in both ET HSCs and MEPs, unlike in PV HSCs and MEPs. Notably, Wnt/ß-catenin signalling was uniquely upregulated during ET haematopoietic differentiation from HSC to MEP, and inhibiting Wnt/ß-catenin signalling blocked MK differentiation in vitro. Consistently, Wnt/ß-catenin inhibitor administration decreased platelet counts in JAK2V617F+ MPN mice by blocking MEPs and MK progenitors and by inhibiting maturation of MKs, while in wild-type mice, Wnt/ß-catenin inhibitor did not significantly reduce platelet counts. In conclusion, our findings provide new insights into the mechanisms underlying phenotypic differentiation of JAK2V617F+ PV and ET and indicate Wnt/ß-catenin signalling as a potential therapeutic target for MPN.

The Influence of Heteroresistance on Minimum Inhibitory Concentration, Investigated Using Weak-Acid Stress in Food Spoilage Yeasts.

Populations of microbial cells may resist environmental stress by maintaining a high population-median resistance (IC50) or, potentially, a high variability in resistance between individual cells (heteroresistance); where heteroresistance would allow certain cells to resist high stress, provided the population was sufficiently large to include resistant cells. This study sets out to test the hypothesis that both IC50 and heteroresistance may contribute to conventional minimal inhibitory concentration (MIC) determinations, using the example of spoilage-yeast resistance to the preservative sorbic acid. Across a panel of 26 diverse yeast species, both heteroresistance and particularly IC50 were positively correlated with predicted MIC. A focused panel of 29 different isolates of a particular spoilage yeast was also examined (isolates previously recorded as Zygosaccharomyces bailii, but genome resequencing revealing that several were in fact hybrid species, Z. parabailii and Z. pseudobailii). Applying a novel high-throughput assay for heteroresistance, it was found that IC50 but not heteroresistance was positively correlated with predicted MIC when considered across all isolates of this panel, but the heteroresistance-MIC interaction differed for the individual Zygosaccharomyces subspecies. Z. pseudobailii exhibited higher heteroresistance than Z. parabailii whereas the reverse was true for IC50, suggesting possible alternative strategies for achieving high MIC between subspecies. This work highlights the limitations of conventional MIC measurements due to the effect of heteroresistance in certain organisms, as the measured resistance can vary markedly with population (inoculum) size. IMPORTANCE Food spoilage by fungi is a leading cause of food waste, with specialized food spoilage yeasts capable of growth at preservative concentrations above the legal limit, in part due to heteroresistance allowing small subpopulations of cells to exhibit extreme preservative resistance. Whereas heteroresistance has been characterized in numerous ecological contexts, measuring this phenotype systematically and assessing its importance are not encompassed by conventional assay methods. The development here of a high-throughput method for measuring heteroresistance, amenable to automation, addresses this issue and has enabled characterization of the contribution that heteroresistance may make to conventional MIC measurements. We used the example of sorbic acid heteroresistance in spoilage yeasts like Zygosaccharomyces spp., but the approach is relevant to other fungi and other inhibitors, including antifungals. The work shows how median resistance, heteroresistance, and inoculum size should all be considered when selecting appropriate inhibitor doses in real-world antimicrobial applications such as food preservation.

Phenotypic heterogeneity in human genetic diseases: ultrasensitivity-mediated threshold effects as a unifying molecular mechanism.

Phenotypic heterogeneity is very common in genetic systems and in human diseases and has important consequences for disease diagnosis and treatment. In addition to the many genetic and non-genetic (e.g., epigenetic, environmental) factors reported to account for part of the heterogeneity, we stress the importance of stochastic fluctuation and regulatory network topology in contributing to phenotypic heterogeneity. We argue that a threshold effect is a unifying principle to explain the phenomenon; that ultrasensitivity is the molecular mechanism for this threshold effect; and discuss the three conditions for phenotypic heterogeneity to occur. We suggest that threshold effects occur not only at the cellular level, but also at the organ level. We stress the importance of context-dependence and its relationship to pleiotropy and edgetic mutations. Based on this model, we provide practical strategies to study human genetic diseases. By understanding the network mechanism for ultrasensitivity and identifying the critical factor, we may manipulate the weak spot to gently nudge the system from an ultrasensitive state to a stable non-disease state. Our analysis provides a new insight into the prevention and treatment of genetic diseases.

Quorum sensing governs a transmissive Legionella subpopulation at the pathogen vacuole periphery.

The Gram-negative bacterium Legionella pneumophila is the causative agent of Legionnaires' disease and replicates in amoebae and macrophages within a distinct compartment, the Legionella-containing vacuole (LCV). The facultative intracellular pathogen switches between a replicative, non-virulent and a non-replicating, virulent/transmissive phase. Here, we show on a single-cell level that at late stages of infection, individual motile (PflaA -GFP-positive) and virulent (PralF - and PsidC -GFP-positive) L. pneumophila emerge in the cluster of non-growing bacteria within an LCV. Comparative proteomics of PflaA -GFP-positive and PflaA -GFP-negative L. pneumophila subpopulations reveals distinct proteomes with flagellar proteins or cell division proteins being preferentially produced by the former or the latter, respectively. Toward the end of an infection cycle (˜ 48 h), the PflaA -GFP-positive L. pneumophila subpopulation emerges at the cluster periphery, predominantly escapes the LCV, and spreads from the bursting host cell. These processes are mediated by the Legionella quorum sensing (Lqs) system. Thus, quorum sensing regulates the emergence of a subpopulation of transmissive L. pneumophila at the LCV periphery, and phenotypic heterogeneity underlies the intravacuolar bi-phasic life cycle of L. pneumophila.

Genomes comparison of two Proteus mirabilis clones showing varied swarming ability.

BACKGROUND: Proteus mirabilis is a Gram-negative bacteria most noted for its involvement with catheter-associated urinary tract infections. It is also known for its multicellular migration over solid surfaces, referred to as 'swarming motility'. Here we analyzed the genomic sequences of two P. mirabilis isolates, designated K38 and K39, which exhibit varied swarming ability. METHODS AND RESULTS: The isolates genomes were sequenced using Illumina NextSeq sequencer, resulting in about 3.94 Mbp, with a GC content of 38.6%, genomes. Genomes were subjected for in silico comparative investigation. We revealed that, despite a difference in swarming motility, the isolates showed high genomic relatedness (up to 100% ANI similarity), suggesting that one of the isolates probably originated from the other. CONCLUSIONS: The genomic sequences will allow us to investigate the mechanism driving this intriguing phenotypic heterogeneity between closely related P. mirabilis isolates. Phenotypic heterogeneity is an adaptive strategy of bacterial cells to several environmental pressures. It is also an important factor related to their pathogenesis. Therefore, the availability of these genomic sequences will facilitate studies that focus on the host-pathogen interactions during catheter-associated urinary tract infections.

Decomposing MRI phenotypic heterogeneity in epilepsy: a step towards personalized classification.

In drug-resistant temporal lobe epilepsy, precise predictions of drug response, surgical outcome and cognitive dysfunction at an individual level remain challenging. A possible explanation may lie in the dominant 'one-size-fits-all' group-level analytical approaches that does not allow parsing interindividual variations along the disease spectrum. Conversely, analysing inter-patient heterogeneity is increasingly recognized as a step towards person-centred care. Here, we used unsupervised machine learning to estimate latent relations (or disease factors) from 3 T multimodal MRI features [cortical thickness, hippocampal volume, fluid-attenuated inversion recovery (FLAIR), T1/FLAIR, diffusion parameters] representing whole-brain patterns of structural pathology in 82 patients with temporal lobe epilepsy. We assessed the specificity of our approach against age- and sex-matched healthy individuals and a cohort of frontal lobe epilepsy patients with histologically verified focal cortical dysplasia. We identified four latent disease factors variably co-expressed within each patient and characterized by ipsilateral hippocampal microstructural alterations, loss of myelin and atrophy (Factor 1), bilateral paralimbic and hippocampal gliosis (Factor 2), bilateral neocortical atrophy (Factor 3) and bilateral white matter microstructural alterations (Factor 4). Bootstrap analysis and parameter variations supported high stability and robustness of these factors. Moreover, they were not expressed in healthy controls and only negligibly in disease controls, supporting specificity. Supervised classifiers trained on latent disease factors could predict patient-specific drug response in 76 ± 3% and postsurgical seizure outcome in 88 ± 2%, outperforming classifiers that did not operate on latent factor information. Latent factor models predicted inter-patient variability in cognitive dysfunction (verbal IQ: r = 0.40 ± 0.03; memory: r = 0.35 ± 0.03; sequential motor tapping: r = 0.36 ± 0.04), again outperforming baseline learners. Data-driven analysis of disease factors provides a novel appraisal of the continuum of interindividual variability, which is probably determined by multiple interacting pathological processes. Incorporating interindividual variability is likely to improve clinical prognostics.