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Mycobacterial glycolipids are important cell envelope structures that drive host-pathogen interactions. Arguably, the most important are lipoarabinomannan (LAM) and its precursor, lipomannan (LM), which are trafficked from the bacterium to the host via unknown mechanisms. Arabinomannan is thought to be a capsular derivative of these molecules, lacking a lipid anchor. However, the mechanism by which this material is generated has yet to be elucidated. Here, we describe the identification of a glycoside hydrolase family 76 enzyme that we term LamH (Rv0365c in Mycobacterium tuberculosis) which specifically cleaves α-1,6-mannoside linkages within LM and LAM, driving its export to the capsule releasing its phosphatidyl-myo-inositol mannoside lipid anchor. Unexpectedly, we found that the catalytic activity of this enzyme is important for efficient exit from stationary phase cultures, potentially implicating arabinomannan as a signal for growth phase transition. Finally, we demonstrate that LamH is important for M. tuberculosis survival in macrophages., (© 2024. The Author(s).)

Background: RNA sequencing combined with machine learning techniques has provided a modern approach to the molecular classification of cancer. Class predictors, reflecting the disease class, can be constructed for known tissue types using the gene expression measurements extracted from cancer patients. One challenge of current cancer predictors is that they often have suboptimal performance estimates when integrating molecular datasets generated from different labs. Often, the quality of the data is variable, procured differently, and contains unwanted noise hampering the ability of a predictive model to extract useful information. Data preprocessing methods can be applied in attempts to reduce these systematic variations and harmonize the datasets before they are used to build a machine learning model for resolving tissue of origins., Results: We aimed to investigate the impact of data preprocessing steps-focusing on normalization, batch effect correction, and data scaling-through trial and comparison. Our goal was to improve the cross-study predictions of tissue of origin for common cancers on large-scale RNA-Seq datasets derived from thousands of patients and over a dozen tumor types. The results showed that the choice of data preprocessing operations affected the performance of the associated classifier models constructed for tissue of origin predictions in cancer., Conclusion: By using TCGA as a training set and applying data preprocessing methods, we demonstrated that batch effect correction improved performance measured by weighted F1-score in resolving tissue of origin against an independent GTEx test dataset. On the other hand, the use of data preprocessing operations worsened classification performance when the independent test dataset was aggregated from separate studies in ICGC and GEO. Therefore, based on our findings with these publicly available large-scale RNA-Seq datasets, the application of data preprocessing techniques to a machine learning pipeline is not always appropriate., (© 2024. The Author(s).)

Microglia are the primary resident immune cells of the central nervous system that maintain physiological homeostasis in the brain and contribute to the pathogenesis of many psychiatric disorders and neurodegenerative diseases. Due to the lack of appropriate human cellular models, it is difficult to study the basic pathophysiological processes linking microglia to brain diseases. In this study, we adopted a microglia-like cellular model derived from peripheral blood monocytes with granulocyte-macrophage colony-stimulating factor (GM-CSF) and interleukin-34 (IL-34). We characterized and validated this in vitro cellular model by morphology, immunocytochemistry, gene expression profiles, and functional study. Our results indicated that the iMG cells developed typical microglial ramified morphology, expressed microglial specific surface markers (P2RY12 and TMEM119), and possessed phagocytic activity. Principal component analyses and multidimensional scaling analyses of RNA-seq data showed that iMG cells were distinct from monocytes and induced macrophages (iMacs) but clustered closer to human microglia and hiPSC-induced microglia. Heatmap analyses also found that iMG cells, but not monocytes, were closely clustered with human primary microglia. Further pathway and relative expression analysis indicated that unique genes from iMG cells were involved in the regulation of the complement system, especially in the synapse and ion transport. Overall, our data demonstrated that the iMG model mimicked many features of the brain resident microglia, highlighting its utility in the study of microglial function in many brain diseases, such as schizophrenia and Alzheimer's disease (AD)., Competing Interests: XC is employed by 410 AI, LLC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Banerjee, Lu, Do, Mize, Wu, Chen and Chen.)

Schizophrenia (SCZ) is a severe psychiatric disorder with a strong genetic component. High heritability of SCZ suggests a major role for transmitted genetic variants. Furthermore, SCZ is also associated with a marked reduction in fecundity, leading to the hypothesis that alleles with large effects on risk might often occur de novo. In this study, we conducted whole-genome sequencing for 23 families from two cohorts with unaffected siblings and parents. Two nonsense de novo mutations (DNMs) in GJC1 and HIST1H2AD were identified in SCZ patients. Ten genes (DPYSL2, NBPF1, SDK1, ZNF595, ZNF718, GCNT2, SNX9, AACS, KCNQ1, and MSI2) were found to carry more DNMs in SCZ patients than their unaffected siblings by burden test. Expression analyses indicated that these DNM implicated genes showed significantly higher expression in prefrontal cortex in prenatal stage. The DNM in the GJC1 gene is highly likely a loss function mutation (pLI = 0.94), leading to the dysregulation of ion channel in the glutamatergic excitatory neurons. Analysis of rare variants in independent exome sequencing dataset indicates that GJC1 has significantly more rare variants in SCZ patients than in unaffected controls. Data from genome-wide association studies suggested that common variants in the GJC1 gene may be associated with SCZ and SCZ-related traits. Genes co-expressed with GJC1 are involved in SCZ, SCZ-associated pathways, and drug targets. These evidences suggest that GJC1 may be a risk gene for SCZ and its function may be involved in prenatal and early neurodevelopment, a vulnerable period for developmental disorders such as SCZ.

Schizophrenia is a complex disorder with many comorbid conditions. In this study, we used polygenic risk scores (PRSs) from schizophrenia and comorbid traits to explore consistent cluster structure in schizophrenia patients. With 10 comorbid traits, we found a stable 4-cluster structure in two datasets (MGS and SSCCS). When the same traits and parameters were applied for the patients in a clinical trial of antipsychotics, the CATIE study, a 5-cluster structure was observed. One of the 4 clusters found in the MGS and SSCCS was further split into two clusters in CATIE, while the other 3 clusters remained unchanged. For the 5 CATIE clusters, we evaluated their association with the changes of clinical symptoms, neurocognitive functions, and laboratory tests between the enrollment baseline and the end of Phase I trial. Class I was found responsive to treatment, with significant reduction for the total, positive, and negative symptoms ( p = 0.0001, 0.0099, and 0.0028, respectively), and improvement for cognitive functions (VIGILANCE, p = 0.0099; PROCESSING SPEED, p = 0.0006; WORKING MEMORY, p = 0.0023; and REASONING, p = 0.0015). Class II had modest reduction of positive symptoms ( p = 0.0492) and better PROCESSING SPEED ( p = 0.0071). Class IV had a specific reduction of negative symptoms ( p = 0.0111) and modest cognitive improvement for all tested domains. Interestingly, Class IV was also associated with decreased lymphocyte counts and increased neutrophil counts, an indication of ongoing inflammation or immune dysfunction. In contrast, Classes III and V showed no symptom reduction but a higher level of phosphorus. Overall, our results suggest that PRSs from schizophrenia and comorbid traits can be utilized to classify patients into subtypes with distinctive clinical features. This genetic susceptibility based subtyping may be useful to facilitate more effective treatment and outcome prediction., Competing Interests: The authors have no conflict of interest., (Copyright © 2020 Jingchun Chen et al.)

Recent studies imply that rare variants contribute to the risk of schizophrenia, however, the exact variants or genes responsible for this condition are largely unknown. In this study, we conducted whole genome sequencing (WGS) of 20 Chinese families. Each family consisted of at least two affected siblings diagnosed with schizophrenia and at least one unaffected sibling. We examined functional variants that were found in affected sibling(s) but not in unaffected sibling(s) within a family. Matching this criterion, a frameshift heterozygous deletion of CA (-/CA) at chromosome 18:24722722, also referred to as rs752084147, in the Carbohydrate Sulfotransferase 9 (CHST9) gene, was detected in two families. This deletion was confirmed by PCR-based Sanger sequencing. With the observed frequency of 0.00076 in Han Chinese population, we performed both case-control and family-based analyses to evaluate its association with schizophrenia. In the case-control analyses, Chi-square test P-value was 6.80e-12 and the P-value was 0.0008 after one million simulations. In family-based segregation analyses, segregation P-value was 7.72e-7 and simulated P-value was 5.70e-6. For both the case-control and family-based analyses, the CA deletion was significantly associated with schizophrenia in the Chinese population. Further investigation of this gene  is warranted in the development of schizophrenia by utilizing larger and more ethnically diverse samples.

Schizophrenia is genetically heterogeneous and comorbid with many conditions. In this study, we explored polygenic scores (PGSs) from genetically related conditions and traits to predict schizophrenia diagnosis using both logistic regression and deep neural network (DNN) models. We used the combined Molecular Genetics of Schizophrenia and Swedish Schizophrenia Case Control Study (MGS + SSCCS) data for training and testing the models, and used the Clinical Antipsychotic Trials for Intervention Effectiveness (CATIE) data as independent validation. We screened 28 conditions and traits comorbid with schizophrenia to identify traits as potential predictors and used LASSO regression to select predictors for model construction. We investigated how PGS calculation influenced model performance. We found that the inclusion of comorbid traits improved model performance and PGSs calculated from two traits were more generalizable in independent validation. With a DNN model using 19 PGS predictors, we accomplished a prediction accuracy of 0.813 and an AUC of 0.905 in the MGS + SSCCS data. When this model was validated with the CATIE data, it achieved an accuracy of 0.721 and AUC of 0.747. Our results indicate that PGSs alone may not be sufficient to predict schizophrenia accurately and the inclusion of behavioral and clinical data may be necessary for more accurate prediction model.

Small molecules are known to stabilize membrane proteins and to modulate their function and oligomeric state, but such interactions are often hard to precisely define. Here we develop and apply a high-resolution, Orbitrap mass spectrometry-based method for analyzing intact membrane protein-ligand complexes. Using this platform, we resolve the complexity of multiple binding events, quantify small molecule binding and reveal selectivity for endogenous lipids that differ only in acyl chain length.

Background: Migrant farmworkers are at risk for heat-related illness (HRI) at work., Purpose: The purpose of this study was to determine which risk factors could potentially reduce the prevalence of HRI symptoms among migrant farmworkers in Georgia., Methods: Trained interviewers conducted in-person interviews of adults who attended the South Georgia Farmworker Health Project clinics in June 2011. The analysis was conducted in 2011-2012. Population intervention models were used to assess where the greatest potential impact could be made to reduce the prevalence of HRI symptoms., Results: In total, 405 farmworkers participated. One third of participants had experienced three or more HRI symptoms in the preceding week. Migrant farmworkers faced barriers to preventing HRI at work, including lack of prevention training (77%) and no access to regular breaks (34%); shade (27%); or medical attention (26%). The models showed that the prevalence of three or more HRI symptoms (n=361, 34.3%) potentially could be reduced by increasing breaks in the shade (-9.2%); increasing access to medical attention (-7.3%); reducing soda intake (-6.7%); or increasing access to regular breaks (-6.0%)., Conclusions: Migrant farmworkers experienced high levels of HRI symptoms and faced substantial barriers to preventing these symptoms. Although data are cross-sectional, results suggest that heat-related illness may be reduced through appropriate training of workers on HRI prevention, as well as regular breaks in shaded areas., (Published by Elsevier Inc.)

Rural emergency departments (EDs) in the United States are less likely to be staffed with emergency medicine (EM) residency-trained and American Board of Emergency Medicine (ABEM)-certified physicians than urban EDs. Rural EM clinical experiences during residency training have been suggested as a strategy to encourage future rural practice, but past Accreditation Council for Graduate Medical Education (ACGME) Residency Review Committee for Emergency Medicine program requirements and a lack of familiarity with rural rotations in the EM graduate medical education (GME) community have limited their availability. To provide a template for the development and implementation of a rural EM clinical experience, Kern's six-step approach was followed., (© 2012 by the Society for Academic Emergency Medicine.)

We examined the prevalence of food insecurity in migrant farmworkers in Georgia. Of these workers 62.83% did not have enough food, and non-H-2A workers had an adjusted risk of food insecurity almost 3 times higher than did H-2A workers. Lack of access to cooking facilities, transportation problems, and having children were additional risk factors. Migrant farmworkers are at extreme risk for food insecurity, although being an H-2A guestworker was protective within this population. Policy interventions are needed to protect these vulnerable farmworkers.

Identification of authenticated cuticular proteins has been based on isolation and sequencing of individual proteins extracted from cleaned cuticles. These data facilitated classification of sequences from conceptual translation of cDNA or genomic sequences. The question arises whether such putative cuticular proteins actually are incorporated into the cuticle. This paper describes the profiling of cuticular proteins from Anopheles gambiae starting with cuticle cleaned by the insect itself in the course of molting. Proteins extracted from cast larval head capsules and cast pupal cuticles were fractionated by 1D SDS gel electrophoresis. Large gel slices were reduced, carbamidomethylated and digested with trypsin. The pellet remaining after SDS extraction was also treated with trypsin. The resulting peptides were separated on a C18 column and then analyzed by tandem mass spectrometry. Two-hundred-ninety-five peptides from putative cuticular proteins were identified; these corresponded to a minimum of 69 and a maximum of 119 different proteins. Each is reported as an authentic Anopheles cuticular protein for the first time. In addition to members of two known cuticular protein families, members of additional families likely to be structural components of the cuticle were identified. Furthermore, other peptides were identified that can be attributed to molting fluid, muscle and sclerotizing agents.

The application of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) for high resolution biomolecular analysis has increased greatly after 30 years of innovation since its conception in 1974. FT- ICR-MS can now routinely be used for the analysis of complex organic mixtures such as biological or petrochemical samples. Many of these new possibilities have been the results of many different instrumental developments. This paper provides a mini review of selected instrumental developments that now allow these measurements. The development of soft ionization techniques such as electrospray ionization and matrix assisted laser desorption and ionisation was crucial for the analysis of biological macromolecules. Improved ion transport optics led to an increase in sensitivity. New ICR cell designs complement the capabilities of FT-ICR-MS by allowing a more thorough study of the mechanism and kinetics of ion reactions in the gas-phase. A selected example of electron capture dissociation (ECD) employs these developments to investigate the role of peptide conformation in ECD. Improved electronics and software allow faster and more flexible experiments. All these improvements led to an increase in speed and sensitivity that are necessary to couple FT-MS to fast separation techniques such as nano-high performance liquid chromatography. The modern FT-ICR-MS instruments can be incorporated in virtual organizations allowing remote access to unique infrastructure. This concept of remote experimentation opens new possibilities for scientific collaborations between expert scientists at different locations and allows the efficient use of this expensive instrumentation.

A new method is described which allows a broad mass range of ions from multiple MALDI events to be accumulated in an FIICR analyzer cell prior to detection. Signal intensity and signal-to-noise ratio are observed to grow in direct proportion to the number of laser shots, providing a substantial improvement to the analysis of low-level samples compared to more commonly used signal-averaging methods. In addition, calibrant ions desorbed from a separate sample spot can be added to the analyzer cell after an analyte population has been accumulated, providing an internal mass standard without the necessity of mixing calibrant and analyte. This method provides excellent mass accuracy by eliminating mass errors due to space charge effects.

The effect of ion space-charge on mass accuracy in Fourier transform ion cyclotron resonance mass spectrometry is examined. Matrix-assisted laser desorption/ionization is used to form a population of high-molecular-weight polymer ions with a wide mass distribution. The density of the ions in the analyzer cell is varied using ion remeasurement and suspended trapping techniques to allow the effect of ion space charge to be examined independently of other experimental influences. Observed cyclotron frequency exhibits a linear correlation with ion population. Mass errors of 100 ppm or more in externally calibrated mass spectra result when ion number is not taken into account. By matching the total ion intensities of calibrant and analyte mass spectra, the protonated ion of insulin B-chain, 3494.6513 Da, is measured with an accuracy of 0.07 ppm (average of 10 measurements, σ = 2.3 ppm, average absolute error 1.6 ppm) using a polymer sample as an external calibrant. Alternatively, the correction for space charge can be made by using a calibration equation that accounts for the total ion intensity of the mass spectrum. A calibration procedure is proposed and is tested with the measurement of the mass of insulin B-chain. A mass accuracy of 2.0 ppm (average of 20 measurements, σ = 4.2 ppm, average absolute error 3.5 ppm) is achieved. Space-charge-induced mass errors are more significant for samples with many components, such as a polymer, than for single-component samples such as purified peptides or proteins.