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Objective: Effective first‐trimester screening for pre‐eclampsia (PE) can be achieved using a competing‐risks model that combines risk factors from the maternal history with multiples of the median (MoM) values of biomarkers. A new model using artificial intelligence through machine‐learning methods has been shown to achieve similar screening performance without the need for conversion of raw data of biomarkers into MoM. This study aimed to investigate whether this model can be used across populations without specific adaptations. Methods: Previously, a machine‐learning model derived with the use of a fully connected neural network for first‐trimester prediction of early (< 34 weeks), preterm (< 37 weeks) and all PE was developed and tested in a cohort of pregnant women in the UK. The model was based on maternal risk factors and mean arterial blood pressure (MAP), uterine artery pulsatility index (UtA‐PI), placental growth factor (PlGF) and pregnancy‐associated plasma protein‐A (PAPP‐A). In this study, the model was applied to a dataset of 10 110 singleton pregnancies examined in Spain who participated in the first‐trimester PE validation (PREVAL) study, in which first‐trimester screening for PE was carried out using the Fetal Medicine Foundation (FMF) competing‐risks model. The performance of screening was assessed by examining the area under the receiver‐operating‐characteristics curve (AUC) and detection rate (DR) at a 10% screen‐positive rate (SPR). These indices were compared with those derived from the application of the FMF competing‐risks model. The performance of screening was poor if no adjustment was made for the analyzer used to measure PlGF, which was different in the UK and Spain. Therefore, adjustment for the analyzer used was performed using simple linear regression. Results: The DRs at 10% SPR for early, preterm and all PE with the machine‐learning model were 84.4% (95% CI, 67.2–94.7%), 77.8% (95% CI, 66.4–86.7%) and 55.7% (95% CI, 49.0–62.2%), respectively, with the corresponding AUCs of 0.920 (95% CI, 0.864–0.975), 0.913 (95% CI, 0.882–0.944) and 0.846 (95% CI, 0.820–0.872). This performance was achieved with the use of three of the biomarkers (MAP, UtA‐PI and PlGF); inclusion of PAPP‐A did not provide significant improvement in DR. The machine‐learning model had similar performance to that achieved by the FMF competing‐risks model (DR at 10% SPR, 82.7% (95% CI, 69.6–95.8%) for early PE, 72.7% (95% CI, 62.9–82.6%) for preterm PE and 55.1% (95% CI, 48.8–61.4%) for all PE) without requiring specific adaptations to the population. Conclusions: A machine‐learning model for first‐trimester prediction of PE based on a neural network provides effective screening for PE that can be applied in different populations. However, before doing so, it is essential to make adjustments for the analyzer used for biochemical testing. © 2023 International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

This article discusses a study that evaluated the performance of a machine learning model for predicting pre-eclampsia (PE) in a large Asian population. The study used data from 10,935 women with singleton pregnancies and applied the machine learning model for first trimester screening. The results showed that after adjusting for biochemical marker analyzers, the predictive performance of the model was comparable to that of the Fetal Medicine Foundation competing risk model in Asian populations. This study suggests that the machine learning model could be a useful tool for predicting PE in Asian populations. [Extracted from the article]

Objectives: To evaluate the performance of an artificial intelligence (AI) and machine learning (ML) model for first-trimester screening for pre-eclampsia in a large Asian population., Methods: This was a secondary analysis of a multicenter prospective cohort study in 10 935 participants with singleton pregnancies attending for routine pregnancy care at 11-13 +6  weeks of gestation in seven regions in Asia between December 2016 and June 2018. We applied the AI+ML model for the first-trimester prediction of preterm pre-eclampsia (<37 weeks), term pre-eclampsia (≥37 weeks), and any pre-eclampsia, which was derived and tested in a cohort of pregnant participants in the UK (Model 1). This model comprises maternal factors with measurements of mean arterial pressure, uterine artery pulsatility index, and serum placental growth factor (PlGF). The model was further retrained with adjustments for analyzers used for biochemical testing (Model 2). Discrimination was assessed by area under the receiver operating characteristic curve (AUC). The Delong test was used to compare the AUC of Model 1, Model 2, and the Fetal Medicine Foundation (FMF) competing risk model., Results: The predictive performance of Model 1 was significantly lower than that of the FMF competing risk model in the prediction of preterm pre-eclampsia (0.82, 95% confidence interval [CI] 0.77-0.87 vs. 0.86, 95% CI 0.811-0.91, P = 0.019), term pre-eclampsia (0.75, 95% CI 0.71-0.80 vs. 0.79, 95% CI 0.75-0.83, P = 0.006), and any pre-eclampsia (0.78, 95% CI 0.74-0.81 vs. 0.82, 95% CI 0.79-0.84, P < 0.001). Following the retraining of the data with adjustments for the PlGF analyzers, the performance of Model 2 for predicting preterm pre-eclampsia, term pre-eclampsia, and any pre-eclampsia was improved with the AUC values increased to 0.84 (95% CI 0.80-0.89), 0.77 (95% CI 0.73-0.81), and 0.80 (95% CI 0.76-0.83), respectively. There were no differences in AUCs between Model 2 and the FMF competing risk model in the prediction of preterm pre-eclampsia (P = 0.135) and term pre-eclampsia (P = 0.084). However, Model 2 was inferior to the FMF competing risk model in predicting any pre-eclampsia (P = 0.024)., Conclusion: This study has demonstrated that following adjustment for the biochemical marker analyzers, the predictive performance of the AI+ML prediction model for pre-eclampsia in the first trimester was comparable to that of the FMF competing risk model in an Asian population., (© 2024 The Authors. International Journal of Gynecology & Obstetrics published by John Wiley & Sons Ltd on behalf of International Federation of Gynecology and Obstetrics.)

Inositol 1,4,5-trisphosphate (IP3) receptor type 1 ( ITPR1 ), 2 ( ITPR2 ), and 3 ( ITPR3 ) encode the IP3 receptor (IP3R), a key player in intracellular calcium release. In four unrelated patients, we report that an identical ITPR3 de novo variant-NM&#95;002224.3:c.7570C>T, p.Arg2524Cys-causes, through a dominant-negative effect, a complex multisystemic disorder with immunodeficiency. This leads to defective calcium homeostasis, mitochondrial malfunction, CD4 + lymphopenia, a quasi-absence of naïve CD4 + and CD8 + cells, an increase in memory cells, and a distinct TCR repertoire. The calcium defect was recapitulated in Jurkat knock-in. Site-directed mutagenesis displayed the exquisite sensitivity of Arg 2524 to any amino acid change. Despite the fact that all patients had severe immunodeficiency, they also displayed variable multisystemic involvements, including ectodermal dysplasia, Charcot-Marie-Tooth disease, short stature, and bone marrow failure. In conclusion, unlike previously reported ITPR1-3 deficiencies leading to narrow, mainly neurological phenotypes, a recurrent dominant ITPR3 variant leads to a multisystemic disease, defining a unique role for IP3R3 in the tetrameric IP3R complex.

Competing Interests: Competing interests: None declared.

The Hardy-Weinberg equilibrium (HWE) assumption is essential to many population genetics models. Multiple tests were developed to test its applicability in observed genotypes. Current methods are divided into exact tests applicable to small populations and a small number of alleles, and approximate goodness-of-fit tests. Existing tests cannot handle ambiguous typing in multi-allelic loci. We here present a novel exact test Unambiguous Multi Allelic Test (UMAT) not limited to the number of alleles and population size, based on a perturbative approach around the current observations. We show its accuracy in the detection of deviation from HWE. We then propose an additional model to handle ambiguous typing using either sampling into UMAT or a goodness-of-fit test test with a variance estimate taking ambiguity into account, named Asymptotic Statistical Test with Ambiguity (ASTA). We show the accuracy of ASTA and the possibility of detecting the source of deviation from HWE. We apply these tests to the HLA loci to reproduce multiple previously reported deviations from HWE, and a large number of new ones., (© The Author(s) 2024. Published by Oxford University Press.)

Objective: Identifying biomarkers that can distinguish trauma-exposed youth at risk for developing posttraumatic pathology from resilient individuals is essential for targeted interventions. As trauma can alter the microbiome with lasting effects on the host, our longitudinal, multimeasure, cross-species study aimed to identify the microbial signature of posttraumatic stress disorder (PTSD)., Method: We followed children exposed to war-related trauma and matched controls from early childhood ( M age = 2.76 years, N = 232) to adolescence ( M age = 16.13 years, N = 84), repeatedly assessing posttraumatic symptomatology and maternal caregiving. In late adolescence, we collected fecal samples from mothers and youth and assessed microbiome composition, diversity, and mother-child microbial synchrony. We then transplanted adolescents' fecal samples into germ-free mice to determine if behavioral changes are observed., Results: Youth with PTSD exhibited a distinct gut microbiome profile and lower diversity compared to resilient individuals, and microbiome diversity mediated the continuity of posttraumatic symptomatology throughout development. Low microbiome diversity correlated with more posttraumatic symptoms in early childhood, more emotional and behavioral problems in adolescence, and poor maternal caregiving. Youth with PTSD demonstrated less mother-child microbial synchrony, suggesting that low microbial concordance between mother and child may indicate susceptibility to posttraumatic illness. Germ-free mice transplanted with microbiomes from individuals with PTSD displayed increased anxious behavior., Conclusions: Our findings provide evidence that the trauma-associated microbiome profile is at least partially responsible for the anxiety component of the PTSD phenotype and highlight microbial underpinnings of resilience. Further, our results suggest that the microbiome may serve as additional biological memory of early life stress and underscore the potential for microbiome-related diagnosis and treatment following trauma. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

mi-Mic, a novel approach for microbiome differential abundance analysis, tackles the key challenges of such statistical tests: a large number of tests, sparsity, varying abundance scales, and taxonomic relationships. mi-Mic first converts microbial counts to a cladogram of means. It then applies a priori tests on the upper levels of the cladogram to detect overall relationships. Finally, it performs a Mann-Whitney test on paths that are consistently significant along the cladogram or on the leaves. mi-Mic has much higher true to false positives ratios than existing tests, as measured by a new real-to-shuffle positive score., (© 2024. The Author(s).)

Intestinal chronic inflammation is associated with microbial dysbiosis and accumulation of various immune cells including myeloid-derived suppressor cells (MDSC), which profoundly impact the immune microenvironment, perturb homeostasis and increase the risk to develop colitis-associated colorectal cancer (CAC). However, the specific MDSCs-dysbiotic microbiota interactions and their collective impact on CAC development remain poorly understood. In this study, using a murine model of CAC, we demonstrate that CAC-bearing mice exhibit significantly elevated levels of highly immunosuppressive MDSCs, accompanied by microbiota alterations. Both MDSCs and bacteria that infiltrate the colon tissue and developing tumors can be found in close proximity, suggesting intricate MDSC-microbiota cross-talk within the tumor microenvironment. To investigate this phenomenon, we employed antibiotic treatment to disrupt MDSC-microbiota interactions. This intervention yielded a remarkable reduction in intestinal inflammation, decreased MDSC levels, and alleviated immunosuppression, all of which were associated with a significant reduction in tumor burden. Furthermore, we underscore the causative role of dysbiotic microbiota in the predisposition toward tumor development, highlighting their potential as biomarkers for predicting tumor load. We shed light on the intimate MDSCs-microbiota cross-talk, revealing how bacteria enhance MDSC suppressive features and activities, inhibit their differentiation into mature beneficial myeloid cells, and redirect some toward M2 macrophage phenotype. Collectively, this study uncovers the role of MDSC-bacteria cross-talk in impairing immune responses and promoting tumor growth, providing new insights into potential therapeutic strategies for CAC., Significance: MDSCs-dysbiotic bacteria interactions in the intestine play a crucial role in intensifying immunosuppression within the CAC microenvironment, ultimately facilitating tumor growth, highlighting potential therapeutic targets for improving the treatment outcomes of CAC., (© 2024 The Authors; Published by the American Association for Cancer Research.)

Prolonging the lifespan of transplanted organs is critical to combat the shortage of this life-saving resource. Chronic rejection, with irreversible demise of the allograft, is often caused by the development of donor-specific HLA antibodies. Currently, enumerating molecular (amino acid) mismatches between recipient and donor is promoted to identify patients at higher risk of developing HLA antibodies, for use in organ allocation, and immunosuppression-minimization strategies. We have counseled against the incorporation of such approaches into clinical use and hypothesized that not all molecular mismatches equally contribute to generation of donor-specific immune responses. Herein, we document statistical shortcomings in previous study design: for example, use of individuals who lack the ability to generate donor-specific-antibodies (HLA identical) as part of the negative cohort. We provide experimental evidence, using CRISPR-Cas9-edited cells, to rebut the claim that the HLAMatchmaker eplets represent "functional epitopes." We further used unique sub-cohorts of patients, those receiving an allograft with two HLA-DQ mismatches yet developing antibodies only to one mismatch (2MM1DSA), to interrogate differential immunogenicity. Our results demonstrate that mismatches of DQα05-heterodimers exhibit the highest immunogenicity. Additionally, we demonstrate that the DQα chain critically contributes to the overall qualities of DQ molecules. Lastly, our data proposes that an augmented risk to develop donor-specific HLA-DQ antibodies is dependent on qualitative (evolutionary and functional) divergence between recipient and donor, rather than the mere number of molecular mismatches. Overall, we propose an immunological mechanistic rationale to explain differential HLA-DQ immunogenicity, with potential ramifications for other pathological processes such as autoimmunity and infections., (© 2024 The Authors. HLA: Immune Response Genetics published by John Wiley & Sons Ltd.)

Background: The effect of microbes on their human host is often mediated through changes in metabolite concentrations. As such, multiple tools have been proposed to predict metabolite concentrations from microbial taxa frequencies. Such tools typically fail to capture the dependence of the microbiome-metabolite relation on the environment., Results: We propose to treat the microbiome-metabolome relation as the equilibrium of a complex interaction and to relate the host condition to a latent representation of the interaction between the log concentration of the metabolome and the log frequencies of the microbiome. We develop LOCATE (Latent variables Of miCrobiome And meTabolites rElations), a machine learning tool to predict the metabolite concentration from the microbiome composition and produce a latent representation of the interaction. This representation is then used to predict the host condition. LOCATE's accuracy in predicting the metabolome is higher than all current predictors. The metabolite concentration prediction accuracy significantly decreases cross datasets, and cross conditions, especially in 16S data. LOCATE's latent representation predicts the host condition better than either the microbiome or the metabolome. This representation is strongly correlated with host demographics. A significant improvement in accuracy (0.793 vs. 0.724 average accuracy) is obtained even with a small number of metabolite samples ([Formula: see text])., Conclusion: These results suggest that a latent representation of the microbiome-metabolome interaction leads to a better association with the host condition than any of the two separated or the simple combination of the two. Video Abstract., (© 2024. The Author(s).)

B cell epitope prediction methods are separated into linear sequence-based predictors and conformational epitope predictions that typically use the measured or predicted protein structure. Most linear predictions rely on the translation of the sequence to biologically based representations and the applications of machine learning on these representations. We here present CALIBER 'Conformational And LInear B cell Epitopes pRediction', and show that a bidirectional long short-term memory with random projection produces a more accurate prediction (test set AUC=0.789) than all current linear methods. The same predictor when combined with an Evolutionary Scale Modeling-2 projection also improves on the state of the art in conformational epitopes (AUC = 0.776). The inclusion of the graph of the 3D distances between residues did not increase the prediction accuracy. However, the long-range sequence information was essential for high accuracy. While the same model structure was applicable for linear and conformational epitopes, separate training was required for each. Combining the two slightly increased the linear accuracy (AUC 0.775 versus 0.768) and reduced the conformational accuracy (AUC = 0.769)., (© The Author(s) 2024. Published by Oxford University Press.)

The outcome of Hematopoietic Stem Cell (HSCT) and organ transplant is strongly affected by the matching of the HLA alleles of the donor and the recipient. However, donors and sometimes recipients are often typed at low resolution, with some alleles either missing or ambiguous. Thus, imputation methods are required to detect the most probably high-resolution HLA haplotypes consistent with a typing. Such imputation algorithms require predefined haplotype frequencies. As such, the phasing of the typing is required for both imputation and frequency generation.We have developed a new approach to HLA haplotype and genotype imputation, where first all candidate phases of a typing are explicated, and then the ambiguity within each phase is solved. This ambiguity is solved through a graph structure of all partial haplotypes and the haplotypes consistent with them.This phasing approach was used to produce an imputation algorithm (GRIMM-Graph Imputation and Matching). GRIMM was then combined with the possibility of combining information from multiple races to produce MR-GRIMM (Multi-Race GRIMM). When family information is available, the phasing of each family member can be restricted by the others. We propose GRAMM (GRaph-bAsed faMily iMputation) to phase alleles in family pedigree HLA typing data and in mother-cord blood unit pairs. Finally, we combined MR-GRIMM with an expectation-maximization (EM) algorithm to estimate haplotype frequencies sharing information between races to produce MR-GRIMME (MR-GRIMM EM).We have shown that these algorithms naturally combine information between races and family members. The accuracy of each of these algorithms is significantly better than its current parallel methods. MR-GRIMM leads to high accuracy in matching predictions. GRAMM better imputes family members than either MR-GRIMM or any existing algorithm and has practically no phasing errors. MR-GRIMME obtains a higher likelihood than existing algorithms.MR-GRIMM, MR-GRIMME, and GRAMM are available as servers or through stand-alone versions in GITHUB and PyPi, as detailed in the appropriate sections., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Allogeneic Hematopoietic Cell Transplantation (HCT) is a curative therapy for hematologic disorders and often requires human leukocyte antigen (HLA)-matched donors. Donor registries have recruited donors utilizing evolving technologies of HLA genotyping methods. This necessitates in-silico ambiguity resolution and statistical imputation based on haplotype frequencies estimated from donor data stratified by self-identified race and ethnicity (SIRE). However, SIRE has limited genetic validity and presents a challenge for individuals with unknown or mixed SIRE. We present MR-GRIMM "Multi-Race Graph IMputation and Matching" that simultaneously imputes the race/ethnic category and HLA genotype using a SIRE based prior. Additionally, we propose a novel method to impute HLA typing inconsistent with current haplotype frequencies. The performance of MR-GRIMM was validated using a dataset of 170,000 donor-recipient pairs. MR-GRIMM has an average 20 % lower matching error (1-AUC) than single-race imputation. The recall metric (sensitivity) of the race/ethnic category imputation from HLA was measured by comparing the imputed donor race with the donor-provided SIRE. Accuracies of 0.74 and 0.55 were obtained for the prediction of 5 broad and 21 detailed US population groups respectively. The operational implementation of this algorithm in a registry search could help improve match predictions and access to HLA-matched donors., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Introduction: The HLA region is the hallmark of balancing selection, argued to be driven by the pressure to present a wide variety of viral epitopes. As such selection on the peptide-binding positions has been proposed to drive HLA population genetics. MHC molecules also directly binds to the T-Cell Receptor and killer cell immunoglobulin-like receptors (KIR)., Methods: We here combine the HLA allele frequencies in over six-million Hematopoietic Stem Cells (HSC) donors with a novel machine-learning-based method to predict allele frequency., Results: We show for the first time that allele frequency can be predicted from their sequences. This prediction yields a natural measure for selection. The strongest selection is affecting KIR binding regions, followed by the peptide-binding cleft. The selection from the direct interaction with the KIR and TCR is centered on positively charged residues (mainly Arginine), and some positions in the peptide-binding cleft are not associated with the allele frequency, especially Tyrosine residues., Discussion: These results suggest that the balancing selection for peptide presentation is combined with a positive selection for KIR and TCR binding., Competing Interests: The 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 © 2023 Levi, Levi and Louzoun.)

Recently, haplo-identical transplantation with multiple HLA mismatches has become a viable option for stem cell transplants. Haplotype sharing detection requires the imputation of donor and recipient. We show that even in high-resolution typing when all alleles are known, there is a 15% error rate in haplotype phasing, and even more in low-resolution typings. Similarly, in related donors, the parents' haplotypes should be imputed to determine what haplotype each child inherited. We propose graph-based family imputation (GRAMM) to phase alleles in family pedigree HLA typing data, and in mother-cord blood unit pairs. We show that GRAMM has practically no phasing errors when pedigree data are available. We apply GRAMM to simulations with different typing resolutions as well as paired cord-mother typings, and show very high phasing accuracy, and improved allele imputation accuracy. We use GRAMM to detect recombination events and show that the rate of falsely detected recombination events (false-positive rate) in simulations is very low. We then apply recombination detection to typed families to estimate the recombination rate in Israeli and Australian population datasets. The estimated recombination rate has an upper bound of 10%-20% per family (1%-4% per individual)., (© 2023 The Authors. HLA: Immune Response Genetics published by John Wiley & Sons Ltd.)

Introduction: H chain rearrangement in B cells is a two-step process where first D H binds J H , and only then V H is joined to the complex. As such, there is no direct rearrangement between V H and J H ., Results: Nevertheless, we here show that the V H JH combinations frequency in humans deviates from the one expected based on each gene usage frequency. This bias is observed mainly in functional rearrangements, and much less in out-of-frame rearrangements. The bias cannot be explained by preferred binding for D H genes or a preferred reading frame. Preferred V H J H combinations are shared between donors., Discussion: These results suggest a common structural mechanism for these biases. Through development, thepreferred V H J H combinations evolve during peripheral selection to become stronger, but less shared. We propose that peripheral Heavy chain V H J H usage is initially shaped by a structural selection before the naive B cellstate, followed by pathogen-induced selection for host specific V H - J H pairs., Competing Interests: The 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 © 2023 Levi, Dvorkin and Louzoun.)