Yoram Louzoun

The emergence of donor-specific antibodies (DSA) against HLA-DQ reduces graft survival and limits future transplant options. Current practice predicts DSA development from HLA antigen mismatches or unique eplet counts (HLAMatchmaker), but predictive accuracy remains limited. Improving the prediction accuracy is crucial for solid organ transplants. We analysed a retrospective cohort of 240 kidney transplants (480 donor-DQ samples) in which the donor DQ allele targeted by each post-transplant DSA was identified. We compared the predictive accuracy of unique eplets (HLAMatchmaker), total eplets, amino acid mismatches, PAM genetic distance, and electrostatic mismatch score (EMS), individually and in combination. DSA emerge through the response of two donor and two patient DQ heterodimers. We evaluate the appropriate combination method of patient and donor alleles. Total eplet or amino acid mismatch counts outperformed unique eplets as used in HLAMatchmaker (AUC 0.79 vs. 0.73). Combining eplets with PAM and EMS distances in an XGBoost classifier achieved AUC 0.84, with a hazard ratio above 7. The most informative features were PAM distance and a subset of eplets distributed across the HLA-DQ alpha and beta chains. When extending prediction from a single donor DQ allele to the donor as a whole, the maximum of the two per-allele scores outperformed a probabilistic combination. The unique-eplet approach is suboptimal for predicting anti-DQ DSA in this cohort; combining total eplets with genetic and biochemical distance metrics yields substantially better prediction, essential for the reduction of DSA emergence. External validation in independent cohorts is the necessary next step.

Most studies of the oral microbiome during pregnancy have focused on the second and third trimesters (T2, T3, respectively). To date, no large-scale longitudinal study has examined oral microbiome development across all three trimesters, leaving early gestational dynamics largely unexplored. We conducted a longitudinal analysis of 346 pregnant Israeli women, validated in an independent cohort of 154 Russian women. In Israel, saliva samples were collected during T1 (11-14 weeks), T2 (24-28 weeks), and T3 (32-38 weeks); in Russia, samples were collected during T2 and T3 at similar gestational ages. Microbial profiles were analyzed for differential abundance and associations with maternal nutrition and lifestyle. Significant shifts in oral microbial composition were observed as early as the transition from T1 to T2. Alpha diversity decreased progressively across pregnancy. Taxonomic changes included a reduction in Verrucomicrobiota and an increase in Synergistota. Gluten-free diet showed the strongest associations with microbiome composition across all trimesters, followed by smoking history and conception method. This study provides the first large-scale evidence of significant oral microbiome changes beginning in early-mid pregnancy, characterized by reduced diversity and a directional shift toward inflammation-associated communities. Strong associations between gluten consumption and smoking suggest a lifestyle effect on the oral microbiome.

Background: The success of hematopoietic stem cell transplantation (HSCT) depends critically on human leukocyte antigen (HLA) matching between donor and recipient. While traditional matching focuses on five classical HLA loci (A, B, C, DRB1, DQB1), clinical practice increasingly considers extended typing at nine loci, including DPA1, DQA1, DPB1, and DRB3/4/5. Furthermore, emerging evidence supports transplantation with up to three HLA mismatches under post-transplant cyclophosphamide (PTCy) regimens. However, current donor search algorithms cannot efficiently identify donors with multiple mismatches across extended HLA loci in real-time. Methods: We developed GRIMM-II (GRaph IMputation and Matching, version II), which comprises two novel algorithms: ML-GRIM (Multi-Locus GRIM) for HLA imputation across multiple loci, and ML-GRMA (Multi-Locus GRMA) for real-time donor-patient matching with up to three mismatches. Both algorithms employ a two-stage approach that combines efficient candidate reduction through graph-theoretic frameworks with detailed genotype comparison. ML-GRIM partitions genotypes into class I (HLA-A, B, C) and class II (remaining loci) components, enabling memory-efficient storage and rapid candidate identification. ML-GRMA searches a pre-imputed donor graph composed of donor genotypes and their sub-components, then computes asymmetric graft-versus-host (GvH) and host-versus-graft (HvG) mismatch probabilities to provide clinically relevant compatibility assessments. Both imputation and matching tools are available as a web application at https://grimmard.math.biu.ac.il/ and through GitHub repositories at https://github.com/nmdp-bioinformatics/py-graph-imputation (imputation) and https://github.com/nmdp-bioinformatics/py-graph-match (matching). Results: We validated ML-GRMA and ML-GRIM using the WMDA3 (World Marrow Donor Association) validation dataset, successfully reproducing all previously reported matches while identifying numerous additional candidate donors not detected by previous algorithms. Further validation of ML-GRMA using 3,000 patients with artificially introduced mismatches (0-3 allele substitutions) demonstrated 100% sensitivity and specificity in identifying matching donors at expected mismatch levels. We validated ML-GRIM using simulated nine-locus typings derived from 8,078,224 US donors in the NMDP registry. The algorithm successfully imputed genotypes across variable numbers of typed loci while incorporating multiethnic haplotype frequencies. The algorithm achieved real-time performance with typical imputation times under one second and matching times of 1-13 seconds per patient for up to three mismatches, even when searching databases exceeding 8 million donors. Notably, ML-GRMA identified substantially more potentially suitable donors than traditional algorithms by accounting for the biological reality that GvH and HvG mismatches often differ, particularly for donors homozygous at specific loci. To evaluate ML-GRIM performance with low-resolution typing, we tested it on simulated 3-locus typings from the same population. The resulting imputation accuracy correlated with the mutual information between typed loci and complete genotypes. Conclusions: GRIMM-II provides a scalable, memory-efficient solution for nine-locus HLA imputation and real-time identification of donors with up to three mismatches. The graph-based framework supports dynamic registry updates and can readily accommodate additional HLA loci and matching criteria as clinical knowledge evolves. By expanding the pool of acceptable donors while maintaining computational efficiency, GRIMM-II addresses a critical need in contemporary transplantation practice, particularly for patients from underrepresented ethnic minorities who face lower probabilities of finding perfectly matched donors.

Introduction: We aimed to develop a machine learning model for first-trimester prediction of gestational diabetes mellitus (GDM) in twin pregnancies using a prospective international, multi-center cohort and identify useful predictive markers. Methods: Pregnant women with two live fetuses were enrolled at 11 + 0 to 13 + 6 weeks’ gestation and followed until delivery. GDM was diagnosed at 24–28 weeks’ gestation using the two-stage GCT and OGTT tests. Biochemical, biophysical, and blood assessments were conducted at three periods during pregnancy. Multiple machine learning models evaluated demographic, clinical, and laboratory parameters, including maternal factors (BMI, age, medical history), sonographic markers (crown rump length, estimated fetal weight, uterine artery pulsatility index), and blood and biochemical markers (placental growth factors, blood glucose, cell counts). LightGBM, XGBoost, and logistic regression models were compared using area under the curve (AUC) analysis. Results: Among 596 women, 99 (16.6%) developed GDM. LightGBM demonstrated superior performance (AUC = 0.72, 95% CI 0.69–0.75). First-trimester high BMI was the strongest predictor, followed by elevated white blood cell counts and platelet levels. Detection rates (DR) were 28% and 42% at 10% and 20% false positive rates (FPR), respectively. Previous GDM was associated with an increased risk for GDM. Discussion: GDM in twins is associated with certain characteristics of the first-trimester. Information from later trimesters has a limited impact. The GDM probability risk score increased with the severity of the treatment. An app to predict this score is available at: twin-pe.math.biu.ac.il.

Background Exposure to violence and psychological distress are positively correlated across populations. Microbiota-gut-brain crosstalk research supports that the microbiota is affected by environmental stressors and may influence mental state. Accordingly, we explored how the microbiota relates to exposure to violence and distress in midlife, a pivotal yet underexplored period. This life stage is marked by emerging vulnerability to chronic stress and mental health decline yet offers opportunities for early identification and intervention. Methods We characterized the fecal microbiota of a previosly snowball-recruited Israeli-Muslim cohort (n = 305, 40-65 yrs) exposed to ongoing and increasing violence (during adulthood) and examined correlations with subjective reports of exposure to violence and psychological distress. We then used machine learning to leverage microbiota profiles and exposure to violence, classifying individuals into high- and low-distress categories. Results We identify unique microbial signatures associated with increasing exposure to violence and distress. Some associated bacteria were previously identified in the literature, while others were not yet described in the context of the gut-brain axis. Microbial profiles associated with violence and distress are largely non-overlapping, yet we are able to classify participants into high- and low distress categories using a combination of microbiota and violence variables. This combined model outperforms those using only microbiota or demographics, but its classification accuracy remains modest (with a median area-under-the-curve of 0.595 (IQR 0.045). Conclusions This research sheds light on the microbiota-gut-brain axis, highlighting that psychological distress and exposure to violence are differentially associated with microbiota composition in midlife. These cross-sectional findings, together with moderate classification into distress classes based on the microbiome, suggest that holistic, context-aware approaches would benefit proactive mental health interventions.

Background Kidney allocation scores aim to balance transplant efficacy and equity but often deprioritize Human Leukocyte Antigen (HLA) matching, leading to reduced graft lifespan and a higher risk of donor-specific antibodies (DSA). This study introduces a “soft allocation” strategy to improve HLA match rates while minimally disrupting the allocation queue or equity. Methods Donors include directed-living (D-LD), non-directed-living (ND-LD), and deceased (DD) types. In the “soft allocation” system, instead of automatically assigning the kidney to the first person in line, the kidney is offered to the best HLA match among the top patients on the waiting list. The predefined size of the top patient group (typically 5–20 patients) is referred to as K. This approach preserves blood type and age compatibility while protecting patients with rare HLA types. Results Analysis of 95,250 US DD transplants shows an increase in HLA-A&B&DR match scores from 1.45 to 1.77. In 42 recent DD cases and 230 ND-LD transplants in Israel, match scores improved from 2.33 to 2.54 and from 1.34 to 2.03, respectively. Similar enhancements are observed in related donor–recipient pairs using crossover transplants, achieving a three-fold match improvement without worsening any recipient’s match. Conclusions For predefined allocation rules, soft allocation enables significant HLA matching improvement with minimal impact on allocation order, benefiting both unrelated and related transplants. Future work will extend this approach to include sensitized donations.

An essential tool in microbiome is the distance between samples. This serves to detect groups of similar samples. We have recently shown that combining the cladogram with the abundance of each microbe into a sorted normalized abundance tree can improve the accuracy of machine learning and differential analysis. We show that smoothing these tree-based images to produce GIMIC (Smoothed Graph IMages of the MICrobiome) leads to an interpretable visualization of microbiome sets and that the difference between the smoothed tree-based images is a better metric than the current state-of-the-art in a wide array of tasks.