Therapeutic antibodies directed at G protein-coupled receptors

G protein-coupled receptors (GPCRs) are one of the most important classes of targets for small molecule drug discovery, but many current GPCRs of interest are proving intractable to small molecule discovery and may be better approached with bio-therapeutics. GPCRs are implicated in a wide variety of diseases where antibody therapeutics are currently used. These include inflammatory diseases such as rheumatoid arthritis and Crohn disease, as well as metabolic disease and cancer. Raising antibodies to GPCRs has been difficult due to problems in obtaining suitable antigen because GPCRs are often expressed at low levels in cells and are very unstable when purified. A number of new developments in overexpressing receptors, as well as formulating stable pure protein, are contributing to the growing interest in targeting GPCRs with antibodies. This review discusses the opportunities for targeting GPCRs with antibodies using these approaches and describes the therapeutic antibodies that are currently in clinical development.Key words: G protein-coupled receptor, transmembrane spanning domain, chemokine receptor, extracellular domain, extracellular loop     

Isolation of Adipose Tissue Immune Cells

The discovery of increased macrophage infiltration in the adipose tissue (AT) of obese rodents and humans has led to an intensification of interest in immune cell contribution to local and systemic insulin resistance. Isolation and quantification of different immune cell populations in lean and obese AT is now a commonly utilized technique in immunometabolism laboratories; yet extreme care must be taken both in stromal vascular cell isolation and in the flow cytometry analysis so that the data obtained is reliable and interpretable. In this video we demonstrate how to mince, digest, and isolate the immune cell-enriched stromal vascular fraction. Subsequently, we show how to antibody label macrophages and T lymphocytes and how to properly gate on them in flow cytometry experiments. Representative flow cytometry plots from low fat-fed lean and high fat-fed obese mice are provided. A critical element of this analysis is the use of antibodies that do not fluoresce in channels where AT macrophages are naturally autofluorescent, as well as the use of proper compensation controls.     

Hyperspectral cytometry at the single-cell level using a 32-channel photodetector

Despite recent progress in cell-analysis technology, rapid classification of cells remains a very difficult task. Among the techniques available, flow cytometry (FCM) is considered especially powerful, because it is able to perform multiparametric analyses of single biological particles at a high flow rate-up to several thousand particles per second. Moreover, FCM is nondestructive, and flow cytometric analysis can be performed on live cells. The current limit for simultaneously detectable fluorescence signals in FCM is around 8-15 depending upon the instrument. Obtaining multiparametric measurements is a very complex task, and the necessity for fluorescence spectral overlap compensation creates a number of additional difficulties to solve. Further, to obtain well-separated single spectral bands a very complex set of optical filters is required. This study describes the key components and principles involved in building a next-generation flow cytometer based on a 32-channel PMT array detector, a phase-volume holographic grating, and a fast electronic board. The system is capable of full-spectral data collection and spectral analysis at the single-cell level. As demonstrated using fluorescent microspheres and lymphocytes labeled with a cocktail of antibodies (CD45/FITC, CD4/PE, CD8/ECD, and CD3/Cy5), the presented technology is able to simultaneously collect 32 narrow bands of fluorescence from single particles flowing across the laser beam in <5 μs. These 32 discrete values provide a proxy of the full fluorescence emission spectrum for each single particle (cell). Advanced statistical analysis has then been performed to separate the various clusters of lymphocytes. The average spectrum computed for each cluster has been used to characterize the corresponding combination of antibodies, and thus identify the various lymphocytes subsets. The powerful data-collection capabilities of this flow cytometer open up significant opportunities for advanced analytical approaches, including spectral unmixing and unsupervised or supervised classification.     

Integration of Lyoplate Based Flow Cytometry and Computational Analysis for Standardized Immunological Biomarker Discovery

Discovery of novel immune biomarkers for monitoring of disease prognosis and response to therapy in immune-mediated inflammatory diseases is an important unmet clinical need. Here, we establish a novel framework for immunological biomarker discovery, comparing a conventional (liquid) flow cytometry platform (CFP) and a unique lyoplate-based flow cytometry platform (LFP) in combination with advanced computational data analysis. We demonstrate that LFP had higher sensitivity compared to CFP, with increased detection of cytokines (IFN-γ and IL-10) and activation markers (Foxp3 and CD25). Fluorescent intensity of cells stained with lyophilized antibodies was increased compared to cells stained with liquid antibodies. LFP, using a plate loader, allowed medium-throughput processing of samples with comparable intra- and inter-assay variability between platforms. Automated computational analysis identified novel immunophenotypes that were not detected with manual analysis. Our results establish a new flow cytometry platform for standardized and rapid immunological biomarker discovery with wide application to immune-mediated diseases.     

Tumor classification using phylogenetic methods on expression data

Tumor classification is a well-studied problem in the field of bioinformatics. Developments in the field of DNA chip design have now made it possible to measure the expression levels of thousands of genes in sample tissue from healthy cell lines or tumors. A number of studies have examined the problems of tumor classification: class discovery, the problem of defining a number of classes of tumors using the data from a DNA chip, and class prediction, the problem of accurately classifying an unknown tumor, given expression data from the unknown tumor and from a learning set. The current work has applied phylogenetic methods to both problems. To solve the class discovery problem, we impose a metric on a set of tumors as a function of their gene expression levels, and impose a tree structure on this metric, using standard tree fitting methods borrowed from the field of phylogenetics. Phylogenetic methods provide a simple way of imposing a clear hierarchical relationship on the data, with branch lengths in the classification tree representing the degree of separation witnessed. We tested our method for class discovery on two data sets: a data set of 87 tissues, comprised mostly of small, round, blue-cell tumors (SRBCTs), and a data set of 22 breast tumors. We fit the 87 samples of the first set to a classification tree, which neatly separated into four major clusters corresponding exactly to the four groups of tumors, namely neuroblastomas, rhabdomyosarcomas, Burkitt's lymphomas, and the Ewing's family of tumors. The classification tree built using the breast cancer data separated tumors with BRCA1 mutations from those with BRCA2 mutations, with sporadic tumors separated from both groups and from each other. We also demonstrate the flexibility of the class discovery method with regard to standard resampling methodology such as jackknifing and noise perturbation. To solve the class prediction problem, we built a classification tree on the learning set, and then sought the optimal placement of each test sample within the classification tree. We tested this method on the SRBCT data set, and classified each tumor successfully.     

Quantification of CD59- mutants in human-hamster hybrid (AL) cells by flow cytometry

Mutation assay is an important approach in evaluating the genotoxic risk of potentially harmful environmental chemicals. The human-hamster hybrid (A(L)) cell mutagenesis system, based on the complement/antibody-mediated cytotoxicity principle, has been used successfully to evaluate the mutagenic potential of a variety of environmental toxicants. The A(L) cells contain a standard set of CHO chromosomes and a single human chromosome 11, which expresses several cell surface proteins including CD59 encoded by the CD59 gene at 11p13.5. A modified mutation assay by flow cytometry was developed to determine the yield of CD59- mutants after either radiation or chemical treatment. After incubation with phycoerythrin-conjugated mouse monoclonal anti-CD59 antibody, the CD59- mutant yields were determined by quantifying the fluorescence of the cells using flow cytometry. This method is faster and eliminates the commonly encountered toxicity problems of the complements with the traditional complement/antibody assay. By comparing the mutant fractions of radiation or chemically treated A(L) cultures using the two methods, we show here that the flow cytometry assay is an excellent substitute in providing an efficient and highly sensitive method in mutant detection for the traditional complement/antibody assay.     

Fluorescent cell barcoding in flow cytometry allows high-throughput drug screening and signaling profiling

Flow cytometry allows high-content, multiparameter analysis of single cells, making it a promising tool for drug discovery and profiling of intracellular signaling. To add high-throughput capacity to flow cytometry, we developed a cell-based multiplexing technique called fluorescent cell barcoding (FCB). In FCB, each sample is labeled with a different signature, or barcode, of fluorescence intensity and emission wavelengths, and mixed with other samples before antibody staining and analysis by flow cytometry. Using three FCB fluorophores, we were able to barcode and combine entire 96-well plates, reducing antibody consumption 100-fold and acquisition time to 5-15 min per plate. Using FCB and phospho-specific flow cytometry, we screened a small-molecule library for inhibitors of T cell-receptor and cytokine signaling, simultaneously determining compound efficacy and selectivity. We also analyzed IFN-gamma signaling in multiple cell types from primary mouse splenocytes, revealing differences in sensitivity and kinetics between B cells, CD4+ and CD4- T cells and CD11b-hi cells.     

Sequence homology: A poor predictive value for profilins cross-reactivity

Background

The basophil activation test (BAT), in which translocation of markers to the surface of blood basophils is measured in response to allergen by flow cytometry, is a rapid assay that is gaining popularity. Two markers are currently being evaluated for the BAT; CD63 and the lineage-specific CD203c. In a recent report, detection of CD203c after lysis with Saponin was shown to be superior to detection of CD63 after lysis with formic acid. We wanted to compare a) lysis with formic acid and lysis with Saponin, b) the response through CD203c and CD63, and c) the definition 10% activated cells above background with the probability binning metric T(χ) > 4, on sets of data generated with blood basophils stimulated with varying concentrations of anti-FcεRI antibody.

Methods

Blood from volunteers was incubated with serial logarithmic dilutions of anti-FcεRI and subsequently with antibodies to CD203c PE and CD63 FITC. Sets of samples set up in parallel were lysed with either Saponin based Whole Blood Lysing reagent or with formic acid based Immunoprep/Q-prep. Samples were acquired on a FACS Calibur, but were compensated and analysed offline. Responders were defined as persons who had 10% or more activated basophils above background, or a T(χ) > 4, for two consecutive dilutions of anti-FcεRI antibody.

Results

More basophils (median 1164 vs. median 397) and better discrimination of upregulated CD203c and CD63 amongst responders were obtained after lysis with Saponin than after lysis with formic acid. We suggest that CD203c may be a more sensitive marker for the BAT than CD63, as 6/11 responders were found with CD203c, compared with 3/11 with CD63. Most responders (7/11) were identified with probability binning.

Conclusion

A combination of lysis with Saponin and the markers CD203c and CD63 computed by probability binning may be the most sensitive method of detecting activation of basophils after stimulation through FcεRI.     

Cell surface receptor-antibody association constants and enumeration of receptor sites for monoclonal antibodies

BACKGROUND: Fluorescent markers (labeled antibodies) and flow cytometry are used to enumerate the average number of receptors (antigens) on formed bodies (cells) in whole blood by using a new method that avoids the extra steps of separating bound from unbound fluorescent markers or the use of external standards. METHODS: Mean channel fluorescence intensities of equilibrated marker-cell suspension mixtures, total concentrations of marker, and targeted cell counts obtained by standard cytometry procedures are used to complete the analyses for receptors per cell. Also, flow cytometric assays using competitive binding between fluorescent marker (CD4-RD1, CD8-FITC, CD3-FITC, CD3-RD1) and unlabeled antibody (CD4, CD8, CD3, CD3-dextran) for receptors on white blood cells in whole blood are described for determination of relative and specific binding constants of unlabeled/labeled antibody for targeted receptors. RESULTS: Ranges that were obtained for receptors per cell (lymphocytes) in normal blood donors were as follows: CD4, 4.9 x 10(4)-1.5 x 10(5); CD8, 5.0 x 10(5)-2.1 x 10(6); CD3, 6.6-7.8 x 10(5). Binding constants were highest for unlabeled CD4 antibody, 2. 7 x 10(10)-2.1 x 10(12) M(-1), and then unlabeled CD3 antibody, 1.1 x 10(10)-1.9 x 10(11) M(-1). FITC- and RD1-labeled antibodies typically had binding constants that were 10-to 100-fold lower than the native antibodies. CONCLUSIONS: Values of receptors per cell and binding constants obtained by the new method from flow cytometric analyses of mixtures of whole blood with FITC- or RD1-labeled CD4, CD8, and CD3 antibodies compare well with literature values determined by other methods.     

Polyethyleneimine-based immunopolyplex for targeted gene transfer in human lymphoma cell lines

Background

Specific and efficient delivery of genes into targeted cells is a priority objective in non‐viral gene therapy. Polyethyleneimine‐based polyplexes have been reported to be good non‐viral transfection reagents. However, polyplex‐mediated DNA delivery occurs through a non‐specific mechanism. This article reports the construction of an immunopolyplex, a targeted non‐viral vector based on a polyplex backbone, and its application in gene transfer over human lymphoma cell lines.

Methods

Targeting elements (biotin‐labeled antibodies), which should recognize a specific element of the target cell membrane and promote nucleic acid entry into the cell, were attached to the polyplex backbone through a bridge protein (streptavidin). Immunopolyplex transfection activity was studied in several hematological cell lines [Jurkat (CD3+/CD19?), Granta 519 (CD3?/ CD19+), and J.RT3‐T3.5 (CD3?/CD19?)] using the EGFP gene as a reporter gene and anti‐CD3 and anti‐CD19 antibodies as targeting elements. Transfection activity was evaluated via green fluorescence per cell and the percentage of positive cells determined by flow cytometry.

Results

A significant selectivity of gene delivery was observed, since the anti‐CD3 immunopolyplex worked only in Jurkat cells while the anti‐CD19 immunopolyplex worked only in the Granta cell line. Moreover, transfection of a CD3+/CD3? cell mixture with anti‐CD3 immunopolyplexes showed up to 16‐fold more transfection in CD3+ than in CD3? cells. Several non‐specific transfection reagents showed poor or no transfection activity.

Conclusion

It is concluded that immunopolyplex is a good non‐viral vector for specific and selective nucleic acid delivery. Immunopolyplex design allows easy replacement of the targeting element (antibody) – the streptavidin–polyplex backbone remaining intact – thereby conferring high versatility. Copyright © 2002 John Wiley & Sons, Ltd.

     

Overfitting,generalization, and MSE in class probability estimation with high‐dimensional data

Accurate class probability estimation is important for medical decision making but is challenging, particularly when the number of candidate features exceeds the number of cases. Special methods have been developed for nonprobabilistic classification, but relatively little attention has been given to class probability estimation with numerous candidate variables. In this paper, we investigate overfitting in the development of regularized class probability estimators. We investigate the relation between overfitting and accurate class probability estimation in terms of mean square error. Using simulation studies based on real datasets, we found that some degree of overfitting can be desirable for reducing mean square error. We also introduce a mean square error decomposition for class probability estimation that helps clarify the relationship between overfitting and prediction accuracy.     

Optimizing classification of drug-drug interaction potential for CYP450 isoenzyme inhibition assays in early drug discovery

In drug discovery, the potential of cytochrome P450 inhibition of new chemical entities is frequently quantified in terms of IC50 values. In early drug discovery, a risk classification into low, medium, or high potential inhibitors is often sufficient for ranking and prioritizing of compounds. Although often 6 or more inhibitor concentrations are used to determine the IC50 value, the question arises whether it is possible to predict the risk class based on fewer inhibitor concentrations with comparable reliability. In this article, the authors propose a new integrated 2-point method with inhibitor concentrations chosen in accordance with the risk classification. They analyze its predictive power and the feasibility of not only classifying the compounds into different risk classes but also ranking those compounds that have been binned into the middle risk class. The proposed integrated 2-point method is thus highly suitable for automation. Altogether, it maintains the quality of the prediction while considerably reducing time and cost. The proposed method is applicable to other IC50 assays and risk classifications.     

Production of an active anti‐CD20‐hIL‐2 immunocytokine in Nicotiana benthamiana

Anti‐CD20 murine or chimeric antibodies (Abs) have been used to treat non‐Hodgkin lymphomas (NHLs) and other diseases characterized by overactive or dysfunctional B cells. Anti‐CD20 Abs demonstrated to be effective in inducing regression of B‐cell lymphomas, although in many cases patients relapse following treatment. A promising approach to improve the outcome of mAb therapy is the use of anti‐CD20 antibodies to deliver cytokines to the tumour microenvironment. In particular, IL‐2‐based immunocytokines have shown enhanced antitumour activity in several preclinical studies. Here, we report on the engineering of an anti‐CD20‐human interleukin‐2 (hIL‐2) immunocytokine (2B8‐Fc‐hIL2) based on the C2B8 mAb (Rituximab) and the resulting ectopic expression in Nicotiana benthamiana. The scFv‐Fc‐engineered immunocytokine is fully assembled in plants with minor degradation products as assessed by SDS‐PAGE and gel filtration. Purification yields using protein‐A affinity chromatography were in the range of 15–20 mg/kg of fresh leaf weight (FW). Glycopeptide analysis confirmed the presence of a highly homogeneous plant‐type glycosylation. 2B8‐Fc‐hIL2 and the cognate 2B8‐Fc antibody, devoid of hIL‐2, were assayed by flow cytometry on Daudi cells revealing a CD20 binding activity comparable to that of Rituximab and were effective in eliciting antibody‐dependent cell‐mediated cytotoxicity of human PBMC versus Daudi cells, demonstrating their functional integrity. In 2B8‐Fc‐hIL2, IL‐2 accessibility and biological activity were verified by flow cytometry and cell proliferation assay. To our knowledge, this is the first example of a recombinant immunocytokine based on the therapeutic Rituximab antibody scaffold, whose expression in plants may be a valuable tool for NHLs treatment.     

Visualizing Antigen Specific CD4+ T Cells using MHC Class II Tetramers

Major histocompatibility complex (MHC) class II tetramers allow the direct visualization of antigen specific CD4+ T cells by flow cytometry. This method relies on the highly specific interaction between peptide loaded MHC and the corresponding T-cell receptor. While the affinity of a single MHC/peptide molecule is low, cross-linking MHC/peptide complexes with streptavidin increases the avidity of the interaction, enabling their use as staining reagents. Because of the relatively low frequencies of CD4+ T cells (~1 in 300,000 for a single specificity) this assay utilizes an in vitro amplification step to increase its threshold of detection. Mononuclear cells are purified from peripheral blood by Ficoll underlay. CD4+ cells are then separated by negative selection using biotinylated antibody cocktail and anti-biotin labeled magnetic beads. Using adherent cells from the CD4- cell fraction as antigen presenting cells, CD4+ T cells are expanded in media by adding an antigenic peptide and IL-2. The expanded cells are stained with the corresponding class II tetramer by incubating at 37 C for one hour and subsequently stained using surface antibodies such as anti-CD4, anti-CD3, and anti-CD25. After labeling, the cells can be directly analyzed by flow cytometry. The tetramer positive cells typically form a distinct population among the expanded CD4+ cells. Tetramer positive cells are usually CD25+ and often CD4 high. Because the level of background tetramer staining can vary, positive staining results should always be compared to the staining of the same cells with an irrelevant tetramer. Multiple variations of this basic assay are possible. Tetramer positive cells may be sorted for further phenotypic analysis, inclusion in ELISPOT or proliferation assays, or other secondary assays. Several groups have also demonstrated co-staining using tetramers and either anti-cytokine or anti-FoxP3 antibodies. Open in a separate windowClick here to view.^{(85M, flv)}     

Isolation of immunoglobulin class switch variants from hybridoma lines secreting anti-idiotope antibodies by sequential sublining

The combination of a sensitive radioimmunoassay with a simple limiting dilution approach designated as sequential sublining (ssl) allowed us to isolate spontaneous class switch variants from two hybridoma lines secreting monoclonal anti-idiotope antibodies against a germ-line encoded antibody with defined hapten-binding specificity. We obtained two families of antibodies, one of which consists of IgG1, IgG2b, and IgG2a, the other consisting of IgG1, IgG2b, IgG2a, and IgE antibodies. The members of a family possess identical anti-idiotypic specificity. We describe serologic and biochemical properties of the class switch variants as well as the frequency and order of "forward" and "reverse" switching, and we compare the ssl approach to other related methods. The ssl allows the rapid isolation of somatic mutants, which have acquired a new predefined antigenic determinant, without complicated equipment and even when the frequency of the mutants is as low as 10(-6) or 10(-7).     

Evaluating the stability of the classification of community data

We propose a method for a posteriori evaluation of classification stability which compares the classification of sites in the original data set (a matrix of species by sites) with classifications of subsets of its sites created by without‐replacement bootstrap resampling. Site assignments to clusters of the original classification and to clusters of the classification of each subset are compared using Goodman‐Kruskal's lambda index. Many resampled subsets are classified and the mean of lambda values calculated for the classifications of these subsets is used as an estimation of classification stability. Furthermore, the mean of the lambda values based on different resampled subsets, calculated for each site of the data set separately, can be used as a measure of the influence of particular sites on classification stability. This method was tested on several artificial data sets classified by commonly used clustering methods and on a real data set of forest vegetation plots. Its strength lies in the ability to distinguish classifications which reflect robust patterns of community differentiation from unstable classifications of more continuous patterns. In addition, it can identify sites within each cluster which have a transitional species composition with respect to other clusters.     

The use of CD38 expression by monoclonal B lymphocytes as a prognostic factor in B-cell chronic lymphocytic leukemia

In B-cell chronic lymphocytic leukemia (B-CLL) the Rai and Binet staging criteria are not always able to accurately predict the prognosis of each patient. Rapidly evolving, violent disease is often seen in the so-called "good-prognosis" group, which highlights the need of additional and more refined prognostic markers. Several of these markers are described in the literature, with varying abilities to predict patient survival. Among the promising prognostic markers is flowcytometric analysis of CD38 on the monoclonal B cells in CLL. Several studies have shown that expression of CD38 is associated with a decreased overall-, or progression free survival. CD38 expression may be analyzed as percentage positive cells or as antibodies bound per cell. Addition of CD38 to the flow cytometry antibody panel for B-CLL analysis is a relatively easy way to obtain important prognostic information.     

细胞因子对脐血B细胞免疫球蛋白类别转换的调节

为了解细胞因子对新生儿Ｂ细胞免疫球蛋白类别转换的调节作用,在体外细胞培养的基础上,采用反向酶联免疫斑点法观察了脐血单个核细胞及添加重组细胞因子后脐血Ｂ细胞免疫球蛋白释放细胞数量（ＩｇＳＣｓ）。结果：正常脐血单个核细胞仅产生少量的ＩｇＳＣｓ。用抗ＣＤ３单抗刺激丝裂霉素Ｃ处理后的脐血Ｔ细胞,并补充ｒＩＬ－２、ｒＩＬ－４、ｒＩＬ－１０及其组合,可诱导脐血Ｂ细胞释放ＩｇＡ、ＩｇＧ和ＩｇＭ。这些结果提示：细胞因子的补充可促进体外新生儿Ｂ细胞免疫球蛋白的类别转换     

Drug-target interaction prediction via class imbalance-aware ensemble learning

Background

Multiple computational methods for predicting drug-target interactions have been developed to facilitate the drug discovery process. These methods use available data on known drug-target interactions to train classifiers with the purpose of predicting new undiscovered interactions. However, a key challenge regarding this data that has not yet been addressed by these methods, namely class imbalance, is potentially degrading the prediction performance. Class imbalance can be divided into two sub-problems. Firstly, the number of known interacting drug-target pairs is much smaller than that of non-interacting drug-target pairs. This imbalance ratio between interacting and non-interacting drug-target pairs is referred to as the between-class imbalance. Between-class imbalance degrades prediction performance due to the bias in prediction results towards the majority class (i.e. the non-interacting pairs), leading to more prediction errors in the minority class (i.e. the interacting pairs). Secondly, there are multiple types of drug-target interactions in the data with some types having relatively fewer members (or are less represented) than others. This variation in representation of the different interaction types leads to another kind of imbalance referred to as the within-class imbalance. In within-class imbalance, prediction results are biased towards the better represented interaction types, leading to more prediction errors in the less represented interaction types.

Results

We propose an ensemble learning method that incorporates techniques to address the issues of between-class imbalance and within-class imbalance. Experiments show that the proposed method improves results over 4 state-of-the-art methods. In addition, we simulated cases for new drugs and targets to see how our method would perform in predicting their interactions. New drugs and targets are those for which no prior interactions are known. Our method displayed satisfactory prediction performance and was able to predict many of the interactions successfully.

Conclusions

Our proposed method has improved the prediction performance over the existing work, thus proving the importance of addressing problems pertaining to class imbalance in the data.

     

Predictions of native American population structure using linguistic covariates in a hidden regression framework

Background

The mainland of the Americas is home to a remarkable diversity of languages, and the relationships between genes and languages have attracted considerable attention in the past. Here we investigate to which extent geography and languages can predict the genetic structure of Native American populations.

Methodology/Principal Findings

Our approach is based on a Bayesian latent cluster regression model in which cluster membership is explained by geographic and linguistic covariates. After correcting for geographic effects, we find that the inclusion of linguistic information improves the prediction of individual membership to genetic clusters. We further compare the predictive power of Greenberg''s and The Ethnologue classifications of Amerindian languages. We report that The Ethnologue classification provides a better genetic proxy than Greenberg''s classification at the stock and at the group levels. Although high predictive values can be achieved from The Ethnologue classification, we nevertheless emphasize that Choco, Chibchan and Tupi linguistic families do not exhibit a univocal correspondence with genetic clusters.

Conclusions/Significance

The Bayesian latent class regression model described here is efficient at predicting population genetic structure using geographic and linguistic information in Native American populations.