Investigadores israelíes desarrollaron un algoritmo para predecir enfermedades infecciosas

Investigadores del Instituto de Ciencia Weizmann afirman que han desarrollado un algoritmo para predecir la aparición de enfermedades infecciosas, incluida la tuberculosis.

Si bien en ciertos casos el sistema inmunitario puede matar las bacterias y, en otros casos, las bacterias pueden vencer las defensas inmunitarias, existen enfermedades como la tuberculosis donde las bacterias pueden permanecer latentes durante años, algunas veces causan enfermedades en una etapa posterior y otras permanecen en hibernación.

Los científicos dirigidos por el Dr. Roi Avraham del Departamento de Regulación Biológica del instituto utilizaron un método desarrollado en el instituto para secuenciar la actividad de los genes en reuniones reales Entre miles de células inmunes y bacterias Salmonella.

A diferencia de las pruebas de laboratorio estándar, el método permitió a los investigadores ver las respuestas de las células a las bacterias y trazar los perfiles de activación de cada célula. Confirmando su hipótesis, los investigadores identificaron diferentes respuestas y patrones de las reuniones iniciales entre las células y las bacterias, y sus resultados posteriores.

Sobre la base de su secuenciación unicelular para la infección por Salmonella, los investigadores desarrollaron un algoritmo, basado en un método conocido como deconvolución, para extraer información similar de propiedades celulares individuales de conjuntos de datos de análisis de sangre estándar.

«El algoritmo que desarrollamos no solo puede definir el conjunto de células inmunitarias que participan en la respuesta, sino que también puede revelar sus niveles de actividad y, por lo tanto, la fuerza potencial de la respuesta inmunitaria», dijo la Dra. Noa Bossel Ben Moshe, quien co-dirigió la investigación junto con el Dr. Shelly Hen-Avivi en el grupo de Avraham.

El algoritmo se probó por primera vez en muestras de sangre tomadas de personas sanas de los Países Bajos. Algunas muestras se infectaron con la bacteria Salmonella y se registró la respuesta inmune. Si bien los métodos existentes de análisis genómico no descubrieron diferencias entre los grupos, el algoritmo reveló diferencias significativas que se vincularon a las variaciones subsiguientes en las habilidades para matar bacterias.

Luego, los investigadores centraron su atención en diagnosticar la aparición de la tuberculosis, causada por bacterias que pueden ocultarse latentes en el cuerpo durante años.

Utilizando una base de datos británica de análisis de sangre que sigue a los pacientes y portadores durante un período de dos años, lo que permite la aplicación del algoritmo a ambos grupos y al subconjunto que cambió de portador a la enfermedad durante ese tiempo. Los investigadores encontraron que los niveles de actividad de las células inmunitarias se denominaron los monocitos podrían predecir el inicio o el curso futuro de la enfermedad.

«El algoritmo se basa en las ‘primeras impresiones’ de las células inmunes y de la salmonela, que causan un tipo de enfermedad muy diferente a la de la tuberculosis micobacteriana», dijo Hen-Avivi.

«Aún así, pudimos predecir desde el principio cuál de los portadores desarrollaría la forma activa de la enfermedad».

Si bien la resistencia a los antibióticos representa un gran desafío para el tratamiento de la tuberculosis hoy en día, los investigadores creen que su algoritmo podría aumentar el éxito del tratamiento.

«Si los que están en riesgo de enfermedad activa podrían identificarse cuando la carga bacteriana es menor, sus posibilidades de recuperación serán mejores», dijo Avraham.

«Y los sistemas médicos estatales en países donde la tuberculosis es endémica podrían tener una mejor manera de reducir el sufrimiento y la incidencia de la enfermedad mientras se reduce el costo del tratamiento».

El equipo de Avraham ahora tiene la intención de continuar su investigación, expandiendo su base de datos sobre tuberculosis y también otros patógenos, para refinar su algoritmo y desarrollar herramientas que pueden usarse en el futuro para predecir el desarrollo y el curso de varias enfermedades infecciosas.

JPost

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